Simulation of polygeneration systems

Accepted Manuscript Simulation of polygeneration systems

Francesco Calise, Giulio de Notaristefani di Vastogirardi, Massimo Dentice d’ Accadia, Maria Vicidomini PII:

S0360-5442(18)31572-X

DOI:

10.1016/j.energy.2018.08.052

Reference:

EGY 13528

To appear in:

Energy

Received Date:

19 December 2017

Accepted Date:

07 August 2018

Please cite this article as: Francesco Calise, Giulio de Notaristefani di Vastogirardi, Massimo Dentice d’Accadia, Maria Vicidomini, Simulation of polygeneration systems, Energy (2018), doi: 10.1016/j.energy.2018.08.052

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Simulation of polygeneration systems

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Francesco Calise*, Giulio de Notaristefani di Vastogirardi, Massimo Dentice d’Accadia, Maria Vicidomini

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Department of Industrial Engineering, University of Naples Federico II, Napoli 80125, Italy;

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[email protected] [email protected] [email protected] * Correspondence: [email protected]; Tel.: +39-081-768-2301

Abstract: This work aims at presenting the current works concerning the polygeneration systems simulation, by specially focusing on the potential integration of different technologies into a single system. Polygeneration allows one to produce energy vectors (power, heating and cooling) as wells as other useful products (hydrogen, syngas, biodiesel, fertilizers, drinking water etc.) by converting one or multiple energy sources. Polygeneration system can be fuelled by renewable sources (geothermal, solar, biomass, wind, hydro), as well as fossil fuels (natural gas, coal, hydrogen, etc.). In this paper innovative energy technologies, such as fuel cells and conventional ones are taken into account, by also focusing on the control strategies implemented for the proper management of polygeneration systems in general. Works regarding energy, economic and exergy analyses and system optimizations are also illustrated. Keywords: renewable energy; polygeneration; distributed generation; dynamic simulations 1.

Introduction

In the last decades, the international energy consumption has considerably increased, principally due to the economic and demographic growth of developing countries. So, more and more urgent issues arose, regarding the environmental impact and availability of fossil or non-renewable fuels (such as oil, gas, nuclear, carbon), which today satisfy most of the worldwide energy demand. At the same time, the consumption of potable water is also significantly increasing. Therefore, some noteworthy actions were taken in order to head towards a sustainable development, aimed at enhancing the energy performance of systems and processes and increasing the use of renewable energy sources [1]. In this framework, polygeneration systems represent an attractive and innovative concept. Such systems can provide simultaneously various energy vectors (power, heating, cooling) and other products (fresh water, syngas, urea, etc.). They become particularly interesting if coupled to renewable energy sources. In spite of their potential, the concept of polygeneration is still scarcely used. In fact, only 10% of the world power generation is obtained by means of polygeneration systems. Exceptions are the countries as The Netherlands, Finland and Denmark, where this ratio increases up to 30%–50%. Presently, many European Union countries are encouraging the development of sustainable and efficient systems based on polygeneration, since it is recognised as a strategic technology able to reduce greenhouse gas emissions. The same strategy was also adopted by the USA, aiming at cutting down the costs of energy production, mainly in the industrial sector. In order to pursue the integration of polygeneration technologies, industries and academic institutions are presently carrying out a noteworthy research effort, for developing and designing plants based on the use of both renewable and fossil energy sources. As for systems fuelled by fossil fuels, numerous investigations aiming at optimizing both trigeneration and cogeneration power plants were performed. As for polygeneration systems fuelled by renewable energy sources, most R&D activities are focused on hybrid plants in which traditional technologies (Stirling, gas turbine, internal combustion engines, combined cycle, etc.) are coupled to renewable energy sources (particularly biomass and solar energy) in a unique polygeneration system. Polygeneration plants are specially interesting when used to produce potable water, along with power, heating and cooling. This is a very attractive option, in particular for remote and inaccessible communities [2]. An example is represented by small or medium islands, where the availability of fossil fuels and potable water is often limited, whereas renewable sources are often abundant and there is obviously a large availability of sea water [3]. Desalination systems, in fact, are a great way to produce potable water when water scarcity represent a crucial issue, especially if one considers that more than 70% of the word population lives within 70 km from a sea [4].

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Polygeneration systems can also contribute to the diffusion of distribute generation (DG) systems. This kind of system is included in the more general concept of distributed energy systems (DES), which consists of the integration of different small-scale DG technologies, instead of a limited number of big, remote power plants (centralized production). In DES systems, the electricity is produced locally near to the final user, and, therefore, the negative effect due to the transmission losses is avoided. At the same time, in such systems, the concept of self-sufficiency, by decreasing the dependence from conventional energy grid, is also promoted. DES systems, fuelled by renewable energy, are usually considered as the best option among all the potential DG technologies. Such systems usually integrate district heating and cooling (DHC) and off-grid electric networks [5]. As mentioned before, the number of possible polygeneration layouts is virtually infinite, since it is possible to combine all the available fossil and renewable conversion technologies. This concept is clearly depicted in Figure 1. Hydrogen

Fertilizer

Methanol

Coal

Gasifier gasificat ion biomn CC

Production unit Syngas / Biogas

Ethanol

Boiler

Biomass

Wind

Electrical desalination unit

Fresh water

ICE Power

Natural gas

Electricity to user Cooling

GT, MGT ST

FC STE

Heat

Electrical chiller

Seawater

Thermal chiller

STC

DHW, Heating

PV Solar PVT

63

ORC

Thermal desalination unit

Fresh water

Geothermal

64

Figure 1. Possible combinations of fossil and renewable conversion technologies in polygeneration systems.

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The topic of polygeneration is not new and several papers tried to analyse it in the past few years. In particular, the majority of the review papers dealing with polygeneration mainly focused on their possible arrangements and integration with renewables and/or buildings [6-8]. However, in authors’ knowledge, none of those papers focused on the simulation techniques implemented in order to analyse such polygeneration systems. In particular, a first attempt was recently performed by some of the authors a recent editorial paper [9], summarizing only a few studies included in a special issue dedicated to the simulation of polygeneration systems. Conversely, the review analysis performed in the present paper provides a comprehensive and detailed state-of-the-art of the available published literature concerning the simulation of polygeneration systems (fuelled by fossil fuels and/or renewable energy source). For these works, the methodology of analysis (exergoeconomic, exergy, environmental, economic, energy and thermoeconomic) as well as the innovative layouts designed to maximize the utilization of the input fuels are also defined. In particular, by collecting a high number of different works, the aim of this review, and therefore, the novelty with respect to the previous work [9], concerns the classification of the polygeneration systems adopting the simulation approach (based on dynamic simulations or steady-state simulations), regarding the following different issues, namely: i) methodology of analysis; ii) energy inputs (renewables and/or fossil fuels);

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iii) iv) v) vi) vii) 2.

energy conversion technologies (gas turbine, steam turbine, combined cycle, ORC, internal combustion engine, FC); energy (power, heating, cooling, etc.) and material outputs (liquid and gaseous fuels, desalinated water, etc.); use of polygeneration in buildings; control strategies and optimization techniques; contribution of polygeneration to the development of distribute generation systems. Methodology of analysis

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This paper analyses different research works investigating polygeneration systems from a plurality of viewpoints, namely: energy, exergy, environmental and economic. From all the studies, it resulted that the design and the operation management of polygeneration systems requires a multidisciplinary approach and analysis, mainly due to high complexity of such systems, since they are always based on the integration of different technologies. In particular, such works were developed by adopting several common or advanced analysis methods, such as: i) energy analysis [10-14] of individual components as well as the system as a whole, oriented to estimate the energy saving of a proposed innovative system with respect the conventional technology; ii) economic analysis [2, 10, 11, 14-20], evaluating operation, maintenance and capital costs, in order to estimate the economic feasibility of the systems; iii) environmental analysis [11, 14, 20-26], usually based on a Life Cycle Analysis (LCA) approach [27]; iv) exergy analysis [10, 13, 21, 22, 28-32], exergoeconomic analysis [33-38] or thermoeconomic analysis [39-45], this is an useful tool for calculating the magnitude of the irreversibilities into a system, in order to evaluate possible enhancements of its performance; aiming at determining the economic value for each material flow or energy, by defining their price.

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As above specified, polygeneration systems typically include a plurality of different technologies integrated into a single system; so, the development of optimized control strategies is a crucial issue in order to improve their performance. Therefore, many works concerning the simulation of the polygeneration systems are focused on the selection of the most appropriate operation control strategy [41, 46-51]. In addition, great attention is paid to the design of the system, based on different optimization methods [52-56], aiming at determining the optimal size of each component. The review presented in the next sections includes works based on dynamic simulations [3, 12, 18, 41, 57] and others in which steady-state simulations were performed [15, 58-63].

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In particular, the works implementing the dynamic approach investigated several topologies of polygeneration systems, supplied both from fossil fuels (natural gas, coal, etc.) and by renewable energy sources (solar, biomass, geothermal, etc.) for the co-production of desalinated water, heating, cooling, electricity, H2, syngas or others. Dynamic simulations present many advantages, since they allow one to analyse with high accuracy the real operation of the system under evaluation, providing important information about the ways in which their performance can be optimized. Such dynamic simulations are often based on the software TRNSYS, a simulation tool diffusely used by the academic community. It provides a library of built-in components often based and validated vs experimental data [64]. Therefore, the results obtained by the works concerning the dynamic simulations carried out by TRNSYS software are usually considered highly reliable. Table 1 shows a classification reporting the studies only performed by the simulation model and the ones which coupled numerical and experimental analyses. For all the cases, the software implemented in the simulations is detailed.

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Table 1. Classification studies: Software, experimental validation and modelling Work

Software

Experimental validation and

Only simulation

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128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143

TRNSYS TRNSYS TRNSYS, Engineering Equation Solver (EES) TRNSYS, EES EES EES EES, LINGO EES, CoolProp EES, BIORAISE, Aspen Plus Aspen Plus Aspen Plus, Dow Chemical Company RO System Analysis (ROSA). Aspen Plus, GE’s GateCycle MATLAB MATLAB, NIST/ASME MATLAB, Simulink MATLAB, Refprop MATLAB TRNSYS DEST ECLIPSE ChemCAD GAMS, CONOPT tool INSEL IPSEpro SIFRE MODEST

model x

x x x x x x x x x x x x x x x x x x x x x x x x x

3. Energy sources Polygeneration systems can be fuelled by fossil fuels (natural gas, coal, diesel etc.) and/or renewable energy sources (geothermal, solar, wind, biomass, etc.). In many cases, a mix of different sources is used. In the next paragraphs, the most significant literature works concerning the simulation of polygeneration systems fuelled by fossil fuels, renewable energy sources and by hybrid energy inputs are described. 3.1 Fossil fuels Several works investigating the simulation of polygeneration plants driven by fossil energy sources are available in literature [11, 17, 48, 73], also due to the fact that coal, natural gas and oil represent the 80% of global energy demand due to their affordability and availability [115]. Coal Currently, the clean and efficient use of coal is one of the most significant goals of the scientific research. In the last years, several advanced coal gasification technologies were developed to maximize the energy efficiency and to achieve a near-zero level of pollutant emissions. An example is the coal partial gasification technology analysed in the work presented by Li et al. [89], where the thermodynamic analysis of this process in a polygeneration system for the methanol and power generation, is carried out. The commercial software Aspen Plus was used to perform the simulation.

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Figure 2. Schematic of a polygeneration system based on partial gasification [89].

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In contrast to traditional coal gasification technologies featured by total gasification of coal through simple or rough oxidization, partial gasification technology, based on pressurized circulating fluidized bed controls the quantities of oxygen agent and detention time during gasification, by achieving, relatively higher cold gas efficiency (up to 65.5%). The investigated system layout is reported in Figure 2. Here, the syngas from the partial gasifier is synthesized to methanol, whereas the unreacted gas enters gas turbine combined cycle to produce power. The system shows an energy and exergy efficiency equal to 51.16% and 50.58%.

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Another novel coal gasification technology, aiming at separating CO2 emissions during combustion process without extra or with few energies required, is based on the concept of chemical looping combustion. This process is simulated in the polygeneration system investigated by Fan et al. [91]. In this system, the combustion flue gases from both air reactor and fuel reactor are sequentially fed into gas turbines for electricity production; here a heat recovery vapour generator unit for further electricity generation with driving an absorption chiller (ACH) in summer and a heat exchanger for daily heat water production is also simulated. This novel system maintains a maximum energy efficiency of 60.34%, whereas the fossil energy saving ratio of this process of 27.20%.

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Utilizing captured CO2 for utility outputs in coal-based polygeneration has twofold advantages: eliminating the subsequent cost of CO2 sequestration and earning additional revenue from utility products using. In the work developed by Bose et al. [90], the performance assessment of a coal-based polygeneration for power with CO2 capture and subsequent use of this captured CO2 for production of a common fertilizer, i.e., urea is proposed. In particular, the syngas produced by gasification, is treated with steam in a water gas shift reactor, after that CO2 is captured. Produced hydrogen is partially utilized for power generation in a combined cycle gas turbine and for urea production. A detailed ASPEN Plus model is developed for this plant. It resulted that, the higher the percentage of captured CO2 used, the higher the production of urea, but a simultaneous decrease of net power output is achieved as both depend on amount of hydrogen of syngas used for these processes.

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The majority of polygeneration energy systems analysed in literature involving coal gasification, use a single gas source and produce only a unique type of chemical product. Conversely, in the study reported by Li et al. [34] a dual-gas source polygeneration process which uses, besides syngas from coal gasification, coke oven gas as gas sources and co-produces dimethyl ether, methanol and dimethyl carbonate via an integrated catalytic synthesis procedure. The system performance is calculated by numerical simulations, based on detailed chemical kinetics which shows the system feasibility, whereas the carried out exergoeconomic analysis, reported the exergy loss and the production cost of product in each functional block of the process.

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Natural gas Over the past few years, combined cooling heating and power (CCHP) systems fuelled by natural gas raised increasing interest [116]. In particular, liquefied natural gas (LNG) is recognized as the best fuel option

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in many countries, in terms of global environmental impact [115]. Therefore, CCHP systems fuelled by LNG were recognized as a potential energy system option for sustainable development and low-carbon society.

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An example of this system was investigated by Li et al. [117], for hotels, offices and residential buildings by an energy, economic and environmental analysis. In the proposed polygeneration system, two prime movers are taken into account: a gas turbine and a gas engine. The produced heat is recovered to meet the thermal demands through the thermal exchanger and the cooling load through the absorption chiller ACH. Such system was compared to a reference system, where the space heating and cooling are produced by a boiler and electric chiller. The resulted biggest economic merits are received in offices, 39.21%, driven by gas turbine and 52.83% driven by gas engine. The biggest reduction of emissions and energy consumption are received in hotels, with a maximum value equal to 60.65% and 42.28%, respectively.

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The concept of chemical looping combustion and gasification investigated in [91], is also included in a new polygeneration system simulated by Salkuyeh and Adams [58]. The simplified layout of the chemical looping gasification is reported in Figure 3.

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Figure 3. Simplified schematic of the chemical looping gasification system [58].

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With respect the previous work using coal, here natural gas and petroleum coke are used as input fuels and different products such as chemicals, olefins, electricity and transportation fuels are co-produced. All process unit operation models were simulated in Aspen Plus 2006.5 simulation software, except for the gas turbines for which a custom model in VBA (Visual Basic for Applications) was linked into the Aspen simulation model instead. The system is not only able to capture 100% of CO2 emissions, but also can be an incentive for those producers to convert stockpiled petroleum coke to more valuable products.

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An innovative polygeneration system fuelled by natural gas for the production of methanol and electricity is investigated by Gao et al. [92]. The novelty regards the integration of partial-reforming and partial-recycle scheme (Figure 4) in methanol synthesis which contribute to enhance the system performance (energy and exergy) with respect to the one of full reforming and without recycle, assumed as reference system. In particular, in this new layout, only a part of natural gas entering the reformer will be converted into syngas, which will be cooled by heat recovery unit, and before will be sent to the methanol synthesis process. Here a partial-recycling operation is adopted, which expects that the unreacted syngas is recycled back to the synthesis reactor, and a part will be fed to the power generation subsystem as fuel, and the rest part will be burnt with the GT exhaust gas to cover the heat demand for the reforming process.

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Figure 4. Flow sheet of part reforming and part cycling (PR/PC) system [92].

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The system simulations were carried out by using ASPEN PLUS steady state simulation software. In this work, the energy saving ratio is used to assess the polygeneration system energy and exergy performance. The energy results are summarised in Table 2. The reference system has rather bad performance with respect the innovative one which also able to reduce about 6% of energy consumption compared with the single product systems.

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Table 2. Performance comparison between systems System

Reference

Fuel consumption (LHV, kW) Net work (kW) Methanol production, M (kg/h) Relative energy saving ratio (%) Exergy efficiency

69753 21211 -0.2 61.02

Innovative 315951 81350 21034 5.8 64.14

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In Table 3, some of the works available in literature concerning polygeneration systems fuelled by natural gas and coal are listed. Whereas, in Table 4 shows several energy indexes (Fuel Energy Saving Ratio, Primary Energy Saving, Primary Energy Rate) and the consequent avoided CO2 emissions of the polygeneration systems fuelled by fossil fuels, when these are compared to the conventional separate production systems.

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Table 3. Works on polygeneration systems fuelled by natural gas and coal. Work Li et al. [34] Lin et al. [17] Li et al. [89] Bose et al. [90] Fan et al. [91] Adams and Barton [118] Zhu et al. [119] Chiesa et al. [120, 121] Yi et al. [93] Khojasteh Salkuyeh and Adams [98] Sibilio [11]

Input

Soutullo [12]

Natural gas

Calise et al. [39] Arsalis et al. [40]

Technology CC, gasifier

Coal

GT, gasifier CC, gasifier GT, ST ICE Engine generator ICE GT

Output dimethyl ether, methanol and dimethyl carbonate methanol, power, CO2 methanol and power CO2, urea, hydrogen, power power, heating, cooling methanol, diesel, gasoline, power hydrogen, power hydrogen, power and CO2 methanol, power power, ethane and propane power, heat, cool H2 power, heat, cool power, heat, cooll

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Calise et al. [49] Khojasteh Salkuyeh and Adams [58] Calise et al. [73] Li et al. et al. [117] Tock and Maréchal [122] Rosato et al. [123] Adams and Barton [118] Rubio-Maya et al. [124, 125]

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CC Reformer

chemicals, olefins, electricity and transportation fuels

ICE GT, ICE GT ICE Gasifier, GT ICE, GT, FC

power, heat, cool H2 power heat and CO2 power, heat, cool methanol, diesel, gasoline and power power, heat, cool and fresh water

Table 4. Economic and environmental indexes of the polygeneration systems fuelled by fossil fuels Value

ΔCO2

Conventional separate production

[13] [11] [14]

Energy index FESR1 PES2 PES

26.6 % 0.2 to 4.3 % 6.6 %

66.7 to 70.5 % 3.9 to 10.6 % 11 to 12 %

Boiler, electric chiller, grid

[17]

PES

8.1 to 15.6 %

52.8 to 88 %

[62] [48] [73] [91]

FESR PES PES FESR

0 to 20.0 % 14.0 to 24.0 % 14.0 to 14.3 % 23.4 to 27.2 %

134 tons/year -

[92]

PES

5.8 %

-

[112]

η

50 % (+11%)

-

[88] [126] [127]

PES PER3 PES

38 % 76. 5 % 20 %

1000 g/kWhe year -

[114]

-

-

225 %

[128]

PES

54 % (maximum)

-

Work

Boiler, air-cooled water electric chiller, grid Synthetic fuel production systems Power generation technologies Standard boiler Boiler, electric chiller, grid Single methanol production Single power generation system Ethanol from wood process Combined heating and electricity system Boiler, cooling absorption machines, grid Vacuum hot water boiler, grid Boiler, grid Single bio-fuel production Single electricity production Gas boiler, compression chiller, grid

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1

Fuel Energy Saving Ratio (FESR): the ratio of the energy saving of the polygeneration system, in comparison with a conventional system with separate productions, to the overall energy consumption of the reference system. 2 Primary Energy Saving (PES): the ratio of the difference between the primary energy of the reference system (separate production) and the primary energy of the polygeneration system, to the primary energy of the reference system. 3

Primary Energy Rate (PER) is defined as the ratio of the primary energy demand to the required output. The system with the lowest value of PER is considered the best with regards to energy consumption.

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3.2 Renewable energies During the past few years, renewable energy sources represented an attractive alternative to fossil fuels, due to their potential contribution to fighting global warming, reducing the emission of pollutants in general, slowing down the depletion of fossil fuels, etc. [129], in order to promote a "sustainable" development [23]. Dozens of papers investigated polygeneration systems supplied by renewable energy sources (biomass, geothermal, solar, wind, hydro etc.); so, a wide number of different system layouts was proposed. The present section provides an overview of those systems classified on the basis of the renewable energy source used. Biomass

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In many cases, biomass-based polygeneration systems use cheap biomass as fuels. Biomass is especially promising in such systems by allowing one to produce a plurality of by-products, along with the conventional energy vectors (electricity, cool and heat). In addition, the possible use of agricultural wastes is extremely promising, since many of these have good potential to be energy resource [130].

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An example of this configuration is presented by Jana and De [15, 24]. They used rice straw, sugarcane bagasse and coconut fibre dust. The polygeneration system (Figure 5) is designed as follows: in a fluidized bed gasifier, the agricultural waste is gasified in syngas, which cleaned, cooled and compressed is utilized for power generation in a combined cycle. An absorption chiller, ACH, is supplied by the heat generated in syngas cooler, whereas an ethanol production unit is supplied by a fraction of the whole generated syngas.

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Figure 5. Schematic of the polygeneration process [15].

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The whole system was modelled in Aspen and a detailed economic analysis was also developed. In Figure 6, a comparison of net power produced for three different agricultural wastes are shown. Power output is contributed mostly by the gas turbine plant, then, by the low pressure steam turbine and the rest by the high pressure steam turbine. It is noted that net power output for coconut fibre dust is the maximum, and then for sugarcane bagasse followed by that for rice straw. The results of the economic analysis showed that the proposed system is very interesting, specifically for rural communities, by achieving a payback period just above 5 years, when the rice straw biomass is taken into account.

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Figure 6. Power outputs for different agricultural wastes per t/year [15].

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The same system layout displayed in Figure 5 was proposed by the authors to evaluate the system performance from the techno-economic point of view, by focusing only on rice straw energy source [94]. Here, the analysis is specific for a district in the state of West Bengal of India. Results show that the polygeneration plant using rice straw as energy resource is a feasible option in energy services for developing countries like India. In fact, the estimated payback period of the plant is 4.76 years. The return on investment (ROI) is 15.5 % and the net present value is 97 million USD.

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Based on the results of the carried out simulations in the previous work, a Life cycle analysis (LCA) was developed in ref. [27], where the environmental impact of this polygeneration plant is discussed. Here, the polygeneration system is compared with stand-alone conventional plants with same utilities. This study helps to estimate the life cycle emission, specifically greenhouse gas emission of this plant. Exergy based allocation method is used for this analysis. Results indicate that global warming potential of the standalone generations of same utilities in conventional ways is 100 times more than corresponding straw based polygeneration. Logistics of biomass cause maximum environmental impacts out of the unit processes of the polygeneration. It shares almost 50% of the global warming potential and the acidification potential of this polygeneration.

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A biofuel-fired trigeneration systems equipped with electrical and thermal energy storages for remote households was simulated by Huang et al. [107]. In particular, in this study wood pellets and willow chips are chosen to supply a Stirling engine and to evaluate the internal combustion engine (ICE) performance, biodiesel is also selected (Figure 7). A thermally-driven chiller is also included.

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Figure 7. Schematic diagram of the trigeneration system, ICE (left) and Stirling engine (right) [107].

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The ECLIPSE process simulation package, successfully used to analyse a wide range of energy conversion systems, such as coal fired power plants and biomass energy systems, was adopted to perform the simulations. In Table 5, some of the main energy results of simulations are reported. From the economic point of view, calculations showed that the economic feasibility of this type of plant, evaluated by the breakeven electricity selling price (BESP) is affected on the renewable energy incentive schemes (Renewable Heat Incentive (RHI), for 7 years at a rate of 12.2 p/kWh. and Feed-In Tariffs (FITs) for 10 years at a rate of 13.2 p/kWh.). If the plant

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obtains both incentives, the BESP for wood pellets, willow chips and biodiesel are 216, 169 and 310 £/MWh, respectively.

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Table 5. Technical results. Feedstock Electrical output, kWe (Net) Overall electricity efficiency, % Heat output, kWth (maximum) Cooling output, kWth Overall CHP efficiency, % Overall trigeneration efficiency, % CO2 emissions, g/kWh (CHP) Reduction in CO2 emissions, t/year

282 283 284 285 286 287 288 289 290 291 292

Willow chips

Bio-diesel

3.2 19.3 10.9 4.4 84.3 66.9 409 8.7

3.2 18.8 11.1 4.5 82.4 65.4 413 8.9

5.2 30 7.6 2.2 74.6 64.1 433 6.1

The performance of system for direct conversion of biomass (sawdust) through chemical looping for coproduction of hydrogen and power was evaluated in ref. [108]. A net power output ranging from 400 to 50 MW and a flexible hydrogen output in the range of 0 to 200 MWth were taken into account. A CC is adopted as prime mover. ChemCAD software is used to model and simulate the several investigated plant configurations, including layouts with biomass and / or coal gasification, with and without carbon capture and biomass direct chemical looping. Authors state that the reason to use coal and biomass is due to the fact that currently there are no industrial size gasifiers able to process only biomass. For these gasifiers, the biomass ratio can be up to 30% from total fuel input. Results regarding power and hydrogen production obtained for biomass direct chemical looping case are summarised in Table 6. From the economic point of view, it resulted that the biomass direct chemical looping has a reduced carbon capture capital cost penalty than the gas–liquid absorption carbon capture case (7% points lower specific investment cost).

293

294 295 296 297 298 299 300 301 302

Wood pellets

Table 6. Energy results of biomass chemical looping case. Main plant data

Units

Power only

Sawdust flow rate Gross electric power output Hydrogen output — LHV Gross electrical efficiency Hydrogen efficiency Carbon capture rate

t/h MWe MWth % % %

558.19 0 46.59 0 99.6

Hydrogen and power cogeneration 268.6 500.31 441.52 100 200 41.76 36.85 8.34 16.69 99.6 99.6

A further sector where the concept of polygeneration was often adopted concerns the co-production of power and fuels via bioorganic wastes. An example of this application is the work reported in reference [110]. Here the multi-generation of different fuels such as DME, methanol, ethanol, FT fuels, fertilizers and biogas from biomass wastes is investigated. The layout of this production process is depicted in Figure 8 and it is based on anaerobic digestion and dry reforming processes. In particular, accurate models for production of biogas are written in order to compute the optimal mixture of biomass wastes between cattle and pig slurry, cattle and pig manure, sludge, urban food waste and urban green waste to be digested to obtain the required biogas. The models are written in GAMS® and in order to identify the optimal solution, corresponding to the optimal mixture which maximize the amount of methane, CONOPT tool was used.

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Figure 8. Scheme of biomass waste processing to fuels and chemicals.

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It resulted that for a syngas optimal production, achieved by biogas dry reforming, a mixture of biogas consisting of 50% CH4, and 48% CO2 is the most appropriate. When the biogas is intended as fuel, a 70% methane content is targeted. In addition, they state that if the aim is obtaining the most typical fertilizer composition, 65% of cattle slurry and 35% urban food waste is the optimal blend. Solar energy

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Several papers also investigated the integration of polygeneration systems with solar thermal collectors and PV panels. In fact, in polygeneration plants fuelled by solar energy, solar technologies can convert the solar incident irradiation in thermal energy used for solar power plant in case of medium or high temperature heat production or, conversely, for space heating or DHW (by STC), in electricity (by PV) and in cooling energy, by thermally driven chillers or electric vapour compression chillers.

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In this framework, an interesting work is reported in reference [57], where a solar trigeneration system, based on sheet and tube PVT solar collectors, a single-stage LiBr/H2O absorption chiller, ACH, storage tanks and auxiliary heaters (Figure 9), is dynamically simulated by means of a dynamic simulation model, developed with TRNSYS.

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In this work [57], an example of integration of solar heating and cooling system with low temperature PVT collectors is reported. System producing electricity, space heating and cooling and domestic hot water for a university building located in Naples (Italy). The system performance is analysed from both energy and economic points of view.

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Figure 9. System layout [57].

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It resulted that the polygeneration system performance is mainly affected on PVT performance that is excellent during the summer, decreasing significantly in winter. The amount of PVT heat used for DHW is 60.7%; the electrical solar fraction is satisfactory since the PVT can cover about 65% of the overall electricity demand. The PVT average electrical efficiency is slightly lower than 10%. This value is not so bad, considering that the nominal efficiency at 25 °C was 16% and taking into account that PVT operates at 50°C and 80°C respectively in winter and in summer. The overall primary energy saving (PES) achieved by the whole system is slightly higher than 70%. The best resulted pay-back period, by the carried out economic analysis, is 3.6 years, when a proper feed-in tariff is considered.

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An example of application of low temperature PVT collectors coupled to a solar-assisted heat pump and an adsorption chiller in a novel polygeneration system based is reported in reference [41]. Here, the dynamic simulation model and a thermo-economic analysis is carried out. The plant produces electricity, space heating and cooling and DHW for a small residential building. During the winter, thermal energy of PVT collectors primarily supplies the heat pump evaporator, whereas in summer, supplies the adsorption chiller (usually activated for lower hot fluid temperatures) providing the required space cooling (Figure 10). The results showed a total energy efficiency of the PVT of 49%, a heat pump yearly coefficient of performance (COP) for heating mode above 4 and a COP of adsorption chiller of 0.55.

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Figure 10. Simplified layout of the proposed system including the main components and loops [41].

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In reference [86] the exergy modelling of a new solar driven trigeneration system was performed. With respect the previous systems, here high temperature PTC collectors are coupled to a high critical temperature efficient ORC, with the n-octane as working fluid. A single-effect ACH from production of cooling energy is also included (Figure 11). Both heating (steam water) and cooling production are achieved by the thermal recovery from the ORC. Different operating conditions, namely power generation, power-heating and powercooling cogeneration and trigeneration were analysed. In particular, by an appropriate integration of heat exchangers and thermal storages, three different modes of operation were taken into account depending on low or high availability of solar radiation and operation during the night time. The exergy analysis was carried out by varying the temperature difference at the pinch point of the ORC evaporator, the inlet temperature of the ORC pump and the turbine inlet pressure. The maximum electrical-exergy efficiency ranged between 7.00% and 3.00% depending the operating condition. In case of trigeneration, global exergy efficiency ranged between 20.0% and 7.00%. Finally, most of the exergy destruction rate occurred in the solar collectors and ORC evaporators.

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Figure 11. Schematic of the solar-trigeneration system [86] .

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359

Biomass and solar energy

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Several studies examined different configurations of polygeneration plants consisted of both solar (STC, PV and PVT) and biomass technologies. An efficient approach to simultaneously use biomass and solar sources is investigated by Calise et al. [57]. Here, a reciprocating engine, fed by rapeseed oil, was coupled to concentrating parabolic trough solar collectors (PTC) to produce thermal energy and a double-stage LiBr/H2O ACH to produce cooling energy. In the framework of polygeneration systems, this type of plant that integrates reciprocating engines with solar thermal collectors is scarcely investigated [65]. In particular, the solar and biomass source are linked because exhaust gases produced by biomass fuelled reciprocating engine are used to provide additional heat to the fluid heated by PTC. In this study, the whole trigeneration system was modelled and dynamically simulated in transient system simulation tool (TRNSYS). The economic results show that the system under investigation is profitable, especially if properly funded and from the energy point of view the rapeseed oil reciprocating engine is suitable for integration in polygeneration systems based on high temperature solar heating and cooling systems, achieving a PES higher than 93%.

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PTC are coupled to a willow pellets boiler in a tri/co-generation plant, consisted of an ORC–Vapour Compression Cycle (VCC) systems in the energy–exergy analysis and economic investigation performed by Karellas and Braimakis [10]. Here, ORC produces power consumed partially by VCC compressor, activated only in summer, whereas the heat generated in the condenser is used to meet hot water demand. With respect the previous work, here PTC and boiler are used to provide the request heat of ORC (Figure 12), and the cooling energy is produced by VCC system. The resulted net electric efficiency is 2.38%; maximized thermal efficiency of the ORC is equal to 5.5% when the evaporation temperature is equal to 90°, while the exergy efficiency is 7% when the biomass boiler operates at full load operation. Findings suggested that superheating process and the adoption of a recuperator lead to higher capacities of the heat exchanger areas and of the solar field extensions, without returning any significant increase of the system efficiency. By considering a typical apartment block on a Greek Island, assuming PTC area of 50 m2 a payback period of 7 years is achieved. A sensitivity analysis was carried out by varying the investment cost and the biomass price, analysing their impact on the IRR and SPB.

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Figure 12. The combined ORC–VCC cycle and biomass boiler and PTC heating water circuits [10].

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A wood chips gasification system and solar evacuated collectors are coupled in the simulation work carried out by Wang and Yang [25]. Here, an ICE supplied by the produced gas by gasifier and a dual-source powered mixed-effect ACH are also included. Waste heat of ICE and produced heat of solar field are utilized to produce cooling and heating energy. The plant is simulated by using the EES software package, and a comparison of the simulated linear curve of the instantaneous efficiency of the evacuated tube collectors between the simulation data and the experimental one, achieved by the solar energy research institute of Beijing is performed. The results indicate that the primary energy ratio and the exergy efficiency are 57.9% and 16.1%, respectively, and the carbon emission reduction ratio is about 95.7%, at the design condition.

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A polygeneration system of generating methanol and power with the solar thermal gasification of the cotton stalk is proposed in ref. [59]. A field of heliostats concentrates the solar radiation on the solar tower where the biomass gasification occurs. Here, the high-temperature solar source is directly employed in the gasification process and, therefore, is considered a promising option for the supply of the process heat (Figure 13).

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Figure 13. Schematic diagram of the proposed polygeneration system [59].

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The syngas from the biomass gasification is used to produce the methanol via a synthesis reactor. The unreacted gas is used for the power generation via a CC power plant. The solar assisted polygeneration system is located in Yanqi (China) and is numerically simulated by the Aspen Plus software to estimate the polygeneration system thermal performances. The highest energy efficiency and the exergy efficiency of the polygeneration system approximately are 56.09% and 54.86%, respectively.

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A hybrid solar-biomass polygeneration system, based on PTC, for the simultaneous production of power, cooling by vapour absorption refrigeration (VAR) and fresh water by a multi-effect humidification and dehumidification (MEHD) desalination unit is investigated by Sahoo et al. [77]. The VAR cooling system operates using the extracted heat taken from steam turbine and condenser heat of the VAR cooling system is used in desalination system for production of drinking water. The system PES is 50.5% and the energy output is increased to 78.12% from this system as compared to simple power plant.

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In Table 7, some of the studies available in literature concerning hybrid solar-biomass polygeneration are listed.

419

Table 7. Works on hybrid solar-biomass polygeneration systems. Work Calise et al. [2] Calise [66] Sahoo et al. [131] Angrisani et al. [132] Rubio-Maya et al. [124] [125] Ray et al. [101] Pantaleo et al. [133] Vidal and Martin [134] Khalid et al. [78]

Solar technology CPVT PTC PTC Scheffler concentrator

Biomass technology Wood-chip auxiliary heater Wood-chip auxiliary heater Boiler Wood pellet fluidized Bed

Prime mover GT, ST Stirling engine (STE)

Evacuated tube collectors

Crops gasifier

ICE, GT, MGT, FC, STE

PV PTC Solar tower

Straw, wood gasifier Wood chips boiler Lignocellulosic gasifier Dry Olive pits combustion chamber

Gas engine GT, ORC GT, ST

Heliostat tower

GT, ORC

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Ghaith and Abusitta [111]

420

flat plate and evacuated tube collectors

Pellet auxiliary heater

-

Hybrid geothermal and solar systems

421 422 423 424 425 426

Integration of geothermal and solar energy is one of the most investigated configurations. In fact, lowmedium enthalpy sources, as the solar and geothermal ones, represent an interesting input for Thermally Activated Technologies (TAT), or Thermally Driven Technology (TDT), such as multi- effect-distillation (MED) systems, as well as Organic Rankine Cycles (ORC) and absorption chillers (ACH) [39]. Therefore, numerous studies can be found in literature about the investigation of different layout schemes, and subsequently some of them are reported.

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A novel trigeneration system obtained by new prototypal flat-plate evacuated solar collectors coupled to a geothermal well is dynamically simulated in the work of Buonomano et al. [18]. The whole system, designed for a hotel building in Ischia Island (Mediterranean Sea), includes a new simulation model for ORC, and produces power, DHW and cooling by a single stage LiBr/H2O ACH driven by geothermal energy only (Figure 14). The ORC is supplied by heat obtained by a geothermal well in which geothermal brine is about at 95°C, but in order to improve system performance, additional heat is provided by solar energy obtained through a 25 m2 solar field. In fact, it resulted that the majority of the thermal energy supplied to the ORC is due to the contributions of geothermal source which is dominant over the solar one. The efficiency of solar collectors, ORC and ACH are 59.2%, 6.4% and 0.68, respectively. Here, a sensitivity analysis in order to investigate the effects of the variation of groundwater temperature, from 90 to 100°C is performed (Figure 15): the higher the well geothermal brine temperature, the higher the ORC inlet temperature. This leads to an increase in the ORC output electricity Eel,ORC, due to the improvement of ORC efficiency, ηORC, from 6.4% to 7.2% and to a decrease of the energy supplied by the auxiliary heater, Eth,AH, since the geothermal source is able to achieve the ORC minimum inlet temperature. Conversely, the produced cooling energy, Ecool,ACH, is not significantly affected by the variation of well temperature.

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Figure 14. Sketch of the system layout [18].

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Figure 15. Sensitivity analysis: energy, efficiency vs. well geothermal brine temperature [18]. A hybrid solar and geothermal polygeneration system combining CPVT field, a single-stage LiBr/H2O ACH and a MED unit was studied through dynamic simulations in [3]. The plant provides electrical, thermal and cooling energy, DHW and a quantity of desalinated water able to cover the whole request of the Pantelleria Island, assumed as case study. In particular, as depicted in Figure 16, solar thermal energy, at a maximum temperature of about 100 °C, in combination with the thermal energy produced by low-enthalpy (about 80 °C) geothermal wells (GW), is used to supply the MED system. Geothermal energy is also used to produce DHW at 45 °C. The performance predicted through dynamic simulations, supported by appropriate economic models, was excellent. This work is particularly interesting because it presents a comprehensive assessment on combination of different kinds of technologies (SHC, CPVT, GW and MED). Several additional islands in the Italian Mediterranean Sea, Ischia and Aeolian Islands, show a similar potential in terms of availability of geothermal and solar energy are also investigated. For all the investigated locations, a parametric analysis aiming at evaluating the variation of Profit Index as a function of the ratio of DHW produced by the system (QHE4) and demanded by the user is performed. It resulted that Profit Index dramatically decreases in case of scarce DHW demand.

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Figure 16. System layout [3].

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Other studies concerning the dynamic simulation of a novel hybrid solar and geothermal polygeneration system capable to produce electric energy, fresh water and space heating and cooling, for the Pantelleria Island was presented and discussed in references [16] and [37]. Here, with respect the previous layout, an ORC supplied geothermal and solar energy is considered. In particular, in reference [3], a low-temperature geothermal well (85°C) is used, mainly for the desalinization process, whereas here a medium-enthalpy

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geothermal source (160°C) is considered. In addition, in this work, the CPVT collectors are replaced by PTC field coupled with a thermal storage tank (Figure 17). The MED unit and ORC were developed in Engineering Equation Solver (EES).

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Figure 17. System layout [16, 37].

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From the energy analysis it resulted that the solar energy input is much lower than the geothermal one, as highlighted by the low value found for the solar fraction, 9.60%; the ORC efficiency is about 11.6%. During the year the MED unit is able to produce fresh water, equal to 54% of the total seawater flowrate. An accurate energy, economic, exergy and exergoeconomic analysis of the system was performed, from which it was found that the global exergy efficiency varies from 40% to 50% during the thermal mode and from 16% to 20% during the cooling one [37]; besides, the exergoeconomic costs of electricity, cooling water, chilled water and desalinated water resulted very interesting, respectively in the ranges 0.1475–0.1722 €/kWh, 0.01612–0.01702 €/kWhex, 0.1863–0.1888 €/kWhex and 0.5695–0.6023 €/kWhex. The previous works [16, 37] are developed by considering that all the useful products are consumed locally. Instead, in a further study [39], based on a similar layout, the system is supposed to be connected to a district electrical, heating and cooling network. In this case, the energy provided by the system must match the real time-dependent demands of electricity and space heating and cooling of typical buildings of Pantelleria Island (assumed as a case study). The system achieved a SPB equal to 8.50 years, with a potential primary source saving of 37.5 GWh/year and a potential CO2 avoided emission of 9451 tons/year. It resulted capable to cover the energy demands of 800 examined buildings. Moreover, the plant produces 1006 103 m3 of desalinated water and it is capable to cover the fresh water global demand. Average ORC efficiency amounts to 15.30%; the system is mainly powered by geothermal energy in fact, the average solar fraction is only 14.6%.

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In reference [79], the authors proposed an interesting renewable multi-generation system, which integrates geothermal energy and solar air PVT system, produces electricity, hot water, drying air and capable to perform space heating and cooling. Power is provided by the PVT system and by the ORC; this latter is powered only by medium–high geothermal source. Cold water is provided by a LiBr/H2O ACH, whose thermal input at the generator is represented by the ORC waste heat. The cooling output is used by a dairy farm, in which heating is provided by an electric heat pump. Ambient hot air coming from the solar PVT system is conditioned for the food drying process in the farm. The overall energy and exergy efficiencies of the multi-generation system are

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calculated to be 11% and 28%, respectively. The exergy efficiency of the solar system is around 12%. The COP and the exergy efficiency of the ACH are 0.73 and 0.21, respectively. COP and exergy efficiency of heat pumps were 4.1 and 0.03, respectively. For the ORC, the energy and exergy efficiencies are 9% and 42%, respectively.

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In reference [80], two novel ORC-based CHP systems powered by solar and low-temperature geothermal (90 °C) sources are presented and compared. Both the configurations are designed to provide 50 kWel, 400 kWth and in both cases three working fluids (e.g. R134a, R236fa, and R245fa) were considered. In particular, one configuration is based on evacuated tube collectors and the power is produced by a single turbine, while the second one is based on a double stage system, in which a field of evacuated solar collectors heats the working fluid up to an intermediate temperature. After this first stage, only a part of the working fluid is heated in a second solar field, composed of direct-steam PTCs, up to the maximum temperature of the cycle. Mechanical work is then produced by two turbo-expanders arranged in series. In both configurations, the best performance in terms of first law and exergy efficiency was obtained with R245fa (Table 8); the best performance in terms of heat recovered was obtained with R134a. Moreover, findings suggested that single-pressure configurations show a better performance, at design conditions.

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Table 8. Results: efficiencies for first and second configuration for three working fluids. Fluid

R245fa

Configuration System efficiency (%) Cycle efficiency (%) Exergy efficiency (%)

513

Second 6.9 8.9 17.5

R245fa First 9.78 11.3 23.3

Second 7.4 9.5 18.4

R245fa First 13.0 15.1 25.0

Second 10.0 12.7 20.0

In Table 9, some of the works concerning hybrid solar-geothermal polygeneration systems are listed.

514

Table 9. Works on hybrid solar-geothermal polygeneration systems.

Calise et al. [3]

Solar technology CPVT

Geothermal temperature 80°C

Calise et al. [16, 37]

PTC

160°C

Work

Suleman et al. [81]

flat-plate evacuated PTC flat-plate evacuated, PTC PTC

Al-Ali and Dincer [82]

PTC

190°C

Islam and Dincer [83]

PTC

167°C

Buonomano et al. [18] Calise et al. [39] Tempesti et al. [80]

515

First 9.1 10.5 22.7

Prime mover -

Power, heat, cool, fresh water

95°C 160°C 80°C-100°C 110°C

Output

Power, heat, cool ORC

Power, heat, cool, fresh water Power, heat Power, heat, cool, dry wet products Power, cool, heat, industrial process heat Power, heat, cool, dry air

3.3 Mixed energy inputs

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One of the most promising peculiarities of the polygeneration system lies in the possibility of hybridizing existing technologies, based on fossil fuels, with renewable energy sources. Several works available in literature on polygeneration systems, present the integration of renewable technologies (solar, geothermal, wind, etc.) into different conventional systems (trigeneration, DHC, gas-fired boiler, electric heat pump) or are based on the coupling of CCHP and renewable energy systems in a plurality of applications [135]. The main potential applications of hybrid polygeneration systems are represented by hospitals, which have large and constant cooling and thermal loads, but also hotels, residential districts, schools, commercial buildings and so on [136].

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In this framework, a theoretical analysis focused on the feasibility of a hybrid plant applied to a real hospital located in Ferrara, Italy, is presented in reference [137]. Several hybrid schemes were investigated and compared: i) the conventional system consisting of electric grid, gas-fired boilers and compression chiller; ii)

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PAFCs (phosporic acid fuel cells) integrated with ACHs and gas-fired boilers; iii) solar thermal collectors coupled to Rankine power cycle and ACHs; iv) PV panels coupled to conventional systems.

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Basrawi et al. [26] investigated a hybrid energy system based on a polycrystalline PV and MGT (Figure 18) system able to produce power, heating and cooling of a group of residential buildings under a tropical region. Some of the electricity produced can be stored to the battery. Exhaust heat of the MGT is recovered by the heat exchanger, and it is used to cover water heating demand of the houses, and the rest is supplied to the absorption chiller. The system was compared with another system without PV.

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Figure 18. Configuration of the overall hybrid energy system for all cases [26].

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It resulted that when the proposed system operates with power-match operation strategy, the highest Net Profit is obtained. This circumstance occurs mainly because in this case no battery system is needed, and the related high cost can be saved. However, this system presents low environmental performance especially if compared to a combined cycle gas turbine, because larger MGTs in this system operated frequently under partial load. Operation strategy that had smaller MGT that operated under full load can still generate Net Profit but at lower degree because it needs larger PV and battery.

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In reference [67], an existing trigeneration system was hybridized with a solar field equipped with concentrating photovoltaic thermal (CPVT) collectors. The system includes a gas turbine, CPVT collectors, ACHs, tanks and balance of the plant (BOP) devices. The system supplies electricity, heat and cooling and was designed and dynamically simulated to be installed at the District Hospital of Naples, (South of Italy). Energy and economical parameters were also evaluated. The system profitability was found to be acceptable (pay-back period equal to 15 years), even in the case of no public funding. However, if a feed-in tariff of 0.30 €/kWh (similar to the one adopted in Italy for PV systems), the SPB period becomes 6.1 years. Considering also a possible feed-in tariff of 0.10 €per kWh of thermal energy produced by the CPVT, the SPB becomes 4.1 years.

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A polygeneration system including renewable energy sources and a natural gas engine, for residential and tertiary buildings, was analysed by Soutullo et al. [12]. The system, modelled in TRNSYS, includes: PV panels, flat plate STC, biomass boilers, wind turbines, polymer electrolyte membrane fuel cells (PEMFCs), ORC (Figure 19) and a single-effect ACH. A natural gas engine is used as a backup when the renewable technologies cannot supply the thermal district loads. The “design of experiments” methodology was used.

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Figure 19. Blocks diagram of the polygeneration plant [12].

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The results of the dynamic simulations were used to analyse the performance of centralized polygeneration system in three representative buildings, located in different Spanish cities (Oviedo, Seville and Zamora). In the thermal model, two cases (Case 1 that maximizes the operation of the solar thermal field and Case 2 that minimizes the power of the biomass boilers) were evaluated, with renewable fractions equal to 90% and 50%, respectively. In both cases, Seville obtained the lowest solar contribution due to the highest cooling district demand. In the electrical model, three configurations were evaluated: PV + PEM, wind + PEM and PV + Wind + PEM. The last configuration achieved the best results.

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An interesting work was presented by Arsalis et al. [40], reporting a thermoeconomic analysis of a 1 MW CCHP system driven by liquefied natural gas (LNG) and assisted by PV. The system includes a gas turbine cycle, ACHs, heat exchangers, LNG storage, a PV solar field and BOP components (pumps, valves, etc.); it is designed to serve a complex of households in Nicosia, Cyprus. The system was modelled in EES and simulated at both part-load and full-load conditions. The simulations results, performed on an annual basis, showed that the primary energy ratio is almost constant at all load condition and that the CCHP can completely fulfil the load profile of 436 households. In addition, without PV integration, the system must generate an additional 1959 MWh/year of electricity by LNG conversion. The economic analysis shows a minimum of the life cycle cost (LCC) around a PV capacity of 300 kW.

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The energy and economic feasibility of a solar-assisted heating and cooling system and DHW production for a school building in different Italian climates was assessed by Calise [68]. Here, evacuated solar collectors, a single-stage LiBr/H2O ACH, and a conventional electric-driven reversible heat pump are simulated by using the software TRNSYS. From the energy point of view, results are encouraging, for the potential of energy saving: 64.7%, 52.4% and 61.4% for Naples, Milan and Trapani, respectively. On the contrary, the economic profitability can be achieved only in case of public funding policies (e.g. feed-in tariffs), as always happens for the great majority of renewable energy systems, since the results SPB are 12.1, 15.7 and 12 years for Naples, Milan and Trapani, respectively.

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Some of the studies regarding the simulation of hybrid polygeneration systems, fuelled by fossil and renewable energy sources are listed in Table 10.

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Table 10. Works on hybrid polygeneration systems. Work

Renewable technology

Fossil fuel

Prime mover

Output

Basrawi et al. [26]

PV

Natural gas

MGT

Power, heat, cool

Calise [19]

PTC

Soutullo et al. [12]

PV, flat plate STC, biomass boilers, wind

Methane, natural gas Natural gas

SOFC PEMFC, ORC

Power, heat, cool, hydrogen Power, heat, cool

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turbines, Arsalis et al. [40] Marrasso et al. [42] Calise et al.[49]

PV Evacuated tube collectors High-vacuum flatplate collectors

Kieffer et al. [138]

GT

Natural gas

ICE

Natural gas

CC

Natural gas / municipal solid waste Coal, Natural gas

CC

Power, transportation fuel

GT, ST, FC

hydrogen, power, heat and captured CO2

Ondeck et al. [135]

Biomass (wood) gasifier Evacuated tube Collectors, Biomass (crops) gasifier PV

Bizzarri et al.[137]

PV, STC

Natural gas

Buonomano et al. [67]

CPVT Evacuated tube collectors

Natural gas

ICE, GT, MGT, FC, STE GT phosporic acid fuel cells, ST GT

Methane

FC

Tock and Maréchal [122] Rubio-Maya et al. [124, 125]

Calise et al. et al. [74]

585

-

Liquefied natural gas

Coal, Natural gas Natural gas

Meerman et al. [96]

Gasifier (Oil residues, and wood)

Coal

GT, CC

Ng et al. [97]

Bio-oil gasifier

Natural gas, coal, CO2

CC, GT

Ozturk and Dincer [84]

Solar power tower

Coal

CC, FC, ST

Power, heat, cool and fresh water

Power, heat, cool

hydrogen, electricity, FTliquids, methanol and urea Power, hydrogen, methanol, methane, acetic acid Power, heat, cool, hydrogen, oxygen

4. Conversion technologies

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Prime movers, converting a fuel into power and heat, usually represents the main components of a polygeneration plant. They can be selected according to different criteria, including input fuels, capacity, investment and operating cost, availability, etc. [139].

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4.1 Gas turbine (GT)

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The use of gas turbines in CCHP systems is investigated in several researches [140-142]. Usually, in such systems, high-temperature flue gas from the turbine are used for heating purposes; if an absorption chiller, ACH, is included, the waste heat can be also used for cooling purposes. An example of this configuration is reported in the work of Ziher and Poredos [140], who evaluated from an economic point of view a trigeneration system in the Slovenian biggest hospital (Figure 20), with a peakload of 4.2 MW. In particular, the main cogeneration unit is a GT, used to cover the high needs of electrical and thermal energy; gas-fuelled reciprocating engines are also used to provide up to 3 MW of power; over this limit, a GT is used. The steam generated in a heat-recovery steam generator, HRSG, is used for heating and sanitary hot water. A hot-water gas boiler provides additional heat during the winter period. A two-stage absorption chiller with a capacity of 2 MW is included, along with two back-up electric chillers with a capacity of 2 MW and 1.5 MW, respectively.

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Figure 20. The concept of energy supply with a trigeneration system [140].

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In this work, the author focused mainly on the following aspects: i) economic analysis (Table 9), evaluating payback period (static method), net present value and profitability index (dynamic method); ii) optimization of the cooling production (with and without cold storage).

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Table 11. Economic evaluation of the trigeneration system analysed in Ref. [112]. Static method

Trigeneration without cold storage Trigeneration with cold storage

Dynamic method

Payback period (years)

Net present value (Mill. Euro)

Profitability index (–)

6.71

2.02

1.07

5.86

2.72

1.09

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Ghaebi et al. [141] performed an energy, exergy and thermoeconomic analysis of a CCHP system with gas turbine. Here, the GT flue gases are used in a dual-pressure HRSG to generate low and high pressure steam; the first one is supplied to an ACH, the second one is used directly. The CCHP has a maximum cooling capacity of 6.96 MW; the thermal capacity is 24.65 MW, the maximum power output is 19.23 MW; first and second law efficiency were 84.01% and 43.92%, respectively.

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A similar configuration is analysed in reference [142]. The thermodynamic performance of such system was assessed in terms of energy efficiency, electrical-to-thermal energy ratio and exergy efficiency. It was found that the maximum exergy destruction is due to the combustion and steam generation processes, representing over 80% of the overall exergy destruction.

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In case of small power applications, micro-gas turbines (MGT) can represent an interesting options [85]. In particular, MGT are sometimes preferred to other small-scale prime movers, such as reciprocating engines, mainly due to their good overall efficiency, low maintenance requirements and low emissions [143]. In this framework, the combination of MGT and ACHs in CCHP systems is often investigated. An example is reported in reference [75] (Figure 21); here, a real system, installed in a supermarket, is analysed from both experimental and numerical points of view. The system is based on a 80 kWe recuperated model (MTG 80RC-G), with a built-in hot water heat exchanger. Three MTG units and several absorber chillers are included. The system was simulated in TRNSYS, and the model was validated by comparison with experimental test results. Finally, the model was used to predict the system performance at different operating conditions, varying ambient temperature, fuel flow rate and pressure ratio, etc.

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Figure 21. Schematic diagram of the tri-generation test facility [75].

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In particular, the Primary Energy Rate (PER) of the system is evaluated, defined as the ratio of the primary energy input (i.e.: the net fuel consumed) to the overall required output (i.e. the sum of the net power generation and cooling capacity). Therefore, the system with the lowest value of PER is considered the best with respect to energy consumption. The results show an interesting trend of PER as a function of ambient air temperature: since both the power generation and cooling capacity decrease in case of a higher ambient air temperature, the PER value, as a result, increases with the rise in ambient air temperature. By varying the pressure ratio from 1.5 to 7.0 (Figure 22), turbine production as well as the compressor consumption increase, with a maximum net power at around 3.0 pressure ratio. Conversely, cooling capacity firstly decreases and gradually rises with a higher pressure ratio. The lowest PER value, about 4.0, can be expected when the pressure ratio is almost 3.0.

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Figure 22. Power and cooling capacity vs. pressure ratio (below) [75].

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Medrano et al. [87] developed a thermodynamic model of an integrated energy system made-up by a MGT and a single-double effect exhaust fired absorption cycle, which recuperates the heat exchanged from the MTG exhaust gases using two generators at two different levels of temperature. An advantage of this system is that the exhaust gases leaving the high temperature generator of the double-effect cycle can then be further utilized to drive the single-effect cycle, producing an extra cooling effect. The overall exergy efficiency of the system resulted equal to 0.285, higher than the ones resulting from the same MTG and exhaust gas fired single or double effect chillers.

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In reference [13], the energy and exergy performance of a small scale CCHP with GT was assessed; the system serves a cluster of buildings, and was analysed through a dynamic simulation, at design and part-load conditions. Here a double-effect ACH is coupled to a MGT with a power capacity of 1748 W. The Fuel Energy Saving Ratio (FESR) is calculated, defined as the ratio of the energy saving of the CCHP system, in comparison with a conventional system with separate productions (SP), to the overall energy consumption of the reference system.

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At design conditions, the FESR has a value as high as 26.6%. The lower the power load, the smaller the FESR value. When the load decreases to approximately 30% of the peak value, the FESR is zero, which means that the energy saving performance of the CCHP system is identical to the SP system. So, the CCHP system allows one to achieve energy savings only when the power output of the gas turbine exceeds 30% of the full load. Similar results are shown for the CO2 emissions.

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A thermodynamic simulation of a trigeneration system consisting of a 28 kWe MGT coupled to a doubleeffect ACH and a heat exchanger to produce hot water is performed in ref. [103]. In particular, a certain amount of the energy of the exhaust gases at high temperature of the MGT are used to supply heat to the generator of the ACH, and the remaining part is used to produce hot water.

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The trigeneration system was evaluated in different operating conditions, varying: i) MGT fuel mass flow rate; ii) ambient temperature: the higher the ambient temperature, the lower the MGT power; iii) generation temperatures, showing the sensitivity of the ACH performance to the variation of the temperatures of the MT exhaust gases (Figure 23): for example, if the ambient temperature is equal to 36 °C, the ACH cannot operate at generation temperatures lower than 146 °C; furthermore, at an ambient temperature of 28 °C a generation temperature close to 166 °C is needed to obtain the highest COP of 1.289.

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Figure 23. COP against generator temperature at five different ambient temperatures [103]. The possibility of integrating an externally-fired micro-gas turbine (EFMGT) with a membrane distillation plant in a stand-alone application for the production of electricity, heat and drinking water is investigated by Rahman and Malmsquit [104]. Here, a simulation model of the components is developed (MGT, electrical and thermal loads, membrane distillation unit, and electrical and thermal storage) in Matlab/Simulink environment. A maximum overall efficiency of 72% was found, at the rated electrical output of 2 kW. The authors state that to maximize the overall efficiency, it is required to maximize the use of thermal waste. However, maximizing the overall efficiency subsequently leads to a lower temperature level at the demand side, and thus, the efficiency of the pure water production decreases. Therefore, such a polygeneration system can perform more efficiently in colder regions. Another important conclusion of this work is that the fuel consumption does not decrease significantly with part-load conditions. As a result, such EFMGT system should always operate at the rated electrical load, to obtain a high efficiency. 4.2 Steam turbine (ST) Several research works available in literature are focused on the way to improve the total efficiency of power plant and CHP based on steam turbines [35, 36]; such technology is often included in polygeneration systems [105]. Three different CHP systems designed to be installed in a sugarcane factory were simulated with the EES software in ref. [4]; they are based on a back-pressure steam cycle (configuration #1, Figure 24 b), a condensing steam cycle (configuration #2, Figure 24 c), and a combined cycle (configuration #3, Figure 24 d), respectively.

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Figure 24. Scheme of sugarcane production and different configurations of cogeneration systems integrated with the sugarcane factory: (a) diagram of integrated production of electricity, sugar and molasses; (b) configuration #1: backpressure steam cycle; (c) configuration #2: condensing steam cycle and (d) configuration #3: combined cycle (gas turbine+steam turbine) [4].

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In this system, the bagasse produced in the extraction system of sugar cane is delivered to the cogeneration system, where it is used as fuel, to produce the electricity and steam consumed by the process. The most relevant results are given in Table 12.

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Table 12. Performance of the systems simulated in ref. [4].

Configuration

Energy efficiency overall (%)

Bagasse (+) excess (−) shortage (t/year)

Electricity excess (MWh/year)

#1 #2 #3

84.2 65.2 56.9

(+) 316,400 0 (−) 301,600

119,300 306,100 503,100

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It resulted that the global energy efficiency of the polygeneration system decreases when passing from configuration #1 to #3, but the economic value and the “quality” (exergy content) of the total product increase, due to the share of electricity production which also increases. Configuration #2 produces a considerably higher amount of electricity (186,800 MWh/year) with respect to #1. This increase of electricity production is obtained by consuming 316,400 t/year of bagasse, which represents a virtual electrical efficiency of 28%. In the case of configuration #3, the surplus electricity production is 503,100 MWh/year higher than #1 one; but for this configuration it requires buying more bagasse.

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In ref. [112], a steam turbine is integrated in a lignocellulosic wood-to-ethanol process. The simulation study was carried out using the software IPSEpro™. The system evaluated is based on an existing CHP plant

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and on the pilot scale plant for the ethanol production. In particular, measured data from operation of the Enköping (Sweden) CHP plant were used for modelling and validating the CHP plant model, whereas the configuration of the pilot scale plant, owned by two universities of Örnsköldsvik (Sweden), was used for modelling the ethanol production. The total efficiency was found to be around 50%, meeting the heating load of the district heating system. The authors concluded that the ethanol production from wood is more efficient when integrated into a CHP plant, in comparison to the case of stand-alone production: in particular, the total biomass consumption is reduced by 13.9% while producing the same amounts of heat, electricity and ethanol as in the stand-alone configurations. In addition, a higher time of operation was obtained for the existing cogeneration system. Thus, the annual production of renewable electricity increased by 2.7%.

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Cormos [109] developed a combined polygeneration process for power, hydrogen and carbon dioxide production, consisting of a steam turbine, a bio-methanol steam reformer and carbon capture technologies. In particular, the study is focused on the hydrogen production from bioethanol at industrial scale, investigating three carbon capture designs: one based on pre-combustion capture using chemical gas–liquid absorption (Case1) and two based on chemical looping, using syngas (Case 2) or direct bioethanol looping (Case 3, shown in Figure 25). In all the cases, the heating duties needed for various processes (e.g. reformer, carbon capture by gas–liquid absorption or chemical looping) are recovered from available hot streams within the plant, therefore, the only energy input of the plant is the bioethanol feedstock. The author modelled and simulated the different carbon capture designs with ChemCAD.

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Figure 25. Layout of bioethanol conversion for hydrogen production with direct bioethanol chemical looping (Case 3) [109].

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From the results, it can be noticed that the chemical looping cases (Cases 2 and 3) reach higher energy efficiency (59–63% vs. 53–58%) and almost total decarbonisation of the fuel (>99% vs. 80–92%). Case 3 resulted the most promising, mainly for the feature to capture almost total carbon of the feedstock and to have a lower plant complexity. In particular, the operation of this configuration expects that the bioethanol is vaporised and then it is introduced in the fuel reactor together with the oxygen carrier. The bioethanol is totally oxidised to CO2 and water. The energy efficiency in Case 3 resulted equal to 63.66%, vs. 59–62% of bioethanol reforming without carbon capture.

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A new power and refrigeration cycle was simulated in ref. [29], where a Rankine cycle and the ejector refrigeration cycle are combined to produce simultaneously power and refrigeration simultaneously with only one heat source. This system can be driven by the flue gas of gas turbine or engine, solar energy, geothermal energy, industrial waste heat. In the paper, a heat source at the constant temperature of 150 °C was considered. An exergy and parametric analysis and a parametric optimization were performed. In particular, the parametric optimization was performed by means of genetic algorithm, aiming at reaching the maximum exergy efficiency. The results show that the biggest irreversibility is located in the heat addition process and in the ejector. An overall efficiency of 13.72% was found, defined as the ratio between the useful energy output (net power plus refrigeration effect) and the total heat input; the exergy efficiency in optimum conditions was 27.10%.

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4.3 Combined Cycles plants (CC)

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CC plants represent the most efficient technology for energy conversion, and a common option for new thermoelectric plants all over the world [100]. In combined cycles (CC), the waste heat of a gas turbine (topping cycle) is used to drive a steam turbine (bottoming cycle), so increasing the overall thermodynamic efficiency up to 55% and over [24].

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An example of integration of CC as prime mover into a trigeneration plant is reported in ref. [21]. Here, the thermodynamic modelling of a trigeneration system based on a gas turbine, a steam turbine and a singleeffect ACH is carried out; an exergy and an environmental analysis are performed. The layout of the system is depicted in Figure 26. The hot gases of GT are utilized in the dual-pressure HRSG with two economizers and two evaporators to generate low-pressure (LP) and high-pressure (HP) steam. The LP steam is used to drive the ACH and the HP steam to generate electricity. Some of the results obtained are reported in Table 13, putting in evidence lower CO2 emissions and higher exergy efficiency with respect to typical CHP systems or GT cycles.

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Figure 26. The schematic of the trigeneration system [21].

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Table 13. Main results of the energy and exergy analyses in ref. [15].

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Parameter

Value

Qheating (MW) Qcooling (MW) Wnet (MW) ηI,Tri (%) ExD,Tot (MW) ηex,Tri (%) Normalized CO2 emission (kg/MWh)

46.60 3.50 35.15 75.50 65.80 47.50 158

A similar layout was investigated by Wang et al. [60], who assessed a new CCHP system integrating a gas turbine and a heat-driven cooling/power cogeneration system, using Aspen Plus . In particular, the layout used in this work includes: an ammonia-water Rankine cycle; a single-double effect LiBr/H2O ACH, which better matches the temperature variation of ammonia-water condensation; a Bryton cycle; a hot water exchanger. The novel system was compared to a reference CCHP system including a gas turbine, a double-effect ACH and a heat exchanger. Two evaluation criteria were adopted to evaluate the CCHP performance: exergy efficiency (ηex) and fuel energy saving ratio (FESR). Thanks to the high-temperature heat cascade utilization in the CCHP,

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it resulted that with the same amount of exergy input (7477.78 kW), the exergy output of the system under analysis is 2833.58 kW, and that of the reference system is 2265.09 kW; ηex reaches 37.89%, which is 7.60% higher than that of the reference system, and the FESR is 31.70%, which is 5.19% higher than that of the reference system.

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Several works are also focused on polygeneration systems coupling CC systems with a gasification technology (coal gasification or biomass gasification), obtaining an integrated gasification combined cycle (IGCC). In an IGCC scheme, the solid feedstock is partially oxidized by oxygen and steam to produce syngas (mainly a mixture of carbon monoxide and hydrogen). An example of this configuration is reported in ref. [119], where the modelling and simulation of a novel polygeneration technology is performed, used for the coproduction of hydrogen and power based on coal gasification integrated to a CC with a dual chemical processes (with carbon capture and storage and with calcium-based chemical looping).

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A further simulation study regarding coal gasification, coupled with methanol synthesis and CC for power production with CO2 recovery, is presented in Lin et al. [17]. Here, the proposed system was analysed with and without CO2 recovery. In particular, the polygeneration system proposed is based on the concept of CO2 generation, reaction, transportation, and conversion into energy, with the production of methanol as a clean fuel (Figure 27). In this way, the polygeneration system can recover CO2 with reduced (or even without) energy penalty.

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Figure 27. Sketch map of the integration of cascading utilization of chemical energy and CO2 capture [17].

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It was found that the polygeneration system without CO2 recovery consumes less energy for the methanol production (about 76% of the traditional methanol production), and achieves higher power generation efficiency, 15% points higher than that of the traditional power generation technology, i.e. IGCC power plants). In case of CO2 recovery, the energy consumption for methanol production is much less than those of the Fischer–Tropsch fuel and conventional methanol production.

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As mentioned above, the biomass gasification represents a further technology which can be used to generate both process heat and significant amounts of electricity. An example where this technology is used in a combined cycle plant is in ref. [61]. Here, the electricity produced is used to match the needs of dry-grind ethanol facilities by utilizing the ethanol process co-products and other biomass sources (corn cobs). The model of the dry-grind ethanol process is simulated by Aspen Plus and, then, used as basis for modelling a gasification system. The authors report that this technology is able to reduce fuel costs for ethanol plants and that the renewable energy ratio of ethanol production could be improved from a typical value of 1.7 up to 5.1; according to this study, a dry-grind ethanol facility with a capacity of 190 million litres per year could produce 30.4 MWe of power while covering all its needs of process heat by using ethanol co-products and corn cobs.

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4.4 Organic Rankine Cycles (ORC) The technology of Organic Rankine Cycles (ORC) is very attractive in applications where heat at relatively low-medium temperature is available for conversion into electric energy [62]. In fact, many organic fluids show a performance significantly better than water in Rankine cycles with low maximum temperature, thanks to a higher molecular weight, lower evaporation heat, positive slope of the saturated vapour curve in the T–s diagram and lower critical and boiling temperatures [44]. In addition, for small-size applications, ORC turbines show a

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few advantages in terms of maintenance, operation life and part-load efficiency. Anyway, for such systems the selection of an appropriate working fluid is crucial [144]. Several works in literature deal with the simulation of polygeneration plants including ORC systems supplied by low-medium temperature sources (geothermal heat [18], solar energy [43, 44], waste heat [45], biomass products [88], etc.).

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In the work reported in reference [22], an ORC-based trigeneration system is analysed, supplied by the waste heat recovered from a micro GT. The system was modelled in Matlab, whereas the properties of the working fluid (n-octane) were calculated with EES; an exergo-environmental analysis of the system was performed and discussed. The system layout mainly consists of a GT cycle, an ORC, a single-effect ACH and a DHW heater (Figure 28), producing heating, cooling, hot water and electricity.

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Figure 28. Schematic of the trigeneration system [22]. The exergy-environmental analysis shows that the highest exergy destruction occurs in the combustion chamber and heat exchanger (Figure 29), mainly due to the irreversibilities associated with the large temperature difference occurring in these components. The absorption cycle does not exhibit significant exergy destructions. It resulted that the system with ORC has less CO2 emissions and higher exergy efficiency than the GT alone, providing a significant motivation for the use of trigeneration cycles.

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Figure 29. Exergy-environmental results [22].

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The simulation of an ORC system, driven by the waste heat recovered from a diesel engine generator, is studied in ref. [145]. In particular, the aim and the novelty of this work is to investigate the system performance and efficiency of a small scale trigeneration system integrating an ORC system into a diesel engine generator,

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used as primary mover. The ORC model is developed in Matlab and the diesel engine generator of the laboratory at Newcastle University is used as the case study. R245fa is used as the working fluid. In addition, in order to expand the system supply capacity, the trigeneration system is also coupled to a smart energy storage system. Based on the simulation results, with the energy storage system, the capacity of the electric cycle increased by 47%. The ORC system may generate 0.737 kW of power, by contributing around 8.91% of the total electricity production, with 9.25% efficiency at the generator full-load operation point. The waste heat recovered from the engine is 35.3% of the total energy. Regarding the heating and cooling energy demand, for the selected household, it is demonstrated that the ratio of the recovered heat by ORC satisfies the 41% of the heating energy demand and that part of waste heat used by ACH produce 9.36% of the cooling energy demand.

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Solar thermal collectors can be adapted to collect solar energy at low temperature and provide the heat necessary to supply ORC plants. An example of this configuration is reported in the study of Sharaf et al. [30], where the thermal energy produced by a field of PTC is used to drive the ORC via evaporator heat exchanger. In this plant, the exhausted energy from the ORC turbine is also used in the first effect of a MED process, producing power and desalted water (power and solar desalination with MED, PSDMED). This system was compared with a further system, where the solar energy is directly utilized from the via evaporator heat exchanger to the first effect of MED process, producing only potable water (solar desalination with MED, SDMED). The most important results obtained in the both systems are summarised in Table 14, considering a MED plant with a capacity of 5000 m3/day.

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Table 14. Results for the systems analysed in [30], for a capacity of distillation equal to 5000 m3/day. Parameter

Unit

SDMED

PSDMED

Solar field area Specific power consumption

m2 kWh/m3

1.009×105 2.17

1.32×105 2.67

-

7.56

3

$/m3 $/h MW % MW kg/s

1.645

1.845

155.7 31.82 – 7.65

157.8 33.1 5.381 46.05

Gain ratio* *Distillate

mass flow rate / Steam mass flow rate

Total water price Total investment, operating and maintenance cost Total exergy destruction Overall exergy efficiency Turbine power Steam mass flow rate

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Comparing the total cost of the desalinated water, the solar field area and the total exergy destruction rate, it is evident that the PSDMED performs slightly better than the SDMED, with the advantage represented by the availability of a significant power provided by the ORC system.

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An ORC-based polygeneration system supplied by biomass, combined with water desalination, was studied by Maraver et al. [62]. The system consists of a small-scale ORC engine, producing power and heat; this latter is recovered for direct heating purposes or DHW production, to generate cooling by a LiBr/H2O ACH and to produce desalted water through a MED unit. The ORC subsystem was modelled using Aspen Plus. The configuration proposed is compared to stand-alone systems producing the same energy and material outputs. The authors investigated the behaviour of the plant by varying the main design and operating parameters of each components, including the working fluid for the ORC unit; among 33 working fluids analysed, it was found that Fluorobenzene and octamethyltrisiloxane offer good cycle efficiencies as well as acceptable densities and specific power capacities. The authors also determined the optimal distribution of the heat generated by the ORC, finding out that the highest savings were obtained when the amount of heat used for heating or DHW production was around 60%. Moreover, an economic analysis was performed, and it was found that the SPB ranged between 4.00 and 20.0 years, depending on the price of biomass and on the capital cost of the MED unit.

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4.5 Internal Combustion Engine (ICE)

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Internal combustion engines (ICEs) represent a very mature, largely available and well-known technology [146], and, depending on their design, can use different fuels: natural gas, biogas, biodiesel, etc. [11]. Such engines are the most widely used prime movers in small- and medium-scale distributed energy resource (DER) systems [147], due to their low capital cost and robustness [148]. Some limitations are the need for frequent maintenance, vibration, noise and emission issues [147]. It must be noted that in an ICE, only 30–40% of an engine's fuel combustion energy is converted into electricity [149]. So, a significant amount of waste heat must be dissipated, mainly through expulsion of exhaust gases and engine cooling system. In order to recover such waste heat, conventional ICEs are often coupled to heat exchangers. In addition, ICEs can be easily coupled with other devices, such as adsorption [126] and absorption chillers [127], heat pumps and electric chillers [63, 150, 151].

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An example of this application is investigated in the simulation and experimental work carried out by Wu et al. [126]. In this work, a micro-CCHP based on a 16 kWe ICE is investigated, whose exhaust heat is used to drive a 7.5 kW adsorption chiller. A thermal management controller (TMC) is also included to manage the waste heat: the heat is sent with the highest priority to a buffer tank, then to a hot water cycle, and finally to the cooling water cycle (Figure 30); this depends on the set point temperatures of buffer and hot water tanks. The heating load is controlled by fan coil 2 and fan coil 3, the cooling load by fan coil 1. The model of ICE is based on the statistical data provided by ASHRAE for naturally aspirated, small ICE. Experimental results were found to be in good agreement with the simulations. It resulted that the system can provide simultaneously a power output of 16 kW electric, up to 17.7 kW for heating purposes and 6.5 kW for cooling (Figure 31). In particular, as shown in this figure, when the electric output is low, the heating output is nearly 0, namely all the waste heat is absorbed by the adsorber. When the electric output is over 6.4 kW, the heating output starts to increase while the cooling output and COP of adsorber both change slightly. Namely, when the electric output is over 6.4 kW, the waste heat recovered by TMC exceeds the heat transfer capacity of the first stage and part of waste heat is assigned to the second stage.

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Figure 30. Layout of experimental micro-CCHP system [126].

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Figure 31. Influence of electric output on heating and cooling output [126].

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The simulation of a medium size ICE (power capacity of 300 kW) with Aspen Plus was presented by Chen et al. [63]. Here, the waste heat recovered from the jacket water circuit (at 90 °C) and from the exhaust gases (at 500 °C) is used to generate additional power, by means of a turbine, and cooling energy, by an ammoniawater absorption chiller. In design conditions, the heat recovered from the exhaust gas and jacket water are 289.56 kW and 180.47 kW, respectively. The net power output of the turbine is 14.76 kW and the refrigeration output is 78.10 kW, calculated as the ratio of the cooling output (225.72 kW) to a reference COP of 2.89. So, the corresponding total equivalent power output is 92.86 kW. An equivalent heat-to-power efficiency of the combined production of power (through the GT) and cooling is defined and resulted equal to 19.76%. An exergy efficiency of 33.69% and a payback period of 2.84 years were estimated.

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An internal combustion engine included in a CHCP system for a small supermarket was simulated by Maidment et al. [127]. The engine produces: i) electricity to drive a low-temperature vapour-compression refrigeration cycle and for lighting, HVAC and other equipment; ii) heat, recovered from engine jackets and exhaust gases, used to satisfy the space-heating and hot-water demands; iii) cool, by an ACH used to refrigerate propylene glycol to −10°C for cooling the chilled-food cabinets in a supermarket. A gas-fired boiler is also included in the system to supplement the heat provided by the combustion engine when necessary and a wasteheat exchanger is used to reject the excess heat to the atmosphere. The investigation has shown that this configuration is extremely efficient, with Primary Energy Savings (PES) up to 20%. A payback period of 6 years has been calculated. For larger stores, even better payback periods could be achieved, as the unit cost of the ACH significantly decreases.

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Sibilio et al. [11] simulated an innovative building-integrated micro-trigeneration system, based on a 6.0 kWel / 11.7 kWth natural gas-fuelled ICE feeding an electric air-cooled vapour compression water chiller for heating, cooling and DHW purposes, in a multi-family house. (Figure 32). The same plant configuration is simulated also in ref. [14], evaluating the energy, environmental and economic effects of electric vehicle charging.

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Figure 32. Scheme of the proposed system in [11].

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The system also includes a hot water tank with three internal heat exchangers, an auxiliary boiler, an electric air-cooled water chiller, a heat exchanger, thermostats, pumps, diverters, and a group of fan-coils installed in the building. The plant was dynamically simulated in TRNSYS, comparing it with the conventional separate energy production. The model of the ICE unit was calibrated and validated based on measured data. As for the operation strategy, a thermal load tracking strategy was adopted. The system was analysed for three different Italian cities (Palermo, Naples and Milan), evaluating the CO2 emissions, Primary Energy Saving (PES) and operating costs (OC). The Figure 33 highlights that PES ranges from 0.2% to 4.3%, ΔCO2 from 3.9% to 10.6%, ΔOC from 11.3% to 19.9%, with the best results in Milan. Although the PES and ΔCO2 are negative during the summer, the authors highlight that in such season the system can reduce the load peaks on the electric grid, alleviating the risk of network congestion and failure events.

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Figure 33. Values of (a) PES, (b) ΔCO2 and (c) ΔOC for the three investigated city [11].

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Rey et al. [152] designed, built, tested and dynamically simulated in TRNSYS a gasoline ICE micro CHP system, whose exhaust gases and engine cooling fluid are recovered for a recreational sailing boats used as mobile homes. The system, producing heating, DHW and electricity, was designed to work independently, and

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therefore no auxiliary system was considered, whereas an electricity and thermal energy storage to match the demand and production were included (Figure 34). The performance of the ICE was compared with similar system based on a Stirling engine (SE). The transient simulations were performed for three different European climates (Helsinki - Finland, Breskens -. The Netherlands and Malaga - Spain) and varying the size of the electricity storage.

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Figure 34. ICE-based micro CHP unit layout [152].

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The authors present their experimental results showing electrical and thermal capacities of ICE system equal to 0.653 kW and 5.414 kW, respectively. From the simulations, it resulted that the system guaranteed the heat demands in all climates. The results of SE and ICE were similar as for electric and thermal energy production, for all the locations, because the simulations are performed assuming that a thermal-load-match control strategy. The slight differences in heat production are due to the small differences in the heating capacity of the two systems (5.93 kW for SE). Annual electrical coverage never reaches 100% (Figure 35). Nevertheless, the ICE supplies less electricity because of its lower electric/thermal capacity ratio: 13%, compared to 15% with SE. The parametric analysis showed that the optimal capacity of the electricity storage system was double with respect to the initial configuration (24 V/100 Ah), leading to a significant increase (up to 11% for Malaga) of the electrical production.

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Figure 35. Annual thermal production in kWh (left) and electrical coverage (right) [152]. 4.6 Fuel cells (FC) Fuel cells are based on the direct conversion of the chemical energy of fuels into electricity, without any combustion nor mechanical drive. Heat, water and other outputs are also available, so that these systems are very attractive for polygeneration applications (Figure 36).

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Figure 36. Polygeneration system based on FC technology.

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FC are known as the most efficient small-scale power plants, featuring high electric efficiency (up to 60%) and overall efficiency (up to 90%) even at mini or micro-scale sizes [11]. They have several benefits, such as: almost zero emissions, a good match with the residential thermal-to-power ratio, reliability, quiet operation, potential for low maintenance, excellent part load behaviour [153, 154]. The work carried out by Soutullo et al. [12], reported above, is not the only one considering the integration of FCs into polygeneration systems. In fact, a number of possible configurations were investigated in literature, such as: Solid Oxide Fuel Cells (SOFC) [19] or PEMFCs [74] integrated with solar heating and cooling systems, as well as PEM integrated with CPVT and an electrolyzer. This last configuration was dynamically simulated in TRNSYS environment and analysed from the energy and economic points of view in the work of Calise et al. [69]. Here, the system proposed consists of a 600 m2 CPVT field (PTC with triple-junction solar PV cells), PEMFCs (180 kWe and 153 kWth), a 375 kW single-stage LiBr/H2O ACH, a 216 kWe alkaline water electrolyzer and an auxiliary gas boiler (Figure 37) and produces space heating / cooling, DHW, electricity for a small real university building. In particular, the electrolyzer system is powered only by the CPVT electricity and produces hydrogen, used to supply the FC and oxygen, which is sold.

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Figure 37. System layout analysed in ref. [69].

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Some of the results of a dynamic simulation performed in TRNSYS, for the climate conditions of Naples (South Italy), are reported in Table 15. The CPVT thermal efficiency, calculated with respect to the beam radiation, is 63.4 %. The electrical one is 19%, due to the use of a multijunction PV cells. The overall efficiency at full load of the FC is 78.4%. 26.8% of the electrical energy supplied by CPVT to produce hydrogen is dissipated due to the EL inefficiencies. The yearly mean ACH COP is about 0.70. In case of a capital investment subsidy of 50%, SPB significantly decreases to about 6 years, in such a way the system can became profitable. A parametric analysis on the PEMFC electrical power variation is also carried out, and from the economic point of view it was observed that when an optimal FC nominal power of 100 kW is selected, the SPB is about 5 years, in presence of incentives. Therefore, this work confirmed the technical and economic feasibility of such innovative polygeneration system.

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Table 15. Efficiency and economic performance for the system analysed in ref. [69] - yearly results.

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Parameter

Value

ηth,CPVT

0.634

ηel,CPVT

0.189

ηth,FC

0.434

ηel,FC

0.35

ηEL

0.732

COPACH

0.729

SPB

12.5

SPB with 50% subsidy

5.84

Unit

–

years

An interesting polygeneration microgrid was simulated in TRNSYS 16 in ref. [55], including a lead acid battery bank, wind turbine, monocrystalline PV panels, a Proton Exchange Membrane (PEM) fuel cell, a PEM electrolyzer, a metal hydride tank, a reverse osmosis (RO) desalination unit using energy recovery, a H2 vehicle and control system. In particular, the hydrogen vehicle is a hybrid Fuel cell – battery scooter with an average hydrogen consumption of 2.4 Nm3 /100 km. The polygeneration microgrid covers the electricity and potable water needs for two households; in addition, the hydrogen produced is used to covers the energy consumption related to transport of the same final users. The system is considered to be installed on a small island in the Aegean Sea, Greece. In this work, the system was evaluated from an economic point of view, evaluating NPV and SPB. A detailed optimization procedure aiming at minimising investment and operational cost was carried out, too. The results showed that, due to current high H2 operating cost, it was preferable to store the produced electricity in a bigger battery bank (1000 Ah at 48 V), by reducing the size of the H2 electricity storage. The FC optimal size resulted equal to 300 W; therefore, the FC is used as a backup energy producer, since its produced energy is significantly lower with respect the ones produced by PV panels and wind turbine (Figure 38). In any case, the authors state that this result does not suggest removing FC in their configuration, since the use of FC electricity avoids to completely discharge the battery and the problems connected with inverter synchronization. The NPV for the polygeneration microgrid is 21,236 €.

Energy (GWh)

PV Produced Energy Wind Turbine Produced Energy

Electrolyzer Unit Consumed Energy Desalination Unit Consumed Energy

Electrical load

998 999

Figure 38. Yearly produced and consumed energy [55].

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1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012

The previous papers investigated FC polygeneration systems as hybrid systems, that is, the FC integration into another electricity generation system. An application regarding polygeneration systems based on SOFC as the unique prime mover is carried out in reference [155]. Here, a planar 5-kW low-temperature SOFC polygeneration system, based on nanocomposite materials, is analysed. The SOFC operating temperature reaches 550°C and its power density is 0.4 W/cm2 or 0.7 W/cm2, when supplied by hydrogen or syngas, respectively. In this work, the system design and the energy and mass balances, based on lumped-parameter model, are presented; a simulation based of the use of syngas as the input fuel is performed, too. The results show that the electrical and thermal efficiency of the SOFC are equal to 19.5% and 57.6%, respectively. In addition, the authors state that there is a considerable amount of heat at the FC outlet, which can be harvested for further use, despite the fact that this SOFC has lower operating temperature than common SOFC systems. Therefore, there is a high potential in low temperature SOFCs for polygeneration application, because they do not have the common problems of high temperature SOFCs and are less expensive. Besides, the use of waste heat coming from the SOFC stack enhanced the efficiency to about 75%.

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Becker et al. [95] presented the simulation, design and techno-economic performance analysis of a 1 MW SOFC polygeneration system for combined production of heat, hydrogen, and power. The system was simulated by Aspen Plus in order to evaluate the thermodynamic performance of the system and its components. The electrical efficiency of the SOFC at the rated power was estimated equal to 48.8%; the overall plant efficiency was found equal to 85.2%.

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In references [156, 157], the trigeneration system proposed is based on SOFC, which was completely analysed from energy, exergy and exergoeconomic points of view. Energy for heating and cooling is obtained by recovering heat from the gases exiting the SOFC. The authors investigated the influence of two significant SOFC parameters, such as current density and the inlet flow temperature, on several output variables. Findings show that the minimum energy efficiency of the trigeneration system is 33%, higher than for the SOFC alone. Furthermore, the maximum energy efficiencies are 79% in case of trigeneration, 69% in case of combined power and heat production, 58% in case of power and cooling cogeneration and 46% in case of electricity production. The maximum exergy efficiency is about 47%, obtained in case of trigeneration. The exergy analysis showed that the main exergy destructions occur in the air heat exchanger, the SOFC stack and the after burner, which was supposed to oxidize all the non-reacted gases exiting the SOFC. As regards the exergoeconomic analysis, the authors investigated the effects of three important SOFC parameters (current density, inlet flow temperature and fuel utilization factor), on several output variables, such as: unit cost of the electrical power, unit cost of the energy for cooling and heating, and total unit cost of the products.

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5. By-products of polygeneration systems In the majority of the works described above, polygeneration systems mainly focused on the production of energy vectors were analysed. However, several other studies presented novel systems, purposely designed to produce other outputs. In general, polygeneration systems are potentially able to provide power, space heating and cooling, DHW, desalinated water, but also chemical products, synthetic fuels and other materials (Figure 39).

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Space heating, DHW Synthetic fuels

Space cooling

Outputs Chemical products

Power

Others

Desalinated water

1037 1038

Figure 39. Outputs of polygeneration systems.

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In particular, several papers are available in literature concerning the use of polygeneration systems that include desalination facilities. In fact, thermally-activated desalination processes can be economically profitable when fed by waste heat, for example in cogeneration plants [158]. However, even electric-driven desalination facilities, based on Reverse Osmosis (RO), are often considered. A polygeneration scheme based on a 24.7 MW natural-gas GT was adopted in [99]; here, the GT cycle is coupled to a bottoming ORC turbine, that is mechanically coupled to the RO unit.The system was developed for on-site hydrocarbon production in the Arabian Gulf. The thermodynamic performances of the combined power cycle were calculated by Aspen Plus V8.4, those of the RO unit by the software Dow Chemical Company RO System Analysis (ROSA). The energy, exergy and economic analyses were carried out for four different organic fluids, but are mainly focused on octamethyltrisiloxane, which yields 6 MW of additional net power output. The RO unit operates with a specific energy consumption of 4.1 kWh/m3 and an exergy efficiency of 29%. The exergy efficiency of the polygeneration system as a whole is estimated equal to 32%, thereby enhancing the efficiency of the GT alone by 6%. The system becomes profitable after approximately 3 years, provided that the desalinated water can be sold at a subsidized price.

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The use of renewable energy sources (solar, biomass, geothermal) to power desalination processes in polygeneration systems is also widely investigated. An interesting dynamic simulation was presented in ref. [106], regarding a solar MED polygeneration system. In particular, in this study, a PTC field coupled to a 50 MWe Rankine cycle is considered. The heat needed to drive the MED unit is provided by the Rankine cycle condenser. The system was dynamically simulated in TRNSYS; only the MED unit was modelled with Matlab. Both thermodynamic and economic analysis are performed, and two case studies are presented, for Venezuela and northern Chile. It was found that the polygeneration system can provide electricity and water for more than 85,000 inhabitants at favourable prices, increasing the total plant annual cost of only 6%÷12%. The plant was also optimised, and it resulted that the desalination capacity in the optimum configuration was 29,323 m3/day for Venezuela and 22,160 m3/day for Chile.

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A polygeneration system integrating solar collectors and membrane distillation was investigated by Mohan et al. for United Arab Emirates locations [76]. Here, the solar energy is directly provided to the desalination unit, as shown in Figure 40. A 35.2 kW single stage LiBr/H2O ACH is also included in the system to meet the cooling load of a group of portacabins, modelled in TRNBUILD and located in Rakric. The production of DHW is also included. The whole system is modelled in TRNSYS. The model is validated by on-site experiments. In this study, three different solar technologies are compared: flat plate, evacuated tube and compound parabolic collectors. The dynamic simulations show that the best pay-back value, equal to 6.75 years, is achieved in case of evacuated tube collectors having gross area of 216 m2. The best performance in terms of daily production of membrane distillation and DHW production are 92.8 kg/day and 301.67 kg/day, respectively.

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Figure 40. Schematic layout of the solar polygeneration system analysed in ref. [76].

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Further interesting configurations analysed in literature are based on the use of geothermal energy, eventually coupled with solar energy. Such kind of polygeneration systems can be ideal in small volcanic islands, where the availability of solar and geothermal energy is high whereas the availability of fresh water is scarce [3]. For example, this idea was investigated in the works reported in references [3, 16, 37, 39].

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In the framework of polygeneration systems, the co-production of energy and chemical products has recently attracted a great interest. In particular, several works are focused on the production of methanol [17, 59, 89], ethanol [15, 24, 61, 112] and hydrogen [69, 90, 109, 119].

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The integration of a methanol-producing biorefinery with an existing CHP unit and a local industry (a butchery) in the Danish city of Horsens is studied in ref. [113]. In this work, the concept of flexible multigeneration system (FMG) is highlighted, defined as integrated systems that generate multiple energy services and are able to adjust operation in response to fluctuating demand patterns and varying price schemes in the overarching energy system. The FMG under evaluation was developed to produce methanol from renewable biomass, in particular wood chips, and to match the local district heating demand and the thermal demand of the butchery. The layout includes a two-stage biomass gasifier, a solid oxide electrolysis cell, a methanol production facility, industrial heat pumps, and novel heat and gas infrastructures. To simulate each system component, the energy system modelling tool SIFRE is used; a detailed procedure of optimization is also described, aiming at maximizing NPV and minimizing the total CO2 emission. For the optimal design, the NPV was estimated to vary within the range 252.5–1471.6 M€, mainly depending on the capital cost and on the price of methanol.

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A techno-economic analysis was presented by Adams and Barton [118] for a polygeneration plant fed by coal and natural gas and co-producing methanol through methanol synthesis, as well as diesel fuel and gasoline through the Fischer–Tropsch process, and electricity. In this work, a new strategy regarding the natural gas reforming for efficient polygeneration systems is also proposed. Such strategy, defined ‘’internal reforming’’, is based on the fact that the gasifier cooling is provided by the reforming of natural gas, rather than by steam generation. The simulations show that the ’’internal reforming’’ provides increased energy efficiency and can be the optimal design choice in many market scenarios. In particular, th einternal reforming is up to 2 percentage points more efficient than the external one, due to the improved heat integration when about half or more of the output was liquid fuels.

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Regarding the ethanol production, a research work aiming at analysing the effects of introducing a lignocellulosic ethanol polygeneration plant into the district heating system in Stockholm, Sweden, was carried out in ref. [114]. The plant has capacity in terms of an ethanol production corresponding to 95 MWth, with biogas, electricity and heat as co-products. The system was studied using MODEST, a model framework based on linear programming, developed for optimisation of dynamic energy systems with time-dependent components and boundary conditions. The results show that such system would produce 110 GWh of electricity annually and furtherly, income from the sale of the biofuels and electricity produced would be about €76 million and €130 million annually, respectively, which is an increase of 70% compared to the income from the electricity produced in the system today. A reduction in global CO2 emissions of about 0.7 million tonnes

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annually, would be also reached. If the electricity price would increase by 20%, the system global CO2 emissions would be even lower and the income from the sold co-products would be about €232 million annually.

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Hydrogen production is another interesting process included in polygeneration systems [159, 160]. In fact, it can be used to produce electricity with low carbon emissions in a GT [161], or in SOFCs [162, 163]. Alternatively, H2 can be properly used for PEMFCs. In this case, high purity H2 is needed. Pure hydrogen is generated from syngas by the pressure swing adsorption (PSA) technique, with 85% hydrogen separation efficiency [120, 121].

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An interesting polygeneration system, aiming at producing hydrogen, oxygen, power, heat and cool, involving the application of both fossil and renewable energy sources is investigated in [84]. Here a solar power tower is combined with a coal gasification system, a GT fuelled by syngas, a ST supplied by wasted heat of GT cycle, a single effect ACH, a hot water production unit, and a hydrogen production unit (Figure 41).

1122 1123

Figure 41. Schematic diagram of the multi-generation energy production system [84].

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In particular, the heat produced from the solar tower is converted by a Rankine cycle into power, in turn used to supply an electrolyser which produces hydrogen. The thermodynamic properties of the working fluids involved into system are calculated by using EES software. The energy efficiencies for the different subsystems, i.e. coal gasification system, Rankine cycles, GT system, ACH and heating unit, hydrogen production unit, and of the overall multi-generation system, were evaluated, from both the energy and exergy point of view. In particular, regarding the hydrogen production, the reached energy and exergy efficiencies were equal to 19.43% and 14.41%, respectively; the obtained energy and exergy efficiencies of multi-generation system were equal to 54.04% and 57.72%, respectively.

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In Table 16, some of the works cited in this section are summarised, classified on the basis of possible inputs (renewable, fossil and hybrid), technologies used and outputs, are reported, along with other information regarding their economic performance (Simple Pay Back or Net Present Value).

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Table 16. Economic performance of some of the systems described in this section. Work

Input

Technology

[99]

Natural gas, seawater

RO, ORC, GT

[67]

Solar, Natural gas

[18]

Geothermal and solar

CPVT, GT Evacuated collectors, ORC

Output Fresh water, power, heat power, heat, cool

Economic index SPB=3 years SPB=4 years SPB=3 years

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1136

[10]

Biomass and Solar

[57] [15, 94]

Solar Agricultural waste

PTC, ORC, willow pellets boiler PVT gasifier, CC

[98]

Coal

GT, ST

[11]

Natural gas

ICE

SPB = 7 years

power, ethanol power, ethane and propane power, heat, cool

SPB = 4 years SPB = 5 years NPV= 353 $ million SPB= 8 years

6. Polygeneration systems for building

1137 1138 1139 1140 1141

The application of trigeneration / polygeneration technologies within buildings, also known as building cooling heating and power (BCHP), is widely studied in literature [147], due to its potential contribution to reduce the energy consumption in buildings, that is responsible for about 40% of global energy consumption and more that 30% of greenhouse gases emissions [164]. Small-scale BCHP systems can be important to promote the transition toward Net Zero Energy Buildings (NZEBs).

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In this framework, the integration of solar technologies within the building in polygeneration systems is investigated in several literature works [18, 39, 67, 68], evaluating the system performance with dynamic simulation tools, by taking into account the hourly power, heating and cooling loads. An interesting example is represented by the work by Marín-Sáez et al. [165], where a building integrated PVT (BIPVT) system is analysed, designed to be superimposed on the blinds of a solar louvre shading system. The system is equipped with cylindrical holographic lens. The simulations are performed calculating the direct normal irradiance spectrum by the SMARTS radiative model, which relies on the following parameters: air mass, aerosol optical depth, precipitable water, and Angstrom exponent. This model is subsequently validated versus experimental data, showing a good agreement. The optical model is based on the Kogelnik’s coupled wave theory and the approximate scalar theory established by Syms [166]. Finally, energy simulations are carried out in TRNSYS; in particular, the system was evaluated under the weather conditions of Sde Boker (Israel) and Avignon (France), for specific case studies. In order to perform a comprehensive simulation, the optimal model, developed in MATLAB, was also integrated in TRNSYS environment. In addition, the developed PVT collectors were coupled with a conventional solar thermal system, obtaining a “combysolar” system able to supply the building with space heating, DHW and electricity (Figure 42).

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Figure 42. Simulated system topology [165].

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The authors found that the optical efficiency in Avignon is higher than that calculated for Sde Boker. In addition, for both cases, optical efficiency dramatically decreases in summer. The maximum monthly average optical efficiency is about 81% (Avignon, December). A similar trend is also detected for the efficiency related to space heating. However, in this case, Sde Boker exhibits higher values. Solar fractions, related to DHW

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production, were 79.3% and 95.5%, for Sde Boker and Avignon, respectively. The solar fraction for space heating was above 15% in both locations. The solar fraction for electric energy was slightly below 10% in both cases.

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The hybridization of trigeneration systems with solar systems was also investigated by Marrasso et al. [42]. The authors developed such system by considering that the increase of energy within building is often due to the growing demand for cooling and solar cooling systems can avoid that such needs are covered by conventional systems. However, for such systems, large roof areas are typically required in order to match the cooling demand. This circumstance makes solar heating and cooling poorly feasible in historical buildings, where the roof area available is often small. Therefore, the authors of this work proposed a novel arrangement, where the solar thermal collectors are coupled with a micro CHP system based on a reciprocating engine and an ACH (Figure 43). The system was used to supply energy to a three-storey office building located in Naples (Southern Italy), dynamically simulated in TRNSYS. The system primarily uses solar energy: the CHP unit is activated only when the tank top temperature falls below a certain set point. An electrical backup heater is also considered during peak heat demands. During the winter, heat is supplied to the zones providing space heating. Conversely, in summer heat is used by the ACH to provide space cooling.

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Figure 43. System configuration in cooling mode [42].

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The results show that, during the heating season, the solar fraction increases as a function of the tilt angle. Conversely, in summer, the opposite trend is detected. The tilt angle, along with other design parameters, were determined through a thermoeconomic optimization. Then, in the optimal configuration, the average efficiency of the solar collectors was 49%. The electrical efficiency of the cogenerator was approximately 26%; the thermal efficiency was higher than 60%. The electrical balance shows that the electricity drawn from the grid is higher than that provided by the CHP, both in winter and summer. Finally, the authors performed a parametric analysis varying the storage tank volume. Such analysis showed that solar fraction increases as a function of the storage capacity.

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The modelling and simulation of 125 m2 BIPVT collectors coupled to a 3-floor office building, for the production of solar heating by radiant floor system, solar cooling by a low-temperature adsorption chiller, solar DHW and electricity is carried out in [70]. The properties of opaque and transparent elements of the modelled building are well-representative of the traditional Italian buildings. The use of an electricity storage system, obtained by lead-acid batteries, coupled to an inverter/regulator system, is also taken into account (Figure 44). System energy, economic and environmental performances for different Italian weather zones (Turin, Florence, Naples and Palermo) are estimated.

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N

Figure 44. Innovative system: building-system layout with roof BIPVT collectors (with electricity storage) and adsorption chiller [70].

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It resulted that, for the weather zone of Naples, the total PES for both electricity consumptions and DHW production resulted about 64%, whereas the PES for DHW only is equal to about 67%. An overall PES for space heating and cooling about 24% is reached. The simple payback period linearly decreases as a function of the heating degree days and increases with the incident solar radiation. The minimum is obtained in Turin (10.6 years), the maximum in Palermo (11.3 years); the avoided CO2 emissions linearly increase as a function of the incident solar radiation, reaching the maximum of about 90% in Palermo. In the remaining zones, the CO2 saving ranges from 76 to 84%.

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The energy and economic performance of roof and/or façades BIPVT collectors for a multi-storey residential building consumptions is carried out in ref. [71], in order to assess the active and passive effects due to the integration of solar technologies within the building. In particular, a comparison among innovative building-plant system configurations is carried out, based on BIPVT collectors for the multi-generation of electricity, thermal energy, and DHW. The simulation models of the proposed system layouts are designed and implemented in TRNSYS simulation environment for the dynamic assessment of their energy and economic performance. Different European climates are investigated and a detailed parametric analysis by varying the thermal resistances and capacitances of the building envelope is performed. The BIPVT collectors produce a decrease of the primary energy demand ranging from 67% to 89%, depending on the weather zone and the building-plant configuration. The economic profitability resulted slightly better for roof BIPVT collectors than for roof and façade applications. For the case studies investigated, the SPB values appear quite high, varying from 11 years for South European weather zones to 20 years for the North European ones.

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In ref. [72], a technical and economic evaluation is carried out for PVT systems coupled with small-scale thermally-driven solar-cooling systems (absorption refrigerator or heat pump) and thermal energy storage, to increase system autonomy. In particular, four scenarios are investigated: 1) PVT thermal output is used for space heating and DHW demands and the electricity is used to cover the space cooling demand; 2) PVT thermal output is used for DHW demand, while space cooling and heating demands are covered by means of a refrigeration cycle; 3) PVT thermal output is used for DHW demand, space heating and cooling needs are covered by an electrically-driven water-to-water HP unit; 4) PVT thermal output is used for DHW, space cooling and heating demands. The systems are investigated in ten European locations, using yearly and monthly averaged solar-irradiance and energy-demand data related to houses with a total floor area of 100 m2 (4–5 persons) and a 50 m2 roof area. Special attention is paid to the total levelized cost of combined energy generation (LCOE) in the housing sector, defined as the net present value of the unit-cost of energy provided over the lifetime (over 20 years) for a given configuration. The yearly result show that: i) Seville, Rome, Madrid and Bucharest are the most promising locations, among those examined; ii) the most efficient system configuration is achieved by coupling PVT panels to water-to-water heat pumps that use the PVT heat to increase the system COP; iii) PVT systems are able to cover 60% of the combined space heating and DHW and almost 100% of the

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cooling demands of houses for all of the four aforementioned locations; iv) the overall LCOE of these systems ranges between 0.06–0.12 €/kWh, which is 30–40% lower than that of equivalent PV-only systems.

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In recent years, the problem of reducing energy consumptions and greenhouse gases emissions in buildings was also addressed by considering the technology of natural-gas-driven BCHP systems, that show high energy efficiency and low greenhouse gases emissions [128]. Such kinds of systems are investigated in the works reported in references [20, 39, 73, 123, 128]. In particular, Zhang et al. [128] studied the feasibility of thermal energy storage in BCHP system consisting of a natural-gas fired GT, an ACH, a heat exchanger and an auxiliary gas boiler (Figure 45). A hotel and an office building were considered as case studies, both located in Beijing. The authors investigated the use of a thermal storage aiming at improving the efficiency of GT and ACH, at part load conditions. In their analysis, they considered the degree of mismatch (DM), based on the relationship between user load demands and the supply thermal power ratio of the GT. In addition, the analytical relationship between PES of whole system and DM was evaluated, using two control strategies: following thermal load (FTL) and following electrical load (FEL). Three typical working conditions were considered: Heating & Electricity, Cooling & Electricity, Heating & Cooling & Electricity.

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Figure 45. Schematic diagram of building energy supply system: (a) traditional separated generation system; (b) BCHP system [128].

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The results show that the more DM approaches to 1 (which means that the energy supplied by the system just equals the demand), the higher is the PES, and that the exportability of the electricity produced by the CHP unit is crucial for achieving good energy savings. The system is found more suitable for cases in which the heating demand is dominant.

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Rosato et al. [123] presented a dynamic simulation, developed to evaluate the thermoeconomic and environmental performance of a building-integrated micro-cogeneration system in an Italian residential application . In this work, the transient nature of building and occupant loads, the part-load characteristics of the cogeneration system, the interaction between the loads and the system output, and system energy management and control were considered. In particular, the CHP unit is based on a 6.0 kWe natural gas-fuelled ICE producing electricity, space heating and DHW for a multi-family house consisting of three floors, located in Naples (south of Italy). The CHP unit and a natural gas-fired boiler co-operate in order to guarantee a given water temperature level (55 °C) within the tank, that has three internal heat exchangers. A group of fan-coils, supplied by the hot water storage, is installed in the building in order to balance the sensible heating load. The electric load-following and the heat demand-following control strategies were compared: it resulted that the system determines a significant reduction of primary energy consumption, CO2 emissions and operating costs. The heat demand-following control strategy appeared more profitable from both environmental and energy point of views. In particular, the simulations results revealed that, by taking into account the Italian electricity grid mix, the thermal load-following approach can offer a primary energy saving of about 6.5%, a reduction of CO2 emissions of about 12.2% and a saving on operating cost of about 20.5%; under electric load-following operation, these values become 5.1%, 10.6% and 31.0%, respectively (Figure 46).

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Figure 46. Comparison between the proposed and conventional system: primary energy consumption (a), ΔCO2 equivalent emissions (b), and operating cost (c) [123].

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An economic and environmental evaluation of two micro CHP systems (namely: gas engine and fuel cell), for residential buildings in Japan was performed by Ren and Gao [20]. The CHP plant is driven by city gas to meet a part of the electrical demand (including cooling load with the use of air conditioning); the deficiency is served by the utility grid. The heat recovered from the micro CHP units is used for heating and hot water requirements. An auxiliary supplementary burner and a storage tank are included. Two different operating modes are taken into consideration, aiming at minimum-cost and minimum-emissions, respectively. The results show that the fuel cell is represent the best option from both economic and environmental points of view. When the operation is aimed at optimising the economic benefits, the annual energy cost is reduced of about 26%. When maximizing the environmental merits, annual CO2 emissions are reduced of about 9%.

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7. Operation control strategy and optimization In many recent works on polygeneration, a special attention is paid to the comparison of different control strategies, often related to the design optimization of the system proposed [41, 50, 55, 58, 167, 168]. To address such problems, dynamic simulation tools are crucial. For example, in references [11, 73, 123, 169], different operation strategies of CHP systems were analysed, namely: rated power strategy (base-load strategy), electrical load-following and thermal load-following; hybrid strategies were also proposed, matching both thermal and electrical requests (on the basis of economic criteria). For example, in the work of Calise et al. [39], three different operating strategies were compared in order to reduce the capital cost and maximize the performance of a trigeneration system consisting of a natural gas fired reciprocating engine and an ACH, producing DHW, space heating / cooling and electricity for a real hospital building. Here, a detailed dynamic simulation model, developed in TRNSYS environment, is used to predict the system real time performance. The investigated operating strategies were: the Thermal Load Tracking strategy (TLT) - the engine partializes to follow the thermal demand, by adopting a proportional controller; the Maximum Power Thermal Load Tracking strategy (MPTLT) - the engine does not partialize and it works always at maximum power, by adopting an On/Off hysteresis controller; the Electricity Load Tracking strategy (ELT) - the engine partializes to follow the electrical request. The MPTLT control strategy, regarding the engine operation, is depicted in Figure 47.

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Figure 47. Cogenerator control strategy [48].

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In particular, when the temperature coming from the sanitary hot water heat exchanger is higher or equal than 72 °C, the engine, managed by the On/Off hysteresis controller, turns off; it turns on again when such temperature is lower than 67.5 °C. If this temperature is too low, the system is managed by a suitable feedback controller that bypasses a part of the heating water flow rate that comes out from the jacket water heat exchanger. Finally, a proportional control allows the cogenerator to work at part-load operation from 50% to 100% of the maximum power, as a function of the engine inlet temperature [39]. From the carried out simulations, it resulted that the best strategy from the energy point of view is the ELT one with the higher electrical production equal to 1203 MWh/year, but the worst value of the thermal efficiency (equal to 38.9%) and global efficiency (equal to 75.6% vs 89.9% for TLT strategy and 90.3% for MPTLT strategy). PES values for the TLT, MPTLT and ELT strategies were respectively: 23, 24 and 14%. ELT strategy is the best one also for the achieved economic results being the SPB values for the ELT, MPTLT and TLT strategies respectively 4, 4.2 and 4.4 years. After the comparison of three strategies, in this work, an optimization, by the TRNEdit tool of TRSNSYS and computer Design of Experiment (DoE), selecting as objective functions the SPB and PES, for the ELT strategy (the best one) was also carried out. From the optimization results it was found out that the lowest SPB value is equal to 3.9 years and the highest PES value is 20.6%. A similar system layout is analysed in reference [73], but in this study the trigeneration system is designed to produce electricity, space heating and cooling for a real industrial building. Here, three control strategies are compared: base-load operation, electric load tracking and a new hybrid strategy based on the simultaneous tracking of electric and thermal-loads. The authors developed several detailed control strategies, including several temperature controllers in the model, able to manage the temperatures of heat recovery exchangers (jacket and flue gas loops), the activation temperature of ACH and fan coil units, the production of DHW, the activation of the engine, etc. Special attention was also paid to the new, above mentioned hybrid strategy: here the engine follows the power demand until the temperature of the DHW outlet water reaches a set-point temperature equal to 75.5 °C; then, the system is partialised at a fixed point, defined by the ratio between the power actually produced by the prime mover and its rated capacity, Pel,PM/Pel,rated,PM, until the temperature reaches a value of 69.5 °C; the fixed value of Pel,PM/Pel,rated,PM is selected to simulate the operating strategy of a real prime mover. When the partialization is activated, the electrical demand (Pel,PM,dem) is also checked, and it must be higher than a fixed ratio, defined as Pel,PM,dem/Pel,rated,PM. This last condition is set in order to obtain a minimum electrical load. Conversely, with the present energy price and legislation, the economic feasibility would be negatively affected. The main results obtained with this new control strategy are reported in Figure 48, showing, in particular: SPB, electrical economic saving (ΔCel,user), global efficiency (ηglob) and economic gain due to excess electricity sold to the grid vs. the ratio Pel,PM/Pel,rated,PM. The lines in the graphs represent the minimum value of Pel,PM,dem/Pel,rated,PM that has to be satisfied in order to activate the prime mover. For low values of the Pel,PM,dem/Pel,rated,PM, ηglob decreases, due to the longtime of part-load operation of the engine; the electricity sold to the grid significantly increases, because the production is greater than the demand. A good value of ηglob - about 0.8 - is reached for Pel,PM/Pel,rated,PM = 0.5. Conversely, for the same value a reduction of economic saving and an increase of SPB is observed. This result is mainly due to adoption of rules of the Italian electricity energy market which boost more electric – driven strategies than efficiency ones. Anyway, it is noted that with this new hybrid control strategy the best SPB is equal to 3.8 years with respect the value of 4.1 and 4.3 years of the electricity tracking and base-load operation strategy, respectively.

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Figure 48. Efficiency and economic results [73].

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In the work of Taljan et al. [102] the simulation of a biomass-fired ORC CHP system with heat storage is presented; the system was optimized, too, aiming at maximizing the profits from heat sales and electricity. The objective of this study was to use optimal operation strategies to size optimally the whole plant. The optimization methodology consisted of three main steps (Figure 49).

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Figure 49. Methodology flowchart [102]. In particular, the optimal sizing routine calls the dispatch optimization routine which sets the hourly optimal operation and economic parameters of the system. After selecting the optimal operation parameters, an economic analysis evaluating a Modified Internal Rate of Return (MIRR) for each iteration is carried out. The MATLAB optimization toolbox and the AMPL mathematical modeling language (based on mixed-integer linear programming optimization models) are used for optimal sizing and optimal operation routine, respectively. MATLAB is also used for the calculation of economic indexes. The results show that the evaluated MIRR at the ORC rated power (100 kW), is equal to 7.80% and 8.74%, respectively, in the case without and

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with thermal storage (50 m3). Therefore, more favourable economic parameters are achieved if the heat storage is not included in the system. In addition, for heat demands higher than 5 GWh/year, a MIRR of 10% was calculated, without thermal storage and at a biomass price lower than 17 €/MWh.

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A multi-objective optimization of a multi-generation energy system, based on an evolutionary algorithm, is presented by Ahmadi et al. [170], aiming at minimizing the total cost (fuel, components purchasing and environmental impact) and maximizing the exergy efficiency. The system includes: a micro GT, a dual pressure heat recovery steam generator, an ACH, an ejector refrigeration cycle, a domestic water heater and a proton exchange membrane electrolyzer, producing power, heating, cooling, hot water and hydrogen. An equation for the Pareto optimal curve is also found, reporting the relationship between exergy efficiency and total cost rate. From this curve it resulted that the total cost rate increases moderately as the total exergy efficiency of the cycle increases to about 65%. An increase of the total exergy efficiency from 65% to 68% leads to a significant increase of the cost rate. The maximum exergy efficiency, equal to 67.89%, corresponds to the highest total cost rate, 615.75 $/h.

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The polygeneration system reported in ref. [2] was also optimized in reference [56], in order to maximize the exergy performance and the economic feasibility of a solar MED polygeneration system. The plant, called Renewable Polygeneration System (RPS), consists of a solar heating and cooling system, CPVT collectors, a biomass heater and a MED unit, and provides electricity, cool and heat and desalted water. A detailed energy, exergy and economic dynamic simulation model of the whole RPS was included in the work. The optimization, based on the computer-aided Design of Experiments (DoE) technique, aims at assessing the optimal configuration by the optimization of economic and exergy objective functions. By DoE technique the sensitivity of the selected design variables the shape of the optimum response surface was also analysed. In particular, the analysis was carried out by varying the number of solar collectors and MED effects, thermal storage specific volume and aims at maximizing the profit index and the exergy efficiency and minimizing the global exergy destruction and operation costs.

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Table 17. Optimization results [56]. Design variables OFs PI Cop ηex Exd,total

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Goal Max. Min. Max. Min.

NSC

Optimal

NEFF

vTK1

qp4

-

-

L·m−2

kg·h−1

760 1000 1000 250

14 14 9 9

25 25 25 25

475 620 100 100

Initial

Value

Unit

OFs

Value

Unit

3.43 −2480 12.80 9.46

k€ % GWh/y

PI Cop ηex Exd,total

2.64 −541 7.20 10.9

k€ % GWh/y

The optimization results (Table 17) suggest that storage tank volume should be relatively small (25 L for unit area of CPVT collectors); in addition, the optimal number of effects in the MED unit was found equal to 14, and an optimal number of 760 CPVT collectors was calculated if the Profit Index was to be maximised (with an optimal value of 3.43); differently, 1000 CPVT are required to minimize the operation costs (-2480 k€) and to maximize the global exergy efficiency (12.80%). Therefore, despite the high capital cost of the CPVT collectors, the economic optimization suggests increasing the solar field area. Anyway, for large solar fields, a high value of exergy is destroyed (87.20% of the fuel exergy entering). Consequently, it resulted that a reduction of the number of CPVT collectors minimizes the global exergy destruction. In references [124, 125], a mathematical optimization process was implemented in order to find the optimal size of a polygeneration plant fuelled by natural gas, solar energy and gasified biomass, which can provide fresh water in a tourist resort in Spain. In particular, a two-step optimization procedure was implemented. In the first one, an optimization process based on a MINLP (Mixed Integer Non-linear Programming) problem was designed to select the technology (between ICE, GT, FC, STE, RO, MED, single or double effect ACH) and the capacities of the components to be included in the polygeneration plant, by evaluating the NPV economic objective function along with energy (PES) and environmental constraints (greenhouse gases emissions). In this step, the monthly averaged requirements were taken into account. It was

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found out that natural gas fuelled engines were the best solution from an economic point of view (NPV=347 k€), but the best value of CO2 emission reduction was obtained using solar and biomass renewable resources (3114.1 ton/year) [124]. The optimized configuration included an ICE as prime mover, a single effect ACH to cool the resort and a MED unit to produce desalted water, as well a plate-fin heat exchanger for the heating demand. In the second step of the optimization procedure [125], instead, the hourly load variability was considered and, therefore, energy storage systems were also taken into account. CONOPT module (by GAMS) was used to solve the operating optimization of the plant. In the optimized configuration, a NPV of almost 300 k€ (reduced with respect the NPV of first step one for the storage system cost), an 18% of PES and more than 850 tonCO2/year avoided emissions were achieved.

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A complete method regarding the design of trigeneration plants which takes into consideration operation mode (control strategy and tariff selection), energy performance (system load coverage, PES, grid utilization factor), and economic aspects (operational profit, return on investment) was presented in ref. [171]. Here, a detailed mathematical model was developed to compare different alternative solutions and to show the magnitude of the selected parameters variation on the plant performance. A new operation strategy, ‘’electrical equivalent load following (ELF)’’, was introduced. In particular, in this strategy, the equivalent required electricity Eleq is the electricity needed to cover electric demand and cooling demand that is not covered by the absorption chiller, which produces cooling energy only if waste heat from the prime mover is available. An example of this strategy is described in Figure 50.

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Figure 50. Example of electrical-equivalent load following strategy [171]. In particular, Eleq and thermal energy produced are plotted versus the prime mover nominal size, for fixed values of the energy demands and the prime mover heat to power ratio (HPR). In summer months, Eleq decreases when the prime mover size increases, because the prime mower waste heat is used to produce cooling. During winter the thermal demand is higher than the cooling one, therefore, the proposed strategy is analogous to electrical load following strategy, since no waste heat from the prime mover to supply the ACH is available. The ELF strategy is compared with three conventional strategies, adopting the following operation assumptions: i) ELF with a minimum electricity load bought from the grid of 0%, 50% or 100% kW of the annual base load (i.e. ELFset = 0, 175 or 350 kW); ii) Continuous Operation (CO) for 8, 16, or 24 h simulating 1,2, or 3 shifts; iii) Peak Shaving (PS) of PSset = 70%, 80% or 90% of the monthly electricity load; iv) Base Load (BL) operation on BLset = 175, 350, 500 kW (50%,100%, 150% of the base load). The calculation of the PES and annual profit is performed in order to carry out the comparison among the different control strategies and adopted tariffs. The case study assumed to compare such strategies regards a trigeneration system consisting

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of an ICE, ACH, auxiliary units (boiler and electrical chiller) and a heat storage. A 350 beds hospital complex located in Greece was considered. The following results were found: more than 80% of electrical, thermal and cooling demands are covered only in ELF, ELFset = 0 and CO, for 24 h with a prime mover nominal power of 0.75 MWe. A 100% coverage of thermal energy can be achieved only with the CO strategy, for 24 h. Strategies that do not achieve a PES of 10% (desired for high efficiency CHP plant) as PS or CO (one shift continuous operation) are not suitable. The lower annual profit (lower than 50 k€) is obtained for CO strategy. The higher PES value (more than 30%) and best annual profit (about 200 k€) were achieved with such ELF strategy.

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8. Distributed generation Polygeneration systems, fuelled by renewable sources and/or conventional fossil fuels, can be furtherly classified as centralized or decentralized systems. Centralized systems are usually characterized by high capacities and are mainly supplied by fossil fuels, due to their high energy density, requiring a constant availability of the energy sources used to drive the system. Decentralized systems are often designed for isolated and remote communities (islands, rural areas), that have no connection with the utility grid, or for distributed generation (DG) coupled with advanced smart-grid systems (Figure 51). These could be on-grid (i.e.: the system operates in combination with the normal power grid to meet the demand of different energy products), or off-grid (stand-alone independent operation), for single house or communities. In DG systems, developed in contrast to the conventional centralized power systems, the input energy sources are locally available (as natural gas, solar, wind, geothermal), and the systems are located near the final users [172]. Therefore, DG is a way to promote sustainable energy development. In this case, factors like land availability, density of population and other socio-economic aspects play a crucial role in the selection of the capacity. DG implies the integration of several small-scale technologies, both fueled by renewable energies (solar power, wind power, hydro power etc.) and fossil fuels (ICE, GT, STE, FC); however, renewable sources are obviously preferred. Polygeneration systems

Decentralized

Centralized

(including or not RES)

(including or not RES)

Distributed Generation

On-Grid (including or not storage)

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Fossil fuels only

RES only

Off-Grid Smart Micro Grid

Figure 51. Decentralized and centralized polygeneration systems.

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Isolated or rural areas

Advantages of such systems are [173]:     

self-sufficiency; reduction of the dependence on the external energy supply; local production, low transmission losses, by avoiding transportation costs; no need for expansion of costly grid interconnection; lower capital costs with respect to centralized plants;

Hybrid Fossil/RES

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An important aspect of DG is that the system has to be able to match the energy request and production. This is the basic concept of the ‘’smart grid’’ (computerized power grid) that, equipped with advanced control and modern communication devices, facilitates the coordination between the energy supplier and the end user [7]. Significant aspects for the correct operation of smart grids are real-time measurement and monitoring, automatic energy loads adjustment, innovative control strategies, accurate assessment, proactive coordination and integrated design. Smart grids are designed to improve the current power grid and to achieve the goal of sustainability, characterized by cost and energy efficiency, reliability, and environmental friendliness [174]. This occurs for the electrical, thermal (heating, DHW) and cold production, distributed separately via local power grid, the district heating (DH) and district cooling (DC) network, respectively, although production of thermal energy and electricity are coupled within the polygeneration plant. Key components for the correct operation of the DG systems are the electrical and thermal storage facilities (electrical vehicle recharge station, electrical battery storage, storage hot and cold tanks), included in order to balance the user demand and energy production, to store the eventual extra-production, by mitigating the effects of renewable energy intermittency. In this framework, by considering the power production by the renewable energy sources as solar and / or wind energy, which are definitely intermittent and variable, several studies were focused on the dynamic nature of the problem caused by charging and discharging of different types of electricity storage facilities, which are important components in the smart polygeneration micro grid. The same importance is assigned to the thermal energy storage, in case of cooling and heating energy production, which can be prepared and stored in storage at off-peak hours and released into district heating and cooling (DHC) network from storage during peak hours [5]. In order to improve the DG systems costs and overall efficiency, the connection to normal long-distance power transmission network (on-grid DG systems) is also possible. This configuration also allows one to overcome issues as the excess production typical of some countries, which are featured by a considerable production due to the large solar field or wind farms. In this way, the power exchanging of DG systems with the normal network of any energy surplus is a feasible and sustainable solution. A further aspect to improve the overall efficiency of DG systems is represented by the proper selection of DG systems scale. Generally, DG polygeneration systems using renewable energy are characterised by small capacities, mainly depending on the availability and distribution of the local resources. From this point of view, DG systems should have a suitable and not too small capacity; however, the final global efficiency could be not high enough if compared with the conventional fossil or renewable energy polygeneration systems or with the separate production plants.

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In the following, some of the works reporting analysis performed on the DG systems, carried out by adopting different optimization techniques [175-179] aiming at contributing their spread in the sector of the sustainable polygeneration, are reported. In these works, the importance of the simulation models, as key tools for evaluating the performance of complex systems, as are the DG systems, is also underlined.

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As many researchers highlighted, the most widespread DG plants are based on the trigeneration systems integrated into DHC networks, by focusing on the optimisation of the energy conversion technologies and their operational strategies. With this kind of system, the layout may be trivial if compared with complex polygeneration systems consisting of a plurality of different technologies. However, the control of such systems is not simple, since thermal, cooling and power demands may considerably vary during the day. This issue was investigated by Conte et al. [51], who addressed the optimal management of trigeneration systems including ACH for cooling production. Their study develops from the literature analysis and shows that, in most cases, flexibility and modularity of efficient and recent trigeneration systems are not adequate to correctly work in DHC at very low partial load ratios. Trigeneration plants usually integrate several chiller devices (double and single effect ACHs, vapour compression): the time-dependent cooling load should be redistributed among such types of chillers aiming at achieving the lowest operating cost and the maximum efficiency. In traditional systems, the management of such chillers is obtained by a sequential approach. This approach is normally employed to obtain a constant chiller flow rate and to reach the same outlet chilled water temperature among the chillers. Nevertheless, this control strategy is rather simple, and it may be considerably improved adopting optimized sharing load methods, as shown in several available literatures works. The novelty

reduction of the fossil fuels dependency and the mitigation of environmental impact; reduction of breakdown effects of a single unit on the whole energy network.

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of the study of Conte et al. [51] is represented by the use of real data taken from an existing trigeneration plant located near Barcelona (Spain). Cooling and thermal capacities are 8.75 MW and 1.40 MW, respectively. The plant consists of three ICEs, each one with an electric capacity equal to 3.35 MW. The heat produced by waste gases supply double-effect ACHs, whereas the jacket water recovered heat is used to drive a single-effect ACH. The operation of system is about 5000 h / year. The system was carefully monitored by flow rates, temperatures and heat flows measuring. A mixed integer linear programming (MILP) approach was adopted to optimize system operation, whereas a general algebraic modelling system (GAMS) is used to model the whole trigeneration system. The authors examined the load sharing strategy for a typical day, by changing the distribution of load as a function of the different chillers part-load performance. Results showed that the single-effect ACH has the lowest efficiency, but its operation is useful for reducing the cost of engine cooling energy rejection and maximizing engine heat recovery. Double and single-effect ACH COP were 1.0 and 0.65, respectively. Such results are satisfactory, by taking into account that such devices usually operate at 40% of their rated capacity. The optimization performed by adopting economic objective functions indicated that the double-effect ACH operation should be preferred to that of the single-effect one.

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The optimization of a distributed cogeneration system is performed in ref. [52]. The plant consists of a DH network, a solar thermal plant, a long-term heat storage, a set of CHP units and conventional components, such as boilers and compression chillers. The considered CHP units were a centralized ICE and small-scale (ICE or MTG) system, properly located close to, or inside, nine industrial facilities located in the North-east of Italy. The optimal energy production system and operation strategy needed to satisfy the energy demand and to minimize the total annual cost were identified, using a MILP model (developed with the commercial software X-press). Different configurations were considered, namely: i) conventional solution; ii) isolated solution with CHP and CCHP systems for partially replacing conventional boilers and compression chillers; iii) distributed cogeneration solution with all users connected each other through a DH network and by adopting CHP and CCHP systems; iv) distributed renewable solution, like the previous one, but with the addition of a centralized solar thermal plant and a thermal storage. Note that in this analysis the objective function, total annual cost, simply corresponds to the heat cost because the cost of electricity and cooling energy are considered constant for the specific set of analysed users. The annual cost for the configuration without solar field and the DH network, with both boilers and cogenerators, allows a 4.5% reduction of the total annual cost, with respect to the conventional solution (5.47 c€/kWh). A 5% reduction is obtained by adopting a distributed solar system, which includes the DH network, the thermal storage and the solar field. In this configuration, a 15% reduction of the primary energy consumption is also achieved. In addition, the optimally sized solar field produces about 55–60% of the user annual thermal demand.

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A main aspect is represented by the allocation of DG sub-systems. This issue is dealt with by Peng et al. [53] presenting a novel approach to resolve the problem of the optimal allocation of DG systems. In this work, a modelling of a PV panels field and wind turbines by using Beta and Weibull distributions, respectively, was carried out. The load was modelled using a normal distribution. The optimal DG allocation (ODGA) was investigated using a multi-objective approach integrating as objective functions total DG costs, pollutant emissions and system power losses. Obviously, the investigated system is subject to both chance and deterministic constraints. This problem was analysed by using the crisscross optimization algorithm with Monte Carlo simulation. The crisscross optimization is used to detect the optimal solution among a set of feasible solutions, whereas the Monte Carlo method is used to deal with system uncertainties and to solve the power flow problem. This novel approach was tested on two benchmark cases widely used in ODGA problems, namely IEEE 33-bus and IEEE 69-bus systems. The novel algorithm is shown to be about 25% faster than particle swarm optimization (PSO). The novel approach also ensured a better stability of the voltage with respect to the case of the PSO technique. In summary, the development of such optimization techniques is very useful to design polygeneration systems including many different power plants, especially when powered by renewable energy sources.

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The optimal design was studied by Melheri et al. [180], taking into account both the capacity and allocation of DG systems for the distribution of power and heating demand at the level of a small neighbourhood. The optimization of a micro-grid configuration in combination with the design of a heating pipeline network, by investigating different distributed energy technologies (PV, FC, STE, gas engine, boiler), is carried out aiming

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at minimizing the investment and operational cost of the overall DG system. Special interest was paid to the design of the heating pipeline network that serves the transportation of heat between the different nodes, where the buildings are located. This was performed by a MILP model, solved by using the GAMS CPLEX solver. The developed optimization model was solved for several cases study (i.e. ten and twenty buildings with and without heat exchange between the buildings, ten buildings with centralized CHP, ten buildings without distributed technologies and heating networks) where the neighbourhood is assumed to be located in Greece. Considering ten buildings and heating distribution networks, the total annualised cost of optimal solution is € 4,742.7, with CHP units of capacity of 4.7 kWe installed in buildings i2, i3, i6, i10, without back-up boilers and PV arrays installed in all buildings. Instead, the optimal heating pipeline network consists of 2 sub networks (Figure 52).

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Figure 52. Heating pipeline network for the “10 Buildings” case. [180]. In ref. [181], several detailed simulation models, aiming at showing their accuracy and influence on optimal operation cost and strategy, with respect the simplified models, for a distributed CCHP system are proposed. The configuration adopted to perform the comparison consists of a natural gas ICE, ACH, and auxiliary devises as an electrical chiller and a boiler. If the ICE electricity production is not sufficient to satisfy the electrical demand (an office building in Tianjin, China), the possibility to use electricity public grid is simulated. No sold for surplus electricity is expected. Results show that 2240 ¥/day, accounting for 2.5% of the total daily cost, is reduced when the detailed model is used to formulate the operation strategy, demonstrating that the detailed model is superior to the simplified model when the economy and rationality of the optimal operation strategy are considered. 9. Conclusions Due to the recent development of small-scale energy technologies and renewable energy sources, energy systems are rapidly changing from the conventional centralized model to new, de-centralized configurations, mostly focused on the use of renewable sources and, therefore, including energy storage devices, needed to face the variability of most renewable sources. In this framework, a very promising solution is represented by polygeneration systems, using multiple energy sources to generate multiple products (electricity, thermal energy, hydrogen, ammonia, etc.). In fact, such systems are potentially able to ensure high energy efficiency, environmental compatibility and high flexibility, and can be profitable even from an economic point of view: for example, they can be more efficient in facing fluctuations of energy prices and changes in end-product demand. In other terms, the systems for DG, that are often intended only for power production, can become much more energy-efficient and interesting when many technologies are combined to simultaneously provide heat, power and other energy products within a single, integrated process. Due to their intrinsic complexity and to the large amount of different possible configurations, an important research effort is needed on polygeneration systems, in order to correctly address their design, optimization and control and to develop efficient decision support tools for designers, policy makers and end users. This is one of the reasons for the recent, significant growth in the number of scientific papers dealing with this type of system. This paper intends to contribute to such research effort.

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Conflicts of Interest: The authors declare no conflict of interest.

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Current literature studies on polygeneration systems simulation The review includes conversion technologies fuelled by fossil and/or renewable energy A classification of methodology, outputs, control and optimization Contribution of polygeneration to the development of distribute generation systems