Combined cooling, heating and power: A review of performance improvement and optimization

Applied Energy 136 (2014) 168–185

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Combined cooling, heating and power: A review of performance improvement and optimization Heejin Cho a,⇑, Amanda D. Smith b, Pedro Mago a a b

Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box 9552, Mississippi State, MS 39762, USA Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA

h i g h l i g h t s  A review on combined cooling, heating, and power (CCHP) systems is presented.  Energetic and exergetic methods to improve CCHP performance are discussed.  Optimization techniques used to improve CCHP performance are reviewed.  The most current research and emerging trends in CCHP technologies are presented.  Gaps in the current CCHP research and development are discussed.

a r t i c l e

i n f o

Article history: Received 4 March 2014 Received in revised form 21 August 2014 Accepted 30 August 2014

Keywords: CCHP Combined cooling, heating and power CCHP review Performance improvement Energetic and exergetic analyses Optimization

a b s t r a c t This paper presents a review on combined cooling, heating, and power (CCHP) systems. This work summarizes the methods used to perform energetic and exergetic analyses, system optimization, performance improvement studies, and development and analysis of CCHP systems, as reported in existing literature. In addition, this work highlights the most current research and emerging trends in CCHP technologies. It is envisioned that the information collected in this review paper will be a valuable source of information, for researchers, designers, and engineers, and provides direction and guidance for future research in CCHP technology. Ó 2014 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance improvement through energetic and exergetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Energetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Energy efficiency studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Studies focused on energy savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Exergetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance evaluation of CCHP systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. CCHP evaluation based on field test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. CCHP Evaluation using thermodynamic analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. CCHP evaluation using transient simulation models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization of CCHP systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Optimization in CCHP design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Optimal operating strategy of CCHP systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⇑ Corresponding author. Tel.: +1 662 325 1959. E-mail address: [email protected] (H. Cho). http://dx.doi.org/10.1016/j.apenergy.2014.08.107 0306-2619/Ó 2014 Elsevier Ltd. All rights reserved.

169 170 170 170 171 172 173 173 174 174 174 175 175

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5.

6.

4.2.1. Optimization with FEL and FTL operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Optimization using mathematical optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Optimal operation of CCHP systems with thermal energy storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4. Advanced control for real-time operation of CCHP systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CCHP Systems current research and development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Economic analysis of CCHP systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. CCHP Systems with renewable and alternative energy sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1. Solar energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2. Biogas, biomass, and biofuels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3. Fuel cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Unique thermodynamic techniques and thermal system arrangements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1. Cooling techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2. Alternative thermodynamic cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2.1. Cascading refrigeration cycles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2.2. Organic Rankine Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3. Unconventional integration schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4. Multigeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4. Advanced mathematical and computational methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1. Sizing and system selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2. Operational modes and dispatch strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5. Other recent progress in thermoeconomic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6. Gaps in current research and development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Traditional power plants convert about 30% of the fuel’s available energy into electric power [1]. The majority of the fuel energy content is lost at the generation facility through waste heat. Further energy losses occur in the transmission and distribution of electric power to individual users. Inefficiencies and environmental issues associated with conventional power plants provide the thrust for developments in ‘‘on-site’’ and ‘‘near-site’’ power generation. Combined cooling, heating, and power (CCHP) systems1 have the potential to increase resource energy efficiency and to reduce air pollutant emissions dramatically. CCHP systems produce both electric and usable thermal energy on-site or near site, converting 75– 80% of the fuel source into useful energy [2]. CCHP systems typically require only 3=4 the primary energy separate heat and power systems require [1]. International Energy Agency (IEA) [3] reported in 2007 that CHP systems produce approximately 9% of global power generation. The global capacity of CHP systems is estimated at around 330 GWe2 [4]. The total installed CHP systems capacities of 37 countries are listed in Table 1. According to IEA’s scenario [4], the G8+5 countries3 have the potential to raise their CHP systems capacity almost 430 GWe in 2015, and over 830 GWe in 2030. In the U.S., the total CHP systems capacity in 2012 is estimated 82 GWe [5]. European CHP systems potential studies indicate that the total capacity in Europe can be raised to within the range of 150–250 GWe by 2025 [3]. The application of CHP systems also has great potential to reduce carbon dioxide emissions: IEA [4] reported that CHP systems can potentially reduce CO2 emissions arising from new generation by

1 CCHP originally stands for combined cooling, heating, and power. In the literature, CCHP is also referred to by various names for slightly different applications such as CHP (combined heat and power), CHCP (combined heating, cooling, and power), BCHP (building cooling, heating, and power), DER (distributed energy resources), cogeneration, and trigeneration. Throughout the study, CCHP is used to refer to applications of combined cooling, heating, and power for Buildings. 2 Gigawatt-electric (GWe) is one billion watts of electric capacity. 3 The G8+5 countries consist of G8 (Group of Eight) nations including Canada, France, Germany, Italy, Japan, Russia, the United Kingdom and the United States and 5 nations of the leading emerging economies including Brazil, China, India, Mexico and South Africa.

169

176 176 177 177 178 178 179 179 179 179 179 179 180 180 180 180 180 181 181 181 181 181 182 182

more than 4% (170 Mt/year)4 in 2015, while in 2030 CO2 reduction could improve to more than 10% (950 Mt/year) which is equivalent to one and a half times of the total annual emissions of CO2 from power generation in India. By 2030 the implementation of CHP systems has the potential to reduce CO2 emissions in the U.S. by 70 Mt/year for buildings and 80 Mt/year for industries [6]. In Europe, CHP systems have been estimated to have been responsible for 15% of greenhouse gas emissions reductions (57 Mt) between 1990 and 2005 [4]. A typical CCHP system for a building consists of a power generation unit (PGU) working together with HVAC (heating, ventilation, and air conditioning) components, such as absorption chillers, cooling towers, and air handling units (AHUs). A variety of PGUs can be used in CCHP systems: micro-turbines, internal combustion (IC) engines, external combustion engines, fuel cells, etc. Fig. 1 illustrates a schematic of a CCHP system. As shown in the figure, Fuel (FPGU) is supplied to the PGU, and it produces electric energy (ElPGU) and rejects heat as a byproduct, normally wasted in many applications. This electric energy is used to power appliances and lights in the building (Elbuilding) and to operate auxiliary cooling and heating components (Elcomp). If the PGU does not generate enough electricity to satisfy the demand, the difference (Elgrid) can be imported from the electric grid (EG). ElPGU and Elgrid can be stored using batteries and super-capacitors, when necessary. On the other hand, if the PGU generates more electricity than needed, the excess electricity (Elexcess) can be exported or sold to the EG in locations where this is option is available. The recovered waste heat (Qrcv) from the PGU is used to produce cooling or heating (Qcool or Qheat) to satisfy the building cooling and heating loads. If the heat recovered from the PGU is not enough to fulfill the thermal energy requirements of the building, a boiler is used to offset the deficit heat (Qboiler). Qrcv and Qboiler can be stored using thermal energy storages if necessary. This paper presents a review on the methods used to perform energetic and exergetic analyses, system optimization, performance improvement studies, and development and analysis of CCHP systems, highlighting the most current research and emerging trends in CCHP technologies. 4 Megaton (Mt) is one million tons. Gas emissions are often expressed in terms of megatons.

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H. Cho et al. / Applied Energy 136 (2014) 168–185

Table 1 Installed CHP capacities [4]. Country

Capacity (MWe)

Country

Capacity (MWe)

Country

Capacity (MWe)

Australia Austria Belgium Brazil Bulgaria Canada China Czech Republic Denmark Estonia Finland France Germany

1864 3250 1890 1316 1190 6765 28,153 5200 5690 1600 5830 6600 20,840

Greece Hungary India Indonesia Ireland Italy Japan Korea Latvia Lithuania Mexico Netherlands Poland

240 2050 10,012 1203 110 5890 8723 4522 590 1040 2838 7160 8310

Portugal Romania Russia Singapore Slovakia Spain Sweden Taiwan Turkey United Kingdom United States

1080 5250 65,100 1602 5410 6045 3490 7378 790 5440 84,707

Electric Grid (EG)

Fuel (FPGU)

Power Generaon Unit (PGU)

Elgrid + Elexcess

El building

Elcomp

ElPGU

Cooling Components

Qrcv

Qcool

(Absorpon Chiller, Cooling Tower, AHU)

Building

+ Fuel (Fboiler)

Boiler

Qboiler

Heang Components

Qheat

(AHU)

Fig. 1. Schematic of a building CCHP system [90].

2. Performance improvement through energetic and exergetic analysis The energetic and exergetic performances of CCHP systems are of interest to many researchers, for both theoretical and existing systems. Energy and exergy performance data allow for comparison with other methods for producing power, cooling, and heating. Often the analysis takes place in a broader context where tradeoffs between energy and exergy performance, emissions or other environment impacts, and economics are addressed. Popular methods for comprehensive analysis include combinations of energy savings and/or energy efficiency, economics, emissions, and exergetic analyses. A selected cross-section of papers covering these topics is presented in Table 2. As an illustration of a combined energetic, economic, and emissions analysis, see Fig. 2, presenting results from Fang et al. [7] for a CCHP system with and without an Organic Rankine Cycle system that provides flexibility in the output power to heat ratio.

2.1. Energetic analysis Energy analysis in some form is the most common method for evaluating the performance of CCHP systems, whether for primary energy5 usage, fuel energy usage, or energy efficiency. These energetic analyses can be used to compare different types of CCHP systems, different operation strategies, or a CCHP system against a reference system or other alternative energy system. 5 Primary energy usage is defined as ‘‘the sum of the energy consumed at a facility and the energy required to extract, convert, and transmit that energy to the facility’’ and is also referred to as source energy [172].

2.1.1. Energy efficiency studies As described by Behboodi et al. [8], the energetic efficiency of a CCHP system is best where there is demand for heating, cooling, and electricity through most or all of the year, and energy efficiency benefits often tend to translate to economic benefits. Simulations by Behboodi et al. [8] also showed that a CCHP power plant located at the site of use and properly sized will yield further economic benefits. Any opportunity to use recovered heat, and especially low quality waste heat, will increase total efficiency [9]. Calise et al. [10], in simulating a thermal photovoltaic CCHP system, stressed the importance of demand for domestic hot water for maximum energy utilization of the CCHP system. Fu et al. [9] tested a number of CCHP configurations in the internal combustion engine-driven test bed constructed at the Building Energy Research Center, Tsinghua University, Beijing, China. The maximum possible efficiency demonstrated was 91–93%, in the winter, with an operation strategy including the following systems: jacket water heat recovery unit (used to regenerate liquid desiccant), exhaust-gasdrive double-effect absorption heat pump, and condensation heat recovery unit. In the summer, the exhaust gas heat was used to drive cooling, but the overall efficiency is lower. Ming et al. [11] tested a natural gas-fired microturbine CCHP system with absorption chiller at Tongji University, Jiading, China. The maximum possible total system efficiency demonstrated was 65–80% in the winter, depending on power output, and 60–65% in the summer. Al-Sulaiman et al. [12] used energy efficiency to assess a proposed CCHP system with parabolic trough solar collectors combined with ORC and found that the energy efficiency of the system was much higher in trigeneration mode, with a maximum efficiency of 94%, when compared with solar power production only, cooling-cogeneration, and heating-cogeneration. Ma et al. [13] proposed a solid oxide fuel cell CCHP system with an ammonia-water waste heat

171

H. Cho et al. / Applied Energy 136 (2014) 168–185 Table 2 Authors using combined methods of energy analysis, exergy analysis, economic analysis, and/or emissions analysis. Study author(s)

Energy savings or energy efficiency

Exergy

Economics

Emissions

Jing et al. [161] Angrisani et al. [21], Cardona et al. [72], Cho et al. [97], Chua et al. [143], Gu et al. [23], Kavvadias and Maroulis [162], Knizley et al. [163], Mago et al. [18], Mago and Hueffed [19] Ahmadi et al. [33], Ahmadi et al. [34], Ghaebi et al. [164], Huangfu et al. [38], Jabbari et al. [130] Gao et al. [30] Badami et al. [165], Badami and Portoraro [166], Calise et al. [10], Harrod et al. [25], Kong et al. [26], Liu et al. [82] Al-Sulaiman et al. [141], Ascione et al. [167], Behboodi et al. [8], Calva et al. [168], Cardona and Piacentino [15], Chicco and Mancarella [169], Cuviella-Suárez et al. [27], Fumo et al. [87], Jing et al. [28], Kazempoor et al. [24], Mancarella and Chicco [16], Ming et al. [11] Ebrahimi et al. [22], Espirito Santo [20], Hosseini et al. [37] Chicco and Mancarella [73], Wang et al. [170] Ghaebi et al. [171] Al-Sulaiman et al. [36]

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

Fig. 2. Primary energy consumption, cost, and carbon dioxide emissions for a CCHP and CCHP-ORC system in each season in a simulated hotel in Beijing [7].

recovery cycle and calculated a possible energy efficiency of 80% or more under specified conditions. Badea et al. [14] compare trigeneration energy efficiency for two prime mover types in residential CCHP applications: fuel cells and Stirling engines. Their simulations showed energy efficiency benefits by using renewable energy sources, and they found that an absorption chiller was better than a thermally activated chiller for energy efficiency performance. 2.1.2. Studies focused on energy savings Component selection and management strategies for CCHP may be evaluated based on primary energy savings. Cardona et al. [15] described many of the typical methods for evaluating CCHP systems based on efficiencies (see Table 3), and they argued that each parameter type only addresses a certain set of energy flows within the system and they can be contradictory. Therefore, they proposed a primary energy savings (PES) management, for the goal

of achieving ‘‘maximum energy savings during the plant life cycle.’’ Mancarella and Chicco [16] explained that evaluation with a primary energy savings indicator will indicate ‘‘the potential of different technologies’’ in terms of both energy performance and emissions reduction. Li et al. [17] pointed out that typical energy savings calculations often compare natural gas against centralized coal-based power production, which may be unfair due to the high efficiency of natural gas generation, and they state explicitly that ‘‘in cooling mode, CCHP systems almost always waste energy.’’ Many researchers find that the energy savings from a CCHP system come primarily from the ‘‘winter’’ mode (little or no cooling), and from the thermal energy recovered and used directly for some form of heating. In describing the effect of climate on primary energy savings, Mago et al. [18] explained that locations where more cooling is required will consume more primary energy. In another article, Mago and

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H. Cho et al. / Applied Energy 136 (2014) 168–185

Table 3 A Selection of criteria used for performance analysis and optimization of CCHP systems in the reviewed literature. Name

Formula

Primary energy savings ratio

PESR ¼

Primary energy savings

PES ¼ ðEPCS  EPAS Þ=EPCS

Trigeneration primary energy saving

F HVAC F F HVAC

SP

F z ¼ 1  Wz TPES ¼ F F SP

Fz

Primary energy savings Fuel energy saving ratio Energy utilization factor ‘Straight heat input or output’ exergy

Exergy efficiency Exergy efficiency of trigeneration

1

Qt

þ

gSP e

gSP t

z þ RSP

gc

1

geq geq el th gel;s þgth;s

FESR ¼

SP

F F y F SP

¼ 1  Wy

_

_

Fy

gSP e

_

þ

Qy

gSP t

_

Esteam þEht þEcw EUF ¼ W net þ _ fuel LHVfuel m EXe þEXh þEXc EXf

gexergy ¼

wtri ¼

_ net;ST þEx _ Heating þEx _ _ _ net;GT þW W Cooling þExHotwater _ Ex f _

_

_

hp;2 Þþmev ðexev ;1 exev ;2 Þ gex;tri ¼ W net þmhp ðexhp;1 exEx _

Comments

Refs.

‘‘Used to evaluate the primary energy saving achieved by CCHP system with respect to the reference system’’ Used ‘‘for comparing the profitability of an alternative system (AS) to a conventional system (CS) Based on a method to ‘‘synthetically describe the energy saving characteristics of the system even without considering the details of the plant structure.’’ Introduced by the European Union Commission ‘‘to evaluate the energetic performance of a cogeneration plant) Used ‘‘to evaluate the fuel energy saving obtained in a cogeneration plant with respect to [separate production] of heat. . .and electricity.’’

[32]

‘‘Defined as the combined energy of the flows produced by the cogeneration system and used to meet. . .demands’’ ‘‘Calculates the efficiencies of CCHP system and HVAC system taking into account the different thermodynamic values of different energy forms and quantities and used’’ ‘‘Product exergy output divided by the exergy input, for the overall polygeneration system’’ [As described]

[20]

‘‘Ratio of the amount of carbon emissions of the CCHP system in comparison to a HVAC system’’ Introduced by Chicco and Mancarella to indicate ‘‘CO2 emission reduction’’ versus standard reference technologies Inverse of ‘‘the ratio of exergy destruction to input exergy’’ which is ‘‘used to relate exergy with environmental impact’’ Includes cost of fuel, environmental (emissions) costs, and capital costs for each component

[32]

[167] [159]

[165] [159]

[32]

[148] [35]

coll

CER

CER ¼

Trigeneration CO2 emission reduction

TCO2 ER ¼

Sustainability index

SI ¼ D1p

Total cost rate

HVAC

CE CE CEHVAC ðmCO2 ÞSP ðmCO2 Þz ðmCO2 ÞSP

C_ tot ¼ C_ f þ C_ env þ

P

k Zk

Hueffed [19] found that a simulated natural gas turbine CCHP system can reduce primary energy consumption by an average of 12% for an office building in Chicago, IL, USA. Espirito Santo [20] simulated a CCHP system with differing operational strategies to serve a hospital in southeastern Brazil. Based on PES analysis, they determined that the feasibility of a trigeneration system is based on three factors: ‘‘(i) grid thermal plant efficiency; (ii) hot water and steam production efficiency; (iii) electrical chiller efficiency; and, of course, (iv) cogeneration efficiency.’’ Angrisani et al. [21] studied an existing micro-trigeneration installation in Frignano, Caserta, Italy, and found that it was less efficient than a conventional system when the power supplied by CCHP was relatively low, but that energy savings could be reached, with a maximum primary energy savings of 19% compared with the conventional system. Similarly, Ebrahimi et al. [22] found that for a theoretical micro-steam turbine CCHP system serving a residential building, the CCHP system saves energy unless the required heating load falls below 150 kW, with the highest overall efficiencies occurring in the winter, when heating demand is high. Gu et al. [23] analyzed four types of prime mover technologies that could be used in CCHP system applications and found that for a high-rise residential building in Shanghai, China, both gas engine and fuel cells had the potential to reduce energy consumption, while gas turbine and Stirling engine systems did not. They also point out the importance of balancing the heat-to-power ratio delivered by the system to the demand of the building. Kazempoor et al. [24] also found that solid oxide fuel cells have significant potential to save energy when used in a CCHP system for multifamily housing. Harrod et al. [25] provided a methodology for sizing analysis of a Stirling engine CCHP system, and their results indicate that a properly sized Stirling engine-based CCHP system can reduce primary energy consumption. Kong et al. [26] found that a Stirling engine-based CCHP system can save more than 33% primary energy compared to the reference case, and they point out that the performance of the associated absorption chiller will have a significant effect on the energy performance of the system. Cuviella-Suárez [27] proposed minimizing primary energy consumption by using a statistical matching method to match gener-

[169] [33] [148]

ation and demand characteristics, with any excess energy not needed for power, heating, or cooling going to water distillation. This is a form of multigeneration (or polygeneration) termed ‘‘tetrageneration.’’ Jing et al. [28] presented a method for using life cycle analysis (LCA) to compare primary energy usage and emissions to evaluate the performance of a solar PV-gas engine powered CCHP system. For their example case of a commercial office building in Beijing, China, they discovered from LCA that the materials contributions and operation and fueling stages are more important than manufacturing and transportation stages for affecting energy consumption. 2.2. Exergetic analysis Exergy analysis provides a measure of not only the energy used by a CCHP system, but the quality of energy provided and consumed, and the amount of availability destroyed within the system. Khaliq et al. [29] assert that exergy analysis is beneficial over energy analysis ‘‘because of the fact that it helps determine the true magnitudes of losses and their causes and locations, and improve the overall system and components.’’ It is possible to quantify exergy destruction, but researchers typically discuss exergy efficiencies and relative exergy contributions. They use exergy analysis to: 1. Calculate the exergy efficiency of a given or proposed system, and compare it with energy efficiency, exergy efficiency of another CCHP system, or exergy efficiency of an alternate system. 2. Find the relative contribution of individual components or subsystems to the overall exergy destruction. A well-designed CCHP system can have significantly less destruction of exergy than a reference system [30] and although the exergy efficiency will vary by system, certain exergetic considerations are critical, based on the body of literature. The usage of electricity and steam (when steam generation is a part of the CCHP system being analyzed) have the most influence on exergetic efficiency [20], and efficient transfer of heat and mass are needed to

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reduce exergy destruction [31]. A CHP system rather than a CCHP system in cooling mode tends to have an advantage in terms of exergy efficiency. Li et al. [32] found that the exergetic efficiency of a CCHP system was lower than that of a reference HVAC system during parts of the summer. Thermodynamic cycles and alternative integration schemes can also play a role in reducing exergy destruction using CCHP systems. In comparing an integrated Organic Rankine Cycle CCHP system with simpler alternatives, Ahmadi et al. [33] found the exergetic efficiency of a gas turbine-ORC CCHP system to be higher than that of a CHP system or gas turbine system alone. Furthermore, they pointed out that in such a system, the combustion chamber is the site of the most exergy destruction. Interestingly, the absorption cycle, which produces cooling, does not exhibit a relatively large amount of exergy destruction because it uses steam rather than a direct conversion of fuel as its energy source. A similar analysis for a gas turbine-steam cycle CCHP system found similar results for the combustion chamber and the absorption cycle [34]. Analyzing the energy flows into and out of individual devices to determine the contributions of different parts of the system provides unique information in the exergy analysis. In the simulated hospital in Brazil, Espirito Santo [20] calculated the energy provided by individual flows of energy relative to the fuel energy consumption, and graphed it with each system flow’s contribution to the overall exergy efficiency in Fig. 3. Exergy destruction can be traced to specific components and subsystems, but also depends on the mode of operation. Al-Sulaiman et al. [35] used exergy analysis to assess a proposed CCHP system with a parabolic trough solar thermal system combined with ORC. In their research, the solar collectors and ORC evaporators were found to be the sites of greatest exergy destruction. Using trigeneration rather than solar mode alone causes a dramatic increase in exergy efficiency, from 7% maximum for solar mode to 20% maximum for trigeneration mode. Al-Sulaiman [36] also found that for a biomass combustor-ORC system, the biomass combustor and ORC evaporators are the sites of greatest exergy destruction. Hosseini et al. [37], in their exergetic assessment of a hybrid photovoltaicsolid oxide fuel cell CCHP system, found that the exergy efficiency was lower than the energy efficiency because the associated absorption chiller had less exergy output than its energy value. While exergetic studies for an individual system provide insight about system performance and can inform design and operation choices, the variety of existing and potential CCHP systems results in a wide variety in the types of results available. To illustrate the findings of several studies of different systems:  Ebrahimi et al. [22] analyzed a steam turbine CCHP system for a residential building and found that the greatest exergy destruction took place in the steam generator, whether in summer or winter.  Fontalvo et al. [31] proposed and modeled a CCHP system powered by a Rankine cycle using an ammonia-water mixture with an absorption refrigeration cycle. They found that the absorber, the boiler, and the turbine were the sites of greatest exergy destruction.  Khaliq et al. [29] analyzed a gas turbine system with absorption and evaporative cooling and found, similarly, that the combustion and heat recovery processes were the sources of the most exergy destruction.  Huangfu et al. [38] analyzed a CCHP system for a residential or light commercial building with an internal combustion gas engine and adsorption chiller, and found that improving its electrical efficiency would provide the greatest exergy benefits. Although no standard exergetic analysis method has been adopted by CCHP researchers, the overall body of research on the

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Fig. 3. Contribution of individual energy flows within a CCHP system to the energy utilization factor and the exergy efficiency [20].

topic is vast and persistent themes are identified. Certain components may be identified as major contributors to exergy destruction: combustor, absorption chiller, and evaporator (if present); and tactics used to optimize for increased exergy efficiency should focus on efficiency of electricity generation, and the performance of the CCHP system in the summer months in cooling mode will greatly affect its exergetic efficiency overall. 3. Performance evaluation of CCHP systems As discussed in the Introduction, the total number of CCHP system installations is increasing worldwide. Consequently, optimal design and operation are becoming important issues. CCHP system performance evaluation is a necessary process to attain economically and environmentally feasible solutions in CCHP systems design and operation. Without proper performance evaluations, improvement in the system design and operation cannot be quantified. A literature review on the topics of CCHP systems evaluation using field test results, thermodynamic analyses, and transient simulation models is given in the following sections. 3.1. CCHP evaluation based on field test results Performance evaluation studies of CCHP systems based on field tests are discussed in this section. Recently, several case studies have been conducted to evaluate CCHP systems based on the field test results from the economical and technical point of view. Jablko et al. [39] made a comparison of technical and economical aspects of micro-CHP using several types of cogeneration, e.g., fuel cells, Stirling engines, and combustion engines. The results from their simulation showed that most cogeneration systems are not better than a conventional condensing boiler for a single-family home with a heat demand of approximately 20,000 kW h per year and 150 m2 floor areas. However, they provided an interesting analysis which shows that a CHP system can be more economical in some scenarios, e.g., when the price of gas and electricity are increased by 10%. Peacock and Newborough [40] evaluated the performance of CHP systems with Stirling engines and fuel cells based on heat and power demand data recorded on a 1-min time base across a full year. They estimated that the annual cost per CO2 savings are £ 90 per 574 kg CO2 for a 1 kW Stirling engine system and £ 142 per 892 kg CO2 for a prospective 1 kW fuel cell system when compared to a conventional system. De Paepe et al. [41] performed experiments with several types of cogeneration system, e.g., gas engines, Stirling engines, and fuel cell, and concluded that although technologies of CHP components are mature enough, it is still not attractive in the market because of high initial and maintenance costs. Kuhn et al. [42] presented an overview on selected microCHP technologies including Stirling and steam machines. They

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assessed CHP performance based on the field tests in Germany, the UK and some other European countries. 3.2. CCHP Evaluation using thermodynamic analyses As discussed in Section 2, both first- and second-law based thermodynamic analyses are fundamental tools to evaluate energy system performance. The exergy analysis is a useful tool in performance assessments of CCHP systems and permits meaningful comparisons of different CCHP systems based on their merits [43]. Several CCHP system performance evaluations using the first and second law of thermodynamics have been presented in the literature. Hasan and Goswami [44] performed an exergy analysis of a combined power and refrigeration cycle driven by a solar heat source. They stated that increasing the heat source temperature does not necessarily result in higher exergy efficiency, as is the case for first law efficiency. Silveira and Tuna [45,46] suggested a new methodology that provides the minimum Exergetic Production Cost based on the second law of thermodynamics for cogeneration plants. They claimed that ‘‘the development of an economic optimization method associated with thermodynamics analysis, overcoming the initials complexities, appears as a powerful tool for a better conception of the investments and operations conditions of the proposed cogeneration system.’’ Jalalzadeh-Azar [47] presented a thermodynamic analysis of a CHP system for a hypothetical commercial building in Atlanta, Georgia, USA. In his paper, the economical benefits of electric-load-following and thermal-load-following operational modes for CHP operation were evaluated. He concluded that the thermal-load-following model was found to be superior to the other previously studied model from first-law thermodynamic standpoint. Moran et al. [48] demonstrated an economical analysis using thermodynamic models of micro-CHP systems with a natural gas internal combustion engine and a diesel engine. According to their results, both systems have similar performance, and total efficiencies of the order of 80% can be obtained in cooler months. They further stated that there is a limit in fuel price that economically prevents the use of CHP systems. Kanoglu and Dincer [43] presented performance assessment of various building cogeneration systems using energy and exergy efficiencies. They stated that ‘‘the diesel-engine and geothermal systems appear to be thermodynamically more attractive, in that they have higher exergy efficiencies, than steam-turbine and gasturbine systems.’’ 3.3. CCHP evaluation using transient simulation models Although the CCHP system performance can be accurately evaluated using actual measurements, it has several limitations: (a) costly data acquisition systems need to be installed; (b) year-round field measurements cannot be easily obtained; and (c) each study is limited to a specific system. On the other hand, computer-based simulations can be used to predict system performance. A validated transient simulation of a CCHP model can provide accurate performance evaluation with (a) relatively simple implementation; (b) time-efficient yearly-simulation using statistical yearly weather data; and (c) straightforward modifications for different sizes and types of CHP systems in different climate conditions. There have been many studies of CHP performance evaluation using transient simulation models in the literature. Hadzikadunic et al. [49] demonstrated how a CHP system can be modeled using a dynamic simulation tool, MODELICA/DYMOLA, and showed that the simulation can deliver accurate results compared to measurements. Using a commercially available software package, Nayak and Radermacher [50] modeled and simulated a 27 MW CHP plant with 10,000 tons of cooling capacity used to provide heating, cooling and electricity to the University of Maryland campus. Two gas turbines were used in the topping cycle to produce 22 MW of elec-

tric power at full load and a backpressure steam turbine was used to supply steam to the campus at about 963 kPa and generate an additional 5.5 MW of electric power in this process. Two models were used in the simulation to show potential energy and cost savings for the existing plant: The first model added inlet air-cooling using either an absorption or electric chiller to increase electrical power output during hot weather; the second model used economizers to provide free cooling and reduce the usage of the electric and stream driven chillers. The results from both simulations showed substantial energy and cost savings. McNeill et al. [51] presented a detailed simulation analyses to investigate the impact of utilizing hybrid cogeneration systems. In their study, DOE-2.2 simulation software was utilized to develop a model of a hybrid cogeneration system that combines liquid desiccant, absorption chiller, natural gas turbine cogeneration system with thermal storage. An effective and efficient approach to assess performance of residential cogeneration systems was presented in the final report of Annex 42 by the International Energy Agency’s Energy Conservation in Buildings and Community Systems Programme [52]. The objectives of Annex 42 are  to develop simulation models that advance the design, operation, and analysis of residential cogeneration system;  to apply these models to assess the technical, environmental, and economic performance of the technologies. They reported that Annex 42 focused on natural-gas-fired cogeneration devices with electrical outputs that varied from under 1 kW to 15 kW and successfully developed models with sufficient precision and resolution for simulating proton exchange membrane fuel cells (PEMFC), solid oxide fuel cells (SOFC), Stirling engines, and internal combustion engines. These models were independently implemented into source code for four widely used building simulation tools (ESP-r, EnergyPlus, TRNSYS and IDA-ICE) and validated empirically using the measured data gathered by Annex 42. These new models were exercised in the building simulation tools to assess the performance of specific prototype, earlymarket, and in some cases, hypothetical cogeneration devices and applications. Dorer and Weber [53] assessed the performance of residential cogeneration systems based on electric and thermal demands specified in IEA Annex 42 [52] using a whole-building energy modeling program, TRNSYS. They demonstrated that natural gas-fuelled (i.e., fuel cell and internal combustion engine based) cogeneration system can significantly reduce primary energy consumption and CO2 emissions compared to conventional systems. In the transient energy simulation, time interval and the number of time steps play a critical role. Hawkes and Leach [54] emphasized that fine temporal precision (e.g., 5-min precision energy demand data) is required in modeling to adequately capture the specific characteristics of residential energy demand and the technical qualities of solid oxide fuel cell and Stirling engine micro-CHP systems. The results of their analysis showed that in some cases optimal design generation capacity of CHP system is reduced by more than half between analyses using 1-h precision and 5-min precision energy demand data. They also show that total CO2 emissions reduction is overestimated by up to 40% using coarse precision data. 4. Optimization of CCHP systems Optimization in system design and operational strategy development is one of the key elements to improve energy efficiency and to reduce overall cost and greenhouse gas emissions. Optimization processes and techniques used in CCHP systems design and operational strategy in the literature are discussed in the following sections.

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4.1. Optimization in CCHP design Design of CCHP systems involves selection of the type and size of the system components, e.g., prime movers, energy storages, heat exchangers, absorption chillers, etc. The selection process must take into consideration the efficiencies of individual components, the system operating strategy, and the building demand for power, heating, and cooling [55–58]. A well designed CHP system should balance cost savings, real energy savings based on primary energy consumption, and net emission of pollutants [18]. The prime mover or power generation unit is one of the main components of a CCHP system. Therefore, it has to be carefully selected to guarantee the desired performance of the CCHP system. Different types of prime movers or PGUs can be used in CCHP systems. Some of them include: reciprocating internal combustion engines, steam turbines and combustion turbines, microturbines, fuel cells, Stirling engines, etc. The advantages, drawbacks, and analyses of prime movers used in various CCHP systems designs are often discussed in many literatures [56,59–63]. Wu and Wang [59] summarized the characteristics and parameters of prime movers in CCHP systems design. They identified that the characteristics of prime movers, such as system efficiency, capacity range, and power-to-heat ratio, are important factors to determine the optimal size and type of prime movers for CCHP applications. A comparative analysis of different prime movers used in CCHP systems and their selection criteria can be found in Al-Sulaiman et al. [64]. Once the type of a prime mover is selected, it is important to determine a rational capacity to maximize availability of the prime mover. This is a complicated task because it is necessary to take into account the systems’ annual operational strategies that consider the variations of electric and thermal energy demands and the deviations of electricity and fuel prices throughout the year [65]. This problem can be effectively dealt with using an optimization programming technique such as linear programming (LP). Yokoyama et al. [65] proposed an optimal planning method for determining the sizes of cogeneration plants under consideration for their annual operational strategies. They demonstrated that sizes of prime movers can be effectively determined using mixed-integer linear programming (MILP) in an example of a gas turbine CHP plant. In the same manner, Beihong and Weiding [66] develop an optimal sizing method to determine the size of a gas turbine cogeneration plant using mixed integer nonlinear programming (MINLP). They applied their method to a cogeneration plant used for a hospital in Shanghai, China to show the effectiveness of their method. Ren et al. [67] demonstrated that a MINLP model of a CHP plant can be effectively used to determine the optimal size of a prime mover and storage tank for residential CHP systems. Maor and Reddy [68] reported their research work to generate the ‘‘necessary data for certain characteristic building types with rationally designed and sized BCHP equipment’’ and Reddy and Maor [69] presented ‘‘a methodology to select representative building types and geographic climates to perform careful design and sizing of the BCHP systems and equipment.’’ The design of CCHP systems should consider the tradeoffs among cost-savings, real energy savings based on primary energy consumption, and net emission of pollutants. A detailed technical

description and analysis of PGUs and thermally activated components that can be used in CCHP systems can be found in Refs. [70,71]. Table 4 presents a summary of the typical performance and cost of a selection of different prime movers, such as: the prime mover efficiency, the overall CHP efficiency, typical capacity, and the installed cost and the operating and maintenance (O&M) cost, for different CHP technologies. This table is intended to serve as a reference to readers and a more accurate number must be estimated for any specific application. 4.2. Optimal operating strategy of CCHP systems CCHP systems can be controlled by several possible operation strategies. The most common kinds of operation strategies found in literature are summarized as follows [15,18,72–79]: 1. Following the electric load (FEL): when the CCHP system operates under this strategy, the power generation unit generates all the electricity needed to satisfy the electric demand and the waste heat is used to satisfy all or part of the building’s thermal load. If the recovered heat is not sufficient to satisfy the thermal demand an auxiliary boiler can be used to supplement the heat needed by the facility. On the other hand, if the recovered heat is more than that required by the building, the excess could be stored or discarded. Some authors defined this strategy as electric demand management (EDM) [72]. 2. Following the thermal load (FTL): under this strategy, the system satisfies the building’s thermal load; and the electricity generated by the power generation unit is used to satisfy part or all of the building’s electric demand. If the electricity produced by the CCHP system cannot meet the electric requirements, additional electricity must be purchased from the grid. On the other hand, if the electricity produced is more than the amount needed by the building, the excess electricity can be stored or sold back to the electric grid. However, this option is not available at all locations. Some authors refer to this strategy as thermal demand management (TDM) [72]. 3. Base load operation: when the system covers only a constant amount of the electric load of the facility. In this case electricity has to be imported from the grid to completely satisfy the electric demand. In addition, an auxiliary boiler can be used if the recovered heat from the power generation unit is not sufficient to satisfy the thermal demand. Although the above mentioned strategies are the most common ones in CCHP system operations, they may not guarantee the best performance of the system. Yokoyama et al. [65] stated that the simple operational strategies may not result an economically feasible solution because the operation of cogeneration system is subjected not only to the variation of load demands, but also to the fuel prices. Sundberg and Henning [80] addressed the influence of fuel price to operate CHP systems in an economically optimal condition. Chao-zhen et al. [81] emphasized the importance of the studies on the energy load demands from the CHP facility to determine an optimal operation strategy. In the following sections, methodologies used to optimize CCHP operations are discussed.

Table 4 Summary of typical performance characteristics and cost for CHP systems with different prime movers [1]. Prime mover

Steam turbine

Reciprocating engines

Gas turbine

Microturbine

Fuel cell

Efficiency Overall efficiency Typical capacity (MWe) CHP Installed costs ($/kWe) O&M costs ($/kW he)

15–38% 80% 0.5–250 430–1100 <0.005

22–40% 70–80% 0.01–5 0.5–1 0.009–0.022

22–36% 70–75% 0.5–250 0.5–2 0.004–0.011

18–27% 65–75% 0.03–0.25 0.4–0.7 0.012–0.025

30–63% 55–80% 0.005–2 1–2 0.032–0.038

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4.2.1. Optimization with FEL and FTL operations Variations of the FEL and FTL operational strategies have been investigated by several authors to optimize the system based on energy, emission, or operational cost. Kavvadias et al. [76] discussed the factors that affect the operation and the feasibility of investment in CCHP systems. They proposed an electrical-equivalent load following strategy where the electrical demand includes only the portion of the cooling demand that the absorption chiller cannot meet. Their results indicated that the proposed strategy was superior to conventional strategies (FEL and FTL) from both the economic and energetic point of view, when applied with maximum demand tariffs, as it was proven to have two major benefits: better load coincidence and peak reduction. Mago and Chamra [78] introduced an operational strategy in which a CCHP system follows a hybrid electric–thermal load strategy (HETS). Results indicated that the HETS was a good alternative for CCHP systems operation since it provides reduction of operational cost, emissions, and primary energy consumption. Gu et al. [23] introduced an operational strategy called energy island mode, which is a variation on FEL in which the system is sized so that it can provide all of the electrical needs of a facility which is not grid connected. Liu et al. [82] proposed a new CCHP system operational strategy based on the electric cooling to cool load ratio, which describes the portion of the cooling load that is met by the electric chiller, and they used an optimization algorithm to determine the optimal power generation unit capacity. Wang et al. [83] discovered in their sensitivity analysis that the PGU capacity has more influence on performance than the ratio of electric cooling to cool load. Chicco and Mancarella [84] developed a matrix modeling of small-scale trigeneration systems and applied the developed model to optimize the operation of the system. The proposed formulation provided the basic framework for formulating optimization problems that deal with management of trigeneration systems within an energy market perspective. Jing et al. [85] optimized the operation strategy of a BCHP system operating FEL and FTL based on life cycle assessment. Their optimization results indicated that FEL strategy provided more environmental benefits than FTL strategy. Fang et al. [86] proposed an optimal operational strategy based on FEL and FTL strategies that depends on an integrated performance criterion (IPC). Using the proposed strategy, the operation of the CCHP system is divided into different regions by one to three border surfaces estimated by the CCHP system energy requirements and the IPC. The IPC simultaneously accounts for the reduction of primary energy consumption, operational cost and carbon dioxide emissions. Fumo et al. [87] presented an emission operational strategy with the objective of minimizing the carbon dioxide emissions from CCHP systems. In their study, the CCHP system was operated FEL, guaranteeing that the CCHP system always generated less emission than the conventional case (separate production of electricity and heat). They reported that although the proposed strategy might not offer the best performance in terms of primary energy consumption or operational cost, it is valuable for facilities that are required to reduce their emissions. In another study, Fumo et al. [88] introduced a building primary energy ratio (BPER) parameter to evaluate the energy performance of CCHP systems. This parameter was used to measure the variation of the primary energy consumption of the CCHP system versus the conventional system, which allows for controlling the CCHP system to operate only when primary energy is being saved. Smith at el. [89] compared with a hybrid method which either follows the thermal or the electric demand in a given time period, in order to minimize the amount of excess electrical or thermal energy produced by the CHP system. The proposed hybrid method showed higher CHP system efficiencies for the simulated building in a wide range of climate conditions.

4.2.2. Optimization using mathematical optimization techniques An optimal operation strategy of a CCHP system can be effectively determined using mathematical optimization techniques, such as genetic algorithms, linear programming and stochastic optimization, with the objective of minimizing the operational cost, primary energy consumptions and/or greenhouse gas emissions. For instance, an optimization problem can be formulated as shown in Eq. (1) to minimize the total operational cost of a CCHP system while satisfying the total energy demand [90].

Minimize

n X

fcel ðtÞElgrid ðtÞ þ cf

pgu ðtÞF pgu ðtÞ

þ cf

boiler ðtÞF boiler ðtÞc

t¼1

 cel

ex ðtÞElexcess ðtÞg

ð1Þ

where variables Elgrid(t), Fpgu(t) and Fboiler(t) represent the electric energy from the electric grid, the fuel energy for the PGU, and the fuel energy to operate the boiler in time period t (t = 1, 2, . . ., n). The term cel(t) represents the cost of purchasing one kW h of electricity, cf_pgu(t) represents the cost of fuel that is used to produce one kW h of energy in the PGU, and cf_boiler(t) represents the cost of fuel that is used to produce one kWh of energy in the boiler. Variable Elexcess(t) represents the amount of electric energy sold back to the electric grid in period t, and cel_ex(t) represents the selling price per kWh of electricity. There is an upper bound on the values for the decision variables Fpgu(t) and Fboiler(t). These bounds are equal to the maximum amount of energy that can be produced in a time period by the PGU and boiler, respectively, due to their production capacity. There is no such limit on the amount of electricity that can be purchased from the grid. Along with the objective function, a set of constraints are formulated to specify conditions for the decision variables that are required to be satisfied. The constraints are determined based on the equipment characteristics (e.g., efficiency of equipment), energy flow relationship and the operational limitations [91]. The effectiveness of using mathematical optimization techniques to determine optimized operations of CCHP systems has been demonstrated in the following studies. Yokoyama et al. [65] demonstrated that an optimal operation planning can be achieved using MILP. Sakawa et al. [92] formulated operational planning problems of district heating and cooling plants as mixed binary linear programming problems and demonstrated the economical feasibility and efficiency of the proposed method. Lahdelma and Hakonen [93] modeled the hourly CHP operation as an LP problem to obtain cost-efficient solutions using their proposed Power Simplex algorithm. Rong and Lahdelma [94] proposed the specialized Tri-Commodity Simplex algorithm to minimize the energy production and purchase costs as well as CO2 emission costs of CHP systems. Wang et al. [91] optimized BCHP systems to maximize the energy savings and reduction of environmental impact using genetic algorithm. In another study, Wang et al. [95] optimized a CCHP system using genetic algorithm based on three criteria: primary energy saving, annual total cost saving, and carbon dioxide emission reduction. Cho et al. [96] developed an optimal energy dispatch algorithm that minimizes the cost of energy based on energy efficiency constraints for each component using a deterministic network flow model of a typical CCHP system. In another study, Cho et al. [97] optimized the operation of CCHP systems for different climate conditions based on operational cost, primary energy consumption, and emissions reduction using an optimal energy dispatch algorithm. They reported that for the different U.S. locations evaluated in their work, there is not a common trend among the three optimization modes since optimizing one parameter may increase or reduce the other two parameters. In a recent study, Hu and Cho [98] proposed a stochastic multi-objective optimization model to optimize the operation strategy of CCHP systems for different climate conditions

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based on operational cost, primary energy consumption, and carbon dioxide emissions. They added the probability constraints into the stochastic model to guarantee the optimized CCHP operation strategy is reliable to satisfy the stochastic energy demand. Lozano et al. [99] also used a linear programming model to determine the optimal operation strategy corresponding to the minimum variable cost. Likewise, Li et al. [17] developed a mixed integer linear program optimal model to minimize the annual total cost, primary energy consumption and carbon dioxide emission of CCHP systems for a given commercial facility. Tichi et al. [100] studied the impacts of the different energy price policies by using the particle swarm optimization (PSO) algorithm. Thorin et al. [101] developed a tool to study long-term optimization of CCHP system based on a mixed integer linear programming (MILP) model. Other than single objective model, multi-objective optimization models are explored to optimize the CCHP system in terms of energy and environmental benefits simultaneously. A multi-objective optimization (MOO) model was proposed in [102] to optimize the CCHP system simultaneously in terms of exergetic efficiency, total levelized cost rate of the system product, and the cost rate of environmental impact. The superior performance of CCHP system in terms of cheaper operational cost and smaller CO2 emission is demonstrated in an urban area [103]. Two objectives, competing fuel cost and environmental impact, were studied using a multi-objective line-up competition algorithm to optimize the CCHP economic dispatch [104]. Another effort to study CCHP operation is the stochastic model which optimizes the performance of the CCHP system with uncertainties in energy demand and energy price. In the real world, the operation strategy derived in the deterministic condition may be infeasible or cost expensive, so the stochastic optimization model which could derive reliable operation strategy is needed. Li et al. [105] proposed a mixed-integer nonlinear programming (MINLP) model to study the impacts of the average, uncertainty, and peaks of energy demands on economic performance of CCHP system. Smith et al. [106] studied uncertainties in the thermal load, natural gas prices, electricity prices, and engine performance, and investigated the performance of CCHP system in terms of operational cost, PEC, and CDE under these uncertainties. An uncertain programming model which integrates the Monte-Carlo method (MCM) and mixed-integer nonlinear programming was developed to derive optimized CCHP operation strategy under energy demand uncertainty [107]. Wang and Singh [108] developed a stochastic model for combined heating and power (CHP) system economic dispatch which could simultaneously optimize the performance of CCHP in terms of production cost, power generation deviation, and heat generation deviation, and propose an improved PSO algorithm to study the stochastic model. Liu et al. [109] proposed a matrix modeling approach to optimize the performance of CCHP systems. They treated the CCHP system as an input–output model. In their paper, the CCHP system was modeled using a conversion matrix including the dispatch factors and components efficiencies. Their proposed optimization technique can be used to optimize the power flow and PGU capac-

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ity of the CCHP system. Flores et al. [110] proposed a dynamic distributed generation dispatch strategy to lower the cost of building energy. They used the proposed technique to simulate various modes of operation, such as electric or thermal load following, peak shaving, peak shifting, or base-load operation and they found that the proposed dispatch strategy was more effective that some of the more traditional dispatch strategies. A list of authors using different mathematical optimization techniques is presented in Table 5. 4.2.3. Optimal operation of CCHP systems with thermal energy storage Thermal energy storages (TESs) are often used to help manage peak energy demand for better performance and economy. According to Haeseldonckx et al. [111], the use of thermal storage tanks prolongs the yearly operation time of a CHP facility and allows the power generation unit to operate more continuously. In their investigation, it is shown that a small TES reduces CO2 emissions to about a third of the reference case without a heat buffer. Wang and Ma [112] suggested that a proper optimization scheme is required when a system includes thermal energy storages. They stated that ‘‘the optimization related to the systems without storage is a quasi-steady, single-point optimization, while the optimization associated with the systems with storage is the dynamic optimization determining a trajectory of setpoints.’’ Modified linear programming has been used in many studies to operate TESs effectively although nonlinear or dynamic programming techniques can also be used. The reason is that nonlinear or dynamic programming techniques may take relatively longer time or may not converge when a large number of variables are used. Yokoyama and Ito [113] presented a revised decomposition method for solving large-scale MILP problems with block angular structure to efficiently conduct the operational planning of TESs. Henze et al. [114] developed an optimization strategy for a chilled water plant using a thermal storage system: mixed integer programming is used to optimize the chiller dispatch, and dynamic programming is accommodated to optimize the charge/discharge strategy of the TES system. Ren et al. [67] developed a mixed integer nonlinear programming model to operate CHP plant with a thermal storage tank. Smith et al. [115] investigated the performance of a CHP system with and without a thermal energy storage option for eight different commercial building types located in Chicago, IL. They reported that for the majority of the buildings, adding thermal storage provides further reductions in operational cost, PEC, and CDE as compared with the CHP system without TS. Yongliang et al. [116] presented a study of using a novel energy storage system that stores excess energy from a trigeneration system in the form of compressed air and thermal heat. They reported that the proposed system is very promising for practical applications especially for the use of renewable energy due to good flexibility and simplicity of the configuration. 4.2.4. Advanced control for real-time operation of CCHP systems Advanced control algorithms are becoming a subject of increasing interest in the CHP research community. For instance, reports

Table 5 Authors using various mathematical optimization techniques. Optimization techniques

Author(s)

Linear programming (linear programming or mixed integer linear programming (MILP)) Mixed integer nonlinear programming (MINLP) Stochastic optimization Genetic algorithm Particle swarm optimization Multi-objective optimization

Li et al. [17], Yokoyama et al. [65], Cho et al. [90], Sakawa et al. [92], Lahdelma and Hakonen [93], Rong and Lahdelma [94], Cho et al. [96], Cho et al. [97], Lozano et al. [99], Thorin et al. [101], Yun et al. [120] Beihong and Weiding [66], Li et al. [105], Li et al. [107] Hu and Cho [98], Wang and Singh [108] Ebrahimi et al. [22], Wang et al. [91], Wang et al. [95], Wang et al. [153], Guo et al. [154] Wang and Singh [108], Tichi et al. [100] Jing et al. [85], Hu and Cho [98], Gholamhossein Abdollahi [102], Bracco et al. [103], Shi et al. [104], Wang and Singh [108], Guo et al. [154], Jing et al. [161], Kavvadias and Maroulis [162]

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Fig. 4. Feed-forward control loop with short-term forecasting for a CCHP system in an optimization framework [90].

[117–119] from Pacific Northwest National Laboratory (PNNL) address a variety of control related tasks concerned with CHP operation. The objectives are to ‘‘ensure optimal performance, increase reliability, and lead to the goal of clean, efficient, reliable and affordable next generation energy systems’’ [119]. The algorithms are categorized into five major groups: (1) performance monitoring, (2) automated commissioning verification, (3) automated fault detection and diagnostics, (4) automated reconfiguration and correction and (5) supervisory controls [118]. Cho et al. [90] proposed a supervisory feed-forward control system as shown in Fig. 4 for CCHP system operation based on short-term weather forecasting. In their proposed system, electric and thermal energy storage systems are included to enhance the electric and thermal energy management of the system. Yun et al. [120] developed an optimal hierarchical CCHP system control algorithm to obtain real-time optimal decisions on the energy management of the CCHP equipment and building.

5. CCHP Systems current research and development Combined cooling, heating and power is a topic of growing interest to researchers globally, including diverse communities in countries such as the United States, China, Iran, and Italy. The term ‘‘CCHP’’ appears in published literature with increasing frequency in the years 2010 and beyond. Major trends in recent literature include: 1. economic analysis of CCHP systems; 2. integration of CCHP systems with renewable and alternative power sources; 3. novel thermodynamic techniques; and 4. advanced methods for improved system selection and operational strategies. Each of these trends are addressed in the following subsections, although several articles overlap between two or more of these categories, and some recent articles address different topics altogether. Some

of these are described in Section 5.5. For the most recent CCHP microgrid research from an electrical engineering perspective, see the recent review by Gu et al. [121]. 5.1. Economic analysis of CCHP systems An economic benefit is an important factor to consider when deciding whether to install or determining a system capacity and an operational strategy of a CCHP system. The economic benefits can be driven by various factors, e.g., equipment cost, equipment efficiency, electricity and fuel cost, facility electric and thermal loads, etc. Because these factors vary widely case by case, it is not straightforward to estimate economic savings for CCHP systems implementation. Additionally, the metric used to assess economic performance is different according to different researchers and in different regions (see Table 3, which presents innovative ways to assess CCHP economics, among other aspects). A simple payback period assessment, which incorporates operating costs and capital costs (see Table 4), may result in a longer payback period than a more conventional reference system, but CCHP systems can also have potential environmental and performance benefits which should be considered in a comprehensive analysis. The facility thermal loads can also be significantly affected by local climate conditions. Therefore, for the same CCHP system, the economic benefits will vary depending on the location where the system is installed. Many researchers (e.g., Mago et al. [18], Smith at el. [89], Cho et al. [97], Hu and Cho [98] in the U.S.; Jiang-Jiang et al. [122], Ren et al. [123], Li and Xia [124] in China; Ebrahimi and Keshavarz [125–127], Tichi et al. [100] in Iran) have tried to estimate and to establish the economic benefits of CCHP systems with various power generation units in different climate locations. The payback period of CCHP systems has been evaluated by several researchers to demonstrate the economic feasibility of a

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system (e.g., [125,128-131]). Optimization techniques are often used in literature to improve the economic benefits of CCHP systems. The introduction of literature related to this topic and more in-depth discussion can be found in Sections 4 and 5.4. Although only some recent papers have been introduced in this section, reader can find many related discussions throughout this manuscript and Table 2 identifies specific papers which deal with both economic and other aspects. 5.2. CCHP Systems with renewable and alternative energy sources 5.2.1. Solar energy Solar energy is growing in popularity worldwide, and because electrical and thermal energy are potentially harvested from a solar energy system, they are increasingly being considered as part of a CCHP system. Rosiek and Batlles [132] performed case studies for CCHP systems in commercial buildings in Spain, comparing a conventional HVAC system (electric compression powered with electricity purchased from the grid) with three types of cooling, heating and power from renewable sources: solar absorption, solar geothermal electric compression, and solar electric compression. Only the solar absorption system produced energy savings; however, the energy savings were significant at 61% over the reference case, and the design was also tailored to reduce space requirements on-site and to take advantage of the abundant solar resources available in the Almería region of Spain. This system is in the early stages of its development and will require further work on performance improvement and integration with CHP technologies. Chua et al. [133] considered several CCHP systems integrated with renewables to serve an electrically isolated island in Singapore. A wide variety of prime movers were analyzed at differing levels of renewable energy penetration: microturbines, solar photovoltaics, solar Stirling dish, fuel cells, and biomass power generation with absorption cooling. Primary energy was reduced for each case, and high renewable penetration (40%) corresponded to the largest potential reduction in CO2 emissions, but the increased capital costs in this case resulted in a net projected economic loss. Meng et al. [134] performed a theoretical study of a novel solar CCHP system based on metal hydrides. They proposed a CCHP system driven by solar energy and industrial waste heat to provide power and refrigeration. They indicated that the proposed system is superior to the traditional CCHP systems based on the integrated performance. Wang et al. [135] presented a parametric analysis of a CCHP system with transcritical CO2 driven by solar energy. In this study, the CCHP system integrates a Brayton cycle and a transcritical CO2 refrigeration cycle with ejector-expansion device, which uses solar energy as the heat source. 5.2.2. Biogas, biomass, and biofuels Bio-based fuels can be used to provide primary energy to a PGU which results in electrical generation and recovered heat, similarly to a fossil fuel-powered CCHP system. Maraver et al. [136] compared biomass-fueled CCHP systems with conventional on-site generation systems using Life Cycle Assessment (LCA) to find potential environmental and primary energy savings benefits. When the plant requires small amounts of cooling compared with heating demands, LCA showed that biomass CCHP systems were environmentally friendly; but when cooling demands were high relative to heating demands, they did not show environmental benefits. The authors were hesitant to state the results of the primary energy savings analysis, concerned that it would eliminate from consideration plants which could still provide some environmental benefit. Huang et al. [137] performed an economic and technical analysis of a biomass fueled CCHP system using an Organic Rankine Cycle (ORC) bottoming cycle to supply electricity, and using the waste heat from combustion exhaust gases and the

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ORC to supply hot water, space heating, and run an absorption chiller. Biomass in the form of willow chips, straw and rice husk was considered and the process efficiency was found to be similar for each type of biofuel. The overall system efficiency for the CHP system without cooling was found to be higher than that of the CCHP system, but the CCHP system showed good potential for cost savings and CO2 emissions reduction. A Stirling engine uses external combustion to fuel a Stirling cycle, and results in electrical generation and waste heat as well. Maraver et al. [138] further reviewed biomass-based CCHP systems based on types of prime movers and methods of integration with chillers. A Stirling engine showed promise thermodynamically but is difficult to obtain and implement in practice. The ORC has some limitations in smaller CCHP systems (below 200 kWe) due to the low sink temperatures. Several configurations were analyzed and some guiding principles for biomass CCHP development were laid out. Again, a rise in the cooling factor, which indicates high cooling loads, resulted in less benefit i.e., in this case, less primary energy savings. Harrod et al. [25] found that using a Stirling engine fueled with biomass (wood chips) as part of a CCHP system for a commercial building would provide cost and energy savings compared with a reference case. The importance of prime mover selection was stressed and the sizing and efficiency of the prime mover were shown to have a pronounced effect on both cost and primary energy savings. 5.2.3. Fuel cells Fuel cells systems convert chemical energy directly to electrical energy, and several types of fuel cells provide both electricity and high-temperature waste heat. In a recent study, Hosseini et al. [37] performed an energetic and exergetic assessment of a solar photovoltaic-water electrolyzer system for a residential application, combined with an SOFC system, and heat recovery unit for space heating or running a steam generator or absorption chiller. For a system located in Toronto, the maximum total energetic and exergetic efficiencies of this arrangement were found to be 55.7% and 49.0%. 5.3. Unique thermodynamic techniques and thermal system arrangements 5.3.1. Cooling techniques A variety of cooling techniques exist for providing cooling to a building using a CCHP system. Gluesenkamp et al. [139] examined small (50 kWe or less) polygeneration system options for prime movers, cooling devices, and system configurations. They pointed out that by separating sensible and latent cooling loads, polygeneration systems can actually provide better human comfort for

Fig. 5. Heat as a fraction of fuel consumed for solid oxide fuel cells, spark ignition internal combustion engine, compression ignition internal combustion engine, microturbine, Stirling engine and Organic Rankine Cycle, high temperature PEM fuel cell, and low temperature PEM fuel cell [139].

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building applications. Their research revealed that advances in adsorbent and dehumidification technologies allow for increasing use of waste heat at low temperatures, which will enable smaller installations to use CCHP, especially systems of 5 kWe or less. They stressed the importance of matching the amount and type of heat provided by a prime mover with a corresponding cooling technology, based on a survey of eight different types of prime movers and several different cooling devices. A plot of the expected heat recovered as a percentage of the fuel consumed is shown for a range of temperatures in Fig. 5. 5.3.2. Alternative thermodynamic cycles Because CCHP systems encompass energy conversion and heat transfer using a variety of devices, many opportunities exist for exploring different thermodynamic cycles and system arrangements to maximize CCHP system benefits. 5.3.2.1. Cascading refrigeration cycles. To provide cooling for supermarket applications, the cooling produced by a sorption system associated with a CCHP plant can be used to condense the refrigerant for a separate cooling cycle. Suamir and Tassou [140] developed a trigeneration system that was integrated with a subcritical CO2 refrigeration system and experimental data and simulations from three different configurations to analyze energy and emissions savings. In one arrangement, the CO2 cycle was part of a system of cascading refrigeration cycles with a sorption refrigeration cycle driven by the heat produced by the CCHP prime mover, a biofuel-fired engine. This unconventional system was designed for a supermarket, a facility with heating loads, refrigeration demands, and significant demand for electricity and hot water. The authors found significant potential for this type of integrated system to reduce energy demands compared with the grid, and the use of biofuels was found to reduce emissions although its cost was greater than conventional purchased fuels. 5.3.2.2. Organic Rankine Cycles. Organic Rankine Cycles (ORCs) are a popular thermodynamic theme in recent CCHP articles due to their ability to take waste heat, especially low-grade waste heat, and obtain useful energy for other processes. Recent advancements in organic materials and cycle design serve to accelerate their popularity and the usefulness of ORCs in combination with novel thermodynamic system designs. Al-Sulaiman et al. [141] compared trigeneration systems using ORC for power generation in combination with three different types of prime movers: SOFC, biomass, and solar-powered. The SOFC-ORC system has the highest electrical efficiency and the solar-ORC system has the lowest CO2 emissions (zero). Furthermore, the authors pointed out that the ratio of electricity to cooling can be controlled by varying the ORC pump inlet temperature, adding flexibility to the cycle. Al-Sulaiman et al. [36] also found that a biomass-ORC system with absorption chiller can increase energetic and exergetic efficiencies over power generation alone, and the use of trigeneration also reduces the CO2 emissions per unit of power produced to one-seventh that of power generation alone. 5.3.3. Unconventional integration schemes Many of the unconventional prime movers and thermodynamic techniques mentioned above can be used in conjunction for additional benefits. Ozcan and Dincer [142] performed a thermodynamic analysis of a CCHP system powered by a solid oxide fuel cell (SOFC), a high-temperature fuel cell system fueled by syngas, integrated with an ORC operating from the heat of the fuel cell stack exhaust gases, and a Li-Br absorption chiller also driven by SOFC exhaust gases. The energetic efficiency resulting from this system arrangement was over 50%, significantly higher than that of an SOFC system operating alone. They also note that incorporat-

Fig. 6. Multigeneration shown for a Multi-Energy System providing multiple energy services with multiple forms of output [145].

ing solar-assisted heating, cooling, and electricity production can further increase the overall system efficiency. Chua et al. [143] performed an analysis of renewable (solar) technologies integrated as part of a hybrid CCHP system with natural gas- and hydrogen-consuming prime movers, using TRNSYS to simulate the integrated components. A high percentage of solar PV and solar thermal making up the CCHP generation result in low fuel consumption but high capital costs. In an optimization exercise, they found that a generation mix of 80% natural gas microturbine, 10% PV, and 10% hydrogen-fueled alkaline fuel cell provided the most economic, energetic, and emissions benefits. 5.3.4. Multigeneration While CCHP systems or trigeneration systems produce three forms of energy outputs (electricity, heating, and cooling), multigeneration systems extend this concept to include multiple forms of energy output from a system (such as electricity, multiple heating or cooling options, gas or hydrogen production, chemical or water outputs). Multigeneration systems are also referred to as polygeneration systems, and Serra et al. define polygeneration systems as ‘‘the combined production of two or more energy services and/or manufactured products,’’ encompassing both cogeneration and trigeneration, while stressing the importance of maximizing thermodynamic efficiencies to conserve natural resources [144]. Mancarella [145] used the term multi-energy systems to include both multi-input and multi-output systems (see Fig. 6). In a comprehensive review of multi-energy systems, he discussed a variety of evaluation perspectives in the literature: spatial, multi-service, multi-fuel, and network perspectives; and asserted that such systems provide performance advantages in terms of energy consumption, the environment, and thermoeconomics. Multigeneration, by providing additional options for energy outputs, can provide increased opportunities to minimize primary energy consumption and maximize exergy efficiency. Multigeneration systems can be analyzed using the same methods as trigeneration systems, and present unique opportunities for creative system design and for optimization. Ahmadi et al. [146–148] have used genetic evolutionary algorithms to design and analyze different types of multigeneration systems. Across these different types, certain patterns emerge in the results based on system thermodynamics: increasing gas turbine inlet pressure increases exergy efficiency and increasing HRSG pinch point temperature reduces system exergy. They also advise quantifying pollution-related costs as part of a multi-objective optimization and call the merged cost and environmental functions ‘‘a useful thermoenvironomic function’’.

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Other researchers have also identified exergy benefits from multigeneration systems. Ozturk and Dincer [149] performed a thermodynamic analysis of solar-driven multigeneration system based on renewable technologies with four subsystems. This combined energy production system produces the following forms of energy: electrical power, heating, cooling, hydrogen, oxygen, and hot water; and the combined exergetic efficiency of the composite system is higher than the subsystems used separately. They also performed a thermodynamic assessment of a solar-driven multigeneration system [150] with coal gasification to use the concentrated solar energy. This unique arrangement, divided into six subsystems, also results in the same six forms of energy production. The exergetic efficiency effects of the system parameters are discussed in Section 2. In a combined economic, energetic, and emissions optimization of a polygeneration system providing electricity, heating, cooling, and fresh water through desalination, Rubio-Maya et al. [151] found ‘‘remarkable benefits’’ were possible in all three dimensions. Ilic et al. [152] found significant CO2 emissions reductions would result from implementing a polygeneration system producing electricity, district heating, and ethanol biofuel. Although a number of computational and analytical studies have optimized and investigated the benefits of multigeneration systems, experimental work to analyze such unique systems in practice is not yet widely available. 5.4. Advanced mathematical and computational methods 5.4.1. Sizing and system selection Several recent research advancements are in the field of applied mathematical and computational modeling to select the best CCHP system and the best operational methodology. Genetic algorithms are a particularly popular method for accelerated decision making and optimization [153] and these techniques are discussed in further detail in Section 4.2.2. Ebrahimi and Keshavarz [125] developed a multi-criteria sizing function for both sizing and operational strategy selection for a CCHP system in different climates in Iran. Furthermore, they perform prime mover selection and sizing using two decision making algorithms based on fuzzy logic and the grey incidence approach for a residential micro-CCHP system in the same five climates in Iran [126]. They studied the impact of these five different climates separately and found that in the tropical, humid or semi-humid climates, the prime mover needed is many times larger and the heating system is not necessary [127]. 5.4.2. Operational modes and dispatch strategies Guo et al. [154] used a multi-objective genetic algorithm to optimize for both cost and CO2 emissions with a CCHP microgrid system. Gholamhossein Abdollahi [102] employed a genetic algorithm to perform a multi-objective optimization of small CCHP systems with respect to exergetic efficiency, levelized cost, and environmental cost. Ebrahimi et al. [22] used a genetic algorithm to optimize a CCHP cycle for energy and exergy efficiency. Li et al. [32] used a mathematical analysis developed from the methods of Cho, Mago, et al. [78,97,155] to analyze a CCHP system in different operating modes for energy savings, exergy efficiency, and emissions reduction over a reference case for a building in China. When they considered instantaneous system performance against instantaneous building loads, the CCHP system’s ability to provide energetic, exergetic, and emission benefits varied in different time periods. However, the authors recommended the following the electric load method overall since the sale of electricity back to the grid was unavailable. Increasing exergetic efficiency was also proven to increase primary energy savings in all operational modes considered.

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Al-Sulaiman et al. [156] developed a mathematical form of thermoeconomic modeling for CCHP systems with ORCs using Powell’s method to minimize cost per unit of exergy. They went on to describe its applications with three types of prime movers considered in Ref. [141]: SOFC, biomass, and solar-powered, with the solar-trigeneration system having the lowest cost per unit of exergy [157]. Hu and Cho [98] proposed a stochastic multi-objective optimization model to optimize the CCHP operational strategy. They introduced a concept of creating an incentive model using a probability constraint optimization framework to assist the multiobjective operational decision process. Their results demonstrate that the examined incentive model can effectively reduce the primary energy consumption and carbon dioxide emissions in CCHP operations for different U.S. climate locations. 5.5. Other recent progress in thermoeconomic analysis Xu and Qu [129] published the first energetic, economic, and environmental case study of a CCHP system in a data center. The authors used TRNSYS modeling to analyze a data center located in San Diego, CA, USA with a gas turbine and double-effect absorption chiller-based CCHP system, using operational performance data collected over two years. They expressed concern with the proliferation of model-based studies on CCHP systems for commercial buildings without corresponding data used for validation. While their study found that a CCHP system was a good fit for this data center, the calculated system efficiency was well below the theoretical efficiency due to the age of the power generation unit and a mismatch between the cooling load and the absorption chiller capacities which required them to operate at partial loading conditions. They found that while the CCHP system cost seven times more than a conventional system, along with increased maintenance costs of $0.07/kW h, the operational savings still result in a 2-year simple payback period. Badami et al. [158] presented experimental results from a gasfired microturbine CCHP system in Turin, Italy, coupled with a liquid desiccant system. They analyzed the energetic and economic performance of the system at rated power and at partial loading. The highest earnings before interest, taxes, depreciation, and amortization was found for constant operation at rated power, while the operational strategy with the least time in operation at rated power actually had negative earnings [158]. They also argued for the Trigeneration Primary Energy Savings (TPES) index method [159] of energetic analysis as a comprehensive way to capture the energy savings performance of a trigeneration system (see Table 3 for some of the methods currently being used). The TPES method showed negative primary energy savings using CCHP for each of three case studies using a partial loading strategy, indicating that implementing a residential CCHP system in this way would only be economically feasible if subsidized. Zhou et al. [160] have also pointed out many of the issues with equipment which operates at off-design conditions, and they found that using multiple pieces of equipment can reduce the amount of off-design operation time, increasing the system’s efficiency and the accuracy of CCHP models. 5.6. Gaps in current research and development As discussed in the above sections, tremendous research efforts have been made in the area of CCHP and many research works have been presented and discussed in literature. The majority of the existing literature focused on the following topics:  Improvement in theoretical performance (e.g., energy and exergy efficiencies) of various CCHP system types through thermodynamic analysis.

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 Advancement in evaluation techniques of CCHP systems performance using field study and computation modeling and simulation, and  Mathematical optimization of the design and operation of CCHP systems. Although the current literature made significant advancement in the above-mentioned areas, there are still gaps in the existing research and opportunities that need to be filled to improve the CCHP technology. Based on the current research and state of the field, the following challenges need to be addressed in current CCHP research and development:

CCHP systems design and operation using various techniques and approaches and it can results in significant improvement of CCHP systems performance reducing overall operational costs, primary energy consumptions and greenhouse gas emissions.  Section 5 presents the current trend of CCHP research and development. Major trends in recent literature include: (a) integration of CCHP systems with renewable and alternative power sources; (b) novel thermodynamic techniques; and (c) advanced methods for improved system selection and operational strategies. References

 Energy policy analysis: A significant amount of concept development has been presented in the CCHP area. However, the existing innovative concepts have not been adapted effectively in real world applications. One of the major reasons is no or insufficient policy support in different regions and locations. An effective energy policy can be implemented successfully when the benefit of CCHP technology is quantified and presented in a proper manner to policy makers. With energy policies in place a CCHP system that is not economically feasible may become economically attractive, which can increase the CCHP system implementation.  Experimental validation: It is evidently shown that the current literature lacks empirical research to validate the existing theoretical concepts (e.g., a thermodynamic model of integrated systems with multiple thermodynamic components).  Technology demonstration: A large amount of highly detailed theoretical optimization methods has been introduced in the current literature. However, there has been very little work demonstrating implementation and performance of optimization methods in physical control systems. Similarly, there is a lack of correlation between techniques and actual energy saving data.  Terminology and metrics: Terminology and metrics for assessments of CCHP systems vary widely in the current literature. Using unified terminologies and metrics can help mitigate such confusion and make for more fair comparisons among different research work. 6. Conclusions CCHP systems have been shown in many situations, for a variety of system types, to have the potential to provide benefits in energy savings, cost savings, and reduced greenhouse gas emissions. Because of its great potential, many researchers have studied CCHP systems to maximize and harvest its potential. This paper reviews more than 170 articles to synthesize the current status of CCHP research regarding energetic and exergetic analyses, optimization methods, and the most current findings and emerging trends in CCHP work.  Section 2 provides a broad review of the existing research on the performance improvement of CCHP systems using the energetic and exergetic analyses. This section demonstrates that in the thermodynamic-based studies CCHP systems are often analyzed in a broader context including aspects of energy savings, economics, emissions, and exergy.  Section 3 discusses the performance evaluation approaches/ techniques used in literature. Mainly three methods/tools are used to evaluate the CCHP system performance: field test measurements, thermodynamic analysis and transient simulation.  Section 4 provides an overview of optimization methods and techniques in literature to improve the CCHP systems performance. Many researchers have performed optimizations of

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