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A pilot facility for analysis and simulation of smart microgrids feeding smart buildings
Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
A pilot facility for analysis and simulation of smart microgrids feeding smart buildings Stefano Bracco a, Federico Delfino a, Fabio Pampararo a, Michela Robba b,n,1, Mansueto Rossi a a b
Department of Naval, Electrical, Electronic and Telecommunication Engineering—DITEN, Via Opera Pia 11a, I-16145 Genova, Italy Department of Informatics, Bioengineering, Robotics and Systems Engineering—DIBRIS, Via Opera Pia 13, I-16145 Genova, Italy
art ic l e i nf o
a b s t r a c t
Article history: Received 4 June 2014 Received in revised form 25 September 2015 Accepted 17 December 2015
In this paper, the economic and environmental assessment of a system composed by a Smart Polygeneration Microgrid (SPM) and a Sustainable Energy Building (SEB) is performed, with specific reference to a pilot facility installed at the Savona Campus and managed by the University of Genoa Power Systems Research Team. The analysis includes the evaluation of operational costs and CO2 emissions, and is carried out in two steps: firstly, the quantification of the economic and environmental benefits provided by the SPM and the SEB on a yearly timescale is determined. Then, the same analysis has been done on a short-term horizon by the use of an optimization algorithm included in the SPM supervisory software. Such a model minimizes an objective function related to daily operational costs and includes the SPM electric and thermal power plants and loads, the distributed heating network, an active building (SEB) and the ICT system responsible for the operational management of the whole research infrastructures. The results of the analysis and the performed experimental research contribute to develop a set of Key Performances Indicators (KPIs) suitable for the smart microgrid concept deployment. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Smart grids Sustainable buildings Experimental test-bed Renewable energy Optimization
Contents 1. 2. 3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The test-bed facilities and the Energy Management System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic and environmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Annual costs, CO2 emissions and primary energy consumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Short term assessment and optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Microgrids represent today one of the most promising technology for Distributed Energy Resources (DERs) integration, as
n Correspondence to: Università degli Studi di Genova, DIBRIS-Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Via Opera Pia 13, 16145 Genova, Italy. Tel.: þ 393805105692, þ 390103532804, þ 390103532748; fax: þ390103532154. E-mail address: [email protected] (M. Robba). 1 c/o Campus Savona, Via A. Magliotto, 217100 Savona, Italy. Tel: þ 3901923027211.
http://dx.doi.org/10.1016/j.rser.2015.12.225 1364-0321/& 2015 Elsevier Ltd. All rights reserved.
1247 1249 1250 1251 1252 1253 1253 1254
they can alleviate management and monitoring burden for the Distribution System Operator (DSO) by clustering several DERs in a single entity which interacts with the grid as a single source [1,2]. Several structures have been proposed, both in AC and DC [3,4]: the latter are gaining more and more attention in the recent years, while the former are still more common. In exchange for this advantage to integrate DERs, microgrids rise the need for planning, day-by-day management and control of a variety of sources, either programmable or stochastic (e.g., renewable sources like wind and solar). Different kinds of production plants may be present in a microgrid and they should be managed in a coordinated way.
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Photovoltaic (PV) is one of the most common technology that can be found in microgrids. In fact, in the last few years, PV generating units have experienced an extraordinary growth all over the world. This trend is expected to continue in the future, especially with an increasing penetration of medium and small-scale installations in the distribution network, both for residential and commercial applications. As highlighted by Bacha et al. in [5], PV generation in particular has the higher potential for an increasing number of application in microgrids, as it is the most distributed means of electricity production from a geographical point of view. Furthermore, as pointed out by Gan and Li in [6], the cost of photovoltaic generation is dramatically decreased over the past decades thanks to technology improvements and scale economies, allowed by the massive diffusion the photovoltaic systems on the market. This trend is expected to continue in the future [7–9], with the introduction of new panels types such has polycrystalline, monocrystalline and amorphous, and of new thin film technology such as Cadmium Telluride (CdTe), Copper Indium Selenide or Copper Indium Gallium Selenide (CIS/ CIGS) [10,11]. In this respect, Morgera et al. present in [10] a detailed review of the state of the market and of the emerging technologies. The main share of the market for electricity generation, over 90%, is covered by crystalline silicon with its variants [12] (polycrystalline or monocrystalline, with higher efficiency). Some promising recent innovations, potentially able to improve panels efficiency, are currently reaching the market, such as metal wrap-through (MWT), even its actual impact in the future is still uncertain [13]. New technologies, currently confined to market niches, are Dye-Sensitized Solar Cells (DSSC, [14]), whose panels are characterized by a lower efficiency if compared with those based on silicon technology, but are considerably cheaper and have better performances in low light, and Organic Photovoltaics (OPV, [15]), which promises low cost of both the panel itself and of production cycle, along with a number of features like flexibility, colorability, low weight and semitransparency. As photovoltaic is an intermittent source (like wind), its integration in microgrids is favored by the installation of electric storage systems, and the application of new management strategies, as well as demand side management schemes [5], in order to ensure a reliable and continuous power supply. For instance, in [16], the use of an electrical storage system to compensate for the photovoltaic production forecasting error is addressed, considering various prediction methods for the forecast, a sensitivity analysis on the relevant technical parameters is carried out and finally and indications are derived on the optimal sizing of the storage system with respect to the PV rated power. From a user perspective, in microgrids, one of the most attractive kinds of small and medium size production units, from an energy efficiency standpoint, are combined heat and power (CHP) generators [17]. The integration of this kind of sources in a microgrid, especially when hosted by structures like districts or campuses, means that also the heating distribution system must be taken into account. A further level of complexity can be introduced by “smart” sustainable buildings, as they include traditional loads, generators (e.g. roof mounted PV), innovative heating and conditioning devices (e.g. geothermal heat pumps) and controllable loads, offering the possibility of Demand Side Management strategies (DSM, [18]). In this respect, as pointed out by Scognamiglio et al. in [19], a new challenge is presented by the ED 2010/31/EU, which requires that new building are Nearly Zero Energy Buildings (NearlyZEBs, [20]). Also in this case, one of the most viable options becomes the use of PV systems,. These new buildings, which are basically nanogrids (i.e. small microgrids), are typically managed by a Building Automation System (BAS) [21,22]. Thus, the microgrid becomes a “system of systems” [23], composed of interacting subsystems (the electrical distribution grid, the heating network, smart buildings with their Energy Management
System, the communication infrastructure, etc…) and several devices belonging to one or more of these subsystems. Each subsystem aims at the satisfaction of different demands (electrical loads, thermal loads, air conditioning) with different degrees of flexibility (interruptible loads, curtailable or deferrable loads, or mandatory loads). Furthermore, the satisfaction of these demands can be optimized according to the following main goals: economic revenues, environmental impacts and technical improvements [24]. Such a complex system yields decision problems at a small/ medium scale, usually involving a single decision maker (the owner/manager of the microgrid), but different physical entities (all the devices and the equipment). Optimization-based Energy Management Systems (EMSs) are fundamental to manage local microgrids [25] and their integration with the external grid [26]. The same approach can be used for general microgrids and grids of any size and with different sets of plants [27–31]. Furthermore, different time horizons have to be considered
Long term decisions regarding the investments related to the
kind, the number, and the size of technologies and components (planning) [32–34]; short term decisions regarding power production scheduling and real time optimal control, generally from a time scale of seconds to one week (operational management) [35–37].
At a planning level, Marnay et al. [38] present an optimization approach to choose the set of generators and storage systems in commercial buildings that minimizes the overall costs. Other approaches are based on the maximization of the Net Present Value using Particle Swarm Optimization [39], and on the minimization of system annual energy losses [40]. Furthermore, issues related to the different kinds of storage systems (e.g., batteries, also for e-car applications) [41] should be considered in planning decisions. At an operational level, in order to take into account different aspects such as the uncertainties of renewable sources availability, prices and demands, the microgrid Energy Management Systems (EMSs) and the Buildings Automation Systems (BASs) should use a Model Predictive Control (MPC) approach [21,22], or should include robust strategies [42]. Two main classes of controllers can be used: centralized controllers [43] or decentralized controllers [44]. Centralized controllers are used when there is a single decision maker, or there is no need to decentralize information, management and optimization, or in presence of standalone microgrids [45]. Decentralized control is in general used when there are different decision makers or microgrids, for complexity reduction in information management and optimization models, or when the microgrid system wants to be used as a “virtual test-bed” for wider smart grids with different actors, decision makers, and other microgrids [46,47]. Experimental tests and demonstration projects are fundamental to derive new methods and tools for the optimal planning and management and for the simulation on the field, as described in [48,49]. The present paper deals with the performance analysis of two new test-bed facilities, belonging to the University of Genoa– Savona Campus: the Smart Polygeneration Microgrid (SPM) [50] and the Sustainable Energy Building (SEB). The SPM was born from the “2020 Energy” Project (special project fully funded with 2.4 M€ by the Italian Ministry of Education, University and Research, started in 2011 and finished in 2013). The SEB Project has been financed by the Italian Ministry for the Environment (3 M €): it deals with the construction of a sustainable building (connected to the SPM as an energy “prosumer”) equipped by renewable power plants and characterized by energy efficiency measures. The SEB project is now in progress and the building will be completed in the next year [50].
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
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Fig. 1. Overview of the Savona Campus SPM and SEB.
The aim of this paper is to present an economic and environmental assessment at a yearly scale (based on operational costs, CO2 emissions and primary energy consumption) for smart private microgrids feeding smart buildings with specific reference to the aforementioned pilot facilities. In addition, an optimization model has been developed economic and environmental assessment at a daily time scale. Since at an operational level the microgrid works on the basis of optimization models (for day-ahead, intra-day, real time), the environmental and economic assessment at the yearly scale can be improved by the use of optimization tools at the daily time scale. However, the aim of this work is not the one of developing and testing an optimization algorithm but the one of providing some information impacts reduction for one day. The paper is organized as follows. In Section 2, the University of Genoa test-bed facilities are described. Section 3 reports the economic and environmental assessment at different time scales. Finally, in Section 4, guidelines for microgrid planning and management are discussed.
2. The test-bed facilities and the Energy Management System An overview of the SPM and the SEB, with the devices location in the Campus, and their main connections, is shown in Fig. 1. The SPM is a three phase low voltage (400 V line to line) distribution system, connecting micro-cogeneration gas turbines fed by natural gas (Capstone C30 and C65), a photovoltaic field, three cogeneration concentrated solar-powered systems (CSP, equipped with Stirling engines), a H2O/LiBr absorption chiller with a storage tank, two types of electrical storage based on batteries technology (long term Na–NiCl2 and short term lithium ion), two electric vehicles charging stations, other electrical devices (inverters and smart metering systems). The smart grid is integrated with the thermal power station, already in operation inside the Campus, composed of two traditional boilers fed by natural gas. The SEB is equipped with photovoltaic modules, thermal solar collectors and a horizontal axis wind turbine on the roof of the building, a geothermal heat pump, a controlled mechanical ventilation plant, and low consumption lamps. The energy consumptions
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of the building and the energy production of its generation units will be monitored in real time in order to evaluate the environmental and economic benefits consequent to have a sustainable building instead of a not sustainable one. The SPM peculiarity is mainly due to
The set of generation units and storage systems for both elec
trical and thermal energy production that make it a complete pilot plant; The possibility of defining and updating a software for the SPM control; a fast telecommunication network (based on the IEC 61850 protocol); its integration with the SEB.
On the other hand, SEB innovation is given by the set of installed renewable technologies, the use of energy-efficient materials, and by the installed BAS. The overall regulation is performed through an EMS, installed in the SPM Operation Centre, which guarantees the SPM functionalities, monitoring the overall system and providing alarms management, through the connections with the RTUs (Remote Terminal Units) and local control panels of devices in field. The EMS is composed by an information system (supervisory control system) developed by Siemens [51] that is responsible of data exchanges to the central controller and of commands to production plants. The supervisory software is designed flexible enough to apply the optimal results of new algorithms coming from research activities. A software tool [52], developed by Siemens, is already active and connected to the information system and can be customized and used for many purposes. However, it would be convenient to test new algorithms to take into account power losses, possible curtailment and lines interruption, multi-objective and multi-decision problems, frequency regulation, decentralized optimal control problems. All software for optimization intra-day and real time purposes should be included within a scheme in order to take into account updates in the system state, forecasts (energy flows from renewable sources, demand variations, prices, etc.), which can be predicted with some difficulty. Fig. 2 reports the interaction among the MPC controller, the Information System, and the different power production units. Both MPC controller and Information System could be also connected to the SEB monitoring system. Thus, the future BAS for the SEB will be designed in a way that can communicate with the Information System of the SPM and with the MPC optimal controller.
The architecture is based on a centralized intelligence that receives forecasts for renewable resources availability, demands, prices, and system state (for example the level of charge of the battery system), and, on the basis of an optimization model, gives commands to controllable production plants, to the storage system, and to the connection between the SPM and the external grid. The optimization model includes an objective function based on costs, and performance indicators for the optimal solution based on emissions, and primary energy use. Constraints are also added to represent the overall system and the different technical requirements. The commands are given to typical hardware (i.e., inverters, switch with the external net, production units, etc.) that are present in the SPM and in the SEB, through the communication system based on the IEC 61850 protocol. Moreover, the central controller can communicate with the local controllers of the production units and the storage systems.
3. Economic and environmental assessment The social benefits of the installation of polygeneration microgrids can be evaluated through a detailed economic and environmental assessment. In this section, the equations used for the assessment of the Savona Campus operating costs, overall CO2 emissions and primary energy consumptions. Other emissions (CO, NOx, SOx, …) coming from fossil fuel power plants (micro gas turbines and boilers) have not been considered since, in the analyzed operating conditions, the limits imposed by environmental standards have been always respected. On the other hand, the analysis has been focused on CO2 emission since one of the main goals of the present study is that of showing the benefits in terms of CO2 reduction due to the adoption of high efficiency cogeneration technologies, such as the cogeneration gas turbines installed in the Savona Campus Smart Polygeneration Microgrid. The study is part of a more complex research activity aimed at quantifying the carbon footprint of the Savona Campus within the European directive scenario that promotes CO2 emission reduction also in the tertiary and residential sectors. First, an analysis on a yearly time-scale related to the impacts of the new facilities on the Campus system is discussed, together with possible guidelines for the planning of polygeneration microgrids feeding sustainable buildings. Then, the economic and environmental analysis has been performed at the daily timescale, through the optimization model summarized in Appendix A.
Fig. 2. The overall Energy Management System.
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
3.1. Annual costs, CO2 emissions and primary energy consumptions The analysis includes two different scenarios that are compared on the basis of economic and environmental assessment:
AS-IS (before the SPM installation and SEB construction): the
electrical energy is purchased from the national grid and the thermal energy is produced by two standard boilers fed by natural gas; TO-BE: both electrical and thermal consumptions are partially satisfied by SPM and SEB power plants.
The total operating costs per year CT for the two scenarios can be respectively expressed as C T_AS IS ¼ EEC AS IS epp þ TEC AS IS TESpp þC m_AS IS
C T_T O BE ¼ Eel_Grid epp þ Eth_Boiler TESpp þ C m_TO BE þðEel_SPM þEel_SEB þ Þef þ
2 X
# ðQ utx;k NGppwf þ Q tx;k NGpp Þ
ð2Þ
k¼1
where EECAS-IS ¼AS-IS electrical energy consumption, TECAS-IS ¼ASIS thermal energy consumption, epp is the electricity purchasing price, TESpp is the thermal energy service purchasing price, Eel_Grid is the electrical energy from the national grid, Eth_Boiler is the thermal energy produced by the boilers, C m_TOBE and C m_ASIS are the maintenance costs in the two scenarios, Eel_SPM is the electricity produced by the SPM (including both renewables and microturbines), Eel_SEB þ is the SEB electrical energy production, ef is the electricity fee for local production, ef is the electricity fee for local production, Q utx;k is the untaxed quantity (due to Italian legislation on high efficiency CHP systems) of natural gas used by microturbine k (C30, C65), Q tx;k is the taxed quantity of natural gas used by the microturbine k (C30, C65), NGppwf is the gas untaxed price, and NGpp is gas full price. Some of the variables in Eqs. (1) and (2) have been calculated on the basis of further equations. Specifically, for the TO-BE scenario, the quantities of natural gas have been derived by the following expressions: Q utx;k ¼ λEel;k , Q k ¼ Eel;k =ðLHV ηel;k Þ, Q tx;k ¼ Q k Q d;k with Eel;k ¼ P el;k t k , where λ is the natural gas untaxed amount [m3/MWhel] according to Italian legislation, LHV (Low Heating Value) is the low heating value of natural gas [MWh/m3], ηel;k is the electrical efficiency of microturbine k at the nominal power, Q k is the overall amount of gas used in microturbine k, P el;k is the rated electrical power of microturbine k, and t k is the number of microturbine k equivalent working hours in a year. Moreover, for the TO-BE scenario, the electricity absorbed from the grid is calculated according to the following equation Eel_Grid ¼ EEC AS IS þ Eel_SEB Eel_SPM Eel_SEB þ þ Eel;CHI Eel;HP
ð3Þ
where Eel_SEB is the SEB electrical energy consumption. As far as Eel;CHI and Eel;HP are concerned, they are quantities referring to the Campus library summer conditioning respectively for the AS-IS and the TO-BE scenarios. In particular, Eel;CHI is the electrical energy consumption of the chiller installed in the SPM to cool the library (using as input the thermal energy coming from the C65 gas turbine), in the TO-BE scenario, whereas Eel;HP is the electrical consumption of the standard heat pump now present (AS-IS scenario) and going to be dismissed. Finally, the electrical energy produced by the SPM (after deducting auxiliaries consumption) is given by Eel_SPM ¼ ERES_SPM þ
2 X
Eel;k ;
quantity, while the second term at the right-hand-side represents the electricity generated by the micro gas turbines C65 and C30. The thermal energy produced by the boiler is given by Eth_Boiler ¼ TEC AS_IS þ Eth_SEB Eth_SPM Eth_SEB þ
ð4Þ
k¼1
where ERES_SPM indicates the contribution of renewables to such
ð5Þ
having indicated with Eth_SEB the SEB thermal energy demand, Eth_SPM the SPM thermal energy total production (by C65 and C30 gas turbines), and with Eth_SEB þ the thermal energy produced by the geothermal heat pump installed in the SEB. The CO2 emissions per year for the two scenarios have been calculated as follows: CO2_AS IS ¼ EEC AS IS
ð1Þ
"
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CO2_TO BE ¼ Eel_Grid
ef n
ηel_Grid
ef n
ηel_Grid
þTEC AS IS "
þ ef NG Of
ef NG Of
ð6Þ
ηBoiler LHV
2 X Eth_Boiler þ Q ηBoiler LHV k ¼ 1 k
# ð7Þ
where ef-n is the emission factor of the Italian electrical mix [tCO2/ MWhel], ef-NG is the natural gas emission factor [tCO2/m3], ηBoiler is the boiler efficiency, and Of is the natural gas oxidation factor. Finally, the primary energy consumptions have been calculated according to " # c2 1 toeAS IS ¼ c1 EEC AS IS þTEC AS IS ð8Þ
ηel_Grid
" toeTO BE ¼ c1 Eel_Grid
c2
ηel_Grid
þ
ηBoiler
Eth_Boiler
ηBoiler
þ LHV
2 X
# Qk
ð9Þ
k¼1
where c2 is the average value of the Italian electrical mix [MWhpe/ MWhel], c1 is a conversion coefficient of the primary energy (from MWh to toe), and ηel_Grid is the national electrical grid efficiency. The main necessary data to perform the economic and environmental assessment are reported in Table 1. The overall results of the economic and environmental analysis are summarized in Table 2, together with the Campus thermal and electrical primary energy demands. Comparing the TO-BE configuration with the AS-IS one, it can be noticed that, in spite of the energy demand increase (of about 130 MWh/y) due to the presence of the new building SEB, there will be a reduction in operating costs (of about 30 k€/y), CO2 emissions (of about 86 tCO2/y), and primary energy consumptions (of about 24 toe/y). In Fig. 3, the contribution of the various sources to the satisfaction of the Campus electrical demand is detailed. It is important to note that about 37% of the electrical energy required by the Campus is self-produced (by SPM and SEB power plants, mainly by the two micro turbines). On the thermal side, Fig. 4 shows the thermal energy produced by boilers, gas turbines and the geothermal heat pump (GHP); about one-fourth of the thermal demand is satisfied by SPM & SEB generation units. The reduction in operating costs due to the SPM & SEB installation is highlighted in Fig. 5. CO2 emissions and primary energy consumptions for the two scenarios are respectively reported in Figs. 6 and 7. It is worth noting that the decrease of these last two quantities is mainly due to the increase of the global efficiency due to the presence of cogeneration units; furthermore, the reduction of both the provision of thermal energy from the boilers and electrical energy from the national grid has a positive impact on CO2 emissions and primary energy consumption.
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Table 1 The main data of the problem. MWhel/y MWhth/y MWhth/y MWhel/y h h h h MWhpe/m3 tCO2/MWhel tCO2/m3 – toe/MWhpe MWhpe/MWhel €/MWhel €/MWhel €/MWhth €/m3 €/m3 € €
Table 2 Economic and environmental results. Savona campus AS-IS Primary energy demand Operating costs CO2 emissions Primary energy consumption
3953 332 826 340
Savona campus TO-BE 4083 304 760 316
Unit
MWhpe/y k€/y tCO2/y toe/y
E_el Grid
Operating cost [k€/y]
982 1426 20 55.0 2000 2000 1000 1000 9.7 10 3 0.465 1.961 10 3 0.995 0.086 2.17 212 12.5 85.3 0.53 0.73 2000 12,000
E_th Boiler C65 C30 PV1 PV2 Wind CSP
AS-IS
TO-BE Fig. 5. Operating costs.
600 500
CO2 emission [tCO2/y]
EECAS-IS TECAS-IS Eth_SEB Eel_SEB C65 operating hours C30 operating hours Library chiller operating hours Library heat pump operating hours LHV of natural gas ef-n ef-NG Of c1 c2 epp ef TESpp NGppwf NGpp Cm_AS-IS Cm_TO-BE
400
Grid
300
Boiler C65
200
C30
100 0
AS-IS
TO-BE Fig. 6. CO2 emissions.
Fig. 3. Electricity generation in the TO-BE scenario.
Primary Energy Consumption [toe/y]
225 200 175 150
Grid
125
Boiler
100
C65
75
C30
50 25 0
AS-IS
TO-BE
Fig. 7. Primary energy consumptions.
Fig. 4. Heat generation in the TO-BE scenario.
3.2. Short term assessment and optimization An energy system characterized by the use of renewable sources has uncertainties related to the forecasts of available power that have to be taken into account. Moreover, it is necessary to minimize costs (or other performance indicators) and to respect technical constraints during a single day. For these reasons, it is convenient to
optimize the management of plants and storage systems in the short-term (day-ahead, intra-day, real time). The Savona Campus test bed facilities use optimization models included in the overall supervisory system for the optimal operation of the generation units and the storage. The outputs of the optimization models are the commands that are given to plants in the short term on the basis of updates of renewables and loads forecasts. Thus, operating costs, CO2 emissions and primary energy consumptions of the TOBE scenario can be further minimized in the short term (i.e., days, hours, minutes, etc.). This objective could be achieved by using specific rules (learnt from day by day operation on field) and/or formalizing different optimization problems for the SPM & SEB overall system running at the microgrid energy management level. In this work, the analysis has been carried out by the use of the
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
optimization model presented in the Appendix A. Specifically, the same equations adopted for the annual assessment are adopted but this time declined to the short-term horizon. The decision variables are 1. P th;B;t , P grid;t , P CHI;t , P S;t ,P th;k;t , and P el;k;t (i.e., the power outputs from boiler, electrical grid, chiller, storage and microturbines, for these last both thermal and electrical), for each time interval t ¼0,…,T 1; 2. P CHI;k;t ; P OUT;k;t (i.e., the thermal power from microturbine k that is used by the chillers or for other thermal users, respectively); 3. P CHI;B;t ; P OUT;B;t (i.e., the thermal power from boilers that is used by the chillers or for other thermal users, respectively); 4. Q utx;k;t , Q tx;k;t , and Q k;t (i.e., the quantities of untaxed, taxed, total natural gas that is used in microturbine k); 5. P HP;t (i.e., the electrical power absorbed by the heat pump to produce cool). The state variables are represented by SOC t for t¼0,…,T, i.e., the state of charge of the battery. The objective function (see Eq. A.1 in Appendix) is given by the sum of operational costs. Different kinds of constraints have been formalized: the microturbines model (Eqs. A.2–A.6 in Appendix), the chiller’s model (Eqs. A.7–A.9 in Appendix), the battery model (Eq. A.10 in Appendix), and power demand balances (Eqs. A.11–A.13 in Appendix). Additional constraints are the technical bounds for the production plants and the storage system. Finally, equations for CO2 emissions and primary energy consumptions, like those reported in Section 3.1, are used to calculate these performance indicators for the optimal solution. This means that the minimization in the optimization algorithm is done for costs while CO2 emissions and primary energy use are calculated after the solution of the decision problem. In fact, the microgrid optimization problem is a multi-objective one where different performance indexes could be tested in order to analyze which is the solution that is the best taking into account all objectives. The definition of a multi-objective decision problem is out of the scope of this paper. Some parameters needed for the short term analysis are considered time-varying for the chosen time scale (i.e., time interval: 15 min; optimization horizon: 24 h). These parameters are: heat, cool, and electricity demands; availability of renewable resources; unit costs for electricity taken from the external grid. The analysis has been here performed for a winter typical day during which the electrical/thermal demand and renewables availability are reported in Fig. 8. As regards unit costs for electricity, they are set to 25.6 c€/kWhel between 8 a.m. and 8 p.m., and 19.8 c€/kWhel in the other time intervals. The optimization problem is non-linear and Lingo 9.0 (www. lindo.com) optimization package has been used to derive the optimal solution. The optimal solution determines operation costs 1000
1253
Table 3 Optimized vs Non-optimized Scenario.
Optimized solution Non-optimized solution
Costs [€/day]
CO2 [t/day]
Primary energy [toe/day]
1532 1720
4 4.4
1.7 1.85
equal to 1531.6 €. The resulting CO2 emissions and primary energy consumptions are respectively equal to 4.04 t and 1.7 toe. In addition, the optimal results are reported in Table 3, in order to be compared with those of the non-optimized scenario (no electrical storage use and microturbines working only at nominal power between 8 a.m and 8 p.m.). In the optimized scenario, the reduction in operating costs is about 11%, whereas the decrease of CO2 emissions and primary energy consumptions is equal to 8%. Finally, a further analysis has been done setting the decision variables related to the storage equal to zero and solving the optimization problem. This means not to include the storage system in the microgrid system but to optimize the operation of gas turbines. In this case, the optimal cost is about 1600 €, resulting in an increase of about 4.5% with respect to the best case.
4. Conclusions In this paper, the "sustainable energy" test-bed facilities (Smart Polygeneration Microgrid-SPM & Sustainable Energy Building-SEB) at the Savona Campus of the University of Genoa have been described and an approach to evaluate how such infrastructures contribute to reduce CO2 emissions, primary energy use and costs of the whole Campus has been proposed. Two main analyzes have been performed: an annual assessment and a short term (daily) optimization that allows managing the infrastructure in order to minimize costs. At an yearly scale, in spite of the energy demand increase (of about 130 MWh/y) due to the presence of the new building SEB, there will be a reduction in operating costs (of about 30 k€/y), CO2 emissions (of about 86 tCO2/y), and primary energy consumptions (of about 24 toe/y). At the daily scale, the performed simulations have shown improvements both for operating costs and greenhouse gas emissions. Future developments of the present work are related to the possible use of the SPM to feed loads in the Savona District near the Campus, and to the management of generating units and loads in other areas through the optimization algorithms developed for the SPM. Moreover, stochastic, multi-objective and distributed optimization models can be conceived and tested through the Savona Campus test bed facilities. Finally, since recent challenges in microgrid development are those related to resiliency and risk assessment [53–55], the impact assessment of a microgrid can be also studied by identifying new indicators that may represent such issues.
Appendix A
900 800 700
min
kW
600 500
C TOT ¼
300
P th;B;t TESpp Δ þ P RES;t ef 10 3 Δ þ C g;t P grid;t Δ
t¼0
RES Electrical Demand Thermal Demand
400
TX 1
þ
2 h X
Q d;k;t NGppwf þ Q nd;k;t NGppwf þ P el;k;t ef 10
Δ
! i
k¼1
200
ðA:1Þ
100 0
3
0
2
4
6
8
10
12
14
16
18
20
22
24
Time [h]
Fig. 8. Electrical/thermal demand and renewables availability.
s.t. Q k;t ¼
P el;k;t Δ LHV ηel;k;t
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:2Þ
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S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
ηel;k;t ¼ f k ðP el;k;t ; T amb;t Þ Q utx;k;t ¼ λP el;k;t Δ
k ¼ 1; 2t ¼ 0; …; T 1
k ¼ 1; 2t ¼ 0; …; T 1
Q tx;k;t ¼ Q k;t Q utx;k;t P th;k;t ¼ δk P el;k;t
ðA:3Þ ðA:4Þ
k ¼ 1; 2 t ¼ 0; …; T 1
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:5Þ ðA:6Þ
P th;k;t ¼ P CHI;k;t þ P OUT;k;t
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:7Þ
P th;B;t ¼ P CHI;B;t þ P OUT;B;t
t ¼ 0; …; T 1
ðA:8Þ
P CHI;t ¼
P CHI;B;t þ
2 X
! t ¼ 0; …; T 1
P CHI;k;t COP CHI
ðA:9Þ
k¼1
SOC t þ 1 ¼ SOC t P S;t Δ De;t ¼
2 X
t ¼ 0; …; T 1
ðA:10Þ
P el;k;t P HP;t þ P grid;t þ P RES;t
k¼1
þ P S;t P CHI;B;t þ
2 X
! P CHI;k;t λcons
t ¼ 0; …; T 1
ðA:11Þ
k¼1
Dh;t r
2 X
P OUT;k;t þ P OUT;B;t þP RESth;t
t ¼ 0; …; T 1
ðA:12Þ
k¼1
Dc;t r P CHI;t þ COP HP P HP;t
t ¼ 0; …; T 1
ðA:13Þ
where Δ is the time interval length [h]; λcons is a coefficient which relates chiller electrical consumption to its thermal input; COP CHI is the chiller coefficient of performance; T amb;t is ambient temperature; Dh;t , Dc;t , De;t are demands for heat, cool, and electricity [kW]; C g;t is the time varying unit sale/purchasing price for the electricity injected into/taken from the grid, f k is a non-linear function typical for each microturbine, δk is the ratio between rated values of thermal and electrical power in microturbine k.
References [1] Asano H, Hatziargyriou N, Iravani R, Marnay C. Microgrids: an overview of on going research, development, and demonstration projects. IEEE Power Energy Mag 2007:78–94. [2] Basu AK, Chowdhury SP, Chowdhury S, Paul S. Microgrids: energy management by strategic deployment of DERs—a comprehensive survey. Renew Sustain Energy Rev 2011;15:4348–56. [3] Patrao I, Figueres E, Garcerá G, González-Medina R. Microgrid architectures for low voltage distributed generation. Renew Sustain Energy Rev 2015;43:415–24. [4] Justo JJ, Mwasilu F, Lee J, Jung JW. AC-microgrids versus DC-microgrids with distributed energy resources: a review. Renew Sustain Energy Rev 2013;24:387–405. [5] Bacha S, Picault D, Burger B, Etxeberria-Otadui I, Martins J. Photovoltaics in microgrids: an overview of grid integration and energy management aspects. IEEE Ind Electron Mag 2015;9:33–46. [6] Gan PY, Li ZD. Quantitative study on long term global solar photovoltaic market. Renew Sustain Energy Rev 2015;46:88–99. [7] European Photovoltaic Industry Association (EPIA). Global market outlook for photovoltaic until 2016; 2012. Available at 〈http://files.epia.org/files/GlobalMarket-Outlook-2016.pdf〉 [accessed 10.05.12]. [8] International Energy Agency (IEA). PVPS report. Snapshot of global PV 1992– 2013. Preliminary trends information from the IEA PVPS programme; 2014. [9] Nemet GF. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Pol 2006;34:3218–32. [10] Morgera AF, Lughi V. Frontiers of photovoltaic technology: a review. Int Conf Clean Electr Power 2015:115–21. [11] Kazmerski LL. 1.03-Solar photovoltaics technology: no longer an outlier. In: Sayigh Ali, editor. Comprehensive renewable energy. Oxford: Elsevier; 2012. p. 13–30. [12] Photovoltaics report, Report of the ISE Fraunhofer Institute; October 2014 [Retreived on Nov 29, 2014]. [13] Roadmap. 2011 International technology roadmap for photovoltaics 2nd edn. Berlin, Germany: SEMI Europe. [14] Gong J, Liang J, Sumathy K. Review on dye-sensitized solar cells (DSSCs): fundamental concepts and novel materials. Renew Sustain Energy Rev 2012;16:5848–60.
[15] Krebs FC. Fabrication and processing of polymer solar cells: a review of printing and coating techniques. Sol Energy Mater Sol Cells 2009;93:394–412. [16] Delfanti M, Falabretti D, Merlo M. Energy storage for PV power plant dispatching. Renew Energy 2015;80:61–72. [17] Basu AK, Chowdhury SP, Chowdhury S. Impact of strategic deployment of CHPbased DERs on microgrid reliability. IEEE Trans Power Deliv 2010;25:1697–705. [18] Cecati C, Citro C, Siano P. Combined operations of renewable Energy systems and responsive demand in a smart grid. IEEE Trans Sustain Energy 2011;2:468–76. [19] Scognamiglio A, Adinolfi G, Graditi G, Saretta E. Photovoltaics in net zero energy buildings and clusters: enabling the smart city operation. Energy Proc 2014;61:1171–4. [20] Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast). Official Journal of the European Union; 2010 [21] Dagdougui H, Minciardi R, Ouammi A, Robba M, Sacile R. Modeling and optimization of a hybrid system for the energy supply of a Green building. Energy Convers Manag 2012;64:351–63. [22] Figueiredo J, Sa da Costa J. A SCADA system for energy management in intelligent buildings. Energy Build 2012;49:85–98. [23] Phillips LR, Jamshidi M. The microgrid as a system of systems. Systems of systems engineering-principles and applications, CRC, ser. SoS Engineering, 1. London, U.K.: Taylor and Francis; 2008. p. 251–80. [24] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. A system of systems model for the control of the Smart Polygeneration Microgrid test-bed facility of Savona University Campus. In: Proceedings of IEEE SOSE; 2012. [25] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. A dynamic optimizationbased architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles. Energy Convers Manag 2015;96:511–20. [26] Delfino F, Minciardi R, Pampararo F, Robba M. A multilevel approach for the optimal control of distributed energy resources and storage. IEEE Trans Smart Grid Special Issue Control Theory Technol Smart Grid 2014;5:2155–62. [27] Norouzi MR, Ahmadi A, Esmaeel Nezhad A, Ghaedi A. Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment. Renew Sustain Energy Rev 2014;29:912–23. [28] Norouzi M, Ahmadi A, Sharaf A, Esmaeel Nezhad A. Short-term environmental/economic hydrothermal scheduling. Electric Power Syst Res 2014;116:117–27. [29] Mavalizadeh H, Ahmadi A. Hybrid expansion planning considering security and emission by augmented epsilon-constraint method. Electr Power Energy Syst 2014;61:90–100. [30] Ahmadi A, Ahmadi MR, Esmaeel-nezhad A. Short term combined heat and power economic/emission dispatch using lexicographic optimization and augmented ε-constraint technique. Electric Power Compon Syst 2014;42:945–58. [31] Ahmadi A, Aghaei J, Shayanfar HA, Rabiee A. Mixed integer programming of multiobjective hydro-thermal self-scheduling. Appl Soft Comput 2012;12:2137–46. [32] Ouammi A, Ghigliotti V, Robba M, Mimet A, Sacile R. A decision support system for the optimal exploitation of wind energy on regional scale. Renew Energy 2012;37:299–309. [33] Shadmand MB, Balog RS. Multi-objective optimization and design of photovoltaic-wind hybrid system for community smart DC microgrid. IEEE Trans Smart Grid 2014;5:2635–43. [34] Hassan MA, Abido MA. Optimal design of microgrids in autonomous and gridconnected modes using particle swarm optimization. IEEE Trans Power Electron 2011;26:755–69. [35] Igualada L, Corchero C, Cruz-Zambrano M, Heredia FJ. Optimal energy management for a residential microgrid including a vehicle-to-grid system. Trans Smart Grid 2014;5:2163–72. [36] Milczarek A, Malinowski M, Guerrero JM. Reactive power management in islanded microgrid—proportional power sharing in hierarchical droop control. IEEE Transactions on Smart Grid [in press]. [37] Rahbar K, Xu J, Zhang R. Real-time energy storage management for renewable integration in microgrid: an off-line optimization approach. IEEE Trans Smart Grid 2015;6:124–34. [38] Marnay C, Venkataramanan G, Stadler M, Siddiqui AS, Firestone R, Chandran B. Optimal technology selection and operation of commercial-building microgrids. IEEE Trans Power Syst 2008;23:975–82. [39] Mohammadi M, Hosseinian SH, Gharehpetian GB. Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO. Sol Energy 2012;86:112–25. [40] Atwa YM, El Saadany EF, Salama MA, Seethapathy R. Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 2010;25:360–70. [41] Beer S, Gomez T, Dallinger D, Momber I, Marnay C, Stadler CM, Lai J. An economic analysis of used electric vehicle batteries integrated into commercial building microgrids. IEEE Trans Smart Grid 2012;3:517–25. [42] Zhang Y, Gatsis N, Giannakis GB. Robust energy management for microgrids with high-penetration renewables. IEEE Trans Sustain Energy 2013:1–10. [43] Tsikalakis AG, Hatziargyriou ND. Centralized control for optimizing microgrids operation. IEEE Trans Energy Convers 2008;23:241–8. [44] Etemadi AH, Davison EJ, Iravani R. A decentralized robust control strategy for Multi-DER microgrids part I: fundamental concepts. IEEE Trans Power Deliv 2012;27:1843–53.
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
[45] Zhao B, Zhang X, Chen J, Wang C, Guo L. Operation optimization of standalone microgrids considering lifetime characteristics of battery. IEEE Trans Sustain Energy 2013:1–10. [46] Fathi M, Bevrani H. Statistical cooperative power dispatching in interconnected microgrids. IEEE Trans Sustain Energy 2013:1–8. [47] Koukoula D, Dimeas AL, Hatziargyriou ND. Scheduling algorithms for agent based control and scheduling of microgrids. In: Proceedings of the 16th international conference on intelligent system application to power systems (ISAP); 2011. [48] Lidula NWA, Rajapakse AD. Microgrids research: a review of experimental microgrids and test systems. Renew Sustain Energy Rev 2011;16:186–202. [49] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. The Smart Polygeneration Microgrid test-bed facility of Savona University Campus: the overall system, the technologies and the research challenges. Renew Sustain Energy Rev 2013;18:442–59.
1255
[50] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. Environmental and economic analysis for the Smart Polygeneration Microgrid test-bed facility of Savona University Campus. In: Proceedings of IEEE ENERGYCON; 2012. p. 587– 92; 2012. [51] Siemens-WinCC online avaiable: 〈http://www.automation.siemens.com/mcms/ human-machine-interface/en/visualization-software/scada/pages/default.aspx〉. [52] Siemens-DEMS online avaiable: 〈https://www.swe.siemens.com/italy/web/IC/ SG/EA/applicazioni/Gestione%20di%20Microgrid%20e%20Virtual%20Power% 20Plant/Pages/DEMS.aspx〉. [53] Khodaei A. Resiliency-oriented microgrid optimal scheduling. IEEE Trans Smart Grid 2014;5:1584–91. [54] Che L, Shahidehpour MDC. Microgrids: economic operation and enhancement of resilience by hierarchical Control. IEEE Trans Smart Grid 2014 [in press]. [55] Manshadi SD, Khodayar ME. Resilient operation of multiple energy carrier microgrids. IEEE Trans Smart Grid 2015;6:2283–92.
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
A pilot facility for analysis and simulation of smart microgrids feeding smart buildings Stefano Bracco a, Federico Delfino a, Fabio Pampararo a, Michela Robba b,n,1, Mansueto Rossi a a b
Department of Naval, Electrical, Electronic and Telecommunication Engineering—DITEN, Via Opera Pia 11a, I-16145 Genova, Italy Department of Informatics, Bioengineering, Robotics and Systems Engineering—DIBRIS, Via Opera Pia 13, I-16145 Genova, Italy
art ic l e i nf o
a b s t r a c t
Article history: Received 4 June 2014 Received in revised form 25 September 2015 Accepted 17 December 2015
In this paper, the economic and environmental assessment of a system composed by a Smart Polygeneration Microgrid (SPM) and a Sustainable Energy Building (SEB) is performed, with specific reference to a pilot facility installed at the Savona Campus and managed by the University of Genoa Power Systems Research Team. The analysis includes the evaluation of operational costs and CO2 emissions, and is carried out in two steps: firstly, the quantification of the economic and environmental benefits provided by the SPM and the SEB on a yearly timescale is determined. Then, the same analysis has been done on a short-term horizon by the use of an optimization algorithm included in the SPM supervisory software. Such a model minimizes an objective function related to daily operational costs and includes the SPM electric and thermal power plants and loads, the distributed heating network, an active building (SEB) and the ICT system responsible for the operational management of the whole research infrastructures. The results of the analysis and the performed experimental research contribute to develop a set of Key Performances Indicators (KPIs) suitable for the smart microgrid concept deployment. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Smart grids Sustainable buildings Experimental test-bed Renewable energy Optimization
Contents 1. 2. 3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The test-bed facilities and the Energy Management System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic and environmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Annual costs, CO2 emissions and primary energy consumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Short term assessment and optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Microgrids represent today one of the most promising technology for Distributed Energy Resources (DERs) integration, as
n Correspondence to: Università degli Studi di Genova, DIBRIS-Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Via Opera Pia 13, 16145 Genova, Italy. Tel.: þ 393805105692, þ 390103532804, þ 390103532748; fax: þ390103532154. E-mail address: [email protected] (M. Robba). 1 c/o Campus Savona, Via A. Magliotto, 217100 Savona, Italy. Tel: þ 3901923027211.
http://dx.doi.org/10.1016/j.rser.2015.12.225 1364-0321/& 2015 Elsevier Ltd. All rights reserved.
1247 1249 1250 1251 1252 1253 1253 1254
they can alleviate management and monitoring burden for the Distribution System Operator (DSO) by clustering several DERs in a single entity which interacts with the grid as a single source [1,2]. Several structures have been proposed, both in AC and DC [3,4]: the latter are gaining more and more attention in the recent years, while the former are still more common. In exchange for this advantage to integrate DERs, microgrids rise the need for planning, day-by-day management and control of a variety of sources, either programmable or stochastic (e.g., renewable sources like wind and solar). Different kinds of production plants may be present in a microgrid and they should be managed in a coordinated way.
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Photovoltaic (PV) is one of the most common technology that can be found in microgrids. In fact, in the last few years, PV generating units have experienced an extraordinary growth all over the world. This trend is expected to continue in the future, especially with an increasing penetration of medium and small-scale installations in the distribution network, both for residential and commercial applications. As highlighted by Bacha et al. in [5], PV generation in particular has the higher potential for an increasing number of application in microgrids, as it is the most distributed means of electricity production from a geographical point of view. Furthermore, as pointed out by Gan and Li in [6], the cost of photovoltaic generation is dramatically decreased over the past decades thanks to technology improvements and scale economies, allowed by the massive diffusion the photovoltaic systems on the market. This trend is expected to continue in the future [7–9], with the introduction of new panels types such has polycrystalline, monocrystalline and amorphous, and of new thin film technology such as Cadmium Telluride (CdTe), Copper Indium Selenide or Copper Indium Gallium Selenide (CIS/ CIGS) [10,11]. In this respect, Morgera et al. present in [10] a detailed review of the state of the market and of the emerging technologies. The main share of the market for electricity generation, over 90%, is covered by crystalline silicon with its variants [12] (polycrystalline or monocrystalline, with higher efficiency). Some promising recent innovations, potentially able to improve panels efficiency, are currently reaching the market, such as metal wrap-through (MWT), even its actual impact in the future is still uncertain [13]. New technologies, currently confined to market niches, are Dye-Sensitized Solar Cells (DSSC, [14]), whose panels are characterized by a lower efficiency if compared with those based on silicon technology, but are considerably cheaper and have better performances in low light, and Organic Photovoltaics (OPV, [15]), which promises low cost of both the panel itself and of production cycle, along with a number of features like flexibility, colorability, low weight and semitransparency. As photovoltaic is an intermittent source (like wind), its integration in microgrids is favored by the installation of electric storage systems, and the application of new management strategies, as well as demand side management schemes [5], in order to ensure a reliable and continuous power supply. For instance, in [16], the use of an electrical storage system to compensate for the photovoltaic production forecasting error is addressed, considering various prediction methods for the forecast, a sensitivity analysis on the relevant technical parameters is carried out and finally and indications are derived on the optimal sizing of the storage system with respect to the PV rated power. From a user perspective, in microgrids, one of the most attractive kinds of small and medium size production units, from an energy efficiency standpoint, are combined heat and power (CHP) generators [17]. The integration of this kind of sources in a microgrid, especially when hosted by structures like districts or campuses, means that also the heating distribution system must be taken into account. A further level of complexity can be introduced by “smart” sustainable buildings, as they include traditional loads, generators (e.g. roof mounted PV), innovative heating and conditioning devices (e.g. geothermal heat pumps) and controllable loads, offering the possibility of Demand Side Management strategies (DSM, [18]). In this respect, as pointed out by Scognamiglio et al. in [19], a new challenge is presented by the ED 2010/31/EU, which requires that new building are Nearly Zero Energy Buildings (NearlyZEBs, [20]). Also in this case, one of the most viable options becomes the use of PV systems,. These new buildings, which are basically nanogrids (i.e. small microgrids), are typically managed by a Building Automation System (BAS) [21,22]. Thus, the microgrid becomes a “system of systems” [23], composed of interacting subsystems (the electrical distribution grid, the heating network, smart buildings with their Energy Management
System, the communication infrastructure, etc…) and several devices belonging to one or more of these subsystems. Each subsystem aims at the satisfaction of different demands (electrical loads, thermal loads, air conditioning) with different degrees of flexibility (interruptible loads, curtailable or deferrable loads, or mandatory loads). Furthermore, the satisfaction of these demands can be optimized according to the following main goals: economic revenues, environmental impacts and technical improvements [24]. Such a complex system yields decision problems at a small/ medium scale, usually involving a single decision maker (the owner/manager of the microgrid), but different physical entities (all the devices and the equipment). Optimization-based Energy Management Systems (EMSs) are fundamental to manage local microgrids [25] and their integration with the external grid [26]. The same approach can be used for general microgrids and grids of any size and with different sets of plants [27–31]. Furthermore, different time horizons have to be considered
Long term decisions regarding the investments related to the
kind, the number, and the size of technologies and components (planning) [32–34]; short term decisions regarding power production scheduling and real time optimal control, generally from a time scale of seconds to one week (operational management) [35–37].
At a planning level, Marnay et al. [38] present an optimization approach to choose the set of generators and storage systems in commercial buildings that minimizes the overall costs. Other approaches are based on the maximization of the Net Present Value using Particle Swarm Optimization [39], and on the minimization of system annual energy losses [40]. Furthermore, issues related to the different kinds of storage systems (e.g., batteries, also for e-car applications) [41] should be considered in planning decisions. At an operational level, in order to take into account different aspects such as the uncertainties of renewable sources availability, prices and demands, the microgrid Energy Management Systems (EMSs) and the Buildings Automation Systems (BASs) should use a Model Predictive Control (MPC) approach [21,22], or should include robust strategies [42]. Two main classes of controllers can be used: centralized controllers [43] or decentralized controllers [44]. Centralized controllers are used when there is a single decision maker, or there is no need to decentralize information, management and optimization, or in presence of standalone microgrids [45]. Decentralized control is in general used when there are different decision makers or microgrids, for complexity reduction in information management and optimization models, or when the microgrid system wants to be used as a “virtual test-bed” for wider smart grids with different actors, decision makers, and other microgrids [46,47]. Experimental tests and demonstration projects are fundamental to derive new methods and tools for the optimal planning and management and for the simulation on the field, as described in [48,49]. The present paper deals with the performance analysis of two new test-bed facilities, belonging to the University of Genoa– Savona Campus: the Smart Polygeneration Microgrid (SPM) [50] and the Sustainable Energy Building (SEB). The SPM was born from the “2020 Energy” Project (special project fully funded with 2.4 M€ by the Italian Ministry of Education, University and Research, started in 2011 and finished in 2013). The SEB Project has been financed by the Italian Ministry for the Environment (3 M €): it deals with the construction of a sustainable building (connected to the SPM as an energy “prosumer”) equipped by renewable power plants and characterized by energy efficiency measures. The SEB project is now in progress and the building will be completed in the next year [50].
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
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Fig. 1. Overview of the Savona Campus SPM and SEB.
The aim of this paper is to present an economic and environmental assessment at a yearly scale (based on operational costs, CO2 emissions and primary energy consumption) for smart private microgrids feeding smart buildings with specific reference to the aforementioned pilot facilities. In addition, an optimization model has been developed economic and environmental assessment at a daily time scale. Since at an operational level the microgrid works on the basis of optimization models (for day-ahead, intra-day, real time), the environmental and economic assessment at the yearly scale can be improved by the use of optimization tools at the daily time scale. However, the aim of this work is not the one of developing and testing an optimization algorithm but the one of providing some information impacts reduction for one day. The paper is organized as follows. In Section 2, the University of Genoa test-bed facilities are described. Section 3 reports the economic and environmental assessment at different time scales. Finally, in Section 4, guidelines for microgrid planning and management are discussed.
2. The test-bed facilities and the Energy Management System An overview of the SPM and the SEB, with the devices location in the Campus, and their main connections, is shown in Fig. 1. The SPM is a three phase low voltage (400 V line to line) distribution system, connecting micro-cogeneration gas turbines fed by natural gas (Capstone C30 and C65), a photovoltaic field, three cogeneration concentrated solar-powered systems (CSP, equipped with Stirling engines), a H2O/LiBr absorption chiller with a storage tank, two types of electrical storage based on batteries technology (long term Na–NiCl2 and short term lithium ion), two electric vehicles charging stations, other electrical devices (inverters and smart metering systems). The smart grid is integrated with the thermal power station, already in operation inside the Campus, composed of two traditional boilers fed by natural gas. The SEB is equipped with photovoltaic modules, thermal solar collectors and a horizontal axis wind turbine on the roof of the building, a geothermal heat pump, a controlled mechanical ventilation plant, and low consumption lamps. The energy consumptions
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S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
of the building and the energy production of its generation units will be monitored in real time in order to evaluate the environmental and economic benefits consequent to have a sustainable building instead of a not sustainable one. The SPM peculiarity is mainly due to
The set of generation units and storage systems for both elec
trical and thermal energy production that make it a complete pilot plant; The possibility of defining and updating a software for the SPM control; a fast telecommunication network (based on the IEC 61850 protocol); its integration with the SEB.
On the other hand, SEB innovation is given by the set of installed renewable technologies, the use of energy-efficient materials, and by the installed BAS. The overall regulation is performed through an EMS, installed in the SPM Operation Centre, which guarantees the SPM functionalities, monitoring the overall system and providing alarms management, through the connections with the RTUs (Remote Terminal Units) and local control panels of devices in field. The EMS is composed by an information system (supervisory control system) developed by Siemens [51] that is responsible of data exchanges to the central controller and of commands to production plants. The supervisory software is designed flexible enough to apply the optimal results of new algorithms coming from research activities. A software tool [52], developed by Siemens, is already active and connected to the information system and can be customized and used for many purposes. However, it would be convenient to test new algorithms to take into account power losses, possible curtailment and lines interruption, multi-objective and multi-decision problems, frequency regulation, decentralized optimal control problems. All software for optimization intra-day and real time purposes should be included within a scheme in order to take into account updates in the system state, forecasts (energy flows from renewable sources, demand variations, prices, etc.), which can be predicted with some difficulty. Fig. 2 reports the interaction among the MPC controller, the Information System, and the different power production units. Both MPC controller and Information System could be also connected to the SEB monitoring system. Thus, the future BAS for the SEB will be designed in a way that can communicate with the Information System of the SPM and with the MPC optimal controller.
The architecture is based on a centralized intelligence that receives forecasts for renewable resources availability, demands, prices, and system state (for example the level of charge of the battery system), and, on the basis of an optimization model, gives commands to controllable production plants, to the storage system, and to the connection between the SPM and the external grid. The optimization model includes an objective function based on costs, and performance indicators for the optimal solution based on emissions, and primary energy use. Constraints are also added to represent the overall system and the different technical requirements. The commands are given to typical hardware (i.e., inverters, switch with the external net, production units, etc.) that are present in the SPM and in the SEB, through the communication system based on the IEC 61850 protocol. Moreover, the central controller can communicate with the local controllers of the production units and the storage systems.
3. Economic and environmental assessment The social benefits of the installation of polygeneration microgrids can be evaluated through a detailed economic and environmental assessment. In this section, the equations used for the assessment of the Savona Campus operating costs, overall CO2 emissions and primary energy consumptions. Other emissions (CO, NOx, SOx, …) coming from fossil fuel power plants (micro gas turbines and boilers) have not been considered since, in the analyzed operating conditions, the limits imposed by environmental standards have been always respected. On the other hand, the analysis has been focused on CO2 emission since one of the main goals of the present study is that of showing the benefits in terms of CO2 reduction due to the adoption of high efficiency cogeneration technologies, such as the cogeneration gas turbines installed in the Savona Campus Smart Polygeneration Microgrid. The study is part of a more complex research activity aimed at quantifying the carbon footprint of the Savona Campus within the European directive scenario that promotes CO2 emission reduction also in the tertiary and residential sectors. First, an analysis on a yearly time-scale related to the impacts of the new facilities on the Campus system is discussed, together with possible guidelines for the planning of polygeneration microgrids feeding sustainable buildings. Then, the economic and environmental analysis has been performed at the daily timescale, through the optimization model summarized in Appendix A.
Fig. 2. The overall Energy Management System.
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
3.1. Annual costs, CO2 emissions and primary energy consumptions The analysis includes two different scenarios that are compared on the basis of economic and environmental assessment:
AS-IS (before the SPM installation and SEB construction): the
electrical energy is purchased from the national grid and the thermal energy is produced by two standard boilers fed by natural gas; TO-BE: both electrical and thermal consumptions are partially satisfied by SPM and SEB power plants.
The total operating costs per year CT for the two scenarios can be respectively expressed as C T_AS IS ¼ EEC AS IS epp þ TEC AS IS TESpp þC m_AS IS
C T_T O BE ¼ Eel_Grid epp þ Eth_Boiler TESpp þ C m_TO BE þðEel_SPM þEel_SEB þ Þef þ
2 X
# ðQ utx;k NGppwf þ Q tx;k NGpp Þ
ð2Þ
k¼1
where EECAS-IS ¼AS-IS electrical energy consumption, TECAS-IS ¼ASIS thermal energy consumption, epp is the electricity purchasing price, TESpp is the thermal energy service purchasing price, Eel_Grid is the electrical energy from the national grid, Eth_Boiler is the thermal energy produced by the boilers, C m_TOBE and C m_ASIS are the maintenance costs in the two scenarios, Eel_SPM is the electricity produced by the SPM (including both renewables and microturbines), Eel_SEB þ is the SEB electrical energy production, ef is the electricity fee for local production, ef is the electricity fee for local production, Q utx;k is the untaxed quantity (due to Italian legislation on high efficiency CHP systems) of natural gas used by microturbine k (C30, C65), Q tx;k is the taxed quantity of natural gas used by the microturbine k (C30, C65), NGppwf is the gas untaxed price, and NGpp is gas full price. Some of the variables in Eqs. (1) and (2) have been calculated on the basis of further equations. Specifically, for the TO-BE scenario, the quantities of natural gas have been derived by the following expressions: Q utx;k ¼ λEel;k , Q k ¼ Eel;k =ðLHV ηel;k Þ, Q tx;k ¼ Q k Q d;k with Eel;k ¼ P el;k t k , where λ is the natural gas untaxed amount [m3/MWhel] according to Italian legislation, LHV (Low Heating Value) is the low heating value of natural gas [MWh/m3], ηel;k is the electrical efficiency of microturbine k at the nominal power, Q k is the overall amount of gas used in microturbine k, P el;k is the rated electrical power of microturbine k, and t k is the number of microturbine k equivalent working hours in a year. Moreover, for the TO-BE scenario, the electricity absorbed from the grid is calculated according to the following equation Eel_Grid ¼ EEC AS IS þ Eel_SEB Eel_SPM Eel_SEB þ þ Eel;CHI Eel;HP
ð3Þ
where Eel_SEB is the SEB electrical energy consumption. As far as Eel;CHI and Eel;HP are concerned, they are quantities referring to the Campus library summer conditioning respectively for the AS-IS and the TO-BE scenarios. In particular, Eel;CHI is the electrical energy consumption of the chiller installed in the SPM to cool the library (using as input the thermal energy coming from the C65 gas turbine), in the TO-BE scenario, whereas Eel;HP is the electrical consumption of the standard heat pump now present (AS-IS scenario) and going to be dismissed. Finally, the electrical energy produced by the SPM (after deducting auxiliaries consumption) is given by Eel_SPM ¼ ERES_SPM þ
2 X
Eel;k ;
quantity, while the second term at the right-hand-side represents the electricity generated by the micro gas turbines C65 and C30. The thermal energy produced by the boiler is given by Eth_Boiler ¼ TEC AS_IS þ Eth_SEB Eth_SPM Eth_SEB þ
ð4Þ
k¼1
where ERES_SPM indicates the contribution of renewables to such
ð5Þ
having indicated with Eth_SEB the SEB thermal energy demand, Eth_SPM the SPM thermal energy total production (by C65 and C30 gas turbines), and with Eth_SEB þ the thermal energy produced by the geothermal heat pump installed in the SEB. The CO2 emissions per year for the two scenarios have been calculated as follows: CO2_AS IS ¼ EEC AS IS
ð1Þ
"
1251
CO2_TO BE ¼ Eel_Grid
ef n
ηel_Grid
ef n
ηel_Grid
þTEC AS IS "
þ ef NG Of
ef NG Of
ð6Þ
ηBoiler LHV
2 X Eth_Boiler þ Q ηBoiler LHV k ¼ 1 k
# ð7Þ
where ef-n is the emission factor of the Italian electrical mix [tCO2/ MWhel], ef-NG is the natural gas emission factor [tCO2/m3], ηBoiler is the boiler efficiency, and Of is the natural gas oxidation factor. Finally, the primary energy consumptions have been calculated according to " # c2 1 toeAS IS ¼ c1 EEC AS IS þTEC AS IS ð8Þ
ηel_Grid
" toeTO BE ¼ c1 Eel_Grid
c2
ηel_Grid
þ
ηBoiler
Eth_Boiler
ηBoiler
þ LHV
2 X
# Qk
ð9Þ
k¼1
where c2 is the average value of the Italian electrical mix [MWhpe/ MWhel], c1 is a conversion coefficient of the primary energy (from MWh to toe), and ηel_Grid is the national electrical grid efficiency. The main necessary data to perform the economic and environmental assessment are reported in Table 1. The overall results of the economic and environmental analysis are summarized in Table 2, together with the Campus thermal and electrical primary energy demands. Comparing the TO-BE configuration with the AS-IS one, it can be noticed that, in spite of the energy demand increase (of about 130 MWh/y) due to the presence of the new building SEB, there will be a reduction in operating costs (of about 30 k€/y), CO2 emissions (of about 86 tCO2/y), and primary energy consumptions (of about 24 toe/y). In Fig. 3, the contribution of the various sources to the satisfaction of the Campus electrical demand is detailed. It is important to note that about 37% of the electrical energy required by the Campus is self-produced (by SPM and SEB power plants, mainly by the two micro turbines). On the thermal side, Fig. 4 shows the thermal energy produced by boilers, gas turbines and the geothermal heat pump (GHP); about one-fourth of the thermal demand is satisfied by SPM & SEB generation units. The reduction in operating costs due to the SPM & SEB installation is highlighted in Fig. 5. CO2 emissions and primary energy consumptions for the two scenarios are respectively reported in Figs. 6 and 7. It is worth noting that the decrease of these last two quantities is mainly due to the increase of the global efficiency due to the presence of cogeneration units; furthermore, the reduction of both the provision of thermal energy from the boilers and electrical energy from the national grid has a positive impact on CO2 emissions and primary energy consumption.
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Table 1 The main data of the problem. MWhel/y MWhth/y MWhth/y MWhel/y h h h h MWhpe/m3 tCO2/MWhel tCO2/m3 – toe/MWhpe MWhpe/MWhel €/MWhel €/MWhel €/MWhth €/m3 €/m3 € €
Table 2 Economic and environmental results. Savona campus AS-IS Primary energy demand Operating costs CO2 emissions Primary energy consumption
3953 332 826 340
Savona campus TO-BE 4083 304 760 316
Unit
MWhpe/y k€/y tCO2/y toe/y
E_el Grid
Operating cost [k€/y]
982 1426 20 55.0 2000 2000 1000 1000 9.7 10 3 0.465 1.961 10 3 0.995 0.086 2.17 212 12.5 85.3 0.53 0.73 2000 12,000
E_th Boiler C65 C30 PV1 PV2 Wind CSP
AS-IS
TO-BE Fig. 5. Operating costs.
600 500
CO2 emission [tCO2/y]
EECAS-IS TECAS-IS Eth_SEB Eel_SEB C65 operating hours C30 operating hours Library chiller operating hours Library heat pump operating hours LHV of natural gas ef-n ef-NG Of c1 c2 epp ef TESpp NGppwf NGpp Cm_AS-IS Cm_TO-BE
400
Grid
300
Boiler C65
200
C30
100 0
AS-IS
TO-BE Fig. 6. CO2 emissions.
Fig. 3. Electricity generation in the TO-BE scenario.
Primary Energy Consumption [toe/y]
225 200 175 150
Grid
125
Boiler
100
C65
75
C30
50 25 0
AS-IS
TO-BE
Fig. 7. Primary energy consumptions.
Fig. 4. Heat generation in the TO-BE scenario.
3.2. Short term assessment and optimization An energy system characterized by the use of renewable sources has uncertainties related to the forecasts of available power that have to be taken into account. Moreover, it is necessary to minimize costs (or other performance indicators) and to respect technical constraints during a single day. For these reasons, it is convenient to
optimize the management of plants and storage systems in the short-term (day-ahead, intra-day, real time). The Savona Campus test bed facilities use optimization models included in the overall supervisory system for the optimal operation of the generation units and the storage. The outputs of the optimization models are the commands that are given to plants in the short term on the basis of updates of renewables and loads forecasts. Thus, operating costs, CO2 emissions and primary energy consumptions of the TOBE scenario can be further minimized in the short term (i.e., days, hours, minutes, etc.). This objective could be achieved by using specific rules (learnt from day by day operation on field) and/or formalizing different optimization problems for the SPM & SEB overall system running at the microgrid energy management level. In this work, the analysis has been carried out by the use of the
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
optimization model presented in the Appendix A. Specifically, the same equations adopted for the annual assessment are adopted but this time declined to the short-term horizon. The decision variables are 1. P th;B;t , P grid;t , P CHI;t , P S;t ,P th;k;t , and P el;k;t (i.e., the power outputs from boiler, electrical grid, chiller, storage and microturbines, for these last both thermal and electrical), for each time interval t ¼0,…,T 1; 2. P CHI;k;t ; P OUT;k;t (i.e., the thermal power from microturbine k that is used by the chillers or for other thermal users, respectively); 3. P CHI;B;t ; P OUT;B;t (i.e., the thermal power from boilers that is used by the chillers or for other thermal users, respectively); 4. Q utx;k;t , Q tx;k;t , and Q k;t (i.e., the quantities of untaxed, taxed, total natural gas that is used in microturbine k); 5. P HP;t (i.e., the electrical power absorbed by the heat pump to produce cool). The state variables are represented by SOC t for t¼0,…,T, i.e., the state of charge of the battery. The objective function (see Eq. A.1 in Appendix) is given by the sum of operational costs. Different kinds of constraints have been formalized: the microturbines model (Eqs. A.2–A.6 in Appendix), the chiller’s model (Eqs. A.7–A.9 in Appendix), the battery model (Eq. A.10 in Appendix), and power demand balances (Eqs. A.11–A.13 in Appendix). Additional constraints are the technical bounds for the production plants and the storage system. Finally, equations for CO2 emissions and primary energy consumptions, like those reported in Section 3.1, are used to calculate these performance indicators for the optimal solution. This means that the minimization in the optimization algorithm is done for costs while CO2 emissions and primary energy use are calculated after the solution of the decision problem. In fact, the microgrid optimization problem is a multi-objective one where different performance indexes could be tested in order to analyze which is the solution that is the best taking into account all objectives. The definition of a multi-objective decision problem is out of the scope of this paper. Some parameters needed for the short term analysis are considered time-varying for the chosen time scale (i.e., time interval: 15 min; optimization horizon: 24 h). These parameters are: heat, cool, and electricity demands; availability of renewable resources; unit costs for electricity taken from the external grid. The analysis has been here performed for a winter typical day during which the electrical/thermal demand and renewables availability are reported in Fig. 8. As regards unit costs for electricity, they are set to 25.6 c€/kWhel between 8 a.m. and 8 p.m., and 19.8 c€/kWhel in the other time intervals. The optimization problem is non-linear and Lingo 9.0 (www. lindo.com) optimization package has been used to derive the optimal solution. The optimal solution determines operation costs 1000
1253
Table 3 Optimized vs Non-optimized Scenario.
Optimized solution Non-optimized solution
Costs [€/day]
CO2 [t/day]
Primary energy [toe/day]
1532 1720
4 4.4
1.7 1.85
equal to 1531.6 €. The resulting CO2 emissions and primary energy consumptions are respectively equal to 4.04 t and 1.7 toe. In addition, the optimal results are reported in Table 3, in order to be compared with those of the non-optimized scenario (no electrical storage use and microturbines working only at nominal power between 8 a.m and 8 p.m.). In the optimized scenario, the reduction in operating costs is about 11%, whereas the decrease of CO2 emissions and primary energy consumptions is equal to 8%. Finally, a further analysis has been done setting the decision variables related to the storage equal to zero and solving the optimization problem. This means not to include the storage system in the microgrid system but to optimize the operation of gas turbines. In this case, the optimal cost is about 1600 €, resulting in an increase of about 4.5% with respect to the best case.
4. Conclusions In this paper, the "sustainable energy" test-bed facilities (Smart Polygeneration Microgrid-SPM & Sustainable Energy Building-SEB) at the Savona Campus of the University of Genoa have been described and an approach to evaluate how such infrastructures contribute to reduce CO2 emissions, primary energy use and costs of the whole Campus has been proposed. Two main analyzes have been performed: an annual assessment and a short term (daily) optimization that allows managing the infrastructure in order to minimize costs. At an yearly scale, in spite of the energy demand increase (of about 130 MWh/y) due to the presence of the new building SEB, there will be a reduction in operating costs (of about 30 k€/y), CO2 emissions (of about 86 tCO2/y), and primary energy consumptions (of about 24 toe/y). At the daily scale, the performed simulations have shown improvements both for operating costs and greenhouse gas emissions. Future developments of the present work are related to the possible use of the SPM to feed loads in the Savona District near the Campus, and to the management of generating units and loads in other areas through the optimization algorithms developed for the SPM. Moreover, stochastic, multi-objective and distributed optimization models can be conceived and tested through the Savona Campus test bed facilities. Finally, since recent challenges in microgrid development are those related to resiliency and risk assessment [53–55], the impact assessment of a microgrid can be also studied by identifying new indicators that may represent such issues.
Appendix A
900 800 700
min
kW
600 500
C TOT ¼
300
P th;B;t TESpp Δ þ P RES;t ef 10 3 Δ þ C g;t P grid;t Δ
t¼0
RES Electrical Demand Thermal Demand
400
TX 1
þ
2 h X
Q d;k;t NGppwf þ Q nd;k;t NGppwf þ P el;k;t ef 10
Δ
! i
k¼1
200
ðA:1Þ
100 0
3
0
2
4
6
8
10
12
14
16
18
20
22
24
Time [h]
Fig. 8. Electrical/thermal demand and renewables availability.
s.t. Q k;t ¼
P el;k;t Δ LHV ηel;k;t
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:2Þ
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S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
ηel;k;t ¼ f k ðP el;k;t ; T amb;t Þ Q utx;k;t ¼ λP el;k;t Δ
k ¼ 1; 2t ¼ 0; …; T 1
k ¼ 1; 2t ¼ 0; …; T 1
Q tx;k;t ¼ Q k;t Q utx;k;t P th;k;t ¼ δk P el;k;t
ðA:3Þ ðA:4Þ
k ¼ 1; 2 t ¼ 0; …; T 1
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:5Þ ðA:6Þ
P th;k;t ¼ P CHI;k;t þ P OUT;k;t
k ¼ 1; 2 t ¼ 0; …; T 1
ðA:7Þ
P th;B;t ¼ P CHI;B;t þ P OUT;B;t
t ¼ 0; …; T 1
ðA:8Þ
P CHI;t ¼
P CHI;B;t þ
2 X
! t ¼ 0; …; T 1
P CHI;k;t COP CHI
ðA:9Þ
k¼1
SOC t þ 1 ¼ SOC t P S;t Δ De;t ¼
2 X
t ¼ 0; …; T 1
ðA:10Þ
P el;k;t P HP;t þ P grid;t þ P RES;t
k¼1
þ P S;t P CHI;B;t þ
2 X
! P CHI;k;t λcons
t ¼ 0; …; T 1
ðA:11Þ
k¼1
Dh;t r
2 X
P OUT;k;t þ P OUT;B;t þP RESth;t
t ¼ 0; …; T 1
ðA:12Þ
k¼1
Dc;t r P CHI;t þ COP HP P HP;t
t ¼ 0; …; T 1
ðA:13Þ
where Δ is the time interval length [h]; λcons is a coefficient which relates chiller electrical consumption to its thermal input; COP CHI is the chiller coefficient of performance; T amb;t is ambient temperature; Dh;t , Dc;t , De;t are demands for heat, cool, and electricity [kW]; C g;t is the time varying unit sale/purchasing price for the electricity injected into/taken from the grid, f k is a non-linear function typical for each microturbine, δk is the ratio between rated values of thermal and electrical power in microturbine k.
References [1] Asano H, Hatziargyriou N, Iravani R, Marnay C. Microgrids: an overview of on going research, development, and demonstration projects. IEEE Power Energy Mag 2007:78–94. [2] Basu AK, Chowdhury SP, Chowdhury S, Paul S. Microgrids: energy management by strategic deployment of DERs—a comprehensive survey. Renew Sustain Energy Rev 2011;15:4348–56. [3] Patrao I, Figueres E, Garcerá G, González-Medina R. Microgrid architectures for low voltage distributed generation. Renew Sustain Energy Rev 2015;43:415–24. [4] Justo JJ, Mwasilu F, Lee J, Jung JW. AC-microgrids versus DC-microgrids with distributed energy resources: a review. Renew Sustain Energy Rev 2013;24:387–405. [5] Bacha S, Picault D, Burger B, Etxeberria-Otadui I, Martins J. Photovoltaics in microgrids: an overview of grid integration and energy management aspects. IEEE Ind Electron Mag 2015;9:33–46. [6] Gan PY, Li ZD. Quantitative study on long term global solar photovoltaic market. Renew Sustain Energy Rev 2015;46:88–99. [7] European Photovoltaic Industry Association (EPIA). Global market outlook for photovoltaic until 2016; 2012. Available at 〈http://files.epia.org/files/GlobalMarket-Outlook-2016.pdf〉 [accessed 10.05.12]. [8] International Energy Agency (IEA). PVPS report. Snapshot of global PV 1992– 2013. Preliminary trends information from the IEA PVPS programme; 2014. [9] Nemet GF. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Pol 2006;34:3218–32. [10] Morgera AF, Lughi V. Frontiers of photovoltaic technology: a review. Int Conf Clean Electr Power 2015:115–21. [11] Kazmerski LL. 1.03-Solar photovoltaics technology: no longer an outlier. In: Sayigh Ali, editor. Comprehensive renewable energy. Oxford: Elsevier; 2012. p. 13–30. [12] Photovoltaics report, Report of the ISE Fraunhofer Institute; October 2014 [Retreived on Nov 29, 2014]. [13] Roadmap. 2011 International technology roadmap for photovoltaics 2nd edn. Berlin, Germany: SEMI Europe. [14] Gong J, Liang J, Sumathy K. Review on dye-sensitized solar cells (DSSCs): fundamental concepts and novel materials. Renew Sustain Energy Rev 2012;16:5848–60.
[15] Krebs FC. Fabrication and processing of polymer solar cells: a review of printing and coating techniques. Sol Energy Mater Sol Cells 2009;93:394–412. [16] Delfanti M, Falabretti D, Merlo M. Energy storage for PV power plant dispatching. Renew Energy 2015;80:61–72. [17] Basu AK, Chowdhury SP, Chowdhury S. Impact of strategic deployment of CHPbased DERs on microgrid reliability. IEEE Trans Power Deliv 2010;25:1697–705. [18] Cecati C, Citro C, Siano P. Combined operations of renewable Energy systems and responsive demand in a smart grid. IEEE Trans Sustain Energy 2011;2:468–76. [19] Scognamiglio A, Adinolfi G, Graditi G, Saretta E. Photovoltaics in net zero energy buildings and clusters: enabling the smart city operation. Energy Proc 2014;61:1171–4. [20] Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast). Official Journal of the European Union; 2010 [21] Dagdougui H, Minciardi R, Ouammi A, Robba M, Sacile R. Modeling and optimization of a hybrid system for the energy supply of a Green building. Energy Convers Manag 2012;64:351–63. [22] Figueiredo J, Sa da Costa J. A SCADA system for energy management in intelligent buildings. Energy Build 2012;49:85–98. [23] Phillips LR, Jamshidi M. The microgrid as a system of systems. Systems of systems engineering-principles and applications, CRC, ser. SoS Engineering, 1. London, U.K.: Taylor and Francis; 2008. p. 251–80. [24] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. A system of systems model for the control of the Smart Polygeneration Microgrid test-bed facility of Savona University Campus. In: Proceedings of IEEE SOSE; 2012. [25] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. A dynamic optimizationbased architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles. Energy Convers Manag 2015;96:511–20. [26] Delfino F, Minciardi R, Pampararo F, Robba M. A multilevel approach for the optimal control of distributed energy resources and storage. IEEE Trans Smart Grid Special Issue Control Theory Technol Smart Grid 2014;5:2155–62. [27] Norouzi MR, Ahmadi A, Esmaeel Nezhad A, Ghaedi A. Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment. Renew Sustain Energy Rev 2014;29:912–23. [28] Norouzi M, Ahmadi A, Sharaf A, Esmaeel Nezhad A. Short-term environmental/economic hydrothermal scheduling. Electric Power Syst Res 2014;116:117–27. [29] Mavalizadeh H, Ahmadi A. Hybrid expansion planning considering security and emission by augmented epsilon-constraint method. Electr Power Energy Syst 2014;61:90–100. [30] Ahmadi A, Ahmadi MR, Esmaeel-nezhad A. Short term combined heat and power economic/emission dispatch using lexicographic optimization and augmented ε-constraint technique. Electric Power Compon Syst 2014;42:945–58. [31] Ahmadi A, Aghaei J, Shayanfar HA, Rabiee A. Mixed integer programming of multiobjective hydro-thermal self-scheduling. Appl Soft Comput 2012;12:2137–46. [32] Ouammi A, Ghigliotti V, Robba M, Mimet A, Sacile R. A decision support system for the optimal exploitation of wind energy on regional scale. Renew Energy 2012;37:299–309. [33] Shadmand MB, Balog RS. Multi-objective optimization and design of photovoltaic-wind hybrid system for community smart DC microgrid. IEEE Trans Smart Grid 2014;5:2635–43. [34] Hassan MA, Abido MA. Optimal design of microgrids in autonomous and gridconnected modes using particle swarm optimization. IEEE Trans Power Electron 2011;26:755–69. [35] Igualada L, Corchero C, Cruz-Zambrano M, Heredia FJ. Optimal energy management for a residential microgrid including a vehicle-to-grid system. Trans Smart Grid 2014;5:2163–72. [36] Milczarek A, Malinowski M, Guerrero JM. Reactive power management in islanded microgrid—proportional power sharing in hierarchical droop control. IEEE Transactions on Smart Grid [in press]. [37] Rahbar K, Xu J, Zhang R. Real-time energy storage management for renewable integration in microgrid: an off-line optimization approach. IEEE Trans Smart Grid 2015;6:124–34. [38] Marnay C, Venkataramanan G, Stadler M, Siddiqui AS, Firestone R, Chandran B. Optimal technology selection and operation of commercial-building microgrids. IEEE Trans Power Syst 2008;23:975–82. [39] Mohammadi M, Hosseinian SH, Gharehpetian GB. Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO. Sol Energy 2012;86:112–25. [40] Atwa YM, El Saadany EF, Salama MA, Seethapathy R. Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 2010;25:360–70. [41] Beer S, Gomez T, Dallinger D, Momber I, Marnay C, Stadler CM, Lai J. An economic analysis of used electric vehicle batteries integrated into commercial building microgrids. IEEE Trans Smart Grid 2012;3:517–25. [42] Zhang Y, Gatsis N, Giannakis GB. Robust energy management for microgrids with high-penetration renewables. IEEE Trans Sustain Energy 2013:1–10. [43] Tsikalakis AG, Hatziargyriou ND. Centralized control for optimizing microgrids operation. IEEE Trans Energy Convers 2008;23:241–8. [44] Etemadi AH, Davison EJ, Iravani R. A decentralized robust control strategy for Multi-DER microgrids part I: fundamental concepts. IEEE Trans Power Deliv 2012;27:1843–53.
S. Bracco et al. / Renewable and Sustainable Energy Reviews 58 (2016) 1247–1255
[45] Zhao B, Zhang X, Chen J, Wang C, Guo L. Operation optimization of standalone microgrids considering lifetime characteristics of battery. IEEE Trans Sustain Energy 2013:1–10. [46] Fathi M, Bevrani H. Statistical cooperative power dispatching in interconnected microgrids. IEEE Trans Sustain Energy 2013:1–8. [47] Koukoula D, Dimeas AL, Hatziargyriou ND. Scheduling algorithms for agent based control and scheduling of microgrids. In: Proceedings of the 16th international conference on intelligent system application to power systems (ISAP); 2011. [48] Lidula NWA, Rajapakse AD. Microgrids research: a review of experimental microgrids and test systems. Renew Sustain Energy Rev 2011;16:186–202. [49] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. The Smart Polygeneration Microgrid test-bed facility of Savona University Campus: the overall system, the technologies and the research challenges. Renew Sustain Energy Rev 2013;18:442–59.
1255
[50] Bracco S, Delfino F, Pampararo F, Robba M, Rossi M. Environmental and economic analysis for the Smart Polygeneration Microgrid test-bed facility of Savona University Campus. In: Proceedings of IEEE ENERGYCON; 2012. p. 587– 92; 2012. [51] Siemens-WinCC online avaiable: 〈http://www.automation.siemens.com/mcms/ human-machine-interface/en/visualization-software/scada/pages/default.aspx〉. [52] Siemens-DEMS online avaiable: 〈https://www.swe.siemens.com/italy/web/IC/ SG/EA/applicazioni/Gestione%20di%20Microgrid%20e%20Virtual%20Power% 20Plant/Pages/DEMS.aspx〉. [53] Khodaei A. Resiliency-oriented microgrid optimal scheduling. IEEE Trans Smart Grid 2014;5:1584–91. [54] Che L, Shahidehpour MDC. Microgrids: economic operation and enhancement of resilience by hierarchical Control. IEEE Trans Smart Grid 2014 [in press]. [55] Manshadi SD, Khodayar ME. Resilient operation of multiple energy carrier microgrids. IEEE Trans Smart Grid 2015;6:2283–92.