Optimum design of a CCHP system based on Economical, energy and environmental considerations using GA and PSO

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1 International Journal of Industrial Engineering Computations 9 (28) Contents lists available at GrowingScience International Journal of Industrial Engineering Computations omepage: Optimum design of a CCHP system based on Economical, energy and environmental considerations using GA and PSO Masoud Rabbani *, Setare Moammadi and Madi Mobini Department of Industrial Engineering, University of Teran, Teran, Iran C H R O N I C L E A B S T R A C T Article istory: Received January 5 27 Received in Revised Format April 27 Accepted April 2 27 Available online April 4 27 Keywords: Combined cooling eating power generation Optimised design Control strategy Particle Swarm Optimisation Genetic algoritm Optimum design and control of a Combined Cooling, Heating and Power generation (CCHP) system, in addition to te economic benefits, could be profitable in environmental and energy consumption aspects. Te aim of tis study is to determine te optimal capacity of equipment and define te best control strategy of a CCHP system. Since determination of optimal system control strategy as a uge impact on improving te objective functions, te system s performance under five different strategies (developed based on well-known Following Electrical Load (FEL) and Following Termal Load (FTL) strategies) is evaluated. In a real case study, a CCHP system is designed for an educational complex located in Mamoudabad, Mazandaran, Iran. Te objective is to minimize capital and operational costs, energy consumption, and CO2 emissions of te system. Due to te complexities of te model, genetic algoritm (GA) and particle swarm optimisation (PSO) algoritm are used to find te optimal values of te decision variables. Te results sow tat using FEL strategy CO2 emissions reduces in compression to FTL strategy. Furtermore, using multiple power generation units under FTL strategy eventuates te least cost but increases CO2 emissions and energy consumption in compression to FEL strategy. 28 Growing Science Ltd. All rigts reserved. Introduction A large portion of national energy consumption is expended for fulfilling te buildings eating, cooling, and electricity demand. Consumed energy in te buildings sector, consisting of residential and commercial end users, accounts for 2.% of te total delivered energy consumption worldwide (International Energy Outlook, 26). Te type and amount of energy consumed by ouseolds can vary significantly witin and across regions and countries (International Energy Outlook, 26). In te USA, residential buildings consume 22% of te total final energy use, compared wit 26% in te EU. Residential buildings energy consumption is 28% of total energy consumption in te UK, well above Spain at 5%, mainly due to a more severe climate and te building types. In 23, energy consumption attributed to residential and te non-domestic sectors is predicted to reac to 67% and 33%, respectively (Pérez-Lombard et al., 28). Lack of efficient construction regulations, in addition to te low energy * Corresponding autor mrabbani@ut.ac.ir (M. Rabbani) 28 Growing Science Ltd. All rigts reserved. doi:.5267/j.ijiec

2 prices in te past ave caused careless and inefficient consumption of energy by te Iranian residential sector compared to industrialized countries (Karbassi et al., 27). Commercial and residential building sector consume about 4% of total energy in Iran. Tis consists,.7% of oil products, 73.3% of natural gas and 3.25% of electricity (Iran Energy Efficiency Organization (IEEO-SABA), 26). In addition to te economic burdens, tis energy consumption trend all around te world is causing severe environmental problems as well as energy security issues (Cai et al., 29). Using Combined Cooling, Heating and Power generation system (CCHP) is a proven metod for enancing energy efficiency. Using CCHPs leads to economic savings, wile reducing te emissions (Zeng et al., 24). Also, possible energy sources for CCHP systems include a vast range of fossil fuels, biomass, geotermal and solar power, giving te flexibility desired for installing tese systems in different geograpical regions. Consequently, CCHP systems are installed in a variety of buildings, suc as otels, offices, ospitals and supermarkets (Ge et al., 29; Wang et al., 28). In tis field te goal is to fulfil a building s energy demand wile minimizing te costs and environmental consequences (Løken, 27). In order to do so, structural design and operational planning of te system need to be optimised. Structural design of te system relates to defining te optimum number and capacity of te equipment; and operational planning of te system relates to te determination of te ourly operation of te equipment (Mago & Camra, 29). One of te main callenges in energy planning field is access to reliable estimation of energy demand. Additionally, te fluctuations in te building energy demand (in terms of eating, cooling, and electricity) makes te design and operational planning of te system a complex task (Cao, 29) since reacing te optimum design requires solving an optimisation model at every interval of time. Te large scale of te optimisation problem makes te models computationally intractable; terefore, operating strategies are proposed to reduce te complexity of te models. Tese strategies determine te state of te Power Generation Unit (PGU) and te proportion of te cooling demand fulfilled by te electric cillers (so called electric cooling to cool load ratio ) in eac period, wic significantly reduce te complexity of te problem. A variety of metods for determining optimum design of CCHP systems are proposed. Initially linear optimisation models were developed to design energy systems. Cao (29) analysed te influence of energy prices on te system s economic feasibility. Te objective function was minimisation of te annual cost, and maximization of te exegetic efficiency. Piacentino and Cardona (28) presented a Mixed Integer Linear Programing (MILP) model to optimize te economic and environmental performance of a tri-generation system (eating, cooling and electricity production). Nonlinear Programming (NLP) and Mixed Integer Programming (MIP) models were used to find te optimum design of te system in researc by Gamou and Yokoyama (998) and Arcuri et al. (27), respectively. Reduced gradient metod was used by Cen and Hong (996) to solve te presented matematical model. In a similar study a matrix approac was employed to model te problem by Geidl and Andresson (27). Tey presented te matematical model of te problem in te matrix form and used Sequential Quadratic Programming to optimize an ourly linear objective function. Due to teir capability in tackling large-scale optimisation problems, artificial intelligence, in te form of euristic and metaeuristic algoritms are commonly employed to optimize te design and operation of CCHP systems. Metaeuristic algoritms ability of exploration and exploitation is admissible wen evaluation of limited number of feasible solutions is desired (Črepinšek et al., 23). Genetic Algoritm (GA) and Particle Swarm (PSO) algoritm ave been applied to optimize of CCHP design and operational parameters. Te PSO algoritm is used by Tici et al. (2) for minimizing te cost of operating various CHP and CCHP systems in an industrial dairy unit. Wu (2) considered te optimisation of operation of a CHP system under uncertainty and used te PSO algoritm to solve te model. Gaebi et al. ( 22) investigated exergoeconomic optimisation of a CCHP system. Te presented economic model was based on te Total Revenue Requirement (TRR) and te total cost of te system was defined as te objective function. Tis model was solved by GA. Designing CCHP systems involves determination of te equipment s capacity as te main goal. Wang et al. (2) designed a CCHP system wit consideration of PGU and storage tank capacity as decision variables. On-off coefficient and

3 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) electric cooling to cool load ratio was considered as decision variables too. Tis researc was extended by an investigation a biomass gasification CCHP system (Wang et al., 24). In tis researc te capacity of te gasification reactor, PGU, absorption ciller, electric ciller, and eat excanger were considered as decision variables. In anoter study by Sanaye et al. (25) a CCHP system was designed wit equipment s capacity, partial load of PGU in eac mont, and electric cooling to cool load ratio as decision variables. Tis study considered a more compreensive design compared wit previous studies; in addition to te capacity of PGU, te number of tem was considered as te decision variables. Wen a ig-capacity PGU is installed, due to fluctuations in electric demand, te optimum solution dictates tat te PGU is in off state a number of courses. Tis will lead to purcase of te wole electricity demand from te grid and supplement of eating demand by auxiliary boiler. Terefore, energy consumption and pollution increase during tese periods; also, te cost of buying electricity will increase significantly. Consequently, it migt be more beneficial to consider a number of smaller-capacity PGU instead of a large-scale one. Tis is wy te number of PGUs is considered as a decision variable. In te previous researc te number and te capacity of te equipment, te on-off coefficient, and electric cooling to cool load ratio under different strategies were not simultaneously considered as decision variables. Terefore, te interconnections between tese variables ave not been taken into account, wic is investigated in tis study. In tis researc various strategies, for simultaneous utilization of several power generation units and adaptation of te operation status of cillers trougout te year, are explored so tat te performance of te system under different circumstances is evaluated. Commonly employed strategies suc as Following Electrical Load (FEL) and Following Termal Load (FTL) are implemented for an actual set of buildings to optimize te performance of te CCHP system. Moreover, different strategies are implemented for a real case and results are analysed. As mentioned before, main goal of using te CCHP systems is to lower te economic costs and te environmental consequences. In tis study, it is endeavoured to reflect te influence of optimum design and operation planning of te system in reduction of economic costs, environmental footprint measured in terms of CO 2 emissions, and energy consumption. As a summary, te followings are te contribution of tis paper: Tree commonly employed strategies in addition to two novel strategies for operational planning of CCHP systems are explored. GA and PSO algoritms are employed to obtain te optimum values of design parameters and teir performance in solving tis optimisation problem are compared. Eigt design parameters (decision variables), including te capacity of gas turbine as te prime mover, teir number and operational strategy, te capacity of te backup boiler and storage tank, te capacity of electrical and absorption cillers, te electric cooling ratio, and te on off coefficient of PGUs are considered and te results under various strategies are compared. Te developed strategies and algoritms are applied to a real case study. 2. Problem Description Conventionally, in Separate Production (SP) systems, electric cillers are used to fulfil cooling demand, wile eating demand of te buildings is supplied wit a boiler (commonly a gas boiler), and te electricity is purcased off te grid. CCHP systems, owever, consist of several separated segments tat perform in an integrated fasion to fulfil te electricity, cooling, and eating demands. Fig. sows general structure of a CCHP system. PGU generates electricity power by consuming te fuel; te eat excanger retrieves eat generated during generation of electricity; depending on te implemented strategy, te recovered eat is eiter used to fulfil te eating demand or is directed to te absorption ciller to fulfil te cooling demand; te electric ciller is used to complement te absorption cillers and fulfil te cooling demand wen needed; te auxiliary boiler and energy storage tank reduce te risk of system failure and increase system reliability.

4 2 To te stack To/From te Grid Heat excanger Electrical load PGU Electrical ciller Fuel Storage tank Absorption ciller + + Cooling load Boi ler Heating load Waste Fig.. Scematic of a CCHP system (Sanaye & Hajabdollai, 25) Operational planning of CCHP systems is usually conducted based on two strategies: FEL and FTL. Te core of FEL strategy is fulfilment of te electrical demand. If te electrical demand of te buildings exceeds te capacity of PGU, it works in full load; oterwise it works in partial load to provide te required amount of electrical power. Te cooling load provided by te electric ciller is determined in eac period (an our) based on te electrical power production. Wen systems operate based on FEL, overproduction of termal energy would be wasted. Wen te energy generated by te PGU is insufficient, lack of electricity is purcased from te grid. Also, termal storage tanks are used to enance termal efficiency of te CCHP systems. Recovered eat from te PGU will be used by te absorption ciller for cooling, or by te eat excanger to supply te eat demand of te buildings. On te contrary to te FEL strategy, in te FTL strategy, te purpose is to fulfil te termal demand of te building, so tere is no excess of termal energy and in case of sortage, termal energy would be supplied by an auxiliary boiler. Wen CCHP system operates based on te FTL, surplus or sortage of electrical power is possible. In case of surplus, if selling te excess electricity to te grid is not possible, surplus electricity would be wasted; and in case of sortage, unmet electricity demand is fulfilled by purcasing from te grid. Anoter commonly adopted strategy for operational planning of CCHP systems is similar to FEL but wit an additional decision variable (x) wic defines te ratio of electric cooling to cool load. In oter words, in tis strategy te proportion of te cooling demand supplied by te electric ciller is a decision variable, compared to te FEL strategy were te priority is always given to te electric ciller and te capacity of te PGU defines te amount of te cooling demand supplied by te electric ciller. Using FEL wit no restriction on te electric ciller utilization migt lead to significant eat waste wic could ave been used by te absorption ciller to supply te cooling demand. In order to prevent increasing te complexity of te model, te value of x is commonly considered to be fixed trougout te year (Sanaye & Kakpaay, 24; J. Wang et al., 2) (one decision variable is added instead of 876 variables). As mentioned before, using several smaller PGUs instead of a single large-capacity PGU could be beneficial in some cases. At te first glance, deployment of several PGUs will dramatically increase te capital cost of te system; owever, te operational costs could be reduced to te extent tat compensate te extra capital cost. Terefore, considering te multi-pgu case provides te opportunity to evaluate te trade-off between te iger capital cost and te reduced operational costs wic is missed wen a single PGU is considered. Specifically, under te FEL strategy, it is anticipated tat multi-pgu approac offers more favourable results wen te minimum and maximum electrical load of te system trougout te year are widely different. If te electric load fluctuations in te system is significant and we ave a ig capacity PGU, it will be turned off in many periods wen te partial load would fall below its economical operational tresold (as explained in section 3.) leading to iger purcased amount of te electricity from te grid and utilizing te auxiliary boiler to fulfil te eat demand. On te contrary wen

5 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 3 several PGUs are available teir status (on/off) could be adjusted respective to te partial load of te system, ence, reducing te operational costs. Similarly, wen using a single ig-capacity PGU under te FTL strategy, in a number of periods during spring and fall te PGU is turned off because te eating demand of te system is reduced and te PGU must operate at a low partial load tat is not economical (as explained in section 3.). Having several PGU wit smaller capacity could reduce purcasing power from te grid and reduce supplying eating demand by te auxiliary boiler, ence, improving system s performance. Considering several PGU will cange ow te operational strategies are applied. Wen using FEL strategy in a multi-pgu system, in order to determine te status (on/off) of eac PGU at eac time step, te PGUs are sorted based on teir capacity and te smallest unit wit te lowest capacity is placed in active status. Te reminder of te electrical demand is assigned to te next PGU and it is activated. Tis is continued until te demand is fully responded or te generation of te electricity by te next PGU is not economically viable. Tis approac is expected to increase te efficiency of te PGUs by increasing teir utilization rate. Wen using te FTL strategy, in order to determine te status (on/off) of eac PGU, te PGUs are sorted based on teir capacity and te smallest unit wit te lowest capacity is placed in active status. Te reminder of eating demand is assigned to te next PGU and it is activated. Tis is continued until te eating demand is fully responded or te activation of te next PGU is not economically viable. 3. Model formulation In te followings, te parameters of te model are introduced and te objective functions and constraints are presented. Table sows a list of te parameters and symbols used in te model. Table Parameters and symbols of te developed model Parameter Subscripts r Heat recovery factor e Electricity grid Transmission efficiency r Recovery plant Plant efficiency f Fuel s Heat storage efficiency i Interest rate temp Temporary n Lifetime of te equipment (year) nom Nominal C Purcase price of te electricity ($/) p Pumps and oter equipment e, buy Cesale, Selling price of te electricity ($/) ea Heating C f, b Buying price of te fuel for boiler ($/) j Number of PGU (counter) Cf, pgu Buying price of te fuel for PGU ($/) k Equipment number (counter) co2 e CO 2 emission of electricity (gr/) req Required CO 2 emission of fuel (gr/) pgu Power Generation Unit co2 f El Electrical demand of te buildings () max Maximum capacity Qc Cooling demand of te buildings () min Minimum capacity Qea Termal demand of te building () total Total amount COP Coefficient of performance of electric ciller best Te best amount ec Coefficient of performance of absorption COP ac symbols ciller Ta Carbon tax ($/kg) PL Partial load c

6 4 Subscripts K Total number of PGUs out Exit N Total number of equipment in Entrance C Capital cost per ($/) E Electricity Generated/Required Cap Capital cost of equipment($) ac Absorption ciller fr Instantaneous fraction ec Electric ciller x Electric cooling to cool load ratio c Cooling Q Heating load () b Boiler F Fuel consumption () s Storage tank Sl Salvage value ($) grid Local grid 3..Under FEL strategy Wen FEL is te adopted strategy, te ratio of cooling load supplemented by te electric ciller per our is calculated by (). Te amount of cooling load supplied by te electric ciller is calculated by (2). if Epgu,max El Ep, Qc, / COP () ec x (( Epgu,max El Ep, ) COP ec ) / Qc, if El Ep, Epgu,max El Ep, Qc, / COP ec if Epgu,max El Ep, Qec, x. Q (2) c, Te electricity consumption of te electric ciller is calculated as sown in (3). Te capacity constraint of te electric ciller is sown in (4). Te total electricity requirement of buildings is sown in (5). Te capacity utilization of te PGU is denoted by,, called te instantaneous fraction of te PGU, and is calculated using (6). E Q COP (3) ec, ec, / ec Q Q Q ec,min ec ec,max E ( El E ) E Ereq, frpgu, E req, p ec, pgu,max Te efficiency of te PGU is igly dependent on its capacity utilization (Wang et al., 2). Below a certain tresold it is not rational to use te PGU since most of te consumed fuel is wasted on eat generation rater tan electricity generation. In (7), is considered as te lower bound on te instantaneous fraction to ascertain te PGU is eiter turned off or is working above te predetermined level. is a decision variable in te model and its upper limit is equal to. if frpgu, Epgu, Ereq, if frpgu, (7) Epgu,max if frpgu, Partial load of PGU is calculated by (8. Te efficiency of te PGU is determined by (9). Increasing te partial load of PGU increases its efficiency (Sanaye & Hajabdollai, 23; Sanaye, Meybodi, & Sokrollai, 28) as sown in te (9). Te electricity purcased from te grid or sold to te grid is calculated using (). (4) (5) (6)

7 PL pgu, E E pgu, pgu,max pgu, 2 pgu, pgu, pgu, nom M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 5.59( PL ).24( PL ).94 (9) E E E E E grid, p ec, pgu, () declares tat power excange wit te grid was purcase off te grid or sold to te grid. Te fuel consumed by PGU is estimated by te (2). Based on te fuel consumption and te efficiency of te PGU, te generated eat is calculated by (3). u,. Esale, u2, Ebuy, Egrid, u u2 u, u2 {,} () E pgu, Fpgu, (2) pgu, Q F ( ) pgu, pgu, pgu, Te amount of te recovered eat is calculated by (4). Fuel consumption related to te electricity purcased from te grid is calculated by (5). Te supplied cooling via absorption ciller is determined by (6). Absorption ciller s cooling range is between minimum and te maximum capacity of absorption ciller, represented in (7). Q Q F r, pgu,. r e, Ebuy, grid plant Q ( x ). Q ac, c, Q Q Q ac,min ac, ac,max Te eat consumed by te absorption ciller is calculated by its coefficient of performance in (8). Te total amount of eat requirement of te system is calculated as (9). Q ( x ) Q / COP ac, in, c, ac Q Q Q req, ac, in, ea, Part of te eat requirement is supplied by recovered eat from te PGU and te rest is provided by te backup boiler. Te generated eat from te auxiliary boiler can be calculated by (2). Capacity constraint of te boiler is represented in (2). Partial load of te boiler is determined by (22) and its efficiency can be calculated by (23) (Sanaye & Hajabdollai, 23; Sanaye et al., 28). Q b, if Qr, Qs, Qreq, Qreq, Qr, Qs, if Qreq, Qr, Qs, Qb,min Qb, Qb,max (2) Qb, PLb, Qb,max (22) b, ( PLb, ).6249( PLb, ) (23) bnom, (8) () (3) (4) (5) (6) (7) (8) (9) (2)

8 6 Fuel consumption of te auxiliary boiler is calculated by (24) and te total fuel consumption in te CCHP system is determined in (25). Te amount of eat carged in or discarged from storage tank can be determined by (26). Te initial investment cost per capacity of equipment ($/) is determined using (27) and (28) (Sanaye & Hajabdollai, 23). Qb, Fb, (24) b, F Fb, Fpgu, Q. Q U. Q U. Q Usin,, & Us,,out {,} U U, s s, s, s, in, s, in, s,out, s,out sin,, s,,out C.4 E pgu 6 6,max Cap C E pgu,max (28) Te gas turbine maintenance cost ($/), expressed based on its capacity, is assumed equal to $.55 (Smit et al., 2). Te initial cost per of te boiler ($/) is estimated by (29 and its total capital cost is calculated by (3. Operating cost of te boiler is assumed $.27 (Sanaye & Hajabdollai, 23). Initial investment per ($/) of absorption and electric ciller are estimated as sown in (3 and (32, respectively (Sanaye et al., 28). Teir total capital cost is calculated by (33 and (34. Operating cost for bot cillers is assumed to be.3 $/ (Sanaye & Hajabdollai, 23). Te initial cost of a storage tank per is considered 33 $/ (Sanaye & Hajabdollai, 23) so te total capital cost of storage tank is calculated by ( C2 25( Q b,max ) (29) Cap2 C2 Q b,max (3) 6.28 C3 54( Q ab,max ) (3) C4 482( Q ec,max ) 59.7 (32) Cap3 C3 Q ab, max (33) Cap C Q (34) 4 4 ec, max Cap 33Q 5 sma, x 3.2. Under FTL strategy For te FTL strategy matematical relationsips are similar to FEL except for te ciller s operation parameters tat are determined in a different way. At first te efficiency of PGU at full load (ƞ, ) is calculated using (9) and te eat generated by te PGU is calculated by (36). Using te eat recovery factor, recovered eat from te CCHP system at te igest capacity is calculated in (37) and te ratio of electric cooling load to cool load is determined as sown in (38). E Q F ( ) ( ) (36) pgu,max pgu,max pgu, pgu, pgu,max pgu,max. r,max pgu,max r Q Q if Qr,max Qea, Qc, / COPac x (( Qr,max Q, ) COP )/ Q, if Q, Qr,max Q, Q, / COP if Qr,max Qea, ea ac c ea ea c ac (25) (26) (27) (35) (37) (38)

9 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 7 By determination of, te required eat can be calculated according to (9). If te required eat is less tan,, recovered eat would be equal to its required eat. By using (39),,, is calculated and using (4 temporary partial load (,, ) is calculated. As sown in (4), is a binary variable wic equals to wen PGU is on and equals to zero wen PGU is off. Using (42), eat recovered from te PGU is calculated based on eat requirement of te system. Based on te recovered eat of PGU te amount of electrical generation (, ) is estimated and te partial load of PGU is calculated by (43). In oter words, at first,, is calculated to determine if PGU sould be in on or off mode. After determination of PGU s state, te amount of, is calculated. E Q. req, pgu, pgu,, temp ( pgu, ) r PL pgu,, temp E E pgu,, temp pgu,max PLpgu,, temp (4) d min, Qrec,max if Qreq, Qrec,max Qrec, (42) d Qreq, oterwise Epgu, j, PLpgu, j, (43) Epgu, j,max 3.3.Under FEL strategy wit fixed ratio of electric cooling to cool load As previously mentioned, in te tird strategy, te ratio of electric cooling to cool load is considered as a decision variable. PGU operates based on FEL but x is determined on te basis of bot electrical and termal load and Eq. () is omitted from te optimisation model Multiple PGUs, Under FEL strategy wit fixed ratio of electric cooling to cool load Te power generation for eac of te PGUs in eac period of time is calculated in (7) until condition, is met. By tis condition te oter units will become inactive. PGUs are sorted in descending order of, as sown in (44). In tis stage, based on FEL strategy te total electrical requirement of te system is determined. Total electricity requirement for eac unit can ten be calculated by (45) and (46). Initially te instantaneous load factor of te active unit is calculated in (47); ten, te amount of electricity generated for eac active unit is determined using (48). Epgu, j,max Epgu, j,max j{,..., K } (44) E E req,, req, Ereqj,, Ereqj,, Epgu, j, j,...,k (46) Ereq, j, frpgu, j, E (47) pgu, j,max if frpgu, j, Epgu, j, Ereq, j, if frpgu, j, (48) Epgu, j,max if frpgu, j, (39) (4) (45)

10 8 At tis stage, te electricity generated by te CCHP system in eac period is determined. If unit number j is placed on inactive status all next units will be in inactive status. Tis restriction is sown in (49); ten, te total electrical load production is calculated by (5). (49) sows tat PGUs are activated respectively and (5) represents total electricity generated by PGUs. Purcased electricity from te grid can be calculated by (5). Calculation of total eat retrieved from te CCHP system in eac period is based on (52). Te rest of te equations are similar to FEL strategy. K j j j,2,..., j, (49) E K E total, pgu, j pgu, j, j E E E grid, req,k, pgu,k, K rec, Qrec, j, j Q (5) (5) (52) 3.5.Multiple PGUs, under FTL strategy wit fixed ratio of electric cooling to cool load (49 is used to sort te PGUs. First, te entire eating system requirement is determined by (9). For te first activated unit te total eating demand of te system is considered as te required eat amount wic is sown in (53). By (54) required eat of oter units can be determined. After te last PGU, if still tere is unmet demand, te auxiliary boiler is used for wic te amount of eating demand is calculated using (55). Q Q (53) req,, req, Qreq, j, Qreq, j, Qrec, j, j,2,..., K (54) Qboiler, Qreq,K, Qrec,K, (55) 3.6.Evaluation Criteria Capital and variable costs, te amount of CO 2 emissions, and amount of energy consumption are considered as te criteria in te objective function in te optimisation model. Te first criterion evaluates te economic costs of te system. It consists of te capital cost of equipment, cost of fuel consumed by te boiler and te PGU, operation cost of equipment, and cost/profit from transfer of electricity from/to te grid. Salvage value of equipment is considered % of teir capital cost (Gibson et al., 25). n i( i) R n (56) ( i) i A n (57) (( i) ) N 876 Z ATCR[ C Cap ] ASl ( E. C F. C F. C E C k k buy, e, buy b, f, b pgu, f, pgu sale, e, sale k 3.55Epgu,.27Qb,.3 Qc, ) Tac CDE 876 Z CDE. F. E 2 co2f co2 e buy, R is te capital recovery factor and calculated as sown in (56 and A is te uniform series sinking fund and its value is calculated using (57), n represents te service life of te equipment and i is te interest rate. Similar to Barami and Farabaks (23), it is assumed tat te values of i and n are equal for all (58) (59)

11 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 9 equipment. Te Annual Total Cost (ATC) is calculated by (58. Te second criterion evaluate te amount of CO 2 Emissions (CDE) to reflect environmental concerns. CO 2 emissions are released by fuel consumption of auxiliary boiler and te PGU. Also, wen electricity is purcased from te grid, te related CO 2 emissions are accounted for as sown in (59). Te tird criterion represents te Primary Energy Consumption (PEC) of te system wic is composed of two parts. Te first part is amount of fuel consumed by te boiler and te PGU, and te second part is te fuel related to te electrical energy purcased from te grid. Total energy consumption of te system is sown in (6). In tis study we use a weigted sum of tese tree criterion to form a single objective function as sown in (6). 876 Z PEC F F 3 e, (6) min Z Z Z Z (6) Z, best Z2, best Z3, best To calculate te optimal value of te multi objective function, first eac of te single-objective functions are optimised to obtain te optimum value for eac of tem, denoted as,,,, and, in (6. Eac single-objective function is normalized and ten sum of tem is calculated. By canging te weigts different results can be acieved. Since te economic cost of fuel consumption (fuel consumption of te PGU, boiler and electricity purcased from te grid) in CCHP system in addition to CO 2 tax are considered in ATC function, te objective functions are largely aligned wit eac oter wic makes using te normalized weigted sum a proper metod in andling te multi-objective optimisation model. 4. Evolutionary Algoritms GA and PSO are bot population-based algoritms commonly employed in te field of energy planning. Genetic algoritm was introduced by Jon Holland (Mitcell, 998) as an evolutionary algoritm inspired by biology concepts suc as ineritance, mutation, selection and crossover. Small random canges are determined by mutation wic is determinative of GA diversity. Crossover operator determines ow te algoritm combines two selected parents, to generate cildren for te next generation. Candidate solutions are assessed by te evaluation function (also known as fitness function). PSO was invented by Kennedy and Eberart in te mid99s (995), inspired by te movement of te particles. Te PSO algoritm includes tree pases, namely, generating particles positions and velocities, updating te velocity of particles, and updating te position of particles. Te tree values tat affect te new direction of a particle are its current motion, te best position in its memory, and swarm influence. (62) sows ow te movement speed of te particle is updated and (63) sows ow te new position of te particle is determined were te inertia coefficient for particle i in its next movement is represented by, new position of te particle i is represented, and is te current motion factor, is particle own memory factor, and is te swarm influence factor., v t wv t cr x t x t c r x t x t i i i best i gbest i 2 2 i i i x t x t v t (63) 4.. Solution Representation Solution representation sceme adopted in tis researc are similar for te bot algoritms, wit minor differences respective to te implemented strategy. In GA, a cromosome is a set of parameters wic define a solution for te problem. An example cromosome for eac strategy is sown Fig. 2. In te FEL strategy a cromosome wit six bits is used. Cromosomes encompass normalized variables, i.e., all te bits filled wit continuous variables between zero and one. Bits represent te capacity of PGU, auxiliary boiler, absorption cillers, electric ciller, eating storage tank and on/off coefficient. In te FTL strategy (62)

12 cromosome consist of 5 bits. In tis strategy cromosome structure is similar to te FEL strategy just te bit corresponding to eat storage tank capacity is removed, because in tis strategy excess eat is not produced at any time and tus te storage tank is removed. In FEL wit fixed ratio of electric cooling load to cool load te cromosome as one bit more tan in te FEL strategy wic is related to te electric cooling load to cool load ratio. In te multi-pgus strategy an upper limit for te number of PGUs is considered and binary bits are used to indicate te instalment of te PGUs; corresponding to eac binary bit tere is one bit related to tat PGU s capacity. Terefore, assuming n as te upper limit on te number of te PGUs, 2*n bits are considered for PGUs to determine teir instalment and capacity. FEL Strategy FTL Strategy FEL Strategy under Fixed Ratio of Cillers : PGU Capacity 2: Boiler Capacity 3: Electric ciller Capacity 4: Absorption Ciller Capacity 5: Storage Tank Capacity 6: On/Off Coefficient : PGU Capacity 2: Boiler Capacity 3: Electric ciller Capacity 4: Absorption Ciller Capacity 5: On/Off Coefficient : PGU Capacity 2: Boiler Capacity 3: Electric ciller Capacity 4: Absorption Ciller Capacity 5: Storage Tank Capacity 6: On/Off Coefficient 7: Electric cooling to cool load FEL Multiple PGU Strategy under Fixed Ratio of Cillers 2... n n+ n n : Zero/One variable of PGU number 2: Zero/One variable of PGU number2 n: Zero/One variable of PGU number n n+: PGU number Capacity n+2: PGU number 2 Capacity 2n: PGU number n Capacity 2n+ 2n+2 2n+3 2n+4 2n+5 2n+6 2n+: Boiler Capacity 2n+2: Electric Ciller Capacity 2n+3: Absorption Ciller Capacity 2n+4: Storage Tank Capacity 2n+5: On/Off Coefficient 2n+6: Electric cooling to cool load FTL Multiple PGU Strategy under Fixed Ratio of Cillers 2... n n+ n n 2n+ 2n+2 2n+3 2n+4 2n+5 : Zero/One variable of PGU number 2: Zero/One variable of PGU number2 n: Zero/One variable of PGU number n n+: PGU number Capacity n+2: PGU number 2 Capacity 2n: PGU number n Capacity 2n+: Boiler Capacity 2n+2: Electric Ciller Capacity 2n+3: Absorption Ciller Capacity 2n+4: On/Off Coefficient 2n+5: Electric cooling to cool load Fig. 2. Cromosomes in different strategies 4.2. Operators Mutation and crossover are te two essential operators in GA. In crossover operator, at first te parent cromosomes are selected, ten by selection of genes from te parents, new off-springs are created. Parent cromosomes are selected according to teir fitness; cromosomes wic ave better fitness function value ave iger cance of being selected. After crossover, mutation takes place wic prevents algoritm from premature convergence to a local optimum by inserting randomly created solutions based on te existing ones. In Fig. 3 examples of crossover and mutation operators for FEL strategy are sown. In tis study a single point crossover is implemented. Te crossover point is randomly selected, and two new solutions are created by swapping te two sides of te point in te parents to form a new solution. Applying te mutation operator, two points of a given cromosome are randomly selected and swapped to form a new solution. An example of crossover and mutation operators for Muti-PGUs strategy is sown in Fig. 4. Tese cromosomes possess binary and continues variables limited to [,]. Crossover operator is exactly te same for te multi-pgu strategy. To apply mutation two random points are selected. If tis selected point is binary, te bit will cange to its contrary form. Te capacity related to tis binary variable will cange based on tis bit s new value. For example, if it becomes zero te capacity related to tis bit will cange to zero; oterwise te capacity

13 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) related to tis bit is updated to a new random value. If selected bit is continuous, it will become updated to a new continuous random variable by mutation operator. In te implemented PSO algoritm te particles structure is similar to te cromosome structure in GA. Mutation is te only operator in PSO wic is same as GA Mutation Operator Crossover Operator Fig. 3. Mutation and crossover example of FEL Crossover Operator Mutation Operator Fig. 4. Crossover and mutation example of Multi-PGU 5. Case study Te proposed CCHP optimisation model and te solution procedures are applied to a real case study, an educational complex located in Mazandaran, Iran. Te CCHP system provides electricity, eating (for

14 2 bot space eating and domestic ot water), and cooling. List of existing buildings, te total area of outer wall, windows, doors and usable area are sown in Table 2. Table 2 List of existing buildings and teir caracteristics Type of building Buildings number Outer wall area Windows area Doors area Usable area Common Villa 4 6, ,87 VIP villa ,5.5 Guest ouse Restaurant , Residential building Market Mecanic Laboratory ,233 Electric Laboratory ,225 Educational sites ,95 Sport all, ,69 Central Kitcen ,356 Te tecnical parameters of CCHP system and te specifications of te components are listed in Table 3. Design parameters (decision variables) and te acceptable range of teir variations are listed in Table 4. Te range of te decision variables is determined by load demand of te buildings; te range of te boiler and storage tank capacity are determined according to eating demand; and cillers capacity range is determined according to cooling demand of te buildings. Table 3 Tecnical parameters and specifications of components Parameter Explanation Value Reference Heat recovery factor.8 (Wang & Fang, 2) r Transmission Efficiency.9 (Iranian Electricity management, 26) grid Plant Efficiency.37 (Iranian Electricity management, 26) plant Heat Storage Efficiency.9 (Iranian Fuel Conservation Organization, 26) s Max Efficiency of boiler.8 (Liu et al., 23) b, nom Max Efficiency of PGU.4 (Iranian Electricity management, 26) pgu, nom i Interest rate.2 (Gaebi et al., 22) n Lifetime of te equipment 5 (Q. Wang & Fang, 2) Sl Salvage value. (Gibson et al., 25) C ebuy, ($/) Purcase price of electricity.2 (Iranian Electricity management, 26) C esale, ($/ ) Selling price of electricity.9 (Iranian Electricity management, 26) C fb, ($/ ) Buying price of gas for boiler.4 (Iranian Fuel Conservation Organization, 26) Cf, pgu($/ ) Buying price of gas for PGU.3 (Iranian Fuel Conservation Organization, 26) (gr/ ) Pollution emission of electricity 968 (Liu et al., 23) co2 e (gr/ ) Pollution emission of fuel 22 (Liu et al., 23) co2 f Ta $/ kg Te carbon tax.3 (Liu et al., 23) c COPec Coefficient of performance of electric ciller 3 (Liu et al., 23) COP Coefficient of performance of absorption ciller.7 (Liu et al., 23) ac

15 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 3 Table 4 Range of variations of te decision variables. number of prime movers -5 Nominal power range of prime movers 5-5 te on off coefficient of PGU.2- Boiler eating capacity -6 Electrical ciller cooling capacity -4 Absorption ciller cooling capacity -4 Electric cooling ratio - Storage tank eating capacity -5 Te electricity, cooling, and eating load curves are estimated using Energy Plus ("Energy Plus ", 24) sown in Fig Cooling Heat Electricity Results and discussions Fig. 5. Electricity, Cooling, and Heating load curves during a year In tis section te results of te algoritms under eac strategy is presented and te comparison is drawn between tem. In tis researc GA and PSO are coded in MATLAB R 23b ("MATLAB," 23) Stopping criteria and parameters for GA and PSO are represented in Table 5. Table 5 Parameters for GA and PSO Variable Value GA Population size Maximum iteration number 2 Crossover probability.6 Mutation probability.4 PSO Population size Maximum iteration number 2 Inertia weigt w.9 Particle own memory factor C 2 Swarm influence factor C 2 2

16 4 6..Results for different strategies for different weigts Te results obtained wen different weigts for eac objective function is used are sown in Fig. 6; 9 different weigts results are represented. It is started by te (,,) vector and ended to (,,) vector for different scenarios. Extreme points wic are related to single objectives are represented in Table 6; since minimizing PEC is equivalent to minimizing CDE, only one row (under eac strategy) is considered for tese two objectives. As ATC decreases, PEC and CDE increase because investment decreases and less electricity is generated by te CCHP system. As a result, buying electricity from te grid increases rising PED and CED. PEC and CDE are aligned because consumption of more fuel consequences more emission. Te range of te results for te tree objectives is less tan % because of te relationsip between te objective functions, reaffirming te suitability of te using te weigted-sum metod to andle te tree objectives simultaneously. Te equal weigts metod is implemented in many decisionmaking problems; tis metod s results are most of te time close to te optimal weigting metods as discussed in (Wang et al., 29, 2). Terefore, in te following all of weigts are assumed equal and w = w2 = w 3 =. Table 6 Results of different strategies in tresolds FEL FTL TC CDE PEC TC CDE PEC Weigts 2,67,692 9,9,994,89 4,2,38 3,8,63 5,542,546,552 59,642,749 [,,] 2,67,74 9,5,345,992 4,954,988 3,8,48 5,533,22,24 59,57,263 [,,] [,,] FEL. Fixed ratio of cooling load Multiple PGU.FTL TC CDE PEC TC CDE PEC Weigts 2,64,252 9,2,87,496 4,45,36,826,494,93,736,82 4,285,55 [,,] 2,64,54 9,7,3,652 4,2,287,826,585,9,462,62 4,58,49 [,,] [,,] Fig. 6. Objectives amount for different strategies in different weigts

17 6.2. Results for FEL strategy M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 5 Te best design of te system in tis strategy is presented in te Table 7 for bot te GA and PSO algoritm. PSO outperforms GA in tis settings as reflected in te last tree rows of Table 7. Numerical results in various segments of te system are sown in Fig. 7. Following tis strategy, PGU is operating in its maximum capacity in 587 periods and in partial load larger tan.8 in 8684 periods (see Fig. 7 d. E PGU). Te results regarding te eat storage tank (Fig. 7 c. Q Storage) indicate tat during two periods of time te storage tank discarges completely. First in summer, to fulfil te eat requirement of te absorption ciller; and second in te winter, to meet te eat demand of te buildings. As sown in Fig. 7 a. Q Boiler, te termal load provided by te boiler is maximized during winter, because recovered eat from te PGU is not sufficient to fulfil te eat demand of te system and te boiler supplies te remaining eat demand. In summer, te boiler is operational in 2 periods because recovered eat is not sufficient to fulfil absorption ciller s eat demand. As sown in Fig. 7 b. Q Electric Ciller, despite te priority given to te use of electric ciller under tis strategy, only 34% of te cooling demand is supplied wit electric ciller and te remaining cooling demand is fulfilled by te absorption ciller. Table 7 Best design of te system at FEL strategy Decision Variable PSO Results GA Results Nominal power of prime movers (),345,489 te on off coefficient of PGU Boiler eating capacity () 3,883 3,67 Electrical ciller cooling capacity (),46,48 Absorption ciller cooling capacity () 2,8 2,368 Storage tank eating capacity () 2,263 2,26 Annual Total Cost ($) 2,65,83 2,63,256 Carbon Dioxide Emission (gr) 9,89,898,936 9,25,387,7 Primary Energy Consumption () 4,77,23 4,787, a.q Boiler b.q Electric Ciller c.q Storage d.e PGU Fig. 7. Performance of system all year long under FEL

18 Results for FTL strategy Te obtained results for tis strategy are listed in Table 8. Te on-off coefficient factor () is valued at te lowest limit (.2). Wen te PGU is turned off te eat requirement of te buildings as to be provided by te boiler as sown in Fig. 8 d. Q Boiler. Te priority in tis strategy is wit te absorption ciller, tus, cooling load provided by te absorption ciller trougout te year is more tan FEL because, 98 % of cooling load is provided by te absorption ciller (c. Q Electric Ciller & b. Q Absorption ciller). Te electric ciller is used in 45 periods trougout te year. Te obtained results from te FTL strategy are dominated by te results from te FEL strategy. Table 8 Best design of te system at FTL strategy Decision Variable PSO Results GA Results Nominal power of prime movers () 2,4 2,665 te on off coefficient of PGU.2.2 Boiler eating capacity () 3,84 3,27 Electrical ciller cooling capacity () Absorption ciller cooling capacity (),736 2,77 Annual Total Cost ($) 3,83,595 3,98,8 Carbon Dioxide Emission (gr) 5,897,884,2 6,2,57,593 Primary Energy Consumption () 58,687,63 59,59, a.e PGU b.q Absorption Ciller c.q Electric Ciller d.q Boiler Fig. 8. Performance of system all year long (FTL) In addition to iger economic costs, FTL strategy cause iger energy consumption and environmental pollution. In 6648 periods PGU is turned off as sown in Fig. 8 a. E PGU and power requirement of te system is supplied troug te network wic in addition to te cost of buying electricity imposes iger emissions and energy consumption to te system Results for FEL, fixed electric cooling ratio Te results of tis strategy, sown in Table 9, indicate tat best performance of te system is obtained wen electric ciller supplies 24% of cooling demand of te system; te electric cooling to cool load ratio is set to.24 in te PSO as sown in Fig. 9 (d. Q Electric Ciller) and to.26 in te GA. Te results

19 M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 7 sow tat tis approac incurs less ATC, PEC and CDE tan te two previous strategies. 96% of te power requirement of te system is provided via PGU. Te total eat requirement is,586,92 wic is less tan FTL (,924,68 ) and more tan FEL (,436,859 ). Table 9 Best design of te system at FEL, fixed electric cooling ratio strategy Decision Variable PSO Results GA Results Nominal power of prime movers (),488,57 te on off coefficient of PGU Boiler eating capacity () 3,325 3,29 Electrical ciller cooling capacity () Absorption ciller cooling capacity () 2,24 2,5 Storage tank eating capacity (),743,735 Electric cooling to cool load ratio Annual Total Cost ($) 2,67,587 2,6,42 Carbon Dioxide Emission (gr) 9,5,246,427 9,7,68,247 Primary Energy Consumption () 4,,759 4,36,568 2 a.e PGU b.q Boiler c.q Storage d.q Electric Ciller Fig. 9. Performance of system all year long (Fixed ratio of electric ciller cool load to cooling demand) Heat required in tis strategy is between FEL and FTL strategy because under te FEL strategy, te priority is wit te electric ciller and eat requirement of te system will decrease. Under FTL and FEL strategies, te priority of te cillers is given so te performance of te cillers is partially predefined wile under FEL wit fixed electric cooling ratio, te model determines te best ratio of cooling load supplied by cillers; terefore, te results sow a better performance. Te capacity of te storage tank in tis strategy is lower tan under te FEL strategy (c. Q Storage). Following tis strategy PGU operates in maximum capacity (a. E PGU) in 2974 periods and in 776 periods operates in partial load above 8% of its capacity Results for FEL multi PGU based on fixed electric cooling ratio Te obtained results sow tat te selection of several PGUs in tis strategy is not efficient and only increases te system costs. Te reason is tat PGU under te FEL strategy operates in partial load iger tan 8% in 776 periods indicating an efficient use of its capacity; as a result, selection of multiple PGUs does not improve te performance and only increases te capital cost of te system. So if te system is to operate based on FEL strategy, it is suggested to use a single PGU in tis case study.

20 Results for FTL, Multi PGU based on fixed electric cooling ratio As sown in Table under FTL strategy using tree PGUs is recommended. As sown Fig. b. Q Boiler, a boiler wit lower capacity compared to te previous strategies is cosen. Tis is because te recovered eat from te PGUs in most periods can fulfill te majority of te eat demand. Most of cooling demand is supplied by te absorption ciller wic is more efficient and electric cooling to cool load ratio is.96. In te peak of te eat demand, during winter and summer, all tree units are in active status and operate in teir maximum capacity as sown in Fig. (a. E PGU). Te costs of FTL strategy wit one PGU is more tan te FTL strategy wit multiple PGUs. It is because at least one PGU is in te active mode wit ig partial load in 485 periods, terefore, provided termal and electrical load covers te demand. Table Te best design of te system under te FTL strategy. Decision Variable PSO Results GA Results Nominal power of prime movers () Nominal power of prime movers2 () Nominal power of prime movers2 () te on off coefficient of PGU.7.7 Boiler eating capacity (),4,65 Electrical ciller cooling capacity () Absorption ciller cooling capacity () 2,774 2,783 Electric cooling ratio Annual Total Cost ($),828,68,829,785 Carbon Dioxide Emission (gr),55,4,63,6,7,542 Primary Energy Consumption () 4,29,29 4,69, a.e PGU b.q Boiler c.q Electric Ciller d.q Required Fig.. Performance of system all year long (FTL, Multi PGU) As a result, in addition to purcasing less electricity from te grid, less termal load is supplied via auxiliary boiler in compression to te FTL strategy.

21 6.7.Electricity excange wit te grid M. Rabbani et al. / International Journal of Industrial Engineering Computations 9 (28) 9 Electricity excanged wit te grid under different strategies is sown in Fig.. Electricity purcased under FEL Electricity purcased under FEL by fixed ratio of cillers Electricity excanged under FTL Electricity excanged under FTL- Multiple PGU Fig.. Electricity excanged wit te grid Te total electricity purcased from te grid in FEL, FTL, FEL wit fixed ratio of electric cooling to cool load, and FTL wit multiple-pgu are respectively,7,688 ; 9,77,65 ; 56,235 ; and 7,8,472. Electricity sold to te grid in FTL and FTL wit Multiple PGU are respectively 978,28, and,26,76. Total electricity purcased form te grid under te FEL strategy wit fixed ratio of electric cooling to cool load is less tan te oter strategies; so if increases in te price of electricity is predicted, tis strategy s cance of being cosen by te decision makers increases. If te selling price of te electricity rises, multiple-pgu under FTL results less ATC wic can alter te decision makers decision. 7. Conclusion A combined cooling, eating and power generation (CCHP) system was optimally designed. Te decision variables were te number of prime movers (PGUs), teir capacity and operational strategy, backup boiler, storage tank eating, absorption ciller and electric ciller capacity, electric cooling ratio, and te on-off coefficient of te PGU. Tis combination of te decision variables, along wit te various strategies considered in tis study, provide a more compreensive view of te real system and, to te best of autors knowledge, is presented for te first time. Due to te complexity of te developed model, PSO and GA were used to solve it. PSO sowed a better performance in solving te optimisation problem altoug te difference between PSO and GA was maximum.7%. Te results obtained for te case study sow tat under te FEL strategy, CDE and PEC are significantly reduced in compression to te FTL strategy. Under FTL strategy wit one PGU, te PGU is put in te inactive mode in a large number of time. Under FTL strategy wit multiple PGU significant reduction in costs was observed but, CDE and PEC were still iger in compression to te FEL strategy. If te approac of decision makers is to reduce te economic costs, it is better to work based on multi-pgu under FTL strategy; and if tey rater to reduce CDE and PEC or are seeking to buy te lowest amount of electricity from te grid, system sould work based on FEL strategy wit fixed ratio of electric cooling to cool load. If selling price of electricity were rising, te system sould operate based on multi-pgu

22 2 under te FTL strategy; and if buying price of electricity were rising, system sould operate based on te FEL strategy wit fixed ratio of electric cooling to cool load. As a direction for future researc, analysing te potential usage of municipality waste in te presented case study, and generally biomass in oter jurisdictions, as te primary source of energy in te CCHP systems is suggested. Anoter avenue of te future researc could be te consideration of te cill storage tanks in te CCHP system and analysing its effect on system s performance. Also, estimating te input parameters suc as electricity, cooling and eating demand of te CCHP by design of a system dynamic model or time series prediction is an interesting topic. Tese metods can be used to predict input data regarding energy consumption. Wen only te energy consumption data of last years and pysical futures of te buildings are used to estimate energy demand of te system, te possible trends in te energy consumption of te system are neglected. Acknowledgement We tank Iranian Fuel Conservation Company (IFCO) for assistance wit te case study, and Mr Moammad Babagolzade for constructive discussions. References Arcuri, P., Florio, G., & Fragiacomo, P. (27). A mixed integer programming model for optimal design of trigeneration in a ospital complex. Energy, 32(8), Barami, S., & Safe, F. (23). A financial approac to evaluate an optimized combined cooling, eat and power system. Energy and Power Engineering, 5(5), 352. Cai, W., Wu, Y., Zong, Y., & Ren, H. (29). Cina building energy consumption: situation, callenges and corresponding measures. Energy Policy, 37(6), Cao, J. (29). Evaluation of retrofitting gas-fired cooling and eating systems into BCHP using design optimization. Energy Policy, 37(6), Cen, B.-K., & Hong, C.-C. (996). Optimum operation for a back-pressure cogeneration system under time-of-use rates. Power Systems, IEEE Transactions on, (2), Črepinšek, M., Liu, S.-H., & Mernik, M. (23). Exploration and exploitation in evolutionary algoritms: A survey. ACM Computing Surveys (CSUR), 45(3), 35. Eberart, R. C., & Kennedy, J. (995). A new optimizer using particle swarm teory. Paper presented at te Proceedings of te sixt international symposium on micro macine and uman science. Energy Plus (24) , from Ge, Y., Tassou, S., Caer, I., & Suguarta, N. (29). Performance evaluation of a tri-generation system wit simulation and experiment. Applied Energy, 86(), Geidl, M., & Andersson, G. (27). Optimal power flow of multiple energy carriers. Power Systems, IEEE Transactions on, 22(), Gaebi, H., Saidi, M., & Amadi, P. (22). Exergoeconomic optimization of a trigeneration system for eating, cooling and power production purpose based on TRR metod and using evolutionary algoritm. Applied termal engineering, 36, Gibson, C. A., Meybodi, M. A., & Benia, M. (25). How Carbon Pricing Impacts te Selection and Optimization of a Gas Turbine Combined Heat and Power System: An Australian Perspective. Journal of Clean Energy Tecnologies, 3(). International Energy Outlook. (26). Retrieved 3 August, 26, from ttp:// Iran Energy Efficiency Organization (IEEO-SABA). (26). Retrieved 3 August, 26, from ttp://en.saba.org.ir Iranian Electricity management. (26). Retrieved 3 August, 26, from Iranian Fuel Conservation Organization. (26). Retrieved 3 August, 26, from Ito, K., Gamou, S., & Yokoyama, R. (998). Optimal unit sizing of fuel cell cogeneration systems in consideration of performance degradation. International Journal of Energy Researc, 22(2),

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24 22 Zeng, C., Wu, J., & Zai, X. (24). A novel operation strategy for CCHP systems based on minimum distance. Applied Energy, 28, by te autors; licensee Growing Science, Canada. Tis is an open access article distributed under te terms and conditions of te Creative Commons Attribution (CC- BY) license (ttp://creativecommons.org/licenses/by/4./).