Optimal Operation of CCHP System Combined Electric Vehicles Considering Seasons

: Energy shortage has always been a problem that the world needs to face. The combined cooling, heating, and power (CCHP) system, as a multi-level energy utilization system that can provide cooling, heating, and electric energy simultaneously, is considered to have good development prospects in alleviating energy problems. In addition, because of the rapid development of electric vehicles (EVs), using EVs as power supply devices has become a hot topic of research. In this paper, EVs are combined with the CCHP system as new power supply equipment, and the inﬂuence of the season on the user’s cooling, heating, and power demand is considered. Aiming at the minimum economic cost, the system is optimized by using the PSO algorithm in two operating modes: following electricity load (FEL) and following thermal load (FTL). The ﬁnal results show that the participation of EVs can reduce costs in both operating modes, especially in FTL mode, which can reduce costs by 4.58%, 13.61%, 12.74%, and 3.57% in spring, summer, autumn, and winter, respectively. In addition, the FEL mode is more economical in spring and winter, and the FTL mode is more economical in summer and winter. In addition, the CO 2 emissions in FEL mode are always less than in FTL mode.


Introduction
Due to the increase in human activities, global energy consumption is also growing, and the demand for energy in urban areas is much higher than in other areas.According to the International Energy Agency, globally, urban areas consume more than 70% of fossil energy and emit more than 60% of greenhouse gases [1,2].It is impossible to stop the global urbanization process.Hence, the key to solving the problem is to determine how to increase energy efficiency and reduce greenhouse gas emissions.The combined cooling, heat, and power (CCHP) system is a multi-generational system that can provide cooling, thermal, and electrical energy at once [3][4][5].With its significant benefits in terms of improved energy efficiency and lower emissions, the CCHP system is thought to be one of the most promising solutions in this area [6][7][8][9][10].
In addition, the enormous popularity of photovoltaic (PV) energy generation has caused a huge imbalance between power generation during the day and at night, which will undoubtedly increase the grid's operational burden and have a negative impact on system stability and planning [11,12].One of the best solutions to this issue is the electrical energy storage system (EESS) [13].Even if the difference between on-peak and off-peak electricity price is only considered from an economic point, an EESS is necessary [14], and then, to reduce the conflict between the different energy demands of users and the supply of the CCHP system, thermal energy storage systems (TESS) and cold energy storage systems (CESS) are also required.Hence, the combination of a CCHP system and energy storage system (ESS) is necessary to improve energy utilization efficiency, reduce pollution emissions, and improve grid stability.Regarding the CCHP system, researchers have conducted a great deal of related research.Some researchers have combined different ESSs with CCHP and optimized and evaluated them.In [15], the combination of a CCHP system with a TESS, EESS, or hybrid energy storage system (HESS) was considered, respectively.The system performance of these different configurations was compared, and the suitable user boundaries were calculated.In [16], a HESS consisting of electric and thermal energy storage systems was proposed.Additionally, a rule-based energy management strategy was designed to realize the decoupling regulation of electric energy and thermal energy in a CCHP system.Razmi et al. [17] proposed a CCHP system using compressed air energy storage (CAES), and the organic Rankine cycle was used to improve the efficiency of the CAES system.In addition, the impact of the CASE vessel's min and max pressure ratios on the CASE volume and round-trip efficiency was taken into account.Shan et al. [18] proposed a CCHP model with a HESS, using the updated Artificial Bee Colony algorithm based on the Beetle Antennae Search method to solve the problem of minimizing the daily generation dispatch cost and daily environmental pollutant control cost of the micro-grid.However, most CCHP systems only consider EESS and TESS and ignore CESS.In [19], based on the supply-demand energy matching principle, an improved design of an active TESS is proposed.The results show that, in most situations, the higher the cooling/heating-to-electricity ratio, the longer the peak electricity load lasts, and the smaller the peak-to-valley ratio of power load, the better thw performance of the system will be.Abdalla et al. [20] focused on the two-stage optimization strategy of the microgrid system, including CCHP and HESS.The capacity and operation plan of each type of equipment are optimized.
In addition, a great deal of research work has also been conducted on the operational strategies and combinatorial design of CCHP systems.In [21], a large urban CCHP system including a gas turbine, absorption chiller, electrical chiller, electrochemical energy storage, thermal energy storage ice storage air conditioning systems, etc., was proposed, and an MINLP model was developed to determine the best combination of technologies to meet the energy demand of district buildings subject to practical constraints.Mao et al. [22] proposed a CCHP system hybridized with photovoltaic/thermal panels and TESS, using the particle swarm optimization (PSO) algorithm to calculate the optimal size of key components.Li et al. [23] proposed an improved method following the balanced thermoelectric load for a CCHP system with heat pumps.When compared to the original hybrid electric-heating load operation strategy, the improved operation strategy improves overall system performance by about 18%.In [24], a new electric load following strategy was proposed.Li et al. [25] proposed an interactive operation strategy based on the energy matching characteristics of the following electric load, following thermal load, and following hybrid electric-heating load operation methods.Melo et al. [26] proposed a multi-objective optimization strategy that integrates renewable and non-renewable energy devices, considering the combined environmental and economic benefits.These operation strategies can effectively improve the operating efficiency of the CCHP system and reduce primary energy consumption.In [27], a modified mayfly algorithm is proposed for the optimization of CCHP.The results show that the proposed optimization algorithm performed 8.15% and 10.06% better compared to the traditional mayfly algorithm and genetic algorithm, respectively.In [28], a conventional waste-driven combined heat and power cycle is combined with a largescale absorption chiller (AC).Taking Danish energy market design and thermodynamic assessment as the case study for this work, the results showed that the thermal and electrical efficiencies of the proposed system are better than the conventional system by 12% and 1.3%, respectively.Lombardo et al. [29] proposed a novel solar driven CCHP system and studied this for three Italian locations, spread along the peninsula, with three different climates.The results show that the effectiveness of the system is greatly affected by solar radiation and weather condition.However, there is great potential for solar-powered micro-CCHP systems based on advanced technology for residential applications.
With the rapid increase in the number of EVs, researchers have conducted many studies on the charging and discharging of EVs.Huang et al. [30] proposed a smart microgrid operation plan combining EVs with photovoltaics and ESS in parallel.The calculation results show that the use of optimized solutions can reduce operating costs and increase overall revenue.In [31], an optimized charging and discharging method of EVs within a campus microgrid is proposed, which would significantly improve grid operation stability.It can be seen that researchers pay more attention to the impact of EV charging on grid load, and the economic benefits that EVs can bring to discharging the grid are not given enough attention.In addition, classical EVs are considered to be an electrical load, with charging being a one-way process from the grid to the EVs [32].Vehicle to Grid (V2G) technology, as a technology that can transfer electricity from EVs to the grid, has received a great deal of attention from researchers [33].Amamra et al. [34] present an optimited bidirectional V2G operation that can reduce EV charging costs.In [35], the uncertainty of wind power generation and the state of charge (SOC) of EVs are modeled as uncertainty prediction sets, and an effective strategy to improve the safety and economy of microgrid systems is proposed.Tepe et al. [36] proposed an optimization method to form an optimized combination of pools based on the power and energy capability profiles of commercial EVs.The results show that, in comparison to pools that were built randomly, an aggregator can enhance income per vehicle by up to seven times across markets by choosing smart pool composition.In [37], a practical energy management system for EV charging stations powered by renewable energy sources is proposed.The method is adaptable enough to prosumers, which are typically modest PV power plants for self-consumption, or to specifically designed PV-based EV charging stations.Gong et al. [38] proposed a smart charging strategy for EVs to mitigate the negative impact of fluctuations of renewable energy source outputs.Results show that the proposed charging strategy is effective in alleviating the output fluctuations of RESs.The charging cost of EVs can be reduced by 7.6% and 10.3%, respectively, in winter and summer.Because of the growing number of EVs, new solutions must be provided for basic charging stations.In [39], Vandewetering et al. provided a full mechanical and economic analysis of three novel PV canopy systems to provide a low-cost PV parking lot canopy to supply EV charging.The results show that each system has good economic benefits.Khoso et al. [40] analyzed different types of carport canopies and compared PV power generation at different standard tilt angles.The comparison of PV generation on mono-pitch, duo-pitch, and barrel-arch canopies is also presented.
Hence, in this paper, a large CCHP system including a TESS, EESS, and CESS combined with EVs is established.

Model of System
The model of the CCHP system is shown in Figure 1.The gas turbine (GT) can provide electricity by burning gas and emitting flue gas as a by-product.The thermal energy in flue gas can be partly recovered by the heat recovery system (HRS) to increase energy efficiency and decrease waste, and the AC can convert thermal energy into cooling energy for users.When the cooling power provided by the AC is not enough to meet the demands of the users, the electric chiller (EC) can compensate for this.In the same way, the gas boiler (GB) can be used to provide a lack of thermal energy if the HRS cannot support enough heat.In addition, users can also buy electricity from the grid instead of just using the electricity provided by the GT.The system is equipped with ESS for all three types of energy, thermal, cooling, and power, which can improve the stability and economic efficiency of the system.All three devices operate independently of each other, and the time when they are charged or discharged needs to be determined by the economics of the whole system.Noticeably, in addition to GT and the grid, EVs can also provide some cheap electricity to the system.

Mathematical Model of Energy Balance
In this system, the GT, the EV, and the grid supply the electricity that users need.The EC requires some electricity when it is operating.In addition, when the EESS is charging or discharging, it will also get or provide some electricity, respectively.Hence, the electricity balance is shown in Equation ( 1).
where E users is the electricity demands of the users, E EC is the electricity used by EC, E cha EESS is the electricity used to charge the EESS, E grid is the electricity purchased from the grid, E GT is the electricity generated by the GT, E dis EESS is the electricity generated when the EESS is discharged, and E EV is the electricity provided by an EV.
The GB and the HRS supply both the thermal energy required by the users and the thermal energy needed to operate the AC, and the cooling demands of users are supplied by the EC and the AC.Considering the charging and discharging of the TESS and CESS, the thermal balance and cold balance in the system are shown in Equations ( 2) and (3), respectively.
where H users is the heat demanded by the users, H AC is the heat required to operate the AC, H cha TESS is the heat used for TESS charging, H GB is the heat supplied by GB, H HRS is the heat recovered by HRS, and H dis TESS is the heat supplied by TESS when it is discharged.
where C users is the cooling energy demanded by users, C cha CESS is the cooling energy used for CESS charging, C EC is the cooling energy supplied by EC, and C dis CESS is the cooling energy supplied by CESS when it is discharged.

Mathematical Model of Components
The GT generates electricity by burning gas, which emits high-temperature flue gas as the by-product.Hence, the mathematical model of GT is shown in Equations ( 4) and ( 5).
where G GT is the energy provided by gas burning in the GT, and η GT is the generating efficiency of the turbine.
where H GT is the heat of the flue gas emitted by the GT.
The GB provides thermal energy only by burning gas.Hence, the mathematical model of GB is shown in Equation ( 6).
where G GB is the energy provided by gas burning in the GB, and η GB is the heating efficiency of the GB.
The HRS can recover and reuse part of the thermal energy in the flue gas emitted by the turbine.Hence, the mathematical model of the HRS is shown in Equation (7).
where η HRS is the recovery efficiency of the HRS.
The AC can convert thermal energy into cooling energy for users.Hence, the mathematical model of AC is shown in Equation (8).
where H AC is the thermal energy consumed by the AC, and COP AC is the coefficient of performance (COP) of the AC.
The EC can convert electricity into cooling energy for users.Hence, the mathematical model of the EC is shown in Equation (9).
where E EC is the electricity consumed by the EC, and COP EC is the COP of the EC.
The EESS, TESS, and CESS can store electric energy, thermal energy, and cooling energy, respectively, but they cannot be charged and discharged at the same time.Hence, the mathematical models of the EESS, TESS, and CESS are shown in ( 10)- (12).
where E t EESS is the SOC of the EESS at the current moment, E t−1 EESS is the SOC of the EESS at the previous moment, and η EESS is the storage efficiency of the EESS.
where H t TESS is the SOC of the TESS at the current moment, H t−1 TESS is the SOC of the TESS at the previous moment, and η TESS is the storage efficiency of the TESS.
where C t CESS is the SOC of the CESS at the current moment, C t−1 CESS is the SOC of the CESS at the previous moment, and η CESS is the storage efficiency of the CESS.
These devices have power constraints in actual operation, and hence they should satisfy the following constraints shown in (13).

EV
In order to get more accurate information about the power available to the EVs at each period, the specific behavior of the EVs needs to be simulated.This includes the arrival and departure times of the EVs and the initial SOC of the EVs at the time of arrival, which is usually simulated using the truncated Gaussian distribution [41].These are shown in Figures 2-4, which refer to [42,43].In order to make the simulation of EV behavior more realistic, some constraints need to be placed on it, which are shown in (14), referring to [42]      T arr = f TG (x; µ arr , σ 2  arr , (T min arr , T max arr )) That is, the departure time must be greater than the arrival time, and at least 20% of the SOC needs to be left when the EV leaves.
In addition, EVs must arrive at the parking lot and connect to the charging pile, and then they can supply energy to the main grid; however, the parking spaces are limited, so the fullness of the parking lot must be considered, which is shown in (15).
where N t is the number of EVs parked in the parking lot at time t, N t−1 is the number of EVs parked in the parking lot at time t − The battery parameters of EVs are also critical factors affecting the available power of EVs.Table 1 shows the battery parameters of the five different manufacturers used in this research [44].These five batteries were applied to different EVs at a 1:1:1:1:1 ratio in the simulation.

Objective Function and Cost
This system takes the minimization of the economic cost as the optimization goal, as shown in Equation ( 16).
where S T is the total cost, S inv is the cost of investment, S CCHP is the operating cost of the system, and S CO 2 is the environmental cost of emitting greenhouse gases such as CO 2 .These three costs are specifically composed of the following parts shown in Equations ( 17)- (19).
This is the investment cost of the GT, GB, HRS, AC, EC, EESS, TESS, and CESS.The cost of EVs is not considered because owners purchase them at their own expense.
S CCHP = (G GT + G GB ) × P gas + E grid × P grid + E EVs × P EVs (18) where P gas is the unit price to purchase gas, P grid is the unit price to buy electricity from the grid, and P EVs is the unit price to purchase electricity from EVs.
where µ gas is the emission factor for burning gas-because the grid generates the electricity purchased from EVs, their CO 2 emission factors are the same, which is µ grid , and P CO 2 is the price of emitting a unit volume of greenhouse gas.

Optimization Method
This research used the particle swarm optimization (PSO) algorithm to solve the optimization problem.The PSO algorithm has the advantages of rapid convergence, remarkable efficiency, and random global search [45].The mathematical formula for PSO is shown in Equations ( 20) and (21).
Matlab was used to model and optimize this research, and the entire optimization process is shown in Figure 5.The cooling, heating, electrical loads, and other parameters were input into the system, and then we set the necessary constraints, used the PSO algorithm to optimize the solution, and obtained optimized economic consumption, greenhouse gas emissions, and other results.

Case Study
In this research, a commercial area in China was used as a case study to analyze the economic benefits of this system [2].Since the energy demand of users varies in hours, 8760 h per year would result in calculations that are too complex and difficult to solve.In order to make the calculation simpler, in this research, we planned to replace the energy demands of users with a typical day.In addition, considering that the energy demands of users vary according to the seasons, four typical days in spring, summer, autumn, and winter were designed to optimize the CCHP system, asshown in Figure 6, and the Table 2 shows some of the technical parameters used in this case study.Table 3 shows the economic equipment parameters for the system.Table 4 shows the range of operating variables for the equipment.Table 5 shows some parameters about gas and electricity and CO 2 emissions.
In this commercial area, there are about 2500 parking spaces for EVs. Figure 7 shows how much electricity EVs can supply to the system per hour.

Operation Mode
In this case, since the capacity of each piece of equipment has been determined, the optimization focuses on optimizing the operation of the equipment.In this study, two modes of operation, followinEDg electrical load (FEL) and following thermal load (FTL), were used to test the system.In FEL mode, the GT first meets the demand of the electric load until it reaches the maximum operating power, and the missing electricity is supplemented by the grid and EVs; the missing thermal is provided by the GB.In FTL mode, GT first meets the demand of the thermal load until it reaches the maximum operating power, and the missing electricity and thermal load are supplemented by the grid, EVs, and GB, respectively.In addition, in each operation mode, this study also divides the system into two cases, with EV participation and without EV participation, and optimizes and compares them.6 shows the cost of operating the system in FEL mode.Through these figures and the data in tables, it can be seen that in spring, the total cost of operating with EVs is a little higher than operating without EVs.This is because when operating with EVs, the EESS, TESS, and CESS store more energy and do not discharge.However, in summer and autumn, the economics of operating with EVs are greater than the economics of operating without EVs, and the total cost is reduced by 6.22% and 6.24%, respectively.Although the cost of gas and CO 2 emissions has increased slightly, the advantage in the cost of electricity has fully made up for these.In addition, in winter, the cost of operating with or without EVs is not much different.The reduction in the cost of electricity is basically the same as the increase in the cost of gas and CO 2 emissions.However, regardless of the season, the participation of EVs leads to a small increase in CO 2 emissions, at 15%, 9.1%, 7.8%, and 5.2% in spring, summer, autumn, and winter, respectively.

System in FEL Mode
Hourly operation of electricity-related equipment

System in FTL Mode
Figures [12][13][14][15] show the equipment operation of the system in FTL mode in different seasons.The part below the zero axis of the ESS represents charging, and the part above represents discharging energy.Table 7 shows the cost of operating the system in FTL mode.These figures and the data in the table show that the total cost of operating with EVs is always less than that of operating without EVs, regardless of the season; it is reduced by 4.58%, 13.61%, 12.74%, and 3.57% in spring, summer, autumn, and winter, respectively.However, in spring and winter, the total cost of the system operating in FTL mode is more expensive than that of the system operating in FEL mode.In spring, it is increased by 11.25% when no EVs participated in the operation and by 2.86% when the EVs participated.In winter, it increased by 8.18% and 4.36% when operating without and with EVs, respectively.However, in summer and autumn, the economic benefit of the FTL mode is much higher than that of the FEL mode.In summer, when the system operates without EVs, the total cost is reduced by 3.81% and by 11.39% when the system operates with EVs.In autumn, when the system operates without EVs, the total cost is reduced by 4.25% and by 10.88% when the system operates with EVs.This is because when the heat load is relatively low, the FTL mode can significantly reduce the operating power of the GT to reduce gas consumption, and the participation of EVs in operation can reduce the cost of electricity purchase.However, in terms of CO 2 emissions, the FTL mode produces more than the FEL mode in any case.than those without the involvement of EVs, especially in the FTL mode.However, on the premise that EVs participate in the operation, it should be noted that the economic benefits of the operating mode are greatly affected by the season.The FEL operation mode should be adopted in spring and winter because the FTL mode costs 2.86% and 4.36% more than the FEL mode in these two seasons, respectively, and in summer and autumn, the FTL mode is definitely better than the FEL mode.The economic cost being reduced by 11.39% and 10.88% cannot be ignored.Choosing the appropriate operating mode according to the season can obviously lead to better economic benefits.In addition, no matter the situation, the CO 2 emission cost of the FEL mode is always less than that of the FTL mode.

Figure 1 .
Figure 1.The model of the CCHP system.

Figure 5 .
Figure 5. Flowchart of the optimization process.

Figure 6 .
Figure 6.(a) Energy demands of users in spring.(b) Energy demands of users in summer.(c) Energy demands of users in autumn.(d) Energy demands of users in winter.

Figure 7 .
Figure 7. Electricity provided by EVs per hour.

Figures 8 -
, respectively, show the equipment operation of the system in FEL mode in different seasons.The part below the zero axis of the ESS represents charging, and the part above represents discharging energy.Table6shows the cost of operating the system in FEL mode.Through these figures and the data in tables, it can be seen that in spring, the total cost of operating with EVs is a little higher than operating without EVs.This is because when operating with EVs, the EESS, TESS, and CESS store more energy and do not discharge.However, in summer and autumn, the economics of operating with EVs are greater than the economics of operating without EVs, and the total cost is reduced by 6.22% and 6.24%, respectively.Although the cost of gas and CO 2 emissions has increased slightly, the advantage in the cost of electricity has fully made up for these.In addition, in winter, the cost of operating with or without EVs is not much different.The reduction in the cost of electricity is basically the same as the increase in the cost of gas and CO 2 emissions.However, regardless of the season, the participation of EVs leads to a small increase in CO 2 emissions, at 15%, 9.1%, 7.8%, and 5.2% in spring, summer, autumn, and winter, respectively.
EVs leaving the parking lot at time t.N max is the maximum capacity of the parking lot; the number of EVs staying in the parking lot cannot be greater than N max .

Table 1 .
Some parameters of EV battery packs.

Table 2 .
The technical parameters of equipment for the system.

Table 3 .
The economic parameters of equipment for the system.

Table 4 .
The range of operating variables for the equipment.

Table 5 .
Some parameters of gas, electricity, and CO 2 .

Table 6 .
Cost of operating the system in FEL mode.