Bargaining‐based energy sharing framework for multiple CCHP systems with a shared energy storage provider

In the present day, when centralized energy storage technology is becoming increasingly mature, the cooperative energy sharing framework between the combined cooling, heating, and power (CCHP) systems and a shared energy storage provider (SESP) can effectively promote the local consumption of renewable energy and reduce the social energy cost. In this study, the optimal operation models of CCHP and SESP are first developed by considering the electric energy trading of the CCHP and the SESP. Then the CCHP–SESP cooperative operation model is established based on Nash bargaining theory, followed by equating it into two subproblems of coalition social energy cost minimization and electric energy trading payment negotiation. The alternating direction method of multipliers approach is introduced to solve the energy‐sharing problems in a distributed way. Numerical simulation results show the effectiveness of the proposed energy‐sharing framework. Simulation results show that the proposed bargaining‐based energy‐sharing framework can significantly reduce the social energy cost and improve the utilization rate of energy storage resources.


| INTRODUCTION
Energy plays a vitally important role in the development of human society.The change of energy application technology drives the progress of social civilization.With the increasing energy shortage and environmental pollution problems, the three issues of how to improve energy utilization, how to reduce environmental pollution problems, and how to achieve sustainable energy development have attracted widespread attention.
By applying high-efficiency equipment, waste heat recovery technology, and energy storage technology, combined cooling, heating, and power (CCHP) systems can efficiently provide cooling, heating, and electricity to end-users. 1,2With the application of energy management strategies and intelligent control technology, CCHP systems can further improve energy utilization efficiency.The role of a shared energy storage service provider is to provide users with a flexible way to manage energy. 3ith shared energy storage facilities, users can use renewable energy more efficiently and store excess energy for emergencies when needed.Shared capacity storage largely solves the challenges of distributed renewable energy management while also providing a way for users to reduce energy costs and carbon emissions. 4he study of the energy-sharing framework originates from the study of the optimal operation strategy for a multispace scale integrated energy system.Current research on the operation strategies of multispace scale integrated energy systems is mainly optimized from the overall operation perspective and most of the operation strategies do not involve the shared energy storage provider (SESP).Moreover, due to the integration of multiple energy sources, there are significant differences in the energy input structure of the integrated energy system, as well as the different response characteristics of various types of equipment, and the coupling and flexible conversion of different energy forms pose great difficulties for integrated energy system economy dispatch and energy management. 5,6In the work by Qian et al., 7 a comprehensive modeling approach for CCHP microgrid was introduced and an economic scheduling optimization model to select five types of basic busbars was developed.In the work by Mao et al., 8 an optimal scheduling method for CCHP-type buildings was proposed, taking into account a demand-side virtual energy storage system.This method integrated the virtual energy storage system into the building scheduling model.In the work by Jiao et al., 9 a dynamic optimal scheduling strategy for the CCHP microgrid based on model predictive control was proposed, which is solved through multistep rolling optimization.This approach enables dynamic adjustments to the microgrid's operation mode based on predicted energy supply and demand, enabling efficient utilization of energy and effective cost control.In the work by Lingmin et al., 10 a bi-level planning and design approach for a CCHP microgrid was proposed.The upper-level optimization problem regarding equipment type and capacity selection was solved using a nondominated sorting genetic algorithm.The lower-level optimization problem for optimal scheduling was formulated as a mixed integer linear programming problem and solved using a commercial solver.In the work by Faraji et al., 11 an optimization model is proposed that utilizes multi-energy complementarity and alternative energy use to achieve a reduction in the impact of uncertainties of renewable energy sources on the operation of the CCHP.The above studies suggest that although the optimal operation strategy of a single CCHP system has been extensively studied, the research on the cooperative operation of multiple CCHP systems is still in its infancy stage.
Energy storage systems can quickly store or release electricity to store excess electricity for users while using the peak-to-valley differential tariff of the grid to reduce the cost of electricity for users. 12However, due to the investment cost and policy issues, the widespread application of user-side energy storage or centralized energy storage is restricted.4][15][16][17] The work 13 proposed a "capacity renting" model was introduced to facilitate the leasing of unused energy storage capacity from one microgrid to another, done at a unit's critical rental cost.The work 14 present principle, control, and communication technologies of a cloud energy storage model and demonstrates its effectiveness and economy through an Irish power system calculation example.The work 15 introduces a novel shared energy storage model, known as cloud energy storage, with a view to devising an operational strategy that effectively reconciles the conflicting interests of cloud energy storage operators and users.The work 16 then suggests the energy storage plant service model and applies it to multi-CCHP systems, thus improving energy utilization.Nevertheless, the report's approach fixates on specified values for the maximum capacity and charging/discharging power of the public energy storage plant, omitting the revenue optimization of the energy storage plant throughout its entire lifespan.The work 17 presents a novel method for determining the optimal configuration of electric/thermal cloud energy storage in an integrated energy system.This innovative approach incorporates a bi-level optimization algorithm to specifically address the unique challenges associated with efficiently storing and managing energy.
The current research on shared energy storage is in its infancy, and the existing work can be divided into two categories: the first category is capacity optimization of shared energy storage systems; the second category is to analyze the business model and profitability of shared energy storage systems.The difference between the proposed "bargaining-based energy cooperation framework" with previous research is mainly reflected in the following three aspects.(1) Pricing mechanisms for shared energy storage.Existing pricing mechanisms for shared energy storage are mainly based on the marginal price approach and rule-ofthumb-based pricing mechanisms.(2) A distributed optimization approach.In this study, a distributed optimization approach is used to realize the optimal energy scheduling problem between multiple CCHPs and shared energy storage plants rather than a centralized optimization approach.(3) The charging and discharging behavior and economic benefits of CCHP users participating in shared energy storage systems.
Motivated by the aforementioned research gaps, we introduce shared energy storage service providers among CCHP user groups, establish an optimal scheduling model based on Nash bargaining with the optimal daily operating cost of the alliance between CCHP user groups and shared energy storage service providers as the target, and analyze the charging and discharging behaviors and economic benefits of CCHP user groups after they are connected to SESP.This study makes the following contributions: The rest of the study is organized as follows.Section 2 introduces system framework and energy cooperation model.Section 3 presents the optimization model of energy cooperation.Sections 4 and 5 show the case studies and the conclusion, respectively.

| MULTI-AGENT ENERGY COOPERATION MODEL
The bargaining-based energy sharing strategy studied in this study is based on the analysis of the multiple CCHP systems and one SESP.The typical schematic of multi-energy systems with SESP in Figure 1.A large-scale shared energy storage power provider is selected and invested in the CCHP system user group to provide energy storage charging and discharging services for the user group.The SESP is responsible for the operation and management of the electric energy storage system and charges the CCHP system users for the shared energy storage system service fee.The SESP service fee is defined as the fee paid by the user for charging and discharging a unit of kWh of electricity using the shared energy storage system.
The photovoltaic power generation, wind power generation, and gas turbine power generation in the CCHP system are given priority to supply the electric load demand and the electric chillers are used as power-consuming equipment, and the insufficient power demand is purchased from the grid or is discharged from the shared storage power provider to supply the users.Considering that the CCHP system cannot meet the technical requirements related to backward power delivery to the grid and related policy restrictions, this study assumes that the CCHP system cannot sell electricity to the grid, 18 and if there is surplus electricity in the CCHP system, the shared energy storage power station absorbs the surplus electricity from the user in the form of charging or the CCHP user directly abandons the electricity.

| CCHP operation model
The typical schematic of a CCHP system is shown in Figure 2. The key equipment of the CCHP system includes the gas turbine, gas-fired boilers, heat recovery systems, absorption chillers, electric chillers, thermal energy storage, wind power generation, and photovoltaic power generation, and the above equipment operation model is referenced in the literature. 18,19he CCHP agent determines the amount of purchased electricity and purchased natural gas by optimizing the operating schedule, as well as determining the amount of electrical energy interaction with the SESP for the purpose of minimizing operating costs.The operating costs of the CCHP agent n include the cost of purchased electricity C n grid , the cost of purchased natural gas C n gas , the inconvenience cost of load shifting C n sl , the network usage fee C n network , and the payment of CCHP n to the provider ψ n .
The typical schematic of multi-combined cooling, heating, and powers (CCHPs) with a shared energy storage provider.
The operational model for minimizing the operating cost of the CCHP agent n can be expressed as: The expressions for each of CCHP's operating costs are as follows: , CCHP agent n transfer its surplus energy to the SESP at time t; whereas if , CCHP agent n absorb energy from SESP for consumption at time t.π n t , C2S is the service fee per kWh paid by CCHP agent n to the SESP at time t.
The operational constraints of the CCHP system include energy flow balance constraints, demand response constraints, and equipment operation constraints.

| Energy flow balance constraints
The energy flow balance constraints of the CCHP system are as follows: )

| Demand response constraints
The demand response constraints of the CCHP system are as follows: where

| Equipment operation constraints
The Equipment operation constraints of the CCHP system are as follows:

| SESP operation model
The operating costs of the SESP agent include the degradation cost C de , the trading cost of buying and selling electricity with the utility C tr , the maintenance and replacement cost C M&R , and the total energy sharing profit  ψ . The operational model for minimizing the operating cost of the SESP agent can be formulated as: where where ξ de is the degradation factor of electric energy storage system; q t ch and q t dis are the charring and discharging profiles of SESP, respectively.τ b and τ s are the utility purchasing price and feed-in tariff of SESP, respectively; q t b and q t s are the electric power absorbing from the grid and feeding to the grid of the SESP, respectively.c 0 maintenance and replacement cost per unit of charging/discharging power of a shared energy storage system.q max is the rated charging/discharging power for shared energy storage system.
The operational constraints of the shared energy storage system are as follows: where ς and B c are the electric energy leakage coefficient and the capacity of the shared energy storage system, respectively.q ch,max and q dis,max are the upper limits for charging and discharging power of the shared energy storage system, respectively.Natural gas is sourced and enters the pipeline, where it is transported through the network to reach the CCHP user.The steady state flow of natural gas can be described as: ( ) where F bd is the steady-state flow rate of pipeline bd; k bd is the parameter of the pipeline; s bd is the parameter of the gas flow direction; p b and p d are the pressures at node b and node d, respectively.
To ensure the reliability of long-distance energy transmission in the natural gas network, compressors should be installed in the gas system to compensate for pressure losses, as shown in Figure 3.In this figure, f com is the natural gas flowing through the compressor, f cp is the amount of natural gas consumed by the compressor, f mi is the natural gas flow rate at the inlet of the compressor, and f on is the natural gas flow rate at the outlet of the pipeline.
Let the compressor outlet be node o and the inlet be node i.The mathematical model of the pipeline containing the compressor can be expressed as follows.
α α cp com gas gas −1 (11)   where k cp is the compression ratio of the compressor; k mi and k on are the inlet and outlet pipe characteristic constants, respectively; p m , p n , pi , and p o are the air pressure in the four nodes of the compressor nodes of the compressor; k on represents the characteristic constants of the inlet and outlet pipelines; q gas refers to the calorific value of natural gas; T gas represents the temperature of the natural gas.
The constraints of the natural gas system mainly include the safe operating limit of node pressure (π i ) and the compressor compression ratio (r b ) constraints, which can be summarized as follows.
where π i min and π i max are the lower and upper limits of nodal pressure, respectively.r b min and r b max are the lower and upper limits of compressor compression ratio, respectively.

| OPTIMIZATION MODEL OF ENERGY COOPERATION
CCHP systems and SESP, as rational participants in the cooperative game players, make the total operating cost of the alliance to be further reduced through the negotiation and cooperation of energy.If the cooperative operation of the CCHP systems and the SESP may proceed smoothly, the negotiation is reached when and only when the power and electricity prices that the CCHP systems expects to interact with the SESP, respectively, are equal to the power and electricity prices that the SESP expects to interact with the CCHP systems.
That is, a reasonable solution model needs to be formulated to obtain the optimal trading power and the optimal trading payment between CCHP systems and SESP, so that the operating cost of the alliance is minimized.By solving two subproblems in turn: (1) the CCHP-SESP alliance operating cost minimization problem P1; (2) the Nash bargaining problem based on benefit sharing P2, we can finally obtain an economic dispatching scheme for energy cooperation that takes into account both individual rationality and alliance rationality.

| Energy cooperation to minimize the total operation cost (P1)
The CCHP-SESP alliance energy cooperation optimization problem can be formulated as: F I G U R E 3 Pipeline with gas turbine driven compressor.f com , the natural gas flowing through the compressor; f cp , the amount of natural gas consumed by the compressor; f mi , the natural gas flow rate at the inlet of the compressor; f on , the natural gas flow rate at the outlet of the pipeline.
s.t.Expressions ( 2)- (12).It is worth noting that in the above objective function of minimizing the total operating cost of the alliance, the service fee paid by the producer and seller n to the SESP and the energy storage revenue received by the SESP are not included in the expression, because at the time of the alliance's energy cooperation.In other words, the transaction payments between the producer and seller and the SESP to each other will not affect the total energy consumption cost of the whole alliance.
Let To solve the problem P1 using alternating direction method of multiplier (ADMM) approach, the augmented Lagrangian function of ( 13) can be formulated as: where λ n , 1  and ρ 1 are Lagrange multiplier and penalty factor.The optimization problem (14) through ADMM can be iteratively execute the following steps in order: The information flow of the transaction power between CCHP and the shared energy storage plant is shown in Figure 4.

| Bargaining-based energy trading payment game problem (P2)
The typical Nash bargaining model of the optimal coordination between the CCHP systems and SESP can be formulated as: subject to: where C SESP,0 and C n CCHP,0 are the disagreement points, fixed parameters denoting the optimal operating cost of SESP and CCHP systems in non-cooperative mode of operation.In this mode, a CCHP system doesn't share energy with SESP and the SESP doesn't share energy with any CCHP systems.
To solve the bargaining-based energy trading payment problem (P2) need to be divided into two steps: 1) Obtain the optimal solution of P1 P * n t , C2S, and plug C2S, into the (16); 2) By taking the maximum of the Logarithm of the ( 16), the typical Nash bargaining model energy trading payment can be further equivalently transformed into the following form (18), as an nonlinear programming problem.
subject to: . | 1375 reaches a globally agreeable solution on benefits sharing.
To solve the problem P2 using ADMM approach, the augmented Lagrangian function of ( 11) can be formulated as: where λ n , n 2,  and ρ 2 are Lagrange multiplier and penalty factor, respectively.The detailed steps of ADMM for P2 problem solving are similar to the optimization P1.
The information flow of the transaction tariff between CCHP and the shared energy storage plant is shown in Figure 5.

| CASE STUDIES
In this section, case studies are solved by the CPLEX and MOSEK solvers in MATLAB 2023b on a 3.5 GHz Intel Core I9 processor carried out on a personal laptop conducted out to verify the correctness of the proposed bargaining-based energy sharing framework and the efficiency of solution algorithm.

| Basic settings
The three CCHP systems proposed in this paper provide energy for the residential building, the data center, and the industrial park.Each CCHP system is directly connected to the shared electric storage provider, and each CCHP system is disconnected.The day-ahead predicted power outputs of each CCHP system with its own renewable energy generation system, PV and WT, are shown in Appendix A:  A1 and A2.The industrial electricity tariff for the CCHP is shown in Appendix A: Table A3 and the gas price is 0.32$/m 3 .The install capacity of SESP is 2000 kWh, the charging and discharging efficiency of energy storage power station is 0.95, the range of stored energy is 10%-90%, and the initial stored energy is 20%.The charge/discharge loss cost of the energy storage system is converted to 0.068$/kWh.A 14-node natural gas distribution network, as depicted in Appendix C: Figure C1, has been chosen as the framework for the natural gas system.The network comprises of Node 1, designated as the balancing node, and the remaining nodes that serve as load nodes.Within the energy system, three CCHP users are integrated, specifically connected to nodes 3, 6, and 13 of the aforementioned natural gas distribution network.In this gas distribution network, the safe range of nodal pressures is [0.2,1.3] and the range of compressor ratios is [1.1,1.8].
In Subproblem 1, the parameters of the ADMM are as follows: maximum number of iterations is 50, the convergence accuracy is 10 −5 , and the penalty factor ρ = 10 1 −3 .In Subproblem 2, the parameters of the ADMM are as follows: maximum number of iterations is 100, the convergence accuracy is 10 −5 , and the penalty factor ρ = 10

| Convergence performance
In this study, the ADMM algorithm is used to achieve the distributed solution of two subproblems, P1 and P2. Figure 6 shows the convergence results of the objective function of each subject for the subproblem P1 of minimizing the total operating cost of the alliance of multi-energy systems with SESP, and it can be seen that the proposed algorithm converges after 39 iterations.Figure 7 shows the convergence results of each CCHP agent and the SESP for the electricity trading negotiation subproblem P2, which shows that the proposed algorithm converges after 60 iterations.It shows that the ADMM, a distributed solution algorithm, for the multi-energy systems with SESP alliance total operation cost minimization subproblem and the electricity trading payment negotiation subproblem both have good convergence characteristics, and can achieve distributed and efficient solutions for the two subproblems while taking into account the protection of privacy information of each subject.

| Performance in minimizing total operating costs
By solving subproblem P1, the optimal scheduling plan for each CCHP agent can be obtained and the cooling, heating, and power balance diagrams for each CCHP system are shown in Appendix B: Figures B1, B2, and B3, respectively.Figure 8 shows the comparison of the net demand for electrical energy before and after the energy cooperation.We can know that before energy cooperation, CCHP interacted with the grid with high power fluctuations.This is because the CCHP feeds its excess power back to the grid when the renewable energy generation is rich.When the internal power supply of the CCHP system cannot meet the load demand, it needs to buy power from the grid.As a result, the CCHP power demand schedule has greater volatility, which is not good for the safe and reliable operation of the grid, and the CCHP is more dependent on the grid.However, after the CCHP agent has energy cooperation with the SESP, as shown in Figure 8B, the CCHP has the gap of electricity demand met by the SESP on top of its own power supply units during the time period from 8:00 to 22:00 h.Therefore, during this time period, the power of the CCHP system interacting with the grid is 0. The results show that the adoption of energy cooperation can improve the local renewable energy efficiency and help to reduce the fluctuation rate of power interaction with the external grid.Table 1 shows the statistical comparison under non-cooperative mode and cooperative mode.Although the net energy demand of the SESP has | 1379 been increased with the energy cooperative model, the mean value of the net electric energy demand of the CCHP system to the grid has been reduced substantially.This also proves that the electric energy variance is further reduced in the energy cooperation framework compared to that in the non-cooperative mode.The results indicate that the use of SESP based on energy cooperation framework can reduce the dependence of CCHP systems on the grid.The energy sharing profiles of multiple CCHPs and SESP are shown in Figure 9, and the energy level of SESP are illustrated in Figure 10.
During the 0:00-8:00 h time period, CCHP purchases power from SESP at a lower price (the trading tariff is discussed in the next subsection), whereas SESP also purchases power from the grid during this time period to ensure the economy of the system and maintain a higher SOC level, as shown in Figure 10.During the time periods 10:00-15:00 h and 19:00-21:00 h, SESP only sells electricity to the CCHP system and maintains a low SOC level due to the higher price of electricity purchased from the grid by SESP, which is in line with the economic operation of the system.
The results of the energy supply reliability indicator LPSP for the CCHP systems with and without participation in the energy cooperation mode are given in Table 2.By introducing a SESP, the loss of power supply probability can be effectively reduced by 4.94%.SESP provider opens up new avenues for improving the efficiency of energy storage equipment utilization and addressing the challenges posed by distributed renewable energy access.
In natural gas distribution network, the variation of nodal pressure is the most important issue to be taken into consideration.The optimization results of the node pressure and flow rate of the natural gas system are shown in Figure 11.Node 1 is the pressure reference node of the system and the pressure value is set to 1200 mbar, whereas Node 2 is the pressure equilibrium point whose pressure can be adjusted within a wide range.The optimization results show that the pressure at each node is within the safe range of (2000 and 2600 mbar).As the compressor is mounted on Line 9 and Line 10, the compressor can be used to increase the pressure at the later nodes, compensating Node 11 and Node 12, respectively, to compensate for pressure loss due to friction and to ensure that the pressure at the nodes is within the safe operating range.The optimized results for compressor (C#1) and compressor (C#2) are 1.16 and 1.49 for compensating the pressure loss in the pipeline, respectively.

| Performance of bargaining-based payment problems for energy transactions
Figure 12 shows the tariff for each CCHP agent to trade electricity with the SESP for the bargaining-based energy sharing framework.In the following, we analyze the potential relationship between the bidirectional energy flow of CCHP agent 1 and SESP, and the trading tariff, using CCHP agent 1 as the object of analysis.Figure 13 shows the energy trading results including energy sharing profiles and trading tariff.From Figure 12, it can be found that during the time period of 1:00-7:00 h, the grid and the SESP system supply electricity to CCHP agent 1 at the same time.Meanwhile the CCHP agent 1 purchases SESP electricity at a price less than the grid purchase price, whereas the SESP system provides electricity to the CCHP agent 1 at a price higher than the price of selling electricity to the grid.Similarly, during the time period from 9:00 to 19:00 h, the CCHP agent 1 sells surplus power to the SESP system at a higher price than the power sold to the grid because its renewable energy generation is greater than its energy demand, and the SESP system receives energy from the CCHP agent 1 at a lower price than the power purchased from the grid to ensure a good economy between the two.
Table 3 shows the detailed energy cost of CCHP agents and SESP.The total alliance cost in the non-cooperative operation mode is $1677.31and the total alliance cost in the energy cooperative operation mode is $1384.72,which is a decrease of $292.59, that is, 17.44%.The optimal transaction amount of each CCHP agent with SESP ψ n and  ψ n n N  can be obtained by multiplying the optimal transaction electricity F I G U R E 13 The energy trading profiles between shared energy storage provider (SESP) and combined cooling, heating, and power (CCHP) agent 1.
T A B L E 3 Total operating cost of CCHP system and SESP ($).

Results
CCHP show that the cost reduction for each participant of the energy cooperation is the same, $73.15.This shows that the cost reduction of each operating participant through the energy cooperation operation of CCHP agents and SESP is about equal, and is 1/4 of the overall cost reduction of the alliance.This shows that the surplus value generated by the energy cooperation based on Nash bargaining is equally distributed among the four parties of the alliance, which reflects the fairness of the benefit distribution by the Nash bargaining method.

| CONCLUSION
This study proposed a bargaining-based energy sharing framework for a multi-energy system consisting of three CCHP systems with a SESP.The original energy sharing problem was decomposed into P1 and P2.In P1, the total operation cost of the alliance is minimized under the energy cooperation mode.The solution of P1 determines the scheduling plan of each CCHP system and the energy sharing profiles of the SESP.In P2, the content of the fairness of cost reduction allocation is maximized under the energy sharing constraints.The numerical results show that the proposed bargaining-based energy sharing framework can significantly reduce the operation costs of users, improve the utilization rate of energy storage resources.Moreover, by adopting the proposed energy cooperation model of shared energy storage, the local energy efficiency can be improved and the reliance on the upstream grid can be reduced.

1 .
A novel bargaining-based energy cooperation framework is proposed for CCHP systems and a SESP to improve local energy efficiency and individual benefits via two-way flow of energy from a SESP.2. The cooperation framework effectively reduces the power interaction between the CCHP systems and the upper grid, reducing the fluctuation rate of the upper grid power and contributing to the safe operation of the power system.3. The social energy cost is minimized.A Nash bargaining model based on distributed partitioned coupled variables is proposed to solve for two-way flow energy trading tariff.

F I G U R E 4
Diagram of energy storage power trading information transmission.CCHP, combined cooling, heating, and power.F I G U R E 5 Diagram of energy storage tariff trading information transmission.
Figure A1.The cooling, heating, and electric loads of each CCHP system are shown in Appendix A: Figure A2.The technical parameters of the equipment inside CCHP and SESP are shown in Appendix A: Tables

F I G U R E 6
The algorithm convergence of total operation cost minimization subproblem P1: (A) operation cost of shared energy storage provider (SESP); (B) operation cost of combined cooling, heating, and power (CCHP) 1; (C) operation cost of CCHP 2; (D) operation cost of CCHP 3. F I G U R E 7 The algorithm convergence of bargaining-based energy trading payment subproblem P2. (A) Convergence of CCHP1 with SESP trading subfunctions; (B) Convergence of CCHP2 with SESP trading subfunctions; (C) Convergence of CCHP3 with SESP trading subfunctions.F I G U R E 8 Net demand profiles: (A) without energy cooperation; (B) with energy cooperation.F I G U R E 9 Energy sharing profiles.F I G U R E 10 Energy level of SESP.T A B L E 2 Reliability indicator in different operation mode.

F
I G U R E 11 Nodal gas pressure and gas network flow in natural gas distribution networks.FI G U R E 12Trading tariff for each cost of combined cooling, heating, and power (CCHP) agent with shared energy storage provider (SESP).

FFF
I G U R E B1 Energy supply and demand scheduling results of combined cooling, heating, and power (CCHP) 1. (A) Electric power.(B) Cooling power.(C) Heating power.MAO ET AL.I G U R E B2 Energy supply and demand scheduling results of combined cooling, heating, and power (CCHP) 2. (A) Electric power.(B) Cooling power.(C) Heating power.I G U R E B3 Energy supply and demand scheduling results of combined cooling, heating, and power (CCHP) 3. (A) Electric power.(B) Cooling power.(C) Heating power.F I G U R E C1 Structure diagram of the gas distribution system with combined cooling, heating, and power (CCHP) user.

T
A B L E A1 Parameter of the CCHP system.

Parameter
, The typical schematic of a combined cooling, heating, and power (CCHP) system.are the electricity consumption of the electric chiller and power consumption of unshiftable loads of the CCHP agent n at time t, respectively; Q n t AC are the cooling loads, the cooling output of electric chiller, and absorption chiller of the CCHP agent n at time t, respectively; Q n t ,GT, P n t ,WT, and P n t , PV are the output of gas turbine, photovoltaic (PV) and wind turbine (WT) of the CCHP agent n at time t, respectively; P n t , C2S is power sharing of the CCHP agent n at time t to the SESP; P t EC, and P n t , uslF I G U R E 2 T A B L E 1 Statistical comparison of net demand profiles (USD).
gain obtained by the energy interaction between CCHP agent n and SESP through energy sharing.From the table we know that both CCHP agent 1 and CCHP agent 3 receive the service fee paid by SESP and CCHP agent 2 pays a larger fee for SESP.The calculation C n indicating energy Parameter of the CCHP system.