Comprehensive energy system cloud energy storage optimal configuration method based on master-slave game

Abstract

The invention discloses an integrated energy system cloud energy storage optimal configuration method based on a master-slave game. The method comprises the following steps: constructing a user aggregate model user total load aggregation model and a user aggregate user load adjustment model; the constructed upper-layer cloud energy storage operator energy storage and lower-layer user aggregation commercial energy optimization configuration model comprises a target function and a constraint condition; and constructing a master-slave game model, and discussing an optimal energy storage service pricing and energy storage configuration scheme by using a master-slave game framework. And the user aggregator is responsible for aggregating energy consumption data on behalf of the user, participating in demand response and negotiating with the cloud energy storage operator. A cloud energy storage operator is guided by cloud energy storage service pricing, energy storage configuration is optimized through a master-slave game model, and cost minimization and income maximization are achieved. Optimization calculation is carried out through the genetic algorithm and the CPLEX solver, the optimization problem of energy storage configuration in the integrated energy system is solved, and the efficiency of energy operation and utilization is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage configuration in integrated energy systems, and particularly relates to an optimization configuration method for cloud energy storage in integrated energy systems based on master-slave game theory. Background Art

[0002] In integrated energy systems, efficient management and optimal configuration of energy are the keys to achieving sustainable energy development. With the development of the energy market and the diversification of energy demands, coordination and optimization among multiple stakeholders have become increasingly important. To meet these demands, game theory has been widely applied to various aspects of integrated energy systems, including energy market design, resource optimal configuration, formulating energy policies for coordinating multi-energy networks, and demand response.

[0003] In the research on energy storage optimal configuration, as an emerging business model, cloud energy storage has gradually attracted attention in the application of combined cooling, heat, and power (CCHP) regional integrated energy systems. Cloud energy storage realizes more efficient energy operation and utilization by centrally managing and optimally allocating user-side loads and rationally utilizing energy resources in integrated energy systems; however, in existing research, the interaction between users and cloud energy storage operators is mostly one-way, the cloud energy storage service pricing is set simply, the cloud energy storage service pricing has not been deeply explored, and the game relationship between users and cloud energy storage operators has not been considered. These factors limit the efficiency and effectiveness of energy storage optimal configuration.

[0004] In summary, the problems existing in the prior art are as follows: (1) The interaction between users and cloud energy storage operators is one-way, lacking a two-way communication and coordination mechanism, which limits the flexibility and response ability of both parties in energy storage configuration.

[0005] (2) In the prior art, the cloud energy storage service pricing is set relatively simply, without deeply exploring the impact of cloud energy storage service pricing on the interests of both users and operators, resulting in the inability to achieve the optimal economic benefits.

[0006] (3) Existing research has not considered the possible game relationship between users and cloud energy storage operators as different stakeholders, which ignores the strategic interaction between the two parties when pursuing their respective maximum interests.

[0007] The difficulty in solving the above technical problems lies in: Problems such as one-way interaction between users and cloud energy storage operators, simplification of cloud energy storage service pricing, and the lack of consideration of the game relationship between the two parties require the development of an advanced two-way communication platform to achieve real-time interaction, the construction of a complex pricing model to comprehensively consider supply and demand dynamics and market changes, the application of game theory to simulate the strategic interaction between the two parties. At the same time, challenges in aspects such as policy adaptability, improvement of user participation, technical implementation and maintenance, and economic and environmental impact assessment also need to be considered. This not only requires interdisciplinary professional knowledge and technical capabilities but also the support of policies and the cooperation of the market environment. It is a complex and long-term process involving multiple fields such as technological innovation, market mechanism design, and behavioral economics. Therefore, it is necessary to propose an optimal configuration method for cloud energy storage in an integrated energy system based on the master-slave game to solve the above problems. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide an optimal configuration method for cloud energy storage in an integrated energy system based on the master-slave game, aiming to solve the problems existing in the prior art, better coordinate the interests of multiple parties, achieve the optimal allocation of energy resources, improve energy utilization efficiency, reduce costs, and promote the sustainable development of the integrated energy system.

[0009] To solve the above technical effects, the technical solution adopted by the present invention is as follows: An optimal configuration method for cloud energy storage in an integrated energy system based on the master-slave game, comprising the following steps: S1, constructing a user load aggregator model, which includes a user total load aggregation model and a model of the user aggregator for adjusting user loads; S2, constructing a power balance constraint after the user aggregator participates in demand response; S3, constructing an upper-layer cloud energy storage operator energy storage optimal configuration model, including an objective function and constraint conditions; S4, constructing a lower-layer user aggregator energy utilization optimal model, including an objective function and constraint conditions; S5, constructing a master-slave game model, including a cloud energy storage operator energy storage service pricing model, pricing constraints during the game process, and the existence of game equilibrium; S6, using a genetic algorithm and a CPLEX solver for solution, and performing simulation calculations under different scenarios, where the different scenarios include: Scenario I, the user aggregator only aggregates user loads and does not participate in demand response; and configures energy storage based on the master-slave game; Scenario II, the user aggregator participates in demand response; and configures energy storage based on the master-slave game; Scenario III, the user aggregator participates in demand response; and configures using the pricing of energy storage services.

[0010] Preferably, in step S1, the method for constructing the user total load aggregation model is as follows: ; (1) In the formula, is the total load aggregated by the user aggregator in the s th period of the typical day. t

[0011] Preferably, in step S1, the method for constructing the model for the user aggregator to adjust the user load, including the electric load adjustment model and the heat / cold load adjustment model, is as follows: Construct the electric load adjustment model: ; (2) ; (3) ; (4) In the formula, , are respectively the flexible electric load and the rigid electric load before adjustment by the user aggregator in the s th period of the typical day; t is s the total electric load after adjustment in the t th period of the typical day; is s the flexible electric load after adjustment in the t th period of the typical day; is s the transferable load power in the t th period of the typical day; is s the curtailable load power in the t th period of the typical day; is s a 0-1 variable for judging whether the load is curtailed in the t th period of the typical day, indicating that the load is curtailed.

[0012] Preferably, the constraints for the curtailable load and the transferable load in the flexible load are constructed as follows: Curtailable load: ; (5) ; (6) ; (7) In the formula, is the maximum curtailable load power; , are respectively the minimum and maximum durations of load curtailment; Transferable load: ; (8) ; (9) Wherein, is the maximum transferable load power; Heat / cooling load adjustment model: ; (10) ; (11) Wherein, are respectively the total heat / cooling loads reduced by the user aggregator at the s th period of the typical day; t th period of the typical day; , are respectively the actual reduction and the maximum allowable reduction of the heat load by the user aggregator within the s th period of the typical day. t th period of the typical day.

[0013] Preferably, in step S2, the power balance constraint constructed after the user aggregator participates in the demand response is specifically: ; (12) ; (13) ; (14) Wherein, , , are respectively the electric power, heat power, and cooling power of the new energy at the s th period of the typical day; t th period of the typical day; , , are respectively the electric power, heat power, and cooling power of the combined heat, power, and cooling system at the s th period of the typical day; t th period of the typical day; , , are respectively the electric power, heat power, and cooling power provided by the cloud energy storage at the s th period of the typical day; t th period of the typical day.

[0014] Preferably, in step S3, the energy storage optimization configuration model constructed by the upper-layer cloud energy storage operator includes an objective function and constraint conditions, including: The objective function is the maximum annual net income function of the cloud energy storage operator : ; (15) The total revenue of the cloud energy storage operator is: ; (16) In the formula, is the total cost for the user aggregator to purchase cloud energy storage services; is the total cost for the cloud energy storage operator to invest in and operate physical energy storage equipment: ; (17) In the formula, is the investment cost; is the operating cost; is the fixed cost; The power balance constraint is constructed as follows: ; (18) ; (19) ; (20) In the formula, is the remaining power charging power of the user aggregator; , is the thermal / cold new energy charging power of the user aggregator; , , is the discharging demand of the user aggregator.

[0015] Preferably, in step S4, constructing the lower-layer user aggregator's energy utilization optimization model includes an objective function and constraint conditions as follows: The objective function is calculated to maximize the revenue of the total user aggregator : ; (21) Where is the cost for the user aggregator to purchase cloud energy storage services, calculated as: ; (22) ; (23) ; (24) In the formula, is the investment cost for the user aggregator to purchase cloud energy storage services; is the operating cost caused by the user aggregator charging the external virtual energy storage for new energy charging; , are the power and capacity of the cloud electricity storage service purchased by the user aggregator respectively; , are the power and capacity of the cloud heat storage service purchased by the user aggregator respectively; , They are the power and capacity for the user aggregator to purchase cloud storage power services respectively; and and are the power of electricity, heat, and cold that the user aggregator needs to charge the virtual energy storage except for new energy charging; and are the market prices of heat energy and cold energy respectively; is the energy purchase cost of the user aggregator, calculated as: ; (25) In the formula, and are the power for the user aggregator to purchase energy from external energy sources to meet the user load; is the penalty cost for the user aggregator to cut the heat / cold load, calculated as: ; (26) In the formula, is the penalty cost for the reduction in comfort caused by cutting the heat load and the cold load; is the demand response revenue of the user aggregator, calculated as: ; (27) In the formula, and are the compensation unit prices for load transfer and load reduction respectively; is the user electricity utility function, calculated as: ; (28) In the formula: a and b and c are the parameters of the user electricity utility function.

[0016] Preferably, the constraint conditions in step S4 include the uniqueness constraint of the energy storage state, the charge and discharge power constraint, and the remaining capacity constraint: ; (29) ; (30) ; (31) ; (32) In the formula, and are 0-1 variables for the user aggregator to control the charge and discharge of the energy storage, controlling the energy storage state. When / is 1, it means charging / discharging; and respectively s typical day t at the end of the time period and t -1 the remaining capacity of the user's virtual energy storage at the end of the time period; The remaining energy / new energy charging demands of the user are as follows: ; (33) ; (34) In the formula, represents the remaining energy charging demand of the user; represents the new energy charging demand of the user.

[0017] Preferably, in step S5, a master-slave game model is constructed, including a cloud energy storage operator's energy storage service pricing model, the pricing constraints of the cloud energy storage service during the game process, and the existence of game equilibrium, including: Calculate the game function in the cloud energy storage operator's energy storage service pricing model : ; (35) In the formula, the cloud energy storage operator CES is the leader, and the user aggregator user is the follower. is the strategy set of the cloud energy storage service price formulated by the cloud energy storage operator, is the strategy set of the user aggregator to purchase the cloud energy storage service, is the strategy set of the user aggregator to regulate the user load, is the strategy set of the goal during the master-slave game process, is the maximum net revenue cost of the cloud energy storage operator, is the maximum revenue of the user aggregator; Construct the pricing constraints of the cloud energy storage service during the game: ; (36) In the formula, , , , , , are the investment coefficients of the physical energy storage; , , , , , are respectively the lower limits of the service unit prices of the power and capacity of cloud electricity storage, cloud heat storage, and cloud cold storage; Construct the existence of game equilibrium: ; (37) If the above conditions are satisfied, then the strategy set ( , , ) is the Stackelberg game equilibrium solution; when the game model satisfies the following conditions, there exists a Stackelberg equilibrium: A. Strategy solution , , are all non-empty, closed, bounded convex sets; B. is a quasiconvex function with respect to ; C. is a quasiconvex function with respect to , ; D. is a continuous function with respect to ; E. is a continuous function with respect to , ;

[0018] Preferably, in step S6, using the genetic algorithm and the CPLEX solver for solving includes: S6.1, initialize the parameters of the cloud energy storage operator and the user aggregator, k =0, set the population number m to 30, the number of iterations to 20, the population mutation rate to 5%, the crossover probability to 80%, and the convergence error ; S6.2, set the initial revenues of the user aggregator and the cloud energy storage operator to a large negative number, and use the genetic algorithm to randomly generate m groups of cloud energy storage service pricing for the cloud energy storage operator, and transfer the parameters to the user aggregator; S6.3, assign k = k +1; S6.4, the user aggregator receives m groups of cloud energy storage service pricing from the CES operator, and according to equations (1)-(4), equations (15)-(17), equations (21)-(28), and equations (33)-(34), use the CPLEX solver to solve the electrical flexible load distribution, heat, and cooling load curtailment amounts, and the power and capacity of purchasing cloud energy storage services, calculate and retain the current cost , and return the power and capacity of purchasing cloud energy storage services and the charging and discharging energy demands of users to the cloud energy storage operator through the user-side management system of the user aggregator; S6.5: The cloud energy storage operator uses the CPLEX solver to solve the capacity and power of the investment entity energy storage based on the power and capacity of the cloud energy storage service returned by the user-side management system and the user's charging and discharging requirements, and calculates and retains the current income. ; S6.6, and If the income is compared with ,implement , ;otherwise, , ; S6.7, if and , end the solution process; otherwise, jump to S6.8; S6.8. According to equations (5)-(14), (18)-(20), (29)-(32), and (35)-(37), use the genetic algorithm to select and mutate to generate the cloud energy storage service pricing of a new cloud energy storage operator, and repeat S6.3-S6.7.

[0019] The beneficial effects of the present invention are as follows: The present invention optimizes the energy storage resource configuration in the integrated energy system by introducing the cloud energy storage business model and the master-slave game theory, and realizes the coordination of interests between users and cloud energy storage operators. It enhances the interactivity of the system, and encourages user aggregators to purchase more cloud energy storage services through reasonable cloud energy storage service pricing, thereby increasing the operator's revenue and reducing the cost of user aggregators; the technical solution effectively reduces electricity demand and improves grid stability through user aggregators' participation in demand response, while improving user satisfaction through load management; in general, the present invention not only improves economic benefits, but also has a positive impact on the environment, demonstrating its technological advancement and scalability in modern energy management, and providing strong support for the healthy development of the energy market. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is the improved framework diagram of the combined heat, cooling and power REIS with cloud energy storage according to the patent of this invention.

[0021] Figure 2 It is the framework diagram of the master-slave game model of the patent of this invention.

[0022] Figure 3 This is a flowchart for solving the cloud energy storage optimization model based on master-slave game in the patent of this invention.

[0023] Figure 4 is a user aggregator aggregated user load curve provided by an embodiment of the present invention, Figure 4 a is the heating season, Figure 4b is the cooling season; Figure 5 It is a schematic diagram of the net income, income, and cost of the cloud energy storage operator in the embodiment of the present invention; Figure 6 It is a schematic diagram of the net income, income, and cost of the user aggregator in the embodiment of the present invention; Figure 7 It is the electricity load adjustment diagram for the heating season of Scenario II provided by the embodiment of the present invention; Figure 8 It is the heat load adjustment diagram for the heating season of Scenario II provided by the embodiment of the present invention; Figure 9 It is the cooling load adjustment diagram for the heating season of Scenario II provided by the embodiment of the present invention; Figure 10 It is the electricity load adjustment diagram for the cooling season of Scenario II provided by the embodiment of the present invention; Figure 11 It is the heat load adjustment diagram for the cooling season of Scenario II provided by the embodiment of the present invention; Figure 12 It is the cooling load adjustment diagram for the cooling season of Scenario II provided by the embodiment of the present invention; Figure 13 It is the electricity load adjustment diagram for the heating season of Scenario III provided by the embodiment of the present invention; Figure 14 It is the heat load adjustment diagram for the heating season of Scenario III provided by the embodiment of the present invention; Figure 15 It is the cooling load adjustment diagram for the heating season of Scenario III provided by the embodiment of the present invention; Figure 16 It is the electricity load adjustment diagram for the cooling season of Scenario III provided by the embodiment of the present invention; Figure 17 It is the heat load adjustment diagram for the cooling season of Scenario III provided by the embodiment of the present invention; Figure 18 It is the cooling load adjustment diagram for the cooling season of Scenario III provided by the embodiment of the present invention. Detailed implementation manners

[0024] Example 1: As Figure 1As shown in the figure, an optimization configuration method for cloud energy storage in an integrated energy system based on master-slave game aims to maximize the net income of the cloud energy storage operator and the net income of the user aggregator. The master-slave game is introduced, with the cloud energy storage operator as the leader and the user aggregator as the follower, and an optimization configuration model for cloud energy storage based on the master-slave game model framework is established. Taking the cloud energy storage service pricing as the strategy, it guides the behavior of users to purchase cloud energy storage services, then feeds back to the cloud energy storage operator, and adjusts the cloud energy storage service pricing. The genetic algorithm and CPLEX solver are used for solving, and simulation calculations are carried out under different scenarios. The calculation results prove the economic advantages of this method.

[0025] As Figure 2 shown, it includes the following steps: S1. Construct a user load aggregator model, which includes a user total load aggregation model and a model for the user aggregator to adjust the user load; S2. Construct the power balance constraint after the user aggregator participates in demand response; S3. Construct an upper-layer cloud energy storage operator energy storage optimization configuration model, including an objective function and constraint conditions; S4. Construct a lower-layer user aggregator energy consumption optimization model, including an objective function and constraint conditions; S5. Construct a master-slave game model, including a cloud energy storage operator energy storage service pricing model, constraint conditions for cloud energy storage service pricing during the game process, and the existence of game equilibrium; S6. Use the genetic algorithm and CPLEX solver for solving, and carry out simulation calculations under different scenarios, where different scenarios include: Scenario I: The user aggregator only aggregates the user load and does not participate in demand response; and the energy storage is configured based on the master-slave game; Scenario II: The user aggregator participates in demand response; and the energy storage is configured based on the master-slave game; Scenario III: The user aggregator participates in demand response; and the configuration is carried out using the pricing of energy storage services.

[0026] Preferably, in step S1, the method for constructing the user total load aggregation model is as follows: ; (1) In the formula, is the total load aggregated by the user aggregator in the s th period of the typical day. t

[0027] Preferably, in step S1, the method for constructing the model for the user aggregator to adjust the user load, including the electric load adjustment model and the heat / cold load adjustment model, is as follows: ​Construct an electric load adjustment model: ; (2) ; (3) ; (4) In the formula, and are respectively the flexible electric load and the rigid electric load before adjustment of the user aggregator at the s th time period of the typical day; t is s the total electric load after adjustment at the t th time period of the typical day; is s the flexible electric load after adjustment at the t th time period of the typical day; is s the transferable load power at the t th time period of the typical day; is s the load shedding power at the t th time period of the typical day; is s a 0-1 variable for judging whether load shedding occurs at the t th time period of the typical day, indicating that load shedding occurs.

[0028] Preferably, the constraint conditions for the load shedding load and the transferable load in the flexible load are constructed as follows: Load shedding load: ; (5) ; (6) ; (7) In the formula, is the maximum load shedding power; and are respectively the minimum and maximum durations of load shedding; Transferable load: ; (8) ; (9) In the formula, is the maximum transferable load power; Thermal / cooling load adjustment model: ; (10) ; (11) In the formula, ​respectively, the total heat / cooling load reduced by the user aggregator at s during the t th time period of a typical day; 、 respectively, the maximum heat load actually reduced and allowed to be reduced by the user aggregator within the s th time period of a typical day. t

[0029] Preferably, in step S2, the power balance constraint constructed after the user aggregator participates in demand response is specifically: ; (12) ; (13) ; (14) In the formula, 、 、 are respectively s the electric power, heat power, and cooling power of new energy during the t th time period of a typical day; 、 、 are respectively s the electric power, heat power, and cooling power of the combined heat and power generation system during the t th time period of a typical day; 、 、 are respectively s the electric power, heat power, and cooling power provided by the cloud energy storage during the t th time period of a typical day.

[0030] As shown in Figure 3 , preferably, in step S3, the energy storage optimization configuration model for the upper-layer cloud energy storage operator includes the objective function and constraints as follows: The objective function is the maximum annual net income function of the cloud energy storage operator : ; (15) is the total income of the cloud energy storage operator: ; (16) In the formula, is the total cost for the user aggregator to purchase cloud energy storage services; is the total cost for the cloud energy storage operator to invest in and operate and maintain physical energy storage equipment: ; (17) In the formula, is the investment cost; ​is the operating cost; is the fixed cost; Construct the power balance constraint as follows: ; (18) ; (19) ; (20) In the formula, is the remaining power charging power of the user aggregator; , are the thermal / cold new energy charging powers of the user aggregator; , , are the discharging demands of the user aggregator.

[0031] Preferably, in step S4, constructing the lower-layer user aggregator energy optimization model includes an objective function and constraint conditions as follows: Calculate the objective function to maximize the revenue of the total user aggregator : ; (21) Where is the cost of the user aggregator purchasing cloud energy storage services, calculated as: ; (22) ; (23) ; (24) In the formula, is the investment cost of the user aggregator purchasing cloud energy storage services; is the operating cost caused by the user aggregator charging the external virtual energy storage for new energy charging; , are the power and capacity of the user aggregator purchasing cloud electricity storage services respectively; , are the power and capacity of the user aggregator purchasing cloud heat storage services respectively; , are the power and capacity of the user aggregator purchasing cloud electricity storage services respectively; , , are the electricity, heat, and cold powers that the user aggregator needs to charge the virtual energy storage except for new energy charging; , are the market prices of heat energy and cold energy respectively; is the energy purchase cost of the user aggregator, calculated as: ; (25) In the formula, , is the power for the user aggregator to purchase energy from external energy sources to meet the user load; is the penalty cost for the user aggregator to reduce the heat / cold load, calculated as: ; (26) In the formula, is the penalty cost for the comfort degradation caused by reducing the heat load and the cold load; is the demand response revenue of the user aggregator, calculated as: ; (27) In the formula, , are the compensation unit prices for load transfer and load reduction respectively; is the user electricity utility function, calculated as: ; (28) In the formula: a , b , c are the parameters of the user electricity utility function.

[0032] Preferably, the constraint conditions in step S4 include the uniqueness constraint of the energy storage state, the charge / discharge power constraint, and the remaining capacity constraint: ; (29) ; (30) ; (31) ; (32) In the formula, , are the 0-1 variables for the user aggregator to control the charge / discharge of the energy storage, controlling the energy storage state. When / is 1, it means charging / discharging; , are respectively s the typical day t the remaining capacity of the user's virtual energy storage at the end of the period and t the remaining capacity of the user's virtual energy storage at the end of the The remaining energy / new energy charging demand of the user is as follows: ; (33) ; (34) In the formula, Indicates the remaining energy charging demand of the user; Indicates the new energy charging demand of the user.

[0033] Preferably, in step S5, a master-slave game model is constructed, including a cloud energy storage operator's energy storage service pricing model, the pricing constraints of cloud energy storage services during the game process, and the existence of game equilibrium, including: Calculate the game function in the cloud energy storage operator's energy storage service pricing model : ; (35) In the formula, the cloud energy storage operator CES is the leader, and the user aggregator user is the follower. is the strategy set of the cloud energy storage service price set by the cloud energy storage operator. is the strategy set of the user aggregator to purchase cloud energy storage services. is the strategy set for the user aggregator to regulate the user load. is the strategy set of the goal during the master-slave game process. The net revenue cost of the cloud energy storage operator is the largest. The revenue of the user aggregator is the largest. Construct the pricing constraints of cloud energy storage services during the game process: ; (36) In the formula, , , , , , are the investment coefficients of physical energy storage. , , , , , are the lower limits of the service unit prices of the power and capacity of cloud electricity storage, cloud heat storage, and cloud cold storage, respectively. Construct the existence of game equilibrium: ; (37) If the above conditions are met, the strategy set ( , , ) is the Stackelberg game equilibrium solution; when the game model meets the following conditions, there exists a Stackelberg equilibrium: A. Strategy solution , , are all non-empty, closed, and bounded convex sets; B. is about quasiconvex function; C. is a quasiconvex function with respect to and ; D. is a continuous function with respect to ; E. is a continuous function with respect to and ;

[0034] Preferably, in step S6, using the genetic algorithm and the CPLEX solver for solving includes: S6.1, initialize the parameters of the cloud energy storage operator and the user aggregator, k =0, set the population number m to 30, the number of iterations to 20, the population mutation rate to 5%, the crossover probability to 80%, and the convergence error ; S6.2, set the initial revenues of the user aggregator and the cloud energy storage operator to a large negative number, and use the genetic algorithm to randomly generate m groups of cloud energy storage service pricing for the cloud energy storage operator, and transfer the parameters to the user aggregator; S6.3, assign k = k +1; S6.4, the user aggregator receives m groups of cloud energy storage service pricing from the CES operator, and according to equations (1)-(4), (15)-(17), (21)-(28), and (33)-(34), use the CPLEX solver to solve the electrical flexible load distribution, heat, and cooling load curtailment amounts, and the power and capacity of purchasing cloud energy storage services, calculate and retain the current cost , and return the power and capacity of purchasing cloud energy storage services and the charging and discharging energy demands of users to the cloud energy storage operator through the user-side management system of the user aggregator; S6.5, the cloud energy storage operator solves the capacity and power of investing in physical energy storage according to the power and capacity of the cloud energy storage service and the charging and discharging energy demands of users returned by the user-side management system, and calculates and retains the current revenue ; S6.6, compare the revenue of with , if , execute , ; otherwise, , ; S6.7, if and , end the solution process; otherwise, jump to S6.8; S6.8, according to equations (5)-(14), equations (18)-(20), equations (29)-(32), and equations (35)-(37), use the selection and mutation of the genetic algorithm to generate the cloud energy storage service pricing of the new cloud energy storage operator, and repeat S6.3-S6.7.

[0035] Embodiment 2: This embodiment provides a specific example structure. This example uses a combined cooling, heating, and power regional integrated energy system with cloud energy storage as Figure 1 shown; the user loads aggregated by the user aggregator are as Figure 4 shown.

[0036] Scenario description: 1) Scenario I, the user aggregator only aggregates user loads and does not participate in demand response; and configures energy storage based on the master-slave game.

[0037] 2) Scenario II, the user aggregator participates in demand response; and configures energy storage based on the master-slave game.

[0038] 3) Scenario III, the user aggregator participates in demand response; uses the pricing of energy storage services for configuration.

[0039] Scenario calculation: This embodiment uses the genetic algorithm and the CPLEX solver for calculation. The specific steps are as follows: S6.1, initialize the parameters of the cloud energy storage operator and the user aggregator, k =0, set the population number m to 30, the number of iterations to 20, the population mutation rate to 5%, the crossover probability to 80%, and the convergence error ; S6.2, set the initial revenues of the user aggregator and the cloud energy storage operator to a large negative number, and use the genetic algorithm to randomly generate m groups of cloud energy storage service pricing of the cloud energy storage operator, and transfer the parameters to the user aggregator; S6.3, k = k +1; S6.4, the user aggregator receives m groups of cloud energy storage service pricing of the CES operator, and uses the CPLEX solver to solve the electrical flexible load distribution, heat and cold load curtailment amounts, and the power and capacity of purchasing cloud energy storage services according to equations (1)-(4), equations (15)-(17), equations (21)-(28), and equations (33)-(34), and calculate and retain the current cost , the power and capacity of the purchased cloud energy storage service and the user's charging and discharging energy demands are returned to the cloud energy storage operator through the user-side management system of the user aggregator; S6.5, based on the power and capacity of the cloud energy storage service and the user's charging and discharging energy demands returned by the user-side management system, the cloud energy storage operator uses the CPLEX solver to solve for the capacity and power of the investment in physical energy storage, calculates and retains the current revenue ; S6.6, compare the with the revenue. If , execute , ; otherwise, , ; S6.7, if and , end the program; otherwise, jump to S6.8; S6.8, according to Equations (5)-(14), (18)-(20), (29)-(32), (35)-(37), use the selection and mutation of the genetic algorithm to generate a new cloud energy storage service pricing for the cloud energy storage operator, and repeat S6.3 - S6.7.

[0040] Result analysis: Among them, Scenario II is the application of the method of the present invention in the embodiment. The load curve of the user load aggregator in Example 1 is as Figure 4 shown. The pricing results, the optimal cloud energy storage purchase plan for the user aggregator, and the optimal energy storage configuration results for the cloud energy storage operator in the three scenarios are calculated by the algorithm and shown in Tables 1 to 3 respectively; Table 1: Pricing strategies for Scenarios I - III;

[0041] Table 2: Optimal cloud energy storage purchase plans for user aggregators in Scenarios I - III;

[0042] Table 3: Optimal energy storage configuration plans for cloud energy storage operators in Scenarios I - III;

[0043] In Scenario I, the user aggregator only aggregates the user load and does not participate in demand response. Compared with Scenario II, the user purchases more power and capacity of cloud electricity storage, cloud heat storage, and cloud cold storage services. The optimized pricing of the power and capacity of the cloud electricity storage, cloud heat storage, and cloud cold storage energy storage services is slightly lower than that of the energy storage services in Scenario II. On the one hand, the cloud energy storage operator slightly reduces the pricing of cloud energy storage services, thus stimulating the user aggregator to purchase more cloud energy storage services. On the other hand, in Scenario II, the user aggregator considers demand response and reduces the user's heat / cold load, changing the user's load curve, thus replacing part of the energy storage demand, and therefore reducing the demand for purchasing cloud energy storage. For Scenario III, with the use of the cloud energy storage service pricing, the power and capacity of the cloud energy storage services purchased by the user aggregator decrease. In order to maximize the benefits, the total cost of the user aggregator is chosen to be reduced, which proves that a reasonable cloud energy storage service pricing can stimulate the cloud energy storage purchase demand of the user aggregator.

[0044] For the configuration results of the cloud energy storage operator, according to the demand of the user aggregator to purchase cloud energy storage services in different scenarios, the cloud energy storage operator will correspondingly increase or decrease the power and capacity of the investment in energy storage equipment. However, since the cloud energy storage service provider does not need to respond to the charging demand of the user aggregator in a timely manner and can purchase a certain amount of energy from the external energy part to meet the demand of the user aggregator, the cloud energy storage operator can still reduce the capacity of the physical energy storage, thus reducing a part of the investment cost.

[0045] Figures 5 - 6 Shows the benefits of the user aggregator and the net benefits of the cloud energy storage operator under three scenarios. Figure 5 As can be seen, in Scenario I, since the user aggregator does not participate in demand response and will purchase more cloud energy storage services, the benefit of the cloud energy storage operator is the highest. However, the cost of the cloud energy storage operator will also increase. Finally, the net benefits of the cloud energy storage operator in Scenario I and Scenario II are almost the same and both are higher than that in Scenario III. For the user aggregator, Figure 6 , since the cost in Scenario II is moderate, the electricity utilization effect in Scenario II is the best and the user satisfaction is the highest. Therefore, the user aggregator also obtains higher benefits compared with Scenario I and Scenario III.

[0046] As Figure 7 and Figure 18 shown, it can be seen that in Scenario II and Scenario III, the user aggregator adjusts its own electricity load, heat load, and cold load in order to reduce its own energy consumption cost. In Scenario II, for the electricity load, before and after demand response, the load curve shows the characteristics of "peak shaving and valley filling", and its load curve is flatter. As Figure 7The load during peak periods from 10:00 - 14:00 and 17:00 - 20:00 was shifted to off-peak periods from 0:00 - 7:00 and 22:00 - 23:00, and the load during some periods was reduced. For the heat / cold load, almost the entire curve was shifted downward. For Scenario III, as Figures 13 - 18 shown, the electrical load shifted part of the load during peak periods and the load during normal periods to off-peak periods, and the adjustment was more significant. This may be because Scenario III purchased less cloud energy storage services than Scenario II. In order to reduce the overall energy consumption cost, the intensity of participating in demand response was increased, while the reduction degree of the heat / cold load was similar to that in Scenario II, without much difference.

Claims

1. A method for optimizing cloud energy storage configuration in an integrated energy system based on master-slave game, characterized in that: The following steps are involved: S1, constructing a user load aggregator model, which includes a user total load aggregation model and a user load adjustment model of the user aggregator; S2, constructs the power balance constraint after the user aggregator participates in demand response; S3, building an upper-layer cloud energy storage operator energy storage optimization configuration model, including objective functions and constraints; S4, constructing a lower-level user aggregation commercial performance optimization model, including objective functions and constraints; S5, constructing a master-slave game model, including the cloud energy storage operator's energy storage service pricing model, cloud energy storage service pricing constraints during the game, and the existence of game equilibrium; S6, using genetic algorithm and CPLEX solver to solve, and perform simulation calculations in different scenarios, including: Scenario I: The user aggregator only aggregates user loads and does not participate in demand response; it configures energy storage based on the master-slave game; Scenario II: User aggregators participate in demand response and configure energy storage based on master-slave game; Scenario III: User aggregators participate in demand response; configuration is based on pricing of energy storage services.

2. According to claim 1, a method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game, characterized in that: In step S1, the method for constructing the user total load aggregation model is: ；（1） In the formula, For user aggregators s Typical day t The total load aggregated over time periods.

3. According to claim 2, a method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game, characterized in that: In step S1, a method for constructing a model for user aggregator to adjust user load, including an electric load adjustment model and a heat / cold load adjustment model, is as follows: Build an electrical load adjustment model: ；（2） ；（3） ；（4） In the formula, , They are user aggregators in s Typical day t Flexible electric load and rigid electric load before adjustment in each period; for s Typical day t Total electricity load after time adjustment; for s Typical day t Flexible electric load after time adjustment; for s Typical day t The transferable load power in each period; for s Typical day t The load power that can be reduced in each period; for s Typical day t A 0-1 variable that determines whether load reduction occurs in each period. Indicates that load reduction has occurred.

4. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 3 is characterized in that: The constraints of the reducible load and transferable load in the flexible load are constructed as follows: Load reduction: ；（5） ；（6） ；（7） In the formula, is the maximum load power that can be reduced; , are the minimum and maximum duration of load shedding, respectively; Transferable load: ；（8） ； （9） In the formula, is the maximum load power that can be transferred; Heating / Cooling Load Adjustment Model: ；（10） ；（11） In the formula, They are user aggregators in s Typical day t Total heating / cooling load after reduction in each period; , They are user aggregators in s Typical day t The maximum heat load that is actually reduced and allowed to be reduced within a time period.

5. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 1, characterized in that: In step S2, the power balance constraint after the user aggregator participates in the demand response is constructed as follows: ；（12） ；（13） ； （14） In the formula, , , They are s Typical day t Electric power, thermal power and cooling power of renewable energy in each period; , , They are s Typical day t Electric power, heating power and cooling power of the combined cooling, heating and power system during the time period; , , They are s Typical day t The electric power, thermal power and cooling power provided by cloud energy storage in different time periods.

6. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 1, characterized in that: In step S3, the upper cloud energy storage operator energy storage optimization configuration model is constructed, including the objective function and constraints: The objective function is the maximum annual net profit function of the cloud energy storage operator : ；（15） Total revenue for cloud energy storage operators: ；（16） In the formula, The total cost of purchasing cloud energy storage services for user aggregators; The total cost of investing in and maintaining physical energy storage equipment for cloud energy storage operators: ；（17） In the formula, For investment costs; For operating costs; is a fixed cost; The power balance constraints are constructed as follows: ；（18） ；（19） ；（20） In the formula, Charging power for the surplus electricity of user aggregators; , Provide hot / cold renewable energy charging power for user aggregators; , , Aggregate the energy needs of users.

7. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 1, characterized in that: In step S4, the lower-layer user aggregation commercial performance optimization model is constructed, including the objective function and constraints including: The objective function is to maximize the total revenue of the user aggregator. : ；（21） in The cost of purchasing cloud energy storage services for user aggregators is calculated as: ；（22） ；（23） ；（24） In the formula, The investment cost of purchasing cloud energy storage services for user aggregators; The operating costs caused by the user aggregator charging the virtual energy storage for new energy charging; , Purchase power and capacity of cloud storage services for user aggregators respectively; , Purchase power and capacity of cloud thermal storage services for user aggregators respectively; , Purchase power and capacity of cloud storage services for user aggregators respectively; , , The electricity, heat and cold power that the user aggregator needs to charge to the virtual energy storage in addition to the new energy charging; , are the market prices of heat and cold energy respectively; is the energy purchase cost of the user aggregator, calculated as: ；（25） In the formula, , The power required for user aggregators to purchase energy from external energy sources to meet user loads; The heat / cool load penalty cost reduction for the user aggregator is calculated as: ；（26） In the formula, Penalty costs for reduced comfort due to reductions in heating and cooling loads; is the demand response revenue of the user aggregator, which is calculated as: ；（27） In the formula, , are the compensation unit prices for load shifting and load reduction, respectively; is the user's electricity utility function, calculated as: ；（28） Where: a , b , c is the parameter of the user's electricity utility function.

8. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 7, characterized in that: The constraints in step S4 include energy storage state uniqueness constraint, charge and discharge power constraint, and remaining capacity constraint: ；（29） ；（30） ；（31） ；（32） In the formula, , It is a 0-1 variable for user aggregators to control the energy storage charging and discharging, and control the energy storage state. / When it is 1, it indicates charging / discharging energy; , They are s typical day t End of period and t -1 The remaining capacity of the user's virtual energy storage at the end of the period; The user's remaining energy / new energy charging requirements are as follows: ；（33） ；（34） In the formula, Indicates the user's remaining energy charging demand; Indicates the user's new energy charging needs.

9. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 1, characterized in that: In step S5, a master-slave game model is constructed, including a cloud energy storage operator energy storage service pricing model, cloud energy storage service pricing constraints during the game, and game equilibrium existence, including: Calculating the game function in the energy storage service pricing model of cloud energy storage operators : ；（35） In the formula, the cloud energy storage operator CES is the leader and the user aggregator user is the follower. A set of cloud storage service price strategies formulated for cloud storage operators. A collection of strategies for user aggregators to purchase cloud energy storage services. A set of policies for user aggregators to regulate user load, is the strategy set of the goal in the master-slave game, The net benefit cost for cloud energy storage operators is the largest. Maximize profits for user aggregators; Constraints on cloud energy storage service pricing during the game construction process: ；（36） In the formula, , , , , , is the investment coefficient for physical energy storage; , , , , , The lower limits of the service unit prices for the power and capacity of cloud electricity storage, cloud heat storage, and cloud cooling storage, respectively; Constructing the game equilibrium exists: ；（37） If the above conditions are met, then the strategy set ( , , ) is the Stackelberg game equilibrium solution; when the game model satisfies the following conditions, there is a Stackelberg equilibrium: A. Strategy Solution , , All are non-empty, closed, bounded convex sets; B. About Quasi-convex function of ; C. About , Quasi-convex function of ; D. About A continuous function of E. About , A continuous function of .

10. The method for optimizing cloud energy storage configuration of an integrated energy system based on master-slave game according to claim 1, characterized in that: In step S6, solving by using genetic algorithm and CPLEX solver includes: S6.1, initialize the parameters of cloud energy storage operators and user aggregators, k =0, set the population m is 30, the number of iterations is 20, the population mutation rate is 5%, the crossover probability is 80%, and the convergence error is ; S6.2, set the initial revenue of user aggregators and cloud energy storage operators to a large negative number, and use the genetic algorithm to generate the initial random m Set up cloud energy storage service pricing for cloud energy storage operators and pass the parameters to user aggregators; S6.3, Assignment k = k +1; S6.4, User Aggregator Receives m The cloud energy storage service pricing of the CES operator is calculated and retained according to equations (1)-(4), (15)-(17), (21)-(28) and (33)-(34). The CPLEX solver is used to solve the distribution of flexible loads, the reduction of heat and cold loads, and the power and capacity of the purchased cloud energy storage service. , the power and capacity of the purchased cloud energy storage service and the user's charging and discharging requirements are returned to the cloud energy storage operator through the user-side management system of the user aggregator; S6.5: The cloud energy storage operator uses the CPLEX solver to solve the capacity and power of the investment entity energy storage based on the power and capacity of the cloud energy storage service returned by the user-side management system and the user's charging and discharging requirements, and calculates and retains the current income. ; S6.6, and If the income is compared with ,implement , ;otherwise, , ; S6.7, if and , end the solution process; otherwise, jump to S6.8; S6.

8. According to equations (5)-(14), (18)-(20), (29)-(32), and (35)-(37), use the genetic algorithm to select and mutate to generate the cloud energy storage service pricing of a new cloud energy storage operator, and repeat S6.3-S6.7.

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