A method for modeling a two-way master-slave game model of economic operation of an energy storage power station and a comprehensive energy system

Abstract

The application discloses a kind of energy storage power station and comprehensive energy system economic operation two-way master-slave game model modeling method, comprising the following steps: respectively constructing active distribution network ADN optimization model, energy storage power station ESPS optimization model, comprehensive energy system IES optimization model;With active distribution network ADN as leader, energy storage power station ESPS as secondary leader, comprehensive energy system IES as follower, construct energy storage power station ESPS and comprehensive energy system IES economic operation mechanism based on two-way master-slave game;Based on the master-slave relationship of active distribution network ADN and energy storage power station ESPS, comprehensive energy system IES, the master-slave relationship of energy storage power station ESPS and comprehensive energy system IES, and the competitive relationship of active distribution network ADN and energy storage power station ESPS, construct two-way master-slave game model.The two-way master-slave game model established by the application can effectively weigh the interest relationship among comprehensive energy system, energy storage power station and active distribution network, realize the benefit balance among each subject, and improve the overall economic benefit of system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system economic operation technology, specifically to a modeling method for a two-way master-slave game model of energy storage power station and integrated energy system economic operation. Background Technology

[0002] With my country's increasing demands for energy efficiency, Integrated Energy Systems (IES) have experienced rapid development due to their ability to achieve multi-energy complementarity and improve energy utilization. In addition to fully utilizing internal resources, IES effectively enhances energy supply reliability by connecting with external energy suppliers such as Active Distribution Networks (ADN), Energy Storage Systems (ESPS), and natural gas networks. However, with ESPS participating in market transactions, achieving economical operation between IES and ESPS remains a topic worthy of in-depth research.

[0003] In existing technical literature: Reference [1]: "Optimization of economic dispatch of multi-microgrid system considering energy storage power station service" (Wu Shengjun, Liu Jiankun, Zhou Qian, et al. Optimization of economic dispatch of multi-microgrid system considering energy storage power station service [J]. Automation of Electric Power System, 2019, 43(10):10-18.) studied an IES optimization strategy considering public ESPS, which realized the time-domain transfer of IES surplus power and the complete absorption of new energy power generation. However, this literature only regards ESPS as an auxiliary energy supply facility of IES, and fails to give full play to the profit-seeking nature of ESPS as an independent subject in the IES optimization process.

[0004] Reference [2]: Research on Shared Energy Storage Mechanism Based on Combined Two-Way Auction (Sun Si, Zheng Tianwen, Chen Laijun, et al. Research on Shared Energy Storage Mechanism Based on Combined Two-Way Auction [J]. Power System Technology, 2020, 44(5): 1732-1739.) proposes an ESPS service mechanism based on combined two-way auction, which solves the problem of monopolistic competition in unidirectional auctions during energy trading. However, this reference does not consider the dual nature of ESPS as both an energy user and a power supplier, and cannot give full play to the initiative of ESPS as an independent entity to participate in economic operation, which directly affects the economic benefits of each entity in the system.

[0005] Reference [3]: "Equilibrium Interaction Strategy of Integrated Energy System Based on Stackelberg Game Model" (Wu Lilan, Jing Zhaoxia, Wu Qinghua, et al. Equilibrium Interaction Strategy of Integrated Energy System Based on Stackelberg Game Model [J]. Automation of Electric Power Systems, 2018, 42(4): 142-150, 207.) takes distributed energy stations as leaders and users as followers, and constructs an energy trading model based on multi-master multi-follower game. This model realizes the equilibrium of interests of each subject in IES. However, this study uses the traditional master-follower game structure to analyze the game among the participating subjects, without considering the duality of the participating subjects as both followers and leaders, as well as the master-follower relationship and competitive relationship that exist between the two subjects in this case. It lacks the motivation to mobilize the subjects to participate in optimization. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a modeling method for a two-way master-slave game model of the economic operation of energy storage power stations and integrated energy systems. The model obtained by this method can effectively balance the interests among integrated energy systems, energy storage power stations, and active distribution networks, achieving a balance of interests among the stakeholders and improving the overall economic efficiency of the system.

[0007] The technical solution adopted in this invention is as follows:

[0008] A modeling method for a two-way master-slave game model of the economic operation of an energy storage power station and an integrated energy system includes the following steps:

[0009] Step 1: Construct optimization models for Active Distribution Network (ADN), Energy Storage Power Station (ESPS), and Integrated Energy System (IES).

[0010] Step 2: With the Active Distribution Network (ADN) as the leader, the Energy Storage Station (ESPS) as the secondary leader, and the Integrated Energy System (IES) as the follower, construct an economic operation mechanism for the Energy Storage Station (ESPS) and the Integrated Energy System (IES) based on a two-way master-slave game.

[0011] Step 3: Based on the master-slave relationship between the Active Distribution Network (ADN) and the Energy Storage Station (ESPS) and the Integrated Energy System (IES), the master-slave relationship between the Energy Storage Station (ESPS) and the Integrated Energy System (IES), and the competitive relationship between the Active Distribution Network (ADN) and the Energy Storage Station (ESPS), construct a two-way master-slave game model.

[0012] In step 1:

[0013] 1) Construct an active distribution network (ADN) optimization model, with its maximum operational efficiency as the optimization objective. The objective function is:

[0014] f ADN =C IES +C ESPS -Cgrid ;

[0015] in:

[0016]

[0017]

[0018]

[0019] In the formula, C IES For the power interaction gains between ADN and IES; C ESPS For the power interaction benefits between ADN and ESPS; C grid The power interaction cost between the ADN and the external power grid; T is the number of scheduling period segments; The electricity price for ADN; The interaction power between IES and ADN; The charging power for ESPS; The electricity price sold to the external power grid. This represents the power that ADN purchases from the external power grid.

[0020] 2): Construct an optimization model for the ESPS (Energy Storage System) of an energy storage power station, with its maximum operational efficiency as the optimization objective. The objective function is:

[0021] f ESPS =C ESPS,d -C ESPS,c -C ESPS,om

[0022] in:

[0023]

[0024]

[0025] C ESO,om =k ESO C ESPS,c

[0026] In the formula: C ESPS,d For the power interaction benefits between ESPS and IES; C ESPS,c For the power interaction cost between ESPS and ADN; C ESPS,om For the operation and maintenance costs of ESPS; The electricity price for ESPS; The interaction power between ESPS and IES; k ESO This is the cost factor for operating and maintaining ESPS.

[0027] 3) Construct an integrated energy system (IES) optimization model, with the goal of maximizing the operational efficiency of the IES. The objective function is:

[0028] f IES =C x +C e -C fuel -C om -C q

[0029] in:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] In the formula, C x C is the power efficiency function; e For the purchase and sale of electricity by IES; C fuel For IES fuel costs; C om For the operation and maintenance costs of IES; C q Penalty costs for reducing comfort caused by lower heat load;

[0036] ρ g For natural gas prices; H g η is the calorific value of natural gas. CHP The power generation efficiency of CHP; k is the output electrical power of CHP. CHP This is the operating and maintenance cost coefficient for CHP; The electricity prices are for ESPS and ADN, respectively. P1 represents the power output of IES sold to the external power grid. t , These represent the interaction power between IES and ESPS and ADN, respectively. denoted as the adjusted electrical load power; a represents the quadratic parameter of the power utility function, and b represents the linear parameter of the power utility function. β is the power reduction for heat load; β is the penalty coefficient for heat load reduction.

[0037] Step 2: The two-way master-slave game includes the following steps:

[0038] Step 2.1: The Active Distribution Network (ADN) acts as the leader and formulates the initial electricity pricing strategy.

[0039] Step 2.2: Based on the electricity price set by the Active Distribution Network (ADN), the ESPS (Energy Storage System) responds to the ADN's pricing strategy and formulates a charging strategy S. ESPS ={P ESPS,c The charging strategy is reported to the Active Distribution Network (ADN); simultaneously, the energy storage power station ESPS formulates its electricity sales pricing strategy to the Integrated Energy System (IES). ADN ={ρ ADN}

[0040] Step 2.3: The Integrated Energy System (IES) implements the electricity pricing strategy S of the Active Distribution Network (ADN). ADN ={ρ ADN} and the electricity sales pricing strategy of energy storage power stations ESPS ESPS ={ρ ESPS} Optimize and adjust the output of internal energy conversion equipment and load demand response, and formulate a power purchase strategy for the integrated energy system (IES). IES ={P IES,ADN,b ,P ESPS,d The strategy is then simultaneously reported to the Active Distribution Network (ADN) and the Energy Storage Station (ESPS).

[0041] Step 2.4: The Active Distribution Network (ADN) updates the electricity sales price based on the electricity purchase strategy reported by the Energy Storage Station (ESPS) and the Integrated Energy System (IES), guiding the ESPS and IES to make dynamic adjustments to ensure that the Active Distribution Network (ADN) itself achieves optimal economic benefits.

[0042] Step 2.5: The energy storage power station ESPS updates the electricity price based on the Active Distribution Network (ADN) and the Integrated Energy System (IES) power purchase strategy. IES ={P IES,ADN,b ,P ESPS,d}, update its charging strategy and electricity sales price S ESPS ={P ESPS,c ,ρ ESPS This allows them to optimize their own economic benefits.

[0043] Step 2.6: Repeat steps 2.3 to 2.5 until the active distribution network (ADN) pricing strategy, energy storage station ESPS charging, and electricity sales pricing strategy S are implemented. ESPS ={P ESPS,c ,ρ ESPS} and Integrated Energy System (IES) power purchase strategy S IES ={P IES,ADN,b ,P ESPS,d The game remains stable and unchanged, reaching a game equilibrium.

[0044] Step 2.7: The system uses the game equilibrium solution as the final trading strategy. Engage in energy trading.

[0045] In this invention, the ADN (Automatic Generation Network) is identified as the reliable and stable energy supplier of the system, playing a leading role in economic operation. The ADN guides the IES (Environmental Engineering Systems) and ESPS (Electronic Power Systems) to respond to its peak-shaving and valley-filling power demand dispatch by setting dynamic electricity prices, thereby maximizing its own benefits. The ESPS, as the secondary leader of the system, utilizes its rapid and flexible charging and discharging characteristics to charge according to the electricity price of the superior ADN and sell electricity to the subordinate system based on its own pricing. The ESPS acts as both a follower and a leader in the system, serving as a crucial intermediate link in the two-way master-slave game. The IES, as a follower, rationally adjusts the output of its internal units and the dispatch of flexible loads based on external electricity prices to meet its diverse load demands and improve economic efficiency. Finally, the game is conducted based on the game subjects determined by this invention, i.e., step 2.

[0046] In step 2: the ESPS and IES economic operation mechanism based on a two-way master-slave game is constructed as follows:

[0047] Active distribution networks (ADNs) are the reliable and stable mainstay of the system's energy supply, playing a leading role in economic operation. They maximize their own benefits by setting dynamic electricity prices to guide IES and ESPS in responding to their peak-shaving and valley-filling demand dispatching.

[0048] The ESPS (Energy Storage System) is the second-level leader of the system. It utilizes its rapid and flexible charging and discharging characteristics to charge according to the electricity price of the upper-level ADN (Automatic Data Center) and sell electricity to the lower-level system according to its own pricing. The ESPS plays both the role of a follower and a leader in the system, and is an important intermediate link in the two-way master-slave game.

[0049] As a follower, the Integrated Energy System (IES) adjusts the output of its internal units and the scheduling of flexible loads according to external electricity prices to meet its various load demands and improve economic efficiency.

[0050] In step 3, the bidirectional master-slave game model is as follows:

[0051]

[0052] Participants consist of leaders (ADN), secondary leaders (ESPS), and followers (IES).

[0053] The strategy set for the leader's electricity pricing strategy is S. ADN ={ρ ADN The charging strategy and electricity pricing strategy of the second-tier leader are S. ESPS ={P ESPS,c ,ρ ESPS The follower's electricity purchasing strategy is S. IES={P IES,ADN,b ,P ESPS,d The utility of each participant is the objective function of each subject.

[0054] G is the set of bidirectional master-slave game strategies proposed in this invention, wherein:

[0055] (1) Participants: Leaders ADN, Secondary Leaders ESPS, and Followers IES identified in Step 2;

[0056] (2) Strategy set: The electricity pricing strategy of the leader AND is S. ADN ={ρ ADN The charging strategy and electricity pricing strategy of the second-level leader ESPS are S ESPS ={P ESPS,c ,ρ ESPS The electricity purchasing strategy of the follower IES is S. IES ={P IES,ADN,b ,P ESPS,d};

[0057] (3) Utility: The utility of each participant is the objective function of each game subject, i.e., in step 1,

[0058] ① Objective function for Active Distribution Network (ADN): f ADN =C IES +C ESPS -C grid ;

[0059] ② Optimization objective function of ESPS for energy storage power stations: f ESPS =C ESPS,d -C ESPS,c -C ESPS,om ;

[0060] ③ Optimization objective function of the Integrated Energy System (IES): f IES =C x +C e -C fuel -C om -C q .

[0061] In the bidirectional master-slave game model proposed in this invention, there exists a strategy that satisfies the following condition:

[0062]

[0063] That is, based on the optimization objective function established in step 1, which aims to maximize the operational benefits of ADN, ESPS, and IES, this strategy... This is the equilibrium solution of the bidirectional master-slave game of this invention.

[0064] This invention provides a modeling method for a two-way master-slave game model of the economic operation of energy storage power stations and integrated energy systems, with the following technical effects:

[0065] 1) Step 1 of the present invention establishes optimization objective functions for maximizing the operating benefits of ADN, ESPS and IES respectively. In the master-slave game mechanism, it can incentivize the players to participate in the game and improve the overall economic efficiency of the system.

[0066] 2) The ESPS and IES economic operation mechanism based on a two-way master-slave game constructed in step 2 of this invention considers the dual nature of the energy storage power station ESPS as both an energy consumer and a power supplier. This fully motivates the ADN and ESPS to participate in bidding. The dual nature of the ESPS as both an energy consumer and a power supplier is not mentioned in other inventions and is an innovation of this invention. Furthermore, the energy storage power station ESPS is a secondary leader in the system. Unlike other inventions that only have a leader and an inventor, this invention not only has an ADN leader and IES followers, but also ESPS as a secondary leader and follower, which is another innovation of this invention.

[0067] 3) Step 3 of this invention constructs a two-way master-slave game economic operation model with ADN as the leader, ESPS as the secondary leader and follower, and IES as the follower. The utility of this model is to maximize the operating benefits of each game subject. Therefore, the model proposed in this invention can guarantee the balance of interests of each subject. Attached Figure Description

[0068] Figure 1 This is a flowchart of the strategy proposed in this invention.

[0069] Figure 2 Electrical load balance diagram.

[0070] Figure 3 It is a heat load balance diagram.

[0071] Figure 4 It refers to the ADN and ESPS electricity pricing strategies.

[0072] Figure 5 This is a graph showing the change in ESPS energy storage capacity.

[0073] Figure 6 Electricity price comparison chart.

[0074] Figure 7 This is a comparison chart of IES electricity purchase situations under Scheme 1 and Scheme 2.

[0075] Figure 8 This is a comparison chart of the changes in ESPS energy storage capacity under Scheme 1 and Scheme 3.

[0076] Figure 9This is a comparison chart of IES electricity purchase situations under Scheme 1 and Scheme 3. Detailed Implementation

[0077] A modeling method for a two-way master-slave game model of the economic operation of energy storage power stations and integrated energy systems. Figure 1 This is a flowchart of the strategy proposed in this invention. First, considering the dual nature of EPS as both an energy consumer and a power supplier, the game relationship between the various entities is established, and optimization models are established for IES, EPS, and ADN respectively. Second, with ADN as the leader, EPS as the secondary leader, and IES as the follower, an economic operation mechanism for EPS and IES based on a two-way master-slave game is constructed. Finally, based on the master-slave relationship between ADN and EPS, the master-slave relationship between EPS and IES, and the competitive relationship between ADN and EPS, a two-way master-slave game model is constructed. The two-way master-slave game model proposed in this invention can effectively balance the interests among the integrated energy system, energy storage power station, and active distribution network, achieve an equilibrium of interests among the various entities, and improve the overall economic efficiency of the system.

[0078] Figure 2 It is an electrical load balance diagram. Figure 2 In the IES (Enhanced Energy System), the transferable electrical load occurs during two periods: 1:00-9:00 and 20:00-24:00. During these periods, the ADN (Average Discharge Rate) is lower, allowing the IES to meet load demands at a low cost and improve user efficiency. Furthermore, the adjusted transferable load is concentrated within these two consecutive periods, effectively ensuring the continuity of electricity use for the transferable load. However, some transferable electrical load still exists during the higher-priced period of 20:00-22:00, mainly due to two factors. Firstly, the CHP (Constant Power Generation) unit of this invention uses a heat-to-power output method, allowing the transferable load to flexibly balance the electrical energy generated by the CHP unit. Secondly, there is an upper limit to the acceptable transferable electrical load within each period.

[0079] Figure 3 It is a heat load balance diagram. Figure 3 The data shows the supply and demand balance of heat load in the IES, indicating that the heat load was reduced throughout the optimization period. The energy source of the heat load reveals that during the 1:00-5:00 period, the heat load is supplied by electric heating equipment, as electricity prices are low at this time, effectively reducing heating costs. As electricity prices rise, the heat load supply gradually shifts to CHP unit heating, thereby reducing operating costs.

[0080] Figure 4 For ADN and ESPS electricity pricing strategies. Figure 5 This is a graph showing the change in ESPS energy storage capacity. Combined with... Figure 4 and Figure 5Analysis shows that, using the method proposed in this invention, the ESPS does not completely adhere to the "low-charge, high-discharge" principle. The ESPS concentrates on charging at maximum power during periods of low electricity prices and sells electricity to the IES during peak electricity prices. This charging and discharging mode effectively reduces the losses incurred by the ADN due to the ESPS charging during off-peak hours, while also reducing the IES's electricity purchase costs during peak hours, effectively balancing the interests of all participating entities.

[0081] Three economic operation scenarios were set up for comparative analysis:

[0082] Option 1: Consider the dual dynamic electricity price of ADN and ESPS, which is the two-way master-slave game economic operation strategy proposed in this invention;

[0083] Option 2: Consider dynamic electricity pricing with ADN, without ESPS participating in economic operation; Option 3: Both ADN and ESPS have fixed electricity prices, meaning there is no game-theoretic relationship between the entities.

[0084] The modeling and simulation results obtained by the above three schemes are shown in Table 1.

[0085] Table 1 Results of Optimization of Benefits for Each Entity

[0086]

[0087] Figure 6 This is a comparison chart of electricity prices after implementing three optimized schemes. Scheme 1 considers both ADN and ESPS dynamic electricity prices, which is the two-way master-slave game economic operation strategy proposed in this invention; Scheme 2 considers ADN dynamic electricity prices, but ESPS does not participate in economic operation; Scheme 3 has fixed electricity prices for both ADN and ESPS, meaning there is no game relationship between the entities. Analysis Figure 6 It can be seen that the electricity prices of ADN and ESPS in Scheme 1 are higher than those in Scheme 2 and Scheme 3 during certain peak electricity consumption periods. This increases the enthusiasm of IES to participate in dispatching and achieves the effect of peak shaving and valley filling.

[0088] The optimized IES power purchase situation after adopting schemes 1 and 2 is as follows: Figure 7 As shown in Table 1, compared to Scheme 2, Scheme 1 saw an increase of 57.9 yuan in the benefit of IES, a decrease of 83.4 yuan in the benefit of ADN, and a benefit of 68.6 yuan for ESPS, resulting in an overall improvement in system benefit. This is because Scheme 1 adds the proactive ESPS, which, while relying on ADN for energy supply, also competes with ADN in the electricity sales process. This game-theoretic interaction among the stakeholders leads to a relatively lower electricity price in Scheme 1. This, in turn, results in... Figure 7While maintaining a largely consistent approach to purchasing electricity from external sources, the efficiency of the IES (Environmental Engineering System) has actually improved. Although the efficiency of the ADN (Active Distribution Network) is negatively impacted in Scheme 1, this scheme helps to improve the overall efficiency of the system and promotes the healthy operation of the energy system.

[0089] Figure 8 This is a comparison chart of the changes in ESPS energy storage capacity under Scheme 1 and Scheme 3. Figure 9 This is a comparison chart of IES electricity purchase situations under Scheme 1 and Scheme 3. To verify the impact of dual dynamic electricity pricing on the economic operation of IES containing ESPS, a comparative analysis of Scheme 1 and Scheme 3 is conducted. The optimized ESPS energy storage capacity under Scheme 1 and Scheme 3 is shown below. Figure 8 As shown, the IES electricity purchase situation is as follows: Figure 9 As shown in Table 1, analysis reveals that compared to Scheme 3, the benefits for IES, ESPS, and ADN are all improved in Scheme 1. Specifically, IES's benefits increased by 723.7 yuan, ESPS's by 22.3 yuan, and ADN's by 63.7 yuan. In Scheme 3, both ADN and ESPS use fixed time-of-use pricing, resulting in no competition among the entities and preventing them from fully utilizing market flexibility to obtain greater benefits. Figure 6 and Figure 8 Analysis revealed that, compared to Option 1, ESPS, driven by profit, primarily charges during off-peak ADN electricity price periods and sells electricity during peak ADN price periods. However, because the electricity price sold by ESPS is fixed, it cannot flexibly increase the price to boost revenue during peak load periods. Furthermore, ESPS is limited by its own charging and discharging power, preventing unlimited charging and discharging, ultimately resulting in significantly lower efficiency compared to Option 1.

[0090] Comprehensive analysis Figure 8 and Figure 9 It can be seen that although the overall electricity price of ADN is relatively high, the "low charging and high discharging" of ESPS results in losses due to increased electricity sales during off-peak hours and reduced revenue during peak hours, ultimately leading to significant losses in efficiency. For IES, using Scheme 3 for optimized operation, the purchase price of electricity increases except for the period from 18:00 to 22:00. Furthermore, during this period, the limited discharge power of ESPS prevents the purchase of large quantities of cheap electricity, ultimately increasing electricity purchase costs and reducing efficiency.

[0091] In summary, the two-way master-slave game model proposed in this invention can fully leverage the flexibility of each entity in participating in market competition, improve the economic efficiency of the system, and achieve a win-win situation for all entities.

Claims

1. A modeling method for a two-way master-slave game model of economic operation of energy storage power stations and integrated energy systems, characterized in that... Includes the following steps: Step 1: Construct optimization models for Active Distribution Network (ADN), Energy Storage Power Station (ESPS), and Integrated Energy System (IES). Step 2: With the Active Distribution Network (ADN) as the leader, the Energy Storage Station (ESPS) as the secondary leader, and the Integrated Energy System (IES) as the follower, construct an economic operation mechanism for the Energy Storage Station (ESPS) and the Integrated Energy System (IES) based on a two-way master-slave game. Step 3: Based on the master-slave relationship between the active distribution network (ADN) and the energy storage power station (ESPS) and the integrated energy system (IES), the master-slave relationship between the energy storage power station (ESPS) and the integrated energy system (IES), and the competitive relationship between the active distribution network (ADN) and the energy storage power station (ESPS), construct a two-way master-slave game model; Step 2 of the bidirectional master-slave game includes the following steps: Step 2.1: The Active Distribution Network (ADN), acting as the leader, formulates the initial electricity pricing strategy; Step 2.2: Based on the electricity price set by the Active Distribution Network (ADN), the ESPS (Energy Storage System) responds to the ADN's pricing strategy and formulates its charging strategy. The charging strategy is reported to the Active Distribution Network (ADN); simultaneously, the Energy Storage Station (ESPS) formulates electricity pricing strategies with the Integrated Energy System (IES). ; Step 2.3: The Integrated Energy System (IES) implements the electricity pricing strategy of the Active Distribution Network (ADN). And the electricity sales pricing strategy of ESPS (Energy Storage System). Optimize and adjust the output of internal energy conversion equipment and load demand response, and formulate a power purchase strategy for the Integrated Energy System (IES). The strategy is simultaneously reported to the Active Distribution Network (ADN) and the Energy Storage System (ESPS). Step 2.4: The Active Distribution Network (ADN) updates the electricity sales price based on the electricity purchase strategy reported by the Energy Storage Station ESPS and the Integrated Energy System IES, guiding the Energy Storage Station ESPS and the Integrated Energy System IES to make dynamic adjustments, so as to ensure that the Active Distribution Network (ADN) itself has the best economic benefits. Step 2.5: The energy storage power station ESPS updates the electricity price based on the Active Distribution Network (ADN) and the Integrated Energy System (IES) power purchase strategy. Update its charging strategy and electricity pricing. To optimize its own economic benefits; Step 2.6: Repeat steps 2.3 to 2.5 until the active distribution network (ADN) pricing strategy, ESPS charging strategy, and electricity sales pricing strategy are implemented. and Integrated Energy System (IES) power purchase strategy The state remains stable, achieving a game equilibrium. Step 2.7: The system uses the game equilibrium solution as the final trading strategy. Engage in energy trading.

2. The modeling method for a two-way master-slave game model of economic operation of energy storage power stations and integrated energy systems according to claim 1, characterized in that... In step 1: 1) Construct an active distribution network (ADN) optimization model, with its maximum operational efficiency as the optimization objective. The objective function is: ； in: ； ； ； In the formula, For the power interaction benefits between ADN and IES; For the power interaction benefits between ADN and ESPS; The power interaction cost between the ADN and the external power grid; This represents the number of time periods in the scheduling cycle. The electricity price for ADN; The interaction power between IES and ADN; The charging power for ESPS; The electricity price sold to the external power grid. This refers to the power purchased by the ADN from the external power grid. 2): Construct an optimization model for the ESPS (Energy Storage System) of an energy storage power station, with its maximum operational efficiency as the optimization objective. The objective function is: in: In the formula: For the power interaction benefits between ESPS and IES; The power interaction cost between ESPS and ADN; For the operation and maintenance costs of ESPS; The electricity price for ESPS; The interaction power between ESPS and IES; This refers to the ESPS operation and maintenance cost coefficient. 3) Construct an integrated energy system (IES) optimization model, with the goal of maximizing the operational efficiency of the IES. The objective function is: in: In the formula, This is a function for the efficiency of electricity use; The cost of purchasing and selling electricity for IES; For IES fuel costs; For the operation and maintenance costs of IES; Penalty costs for reducing comfort caused by lower heat load; For natural gas prices; This refers to the calorific value of natural gas. The power generation efficiency of CHP; This refers to the output electrical power of the CHP. This is the operating and maintenance cost coefficient for CHP; , The electricity prices are for ESPS and ADN, respectively. This refers to the amount of electricity that IES sells to the external power grid. , These represent the interaction power between IES and ESPS and ADN, respectively. This refers to the adjusted electrical load power. For the quadratic parameters of the power utility function, The parameters of the power efficiency function are linear. To reduce power consumption due to heat load; This is the penalty factor for heat load reduction.

3. The modeling method for a two-way master-slave game model of economic operation of energy storage power stations and integrated energy systems according to claim 1, characterized in that... In step 3, the two-way master-slave game model is as follows: Participants consist of leaders (ADN), secondary leaders (ESPS), and followers (IES). This is a two-way master-slave game strategy set; the strategy set is the leader's electricity sales price strategy. The charging strategy and electricity pricing strategy of the second-tier leaders are The follower's electricity purchase strategy is The utility of each participant is the objective function of each subject.

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