A generalized energy storage park integrated energy system coordinated optimization operation method considering multi-party participation

Abstract

The application discloses a kind of generalized energy storage park integrated energy systems coordination optimization operation method considering multi-party participation. First, the operation framework of park integrated energy systems (PIES) is established, and the interest relationship between the upper microgrid operators (MGO) and the lower user aggregators (UA) is analyzed. Second, to maximize the interests of each subject in the park, a mixed game model involving multiple users and microgrid operators is constructed. The operator sets the energy selling price through the principal-agent game, and the user aggregators conduct a cooperative game process based on the Nash negotiation theory after receiving the price. Then, considering the high initial investment of energy storage devices, the cluster dispatching potential of electric vehicles is fully tapped, and the operation strategy of using electric vehicles and intelligent buildings as generalized energy storage is developed. Finally, a certain city's park integrated energy system is analyzed. The results show that the established model can effectively reduce carbon emissions and achieve a win-win situation for operators and multiple users.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization operation, and in particular relates to a method for coordinated optimization operation of a comprehensive energy system in a generalized energy storage park that takes into account the participation of multiple parties. Background Technology

[0002] With the introduction of the "dual carbon" target, new energy sources have become an important solution for achieving low-carbon emission reduction. Among them, park integrated energy systems (PIES), which have the advantages of multiple energy complementarities, are an important path to solve the carbon emission problem. Due to the complexity of PIES, how to reduce costs and achieve a win-win situation for multiple stakeholders within the park is an urgent problem to be solved. A hybrid game model combining master-slave game and cooperative game provides a theoretical basis for addressing the needs of multiple stakeholders. In recent years, with the continuous development of the building energy storage field, a series of technical paths suitable for PIES have emerged, such as "photovoltaic-storage-DC-flexible" technology. At the same time, with the increase in the number of electric vehicles (EVs) and the development of research on vehicle-grid interaction, exploring the dispatchable potential of EV clusters as PIES energy storage devices has a certain practical basis.

[0003] With the introduction of the "photovoltaic-storage-direct-drive-flexible" strategy and the increase in EV ownership, multi-energy synergy within PIES (Plant Energy Systems) has become the future development direction for industrial parks. Therefore, balancing the interests of various stakeholders within PIES and considering the dispatchable potential of EV clusters with the relative advantages of building thermal storage forming a broad energy storage system are crucial for achieving efficient operation of the park's integrated energy system.

[0004] Based on the above analysis, and addressing the issues of high upfront investment in energy storage equipment and low energy storage utilization rates due to unreasonable planning, this invention proposes a coordinated and optimized operation method for a generalized energy storage park integrated energy system that considers the participation of multiple parties. This method fully explores the dispatchable potential of EV clusters and addresses the interests of multiple users within the PIES (Multi-User Group) and UA (Multi-User Area). It utilizes Nash negotiation theory to allocate the cooperative benefits of multiple users and establishes a PIES model based on hybrid game theory for generalized energy storage. This achieves a win-win situation for both the MGO and UA while reducing system carbon emissions. The effectiveness and feasibility of the proposed scheme are verified through numerical examples. Summary of the Invention

[0005] The problem this invention aims to solve is the issue of different interests among multiple stakeholders in a PIES and the initial investment in energy storage equipment. It proposes to solve the multi-user benefit problem through hybrid game theory and utilize the shared energy storage characteristics of EV clusters as energy storage equipment to reduce PIES costs.

[0006] Technical Solution: A method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park, involving multiple parties, characterized by the following steps:

[0007] (1) In the data processing stage, based on the historical data of EVs in the charging station, the bi-directional long short-term memory (Bi-LSTM) method is used to process the historical data to obtain the allowable range of charging and discharging power and state of charge (SOC) of EVs in the master-slave game stage.

[0008] (2) Perform master-slave game calculation, use genetic algorithm to randomly generate the initial MGO energy sales strategy, and transmit it to the UA side;

[0009] (3) UA receives MGO’s pricing strategy, uses CPLEX solver to solve it, optimizes its own electric and heat load distribution and transfer amount, calculates the revenue within a cycle, and returns the energy purchase information to MGO.

[0010] (4) MGO calculates the revenue within a cycle based on the energy sales price and UA's energy purchase information, and determines whether it meets the requirement of maximizing revenue;

[0011] (5) Based on the update iteration of the genetic algorithm, MGO generates a new set of energy sales strategies, and repeats steps (3) and (4) until... Until the maximum value is reached;

[0012] (6) If the new MGO and UA revenues are greater than the previous revenues, then update;

[0013] (7) After the data is updated, if the error is less than the set value, complete the algorithm and perform cooperative game calculation; otherwise, return to step (2) to continue execution.

[0014] (8) Input the MGO energy sales price generated in step (6) and the transaction volume of user cooperation game;

[0015] (9) Set the initialization parameters for the alternating direction method of multipliers (ADMM) and input the user's transaction volume;

[0016] (10) Calculate the electricity price between users using the ADMM algorithm;

[0017] (11) Determine whether the electricity prices among users have converged. If they have converged, return the final price and end the program. Otherwise, return to (10) and continue.

[0018] Furthermore, the formulas for MGO, EV, UA, and the generalized energy storage model in step (1) are as follows:

[0019] MGO model:

[0020]

[0021] In the formula: For CHP's gas costs, For the power generation efficiency of CHP, The power output of CHP For natural gas prices, The heat production capacity of CHP This is the thermoelectric ratio coefficient.

[0022] EV Cluster Shared Energy Storage Model: The dispatchable potential of an EV cluster refers to the initial SOC of EVs and the time EVs arrive at and leave the charging station. , Historical data is used for prediction to clarify the range of EV capacity and charge / discharge power available for energy storage in real-time. Minkowski theory is used to aggregate EV clusters into a shared energy storage model, and the envelope space boundary of the EV cluster's dispatchable potential is calculated.

[0023]

[0024] In the formula: , and They are respectively The charging and discharging power and SOC of the EV cluster at any given time; , These are the charging and discharging power limits for EV clusters; , These are the maximum and minimum capacities of the EV cluster, respectively. for The change in SOC of EV within the time period. , The component represents the charging and discharging power of an individual EV at time t; , These represent the maximum and minimum permissible values ​​for the charging and discharging power of an individual EV; , These are Boolean variables representing the charging and discharging states, respectively. The SOC represents the individual EV; , These represent the upper and lower limits of the capacity of an individual EV; , These represent the battery levels of the EV when it arrives at and leaves the charging station, respectively. =1 indicates that the EV is located at a charging station and can be charged and discharged.

[0025] UA Model: The user's electrical and thermal rigid loads and flexible loads can be represented as:

[0026]

[0027] In the formula: This refers to the electricity that UA purchases from the MGO. , These are the electrical load and flexible electrical load values ​​after UA completes DR, respectively; The electrical power of the photovoltaic device; For cooperative game trading volume; , For UA, rigid loads and flexible electrical loads; The electrical load consumed by electric heating equipment; Users Net electrical load for a given period; The electrical load is adjusted for users after the electrical and thermal flexible loads are completed.

[0028] The power exchanged between users should be within the specified range. Inside:

[0029]

[0030] Users participating in the cooperative game must satisfy the electricity trading balance constraint and the electricity purchase price balance constraint:

[0031]

[0032] Regarding heating, Indoor temperature of buildings during the period It can be represented as:

[0033]

[0034] In the formula: , They are respectively Solar radiation and heat radiated outward from buildings during different time periods; It is the specific heat of air.

[0035] Generalized Energy Storage Model: Based on the shared energy storage characteristics of smart buildings and EV clusters within PIES, building thermal storage and EV charging station electricity storage are considered as generalized adjustable energy storage resources. The mathematical form of the generalized energy storage model is summarized as follows:

[0036]

[0037] In the formula: This refers to the capacity of energy storage in a broad sense. , These are the State of Charge (SOC) of generalized energy storage devices.

[0038] Furthermore, the MGO energy sales strategy set in step (2) can be represented as:

[0039]

[0040]

[0041] In the formula: , These are the energy sales prices set by MGO after a master-slave game; , The time-of-use electricity price for the power grid; , These are the upper and lower limits for the price of heat.

[0042] To prevent MGO from setting the highest possible energy selling price to maximize profits, the constraint on its average energy selling price is as follows:

[0043]

[0044]

[0045] In the formula: , The average price of energy sales is constrained.

[0046] Furthermore, the formula for calculating the return of UA within one cycle in step (3) is as follows:

[0047]

[0048] In the formula: Let UA be the power consumption utility function; This is a penalty function for reducing comfort caused by load reduction; To take into account the electrical load after the EV charging station; , , These are parameters related to electricity efficiency.

[0049] Furthermore, the formula for calculating MGO revenue in step (4) is as follows:

[0050]

[0051] In the formula: and Revenue from MGO selling energy to UA; For the revenue generated by MGO from electricity trading with the distribution network, The cost of the CET mechanism is described in detail below:

[0052]

[0053]

[0054]

[0055] In the formula, This represents the net electricity load value after the user has responded to demand.

[0056] Furthermore, the calculation of MGO revenue and UA revenue, which change with the number of iterations, in steps (5) and (6) is specifically described as follows:

[0057] like ,but , ;otherwise , .

[0058] In the formula, and The values ​​represent the gains of MGO and UA after iteration, respectively. , , , The first Next and first The value after the next iteration.

[0059] Furthermore, the error accuracy formula in step (7) is as follows:

[0060]

[0061] In the formula To account for the accuracy error, cooperative game calculations are performed when the energy sales strategy of MGO meets the accuracy requirements.

[0062] Furthermore, in step (8), the MGO energy sales price generated in step (6) needs to be input. , Partnering with users to compete on transaction volume .

[0063] Furthermore, in step (9), parameters such as the initialization dual variables of the ADMM algorithm are set:

[0064]

[0065]

[0066]

[0067] In the formula: The total electricity volume transacted by the user in the partnership; The user's bargaining power; Costs incurred before collaborating with users.

[0068] Furthermore, in step (10), the electricity price between users is calculated using the ADMM algorithm;

[0069]

[0070] In the formula: Indicates user The transaction volume obtained during the master-slave game process The expected price of electricity traded; For Lagrange multipliers; As a penalty factor; Costs incurred before collaborating with users; This represents the total electricity volume traded by the user in the partnership.

[0071] Among them, users Update trading decisions :

[0072]

[0073] user Update after receiving decision :

[0074]

[0075] Lagrange multipliers The iterative process is as follows:

[0076]

[0077] Furthermore, step (11) determines the convergence condition of electricity prices among users:

[0078]

[0079] In the formula: The convergence coefficient is denoted as . If convergence is achieved, the algorithm terminates; otherwise, it returns to step (10) to continue.

[0080] Beneficial effects: This invention proposes a coordinated and optimized operation method for a comprehensive energy system in a generalized energy storage park that takes into account the participation of multiple parties. This method can overcome the problems of high initial investment in energy storage equipment and low energy utilization rate, improve the economic efficiency of system operation, and increase the economic income of users and PIES when MGO and UA conduct electricity and heat transactions. Attached Figure Description

[0081] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0082] Figure 2 A schematic diagram of the PIES framework for the park;

[0083] Figure 3 This is a schematic diagram of the mixed game process of MGO and UA;

[0084] Figure 4 and Figure 5 This example compares the energy sales prices of MGO using different methods.

[0085] Figure 6 and Figure 7 This example compares the electrothermal power balance diagrams on the UA side using different methods.

[0086] Figure 8 and Figure 9 This refers to the volume and price of electricity transactions between users after cooperative game theory. Detailed Implementation

[0087] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0088] like Figure 1 As shown, the master-slave game process of the embodiment system is described using the method of the present invention, and the specific content is as follows:

[0089] MGO model:

[0090]

[0091] In the formula: For CHP's gas costs, For the power generation efficiency of CHP, The power output of CHP For natural gas prices, The heat production capacity of CHP This is the thermoelectric ratio coefficient.

[0092] EV Cluster Shared Energy Storage Model: The dispatchable potential of an EV cluster refers to the initial SOC of EVs and the time EVs arrive at and leave the charging station. , Historical data is used for prediction to clarify the range of EV capacity and charge / discharge power available for energy storage in real-time. Minkowski theory is used to aggregate EV clusters into a shared energy storage model, and the envelope space boundary of the EV cluster's dispatchable potential is calculated.

[0093]

[0094] In the formula: , and They are respectively The charging and discharging power and SOC of the EV cluster at any given time; , These are the charging and discharging power limits for EV clusters; , These are the maximum and minimum capacities of the EV cluster, respectively. for The change in SOC of EV within the time period. , The component represents the charging and discharging power of an individual EV at time t; , These represent the maximum and minimum permissible values ​​for the charging and discharging power of an individual EV; , These are Boolean variables representing the charging and discharging states, respectively. The SOC represents the individual EV; , These represent the upper and lower limits of the capacity of an individual EV; , These represent the battery levels of the EV when it arrives at and leaves the charging station, respectively. =1 indicates that the EV is located at a charging station and can be charged and discharged.

[0095] UA Model: The user's electrical and thermal rigid loads and flexible loads can be represented as follows:

[0096]

[0097] In the formula: This refers to the electricity that UA purchases from the MGO. , These are the electrical load and flexible electrical load values ​​after UA completes DR, respectively; The electrical power of the photovoltaic device; For cooperative game trading volume; , For UA, rigid loads and flexible electrical loads; The electrical load consumed by electric heating equipment; Users Net electrical load for a given period; The electrical load is adjusted for users after the electrical and thermal flexible loads are completed.

[0098] The power exchanged between users should be within the specified range. Inside:

[0099]

[0100] Users participating in the cooperative game must satisfy the electricity trading balance constraint and the electricity purchase price balance constraint:

[0101]

[0102] Regarding heating, Indoor temperature of buildings during the period It can be represented as:

[0103]

[0104] In the formula: , They are respectively Solar radiation and heat radiated outward from buildings during different time periods; It is the specific heat of air.

[0105] Generalized Energy Storage Model: Based on the shared energy storage characteristics of smart buildings and EV clusters within PIES, building thermal storage and EV charging station electricity storage are considered as generalized adjustable energy storage resources. The mathematical form of the generalized energy storage model is summarized as follows:

[0106]

[0107] In the formula: This refers to the capacity of energy storage in a broad sense. , These are the State of Charge (SOC) of generalized energy storage devices.

[0108] (2) The energy sales strategy set of MGO can be represented as:

[0109]

[0110]

[0111] In the formula: , These are the energy sales prices set by MGO after a master-slave game; , The time-of-use electricity price for the power grid; , These are the upper and lower limits for the price of heat.

[0112] To prevent MGO from setting the highest possible energy selling price to maximize profits, the constraint on its average energy selling price is as follows:

[0113]

[0114]

[0115] In the formula: , The average price of energy sales is constrained.

[0116] Example:

[0117] (a) Model building

[0118] UA's revenue can be expressed as:

[0119]

[0120] In the formula: Let UA be the power consumption utility function; This is a penalty function for reducing comfort caused by load reduction; To take into account the electrical load after the EV charging station; , , These are parameters related to electricity efficiency.

[0121] MGO's revenue can be expressed as:

[0122]

[0123] In the formula: and Revenue from MGO selling energy to UA; For the revenue generated by MGO from electricity trading with the distribution network, The cost of the CET mechanism is described in detail below:

[0124]

[0125]

[0126]

[0127] In the formula: This represents the net electricity load value after the user has responded to demand.

[0128] (b) Analysis of test results of the embodiment

[0129] This study analyzes a PIES (Powered Energy Storage) system in a certain city. Considering the diversity of EVs and charging stations, three types of EV cluster models and four types of charging station models are considered. Historical data for EV clusters are processed using the Bi-LSTM method. For the PIES model incorporating generalized energy storage and hybrid game theory strategies, various scenarios are established using MATLAB for research, and the advantages of the proposed model are verified from the perspectives of PIES economics and low carbon emissions. In the scenario research, the MGO (Maintenance, Gas, and Electricity) side is equipped with a CHP (Consumer Power Plant) unit, which consumes natural gas to output electric and thermal power for UA (User Electricity, Undertaken Energy). Simultaneously, based on the UA's energy purchases, revenue is obtained by balancing the output of the CHP unit with the electricity purchased from the upper-level grid. The goal of the MGO is to maximize social welfare. When the MGO provides a high heat price and has sufficient electricity, the UA can utilize the EH (Energy, Heating, and Power) device to obtain heat energy to meet user comfort needs and reduce its own energy costs. EV charging stations purchase electricity from the distribution network according to their own energy needs and sell electricity to UAs when there is sufficient electricity to obtain revenue.

[0130] For the above embodiment system, scenario 1, which includes master-slave game theory and ordinary battery energy storage characteristics, and scenario 2, which includes hybrid game theory and EV shared energy storage characteristics, are respectively considered to test and verify the overall economic efficiency of PIES, UA revenue and carbon emissions.

[0131] The carbon emissions of PIES and the income of MGO and UA were analyzed using the different methods described above. Figure 3 A schematic diagram illustrating the process of a hybrid game between MGO and UA. Figure 4 , Figure 5 The graphs show the MGO energy sales price curves for scenarios 1 and 2, respectively. The results show that after adding the EV cluster for shared energy storage, the MGO energy sales price decreased compared to when EVs were not included. This indicates that adding EV charging stations to the PIES can effectively reduce the UA energy sales price and lower the UA's energy costs. Figure 6 and Figure 7 This illustrates the power balance diagrams for user 3 under scenarios 1 and 2. Compared to scenario 1, which considers energy storage devices, scenario 4, after replacing it with an EV cluster shared energy storage model in PIES and considering the cooperative game among UAs, shows that user 3 has more available periods for transferable and reduceable loads in scenario 2 compared to scenario 1. This is because, to reduce their own energy costs, users choose to shift flexible loads from periods with higher energy prices to periods with lower energy prices, proving that the EV cluster shared energy storage model can improve the flexibility of UA-side loads. Simultaneously, during periods when the MGO provides electricity at higher prices, UAs can distribute benefits through cooperative game among users, reducing energy purchase costs while improving energy stability.

[0132] Table 1 shows the impact of different scenarios on the carbon emissions of UA, MGO, and PIES in the system of the example. It can be seen that after adding the EV model and considering the cooperative game among users, the carbon emissions of the system are significantly reduced, while the overall revenue of UA is increased, and the initial energy storage construction cost of the system is reduced.

[0133] Table 2 presents the profit distribution in the cooperative game of Scenario 2. As shown in Table 2, because Scenario 4 further considers the cooperative game among users in UA, UA's overall profit is 466 yuan higher than in Scenario 3. This is because UA, as a whole, negotiates with MGO, and each user fully utilizes its own electrical and thermal load structure characteristics to achieve energy complementarity through a P2P process, reducing UA's dependence on MGO. Furthermore, the results in Table 2 also indicate that considering Nash negotiation theory not only reduces the total cost of the IEM alliance but also protects the interests of the cooperative participants. The final profits of each user in UA increased by 155.2 yuan, 154.6 yuan, and 156.5 yuan respectively, demonstrating the role of Nash negotiation theory in the fair distribution of cooperative profits among users.

[0134] In summary, the following conclusions can be drawn: The method for coordinated and optimized operation of a generalized energy storage park integrated energy system that takes into account the participation of multiple parties proposed in this invention has better economic efficiency, can effectively reduce the high upfront cost of PIES energy storage equipment in the park, balance the interests of all parties, bring additional revenue to UA and ensure the distribution of benefits among users, and at the same time significantly reduce the carbon emissions of PIES.

[0135] Table 1. Economic Benefit Indicators and Carbon Emission Analysis for Different Scenarios

[0136] Scene Microgrid operator revenue / yuan User aggregator revenue / yuan EV charging station revenue / yuan Carbon trading cost / yuan Actual carbon emissions / kg 1 7816 29735 / 4234 33528 2 6793 27928 2191 4145 29705

[0137] Table 2 User Benefit Analysis Before and After Cooperation

[0138] user Revenue before cooperation / yuan Revenue after cooperation / yuan User's final revenue / yuan Increase in income / yuan 1 8967.5 8957 9122.7 155.2 2 9244.6 9270.4 9399.2 154.6 3 9249.4 9320.9 9405.9 156.5

Claims

1. A method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park, considering the participation of multiple parties, characterized in that: Includes the following steps: (1) In the data processing stage, based on the historical data of EVs in the charging station, the bidirectional long short-term memory network method is used to process the historical data to obtain the allowable range of charging and discharging power and state of charge of EVs in the master-slave game stage. (2) Perform master-slave game calculation, use genetic algorithm to randomly generate the initial MGO energy sales strategy, and transmit it to the UA side; (3) UA receives the pricing strategy of MGO, uses the CPLEX solver to solve it, optimizes the distribution and transfer of its own electric and heat loads, calculates the revenue within a cycle, and returns the energy purchase information to MGO. (4) MGO calculates the revenue within a cycle based on the energy sales price and UA's energy purchase information, and determines whether it meets the requirement of maximizing revenue. (5) Based on the update iteration of the genetic algorithm, MGO generates a new set of energy sales strategies, and repeats steps (3) and (4) until k reaches its maximum value; (6) If the new MGO and UA revenues are greater than the previous revenues, then update; (7) After the data is updated, if the error is less than the set value, complete the algorithm and perform cooperative game calculation; otherwise, return to step (2) to continue execution. (8) Input the MGO energy sales price and user cooperative game transaction volume generated in step (6); (9) Set the initialization parameters for the alternating direction multiplier method and input the user's transaction volume; (10) Calculate the electricity price between users using the ADMM algorithm; (11) Determine whether the electricity prices among users have converged. If they have converged, return the final price and end the program. Otherwise, return to (10) and continue. In step (9), the parameters of the ADMM algorithm initialization dual variables are set, and the calculation formula is as follows: In the formula: The total electricity volume transacted by the user in the partnership; The user's bargaining power; Costs incurred before collaborating with users; In step (10), the electricity price between users is calculated using the ADMM algorithm. The calculation method is as follows: In the formula: Indicates user The transaction volume obtained during the master-slave game process The expected price of electricity traded; For Lagrange multipliers; As a penalty factor; Costs incurred before collaborating with users; The total electricity volume transacted by the user in the partnership; Among them, users Update trading decisions : user Update after receiving decision : Lagrange multipliers The iterative process is as follows: 。 2. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, Step (1) also includes the construction of MGO, EV, UA, and the generalized energy storage model, the specific methods of which are as follows: MGO model: In the formula: The gas cost for MGOs equipped with combined heat and power units, For the power generation efficiency of CHP, The power output of CHP For natural gas prices, The heat production capacity of CHP Thermoelectric ratio; EV Cluster Shared Energy Storage Model: The dispatchable potential of an EV cluster refers to the initial SOC of EVs and the time EVs arrive at and leave the charging station. , Historical data is used for prediction to clarify the range of EV capacity and charging / discharging power available for energy storage in real time. Minkowski theory is used to aggregate EV clusters into a shared energy storage model, and the envelope space boundary of the EV cluster's dispatchable potential is calculated. In the formula: , and They are respectively The charging and discharging power and SOC of the EV cluster at any given time; , These are the charging and discharging power limits for EV clusters; , These are the maximum and minimum capacities of the EV cluster, respectively. for Changes in SOC of EV over the period; , The component represents the charging and discharging power of an individual EV at time t; , These represent the maximum and minimum permissible values ​​for the charging and discharging power of an individual EV; , These are Boolean variables representing the charging and discharging states, respectively. The SOC represents the individual EV; , These represent the upper and lower limits of the capacity of an individual EV; , These represent the battery levels of the EV when it arrives at and leaves the charging station, respectively. =1 indicates that the EV is located at a charging station and can be charged and discharged. UA Model: The user's electrical and thermal rigid loads and flexible loads can be represented as: In the formula: This refers to the electricity that UA purchased from the MGO; , These are the electrical load and flexible electrical load values ​​after UA completes DR, respectively; The electrical power of the photovoltaic device; For cooperative game trading volume; , For UA, rigid loads and flexible electrical loads; The electrical load consumed by electric heating equipment; Users Net electrical load for a given period; The electrical load after the user completes the adjustment of the flexible electrical and heating loads; The power exchanged between users should be within the specified range. Inside: Users participating in the cooperative game must satisfy the electricity trading balance constraint and the electricity purchase price balance constraint: Regarding heating, Indoor temperature of buildings during the period It can be represented as: In the formula: , They are respectively Solar radiation and heat radiated outward from buildings during different time periods; The specific heat of air; Generalized Energy Storage Model: Based on the shared energy storage characteristics of smart buildings and EV clusters within PIES, building thermal storage and EV charging station electricity storage are considered as generalized adjustable energy storage resources. The mathematical form of the generalized energy storage model is summarized as follows: In the formula: This refers to the capacity of energy storage in a broad sense. , These are the State of Charge (SOC) of generalized energy storage devices.

3. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In step (2), the MGO energy sales strategy set can be represented as: In the formula: , These are the energy sales prices set by MGO after a master-slave game; , The time-of-use electricity price for the power grid; , These are the upper and lower limits of the heating price; The constraint condition for the average energy sales price is: In the formula: , The average price of energy sales is constrained.

4. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In step (3), the formula for calculating UA's revenue over one period is: In the formula: Let UA be the power consumption utility function; This is a penalty function for reducing the load that leads to a decrease in comfort; To take into account the electrical load after the EV charging station; , , These are parameters related to electricity efficiency.

5. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that... In step (4), the formula for calculating MGO revenue is as follows: In the formula: and Revenue from MGO selling energy to UA; For the revenue generated by MGO from electricity trading with the distribution network, The cost of the CET mechanism is described in detail below: In the formula, This represents the net electricity load value after the user has responded to demand.

6. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In steps (5) and (6), the MGO revenue and UA revenue, which change with the number of iterations, are calculated, as detailed below: like ,but , ;otherwise , ; In the formula: and The returns for MGO and UA after iteration are shown below; , , , The first Next and first The value after the next iteration.

7. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In step (7), the error accuracy formula is as follows: In the formula: To account for the accuracy error, cooperative game calculations are performed when the energy sales strategy of MGO meets the accuracy requirements.

8. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In step (8), the MGO energy sales price generated in step (6) needs to be input. , Partnering with users to compete on transaction volume .

9. The method for coordinated and optimized operation of a comprehensive energy system in a generalized energy storage park according to claim 1, characterized in that, In step (11), the convergence condition of electricity prices between users is determined: In the formula: δ is the convergence coefficient; if convergence is achieved, the algorithm ends; if convergence is not achieved, the algorithm returns to step (10) to continue.

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