Integrated energy microgrid system master-slave game optimization operation method and device

Abstract

The application relates to a master-slave game optimization operation method and device of a comprehensive energy microgrid system, wherein the method comprises the following steps: determining a revenue target function of a microgrid operator, a user aggregator and a shared energy storage operator respectively based on single-day operation revenue and power load of the comprehensive energy microgrid system under an energy concentrator architecture; determining a constraint condition of the comprehensive energy microgrid system according to multi-energy flow coupling characteristics of the comprehensive energy microgrid system; constructing a master-slave game optimization model of the comprehensive energy microgrid system based on the revenue target function and the constraint condition, and solving the master-slave game optimization model to generate a scheduling strategy of the comprehensive energy microgrid system. Thus, the problems in the related art that the system flexibility cannot be fully utilized due to the focus on only a single type of flexible power load, efficient collaboration is difficult to achieve, and the multi-user complex interaction characteristics cannot be reflected due to the lack of a shared energy storage mechanism, and the energy storage resources cannot be fairly and efficiently allocated are solved.

Description

Technical Field

[0001] This application relates to the field of microgrid system optimization and scheduling technology, and in particular to a master-slave game optimization operation method and device for integrated energy microgrid systems. Background Technology

[0002] Currently, against the backdrop of the global energy system's accelerated transition to a low-carbon model, integrated energy microgrid systems, as an important means of achieving efficient utilization of distributed energy, are receiving increasing attention from academia and industry. Facing challenges such as operational fluctuations in the power system caused by the increasing proportion of renewable energy, microgrids, through energy hub technology, organically integrate various energy units such as distributed clean power generation, electric heating equipment, and gas turbines to achieve multi-energy coordinated optimization, effectively improving the overall energy utilization efficiency and power supply reliability. Energy storage plays an indispensable role in the dynamic balance and optimal scheduling of microgrids. Modern microgrids integrate flexibly convertible energy forms such as electricity and heat, and game theory provides a key analytical framework for grid optimization involving multiple stakeholders. Optimal scheduling of multi-energy microgrids has become a key research direction in the energy field.

[0003] Among related technologies, multiple energy units such as distributed clean power generation, electric heating equipment, and gas turbines are integrated based on the energy hub architecture to carry out multi-energy coordinated optimization scheduling. Alternatively, a shared energy storage model based on the sharing economy concept can be adopted, in which a third-party operator uniformly completes the construction and operation and maintenance of energy storage facilities, providing users with flexible rental services while taking into account energy efficiency and cost control. In addition, game theory methods can be used to build a microgrid scheduling model around electricity trading to coordinate various independent decision-making entities.

[0004] However, in related technologies, because the optimization scheduling only focuses on a single type of flexible electrical load in the system and fails to explore the collaborative optimization mechanism between multiple flexible loads, the system's flexibility cannot be fully utilized, making it difficult to achieve efficient collaboration. Due to the lack of integration of shared energy storage mechanisms and optimization based on single user data, it is difficult to reflect the complex interactive characteristics of multiple users. There is a lack of a systematic scheduling method that takes into account the collaborative behavior of users, making it difficult to allocate energy storage resources fairly and efficiently, and failing to simultaneously guarantee the overall benefits of the system and the interests of individual users. Furthermore, since most game theory models only consider electricity trading and ignore the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity, they cannot fully explore the optimization potential of multi-energy collaboration, resulting in low overall system energy efficiency, which urgently needs improvement. Summary of the Invention

[0005] This application provides a master-slave game optimization operation method and device for integrated energy microgrid systems to solve the problems in related technologies, such as the inability to fully utilize system flexibility and achieve efficient collaboration due to focusing only on a single type of flexible electrical load in the system, the inability to fully integrate shared energy storage mechanisms to reflect the complex interaction characteristics of multiple users, resulting in the inability to fairly and efficiently allocate energy storage resources and simultaneously guarantee the overall benefits of the system and the interests of individual users, or the low overall energy efficiency of the system due to the game model ignoring the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity.

[0006] The first aspect of this application provides a master-slave game optimization operation method for an integrated energy microgrid system, comprising the following steps: determining the revenue objective functions for the microgrid operator, user aggregator, and shared energy storage operator based on the daily operating revenue and electricity load of the integrated energy microgrid system under an energy hub architecture; determining the constraints of the integrated energy microgrid system according to the multi-energy flow coupling characteristics of the integrated energy microgrid system; constructing a master-slave game optimization model of the integrated energy microgrid system based on the revenue objective function and the constraints, and solving the master-slave game optimization model to generate a scheduling strategy for the integrated energy microgrid system.

[0007] Through the aforementioned technical means, the embodiments of this application can construct the revenue objective functions of microgrid operators, user aggregators, and shared energy storage operators, and clarify the operational constraints in conjunction with the multi-energy flow coupling characteristics of the system. Based on the revenue objective function and constraints, a master-slave game optimization model of the integrated energy microgrid system is constructed, and a system scheduling strategy is generated. This constructs a refined integrated energy microgrid system under the energy hub architecture, breaking through the limitations of static or single energy models, improving the system's adaptability and robustness in the face of different user compositions and changes in market conditions, enhancing the microgrid's acceptance of renewable energy and overall energy utilization efficiency, and also helping to promote the collaborative operation and intelligent scheduling of distributed energy, thereby laying a solid technical foundation for the construction of a new type of power system.

[0008] Optionally, in one embodiment of this application, the daily operating revenue and electricity load of the integrated energy microgrid system based on the energy hub architecture are used to determine the revenue objective functions of the microgrid operator, user aggregator, and shared energy storage operator, respectively. This includes: obtaining the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales based on the daily operating revenue and electricity load, to determine the microgrid operator's revenue objective function; obtaining the user aggregator's electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and cost of purchasing heat based on the daily operating revenue and electricity load, to determine the user aggregator's revenue objective function; and determining the shared energy storage operator's revenue objective function based on the shared energy storage operator's energy storage charging and discharging cost and energy storage subsidy fee.

[0009] Through the above-mentioned technical means, the embodiments of this application can construct reasonable revenue objective functions for microgrid operators, user aggregators, and shared energy storage operators based on the actual operating characteristics and cost structure of various entities. This provides a target orientation that fits the actual operating scenario for subsequent game optimization, thereby realizing multi-entity economic optimization and ensuring that the final generated scheduling strategy has stronger feasibility and rationality.

[0010] Optionally, in one embodiment of this application, determining the constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system includes: establishing energy balance constraints, load management constraints, and equipment boundary constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system; establishing energy storage constraints of the integrated energy microgrid system based on the operational constraints of the shared energy storage operator; and determining the constraints of the integrated energy microgrid system based on the energy balance constraints, the load management constraints, the equipment boundary constraints, and the energy storage constraints.

[0011] Through the above-mentioned technical means, the embodiments of this application can combine the multi-energy flow coupling characteristics of the integrated energy microgrid system to establish energy balance constraints, load management constraints and equipment boundary constraints. At the same time, the operating rules of the shared energy storage operator are incorporated to form energy storage constraints. The safety boundary of system operation is defined from multiple dimensions such as energy supply and demand, load regulation, equipment operating limits and energy storage operating rules. This ensures that the scheduling scheme conforms to the law of energy conservation and the physical characteristics of the equipment, provides a reliable physical basis for game optimization, effectively avoids infeasible scheduling schemes, and ensures the engineering practicality of the scheduling strategy.

[0012] Optionally, in one embodiment of this application, the step of constructing a master-slave game optimization model of the integrated energy microgrid system based on the revenue objective function and the constraints, and solving the master-slave game optimization model to generate a scheduling strategy for the integrated energy microgrid system, includes: determining the pricing strategy set of the microgrid operator based on the revenue objective function and the constraints, and determining the user optimization strategy set of the user aggregator based on the pricing strategy set; constructing the master-slave game optimization model based on the pricing strategy set and the user optimization strategy set; solving the electricity pricing strategy of the microgrid operator using a preset algorithm based on the master-slave game optimization model, and calling a preset solver to solve the optimization strategy of the user aggregator, so as to generate a scheduling strategy for the integrated energy microgrid system according to the electricity pricing strategy and the optimization strategy.

[0013] Through the aforementioned technical means, the embodiments of this application can determine the pricing strategy set of microgrid operators based on the revenue objective function and constraints, and derive the user optimization strategy set of user aggregators accordingly, so as to build a complete master-slave game optimization model, clarify the decision space and interaction mechanism of each subject, use a preset algorithm to solve the electricity pricing strategy of microgrid operators and call a preset solver to solve the optimization strategy of user aggregators, which can efficiently obtain equilibrium solutions, facilitate actual engineering deployment, and the generated scheduling strategy can effectively balance the overall benefits of the system and the interests of individual users, achieving the dual goals of multi-energy collaborative optimization and resource sharing.

[0014] Optionally, in one embodiment of this application, the expression of the master-slave game optimization model is: , in, For the microgrid operator, For the user aggregator, For the set of pricing strategies, Optimize the strategy set for the user. For the utility set of the microgrid operator, This refers to the utility set of the user aggregator.

[0015] Through the above-mentioned technical means, the embodiments of this application can construct a master-slave game optimization model for an integrated energy microgrid system under a hub architecture, providing a clear objective and boundary for solving the problem. This model can clearly depict the master-slave game relationship between the microgrid operator and the user aggregator, thereby improving the rigor and reproducibility of the model, providing a unified mathematical benchmark for subsequent solutions, and ensuring the standardization of the optimization process and the accuracy of the results.

[0016] A second aspect of this application provides a master-slave game optimization operation device for an integrated energy microgrid system, comprising: a first determining module, used to determine the revenue objective functions of the microgrid operator, user aggregator, and shared energy storage operator based on the daily operating revenue and electricity load of the integrated energy microgrid system under an energy hub architecture; a second determining module, used to determine the constraints of the integrated energy microgrid system according to the multi-energy flow coupling characteristics of the integrated energy microgrid system; and a generating module, used to construct a master-slave game optimization model of the integrated energy microgrid system based on the revenue objective function and the constraints, and solve the master-slave game optimization model to generate a scheduling strategy for the integrated energy microgrid system.

[0017] Through the aforementioned technical means, the embodiments of this application can construct the revenue objective functions of microgrid operators, user aggregators, and shared energy storage operators, and clarify the operational constraints in conjunction with the multi-energy flow coupling characteristics of the system. Based on the revenue objective function and constraints, a master-slave game optimization model of the integrated energy microgrid system is constructed, and a system scheduling strategy is generated. This constructs a refined integrated energy microgrid system under the energy hub architecture, breaking through the limitations of static or single energy models, improving the system's adaptability and robustness in the face of different user compositions and changes in market conditions, enhancing the microgrid's acceptance of renewable energy and overall energy utilization efficiency, and also helping to promote the collaborative operation and intelligent scheduling of distributed energy, thereby laying a solid technical foundation for the construction of a new type of power system.

[0018] Optionally, in one embodiment of this application, the first determining module includes: a first determining unit, configured to determine the microgrid operator's revenue objective function by obtaining the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales based on the daily operating revenue and the electricity load; a second determining unit, configured to determine the user aggregator's revenue objective function by obtaining the user aggregator's electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and cost of heat purchase based on the daily operating revenue and the electricity load; and a third determining unit, configured to determine the shared energy storage operator's revenue objective function based on the shared energy storage operator's energy storage charging and discharging cost and energy storage subsidy fee.

[0019] Through the above-mentioned technical means, the embodiments of this application can construct reasonable revenue objective functions for microgrid operators, user aggregators, and shared energy storage operators based on the actual operating characteristics and cost structure of various entities. This provides a target orientation that fits the actual operating scenario for subsequent game optimization, thereby realizing multi-entity economic optimization and ensuring that the final generated scheduling strategy has stronger feasibility and rationality.

[0020] Optionally, in one embodiment of this application, the second determining module includes: a first establishing unit, configured to establish energy balance constraints, load management constraints, and equipment boundary constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system; a second establishing unit, configured to establish energy storage constraints of the integrated energy microgrid system based on the operating constraints of the shared energy storage operator; and a fourth determining unit, configured to determine the constraints of the integrated energy microgrid system based on the energy balance constraints, the load management constraints, the equipment boundary constraints, and the energy storage constraints.

[0021] Through the above-mentioned technical means, the embodiments of this application can combine the multi-energy flow coupling characteristics of the integrated energy microgrid system to establish energy balance constraints, load management constraints and equipment boundary constraints. At the same time, the operating rules of the shared energy storage operator are incorporated to form energy storage constraints. The safety boundary of system operation is defined from multiple dimensions such as energy supply and demand, load regulation, equipment operating limits and energy storage operating rules. This ensures that the scheduling scheme conforms to the law of energy conservation and the physical characteristics of the equipment, provides a reliable physical basis for game optimization, effectively avoids infeasible scheduling schemes, and ensures the engineering practicality of the scheduling strategy.

[0022] Optionally, in one embodiment of this application, the generation module includes: a fifth determining unit, configured to determine the microgrid operator's pricing strategy set based on the revenue objective function and the constraints, so as to determine the user optimization strategy set of the user aggregator based on the pricing strategy set; a construction unit, configured to construct the master-slave game optimization model based on the pricing strategy set and the user optimization strategy set; and a solving unit, configured to solve the microgrid operator's electricity pricing strategy using a preset algorithm based on the master-slave game optimization model, and call a preset solver to solve the user aggregator's optimization strategy, so as to generate the scheduling strategy of the integrated energy microgrid system according to the electricity pricing strategy and the optimization strategy.

[0023] Through the aforementioned technical means, the embodiments of this application can determine the pricing strategy set of microgrid operators based on the revenue objective function and constraints, and derive the user optimization strategy set of user aggregators accordingly, so as to build a complete master-slave game optimization model, clarify the decision space and interaction mechanism of each subject, use a preset algorithm to solve the electricity pricing strategy of microgrid operators and call a preset solver to solve the optimization strategy of user aggregators, which can efficiently obtain equilibrium solutions, facilitate actual engineering deployment, and the generated scheduling strategy can effectively balance the overall benefits of the system and the interests of individual users, achieving the dual goals of multi-energy collaborative optimization and resource sharing.

[0024] Optionally, in one embodiment of this application, the expression of the master-slave game optimization model is: , in, For the microgrid operator, For the user aggregator, For the set of pricing strategies, Optimize the strategy set for the user. For the utility set of the microgrid operator, This refers to the utility set of the user aggregator.

[0025] Through the above-mentioned technical means, the embodiments of this application can construct a master-slave game optimization model for an integrated energy microgrid system under a hub architecture, providing a clear objective and boundary for solving the problem. This model can clearly depict the master-slave game relationship between the microgrid operator and the user aggregator, thereby improving the rigor and reproducibility of the model, providing a unified mathematical benchmark for subsequent solutions, and ensuring the standardization of the optimization process and the accuracy of the results.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the master-slave game optimization operation method for an integrated energy microgrid system as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described master-slave game optimization operation method for an integrated energy microgrid system.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described master-slave game optimization operation method for integrated energy microgrid systems.

[0029] This application's embodiments can construct the revenue objective functions for microgrid operators, user aggregators, and shared energy storage operators, and clarify operational constraints by combining the system's multi-energy flow coupling characteristics. Based on the revenue objective function and constraints, a master-slave game optimization model for the integrated energy microgrid system is constructed, and a system scheduling strategy is generated. This constructs a refined integrated energy microgrid system under an energy hub architecture, breaking through the limitations of static or single-energy models, improving the system's adaptability and robustness in the face of different user compositions and market conditions, enhancing the microgrid's acceptance of renewable energy and overall energy utilization efficiency, and also helping to promote the collaborative operation and intelligent scheduling of distributed energy, thus laying a solid technical foundation for building a new type of power system. Therefore, it solves the problems in related technologies, such as focusing only on a single type of flexible electrical load in the system, resulting in insufficient system flexibility and difficulty in achieving efficient collaboration; the inability to fully reflect the complex interaction characteristics of multiple users due to insufficient integration of shared energy storage mechanisms, leading to difficulties in the fair and efficient allocation of energy storage resources, and the inability to simultaneously guarantee the overall system benefits and individual user interests; or the low overall system energy efficiency due to the game model ignoring the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a master-slave game optimization operation method for an integrated energy microgrid system according to an embodiment of this application. Figure 2 This is a schematic diagram of an integrated energy microgrid framework under an energy hub architecture according to an embodiment of this application; Figure 3 This is a schematic diagram of a microgrid operator optimization strategy provided according to an embodiment of this application; Figure 4 This is a schematic diagram of a user aggregator optimization strategy provided according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a master-slave game optimization operation device for an integrated energy microgrid system according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Integrated energy microgrid system master-slave game optimization operation device; 100-First determination module, 200-Second determination module, 300-Generation module; 601-Memory, 602-Processor, 603-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, outlines a master-slave game-theoretic optimization method and apparatus for an integrated energy microgrid system, based on embodiments of this application. Addressing the issues raised in the background section regarding related technologies, which focus solely on a single type of flexible electrical load, resulting in insufficient system flexibility and difficulty in achieving efficient collaboration, and the lack of fully integrated shared energy storage mechanisms, which hinders the reflection of complex multi-user interactions and leads to unfair and inefficient allocation of energy storage resources, failing to simultaneously guarantee overall system benefits and individual user interests, or exhibiting low overall system energy efficiency due to the game-theoretic model neglecting the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity, this application provides a master-slave game-theoretic optimization method for an integrated energy microgrid system. In this method, a master-slave game-theoretic optimization mechanism can be constructed involving a microgrid operator, a user aggregator, and a shared energy storage system. This paper proposes a comprehensive energy microgrid system based on a refined energy hub architecture. This model leverages the revenue objective function of energy storage operators and clarifies operational constraints by considering the multi-energy flow coupling characteristics of the system. It constructs a master-slave game optimization model for the integrated energy microgrid system based on the revenue objective function and constraints, generating a system scheduling strategy. This overcomes the limitations of static or single-energy models, enhancing the system's adaptability and robustness in the face of different user compositions and market conditions. It also strengthens the microgrid's acceptance of renewable energy and overall energy utilization efficiency, and promotes the collaborative operation and intelligent scheduling of distributed energy resources, thus laying a solid technical foundation for building a new type of power system. This approach addresses several issues in related technologies. For example, focusing solely on a single type of flexible electrical load in the system leads to insufficient system flexibility and difficulty in achieving efficient collaboration. Furthermore, the lack of integration of shared energy storage mechanisms makes it difficult to reflect the complex interactive characteristics of multiple users, resulting in unfair and inefficient allocation of energy storage resources and an inability to simultaneously guarantee the overall system benefits and individual user interests. Additionally, the game model's neglect of the coupling and mutual conversion capabilities between heat, electricity, and other energy flows leads to low overall system energy efficiency.

[0035] Specifically, Figure 1 This is a flowchart illustrating a master-slave game optimization operation method for an integrated energy microgrid system provided in an embodiment of this application.

[0036] like Figure 1 As shown, the master-slave game optimization operation method of this integrated energy microgrid system includes the following steps: In step S101, based on the daily operating revenue and electricity load of the integrated energy microgrid system under the energy hub architecture, the revenue objective functions of the microgrid operator, user aggregator, and shared energy storage operator are determined respectively.

[0037] It is understood that the energy hub architecture in this application embodiment can be understood as the core carrier for realizing multi-energy integration and conversion, which can uniformly connect distributed generation, electric heating equipment, gas turbines and other units and realize energy scheduling and distribution. The integrated energy microgrid system realizes the interconnection of three objects—microgrid operators, user aggregators and shared energy storage operators—through the energy hub, such as... Figure 2 As shown.

[0038] In actual implementation, the embodiments of this application can collect historical operating data of the integrated energy microgrid system, including market information such as electricity price, heat price, and gas price at various time periods, as well as operating efficiency parameters of various energy equipment. Based on the multi-energy flow conversion relationship of the energy hub, combined with daily operating revenue and electricity load, the revenue objective functions of the microgrid operator, user aggregator and shared energy storage operator are determined respectively.

[0039] Specifically, the core business of a microgrid operator is as an energy trading and supply service provider. Its revenue primarily comes from selling electricity and heat to end users, profiting from market price differences through optimized pricing strategies. Simultaneously, it possesses independent production capabilities, utilizing its configured gas turbines to ensure a stable and reliable energy supply to users. This application's embodiments can divide the microgrid operator's benefits into electricity revenue and heat revenue, thereby determining the microgrid operator's revenue objective function based on these two revenue streams.

[0040] Furthermore, user-side electricity loads can be categorized into rigid and flexible types. Rigid loads must be supplied with electricity during fixed time periods; flexible loads, on the other hand, can adjust their supply time according to electricity price signals, thereby achieving demand-side response. It should be noted that the operation of user-owned electric heating equipment generates additional power demand. The revenue of shared energy storage providers is calculated independently, and the optimization objective is not based on a master-slave game.

[0041] The embodiments of this application can determine the revenue objective functions of the microgrid operator, user aggregator, and shared energy storage operator based on the daily operating revenue and electricity load of the integrated energy microgrid system. This effectively reflects the complex interest distribution relationship in the integrated energy microgrid system, provides a basis for subsequent game optimization, and ensures that the optimization results can maximize the overall benefits of the system while taking into account the reasonable benefits of each participating entity, thus promoting the sustainable operation of the system.

[0042] Optionally, in one embodiment of this application, based on the daily operating revenue and electricity load of the integrated energy microgrid system under the energy hub architecture, the revenue objective functions of the microgrid operator, user aggregator, and shared energy storage operator are determined respectively. This includes: obtaining the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales based on the daily operating revenue and electricity load, to determine the microgrid operator's revenue objective function; obtaining the user aggregator's electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and cost of purchasing heat based on the daily operating revenue and electricity load, to determine the user aggregator's revenue objective function; and determining the shared energy storage operator's revenue objective function based on the shared energy storage operator's energy storage charging and discharging costs and energy storage subsidy fees.

[0043] It is understood that in the embodiments of this application, the energy storage fee refers to the rental expenditure incurred by the user aggregator in renting shared energy storage capacity and services, and the heat reduction cost refers to the compensatory expenditure incurred by the user side in adjusting the heat load.

[0044] In actual implementation, this application embodiment can be set as a day divided into H time periods. The microgrid operator's benefits are divided into electricity revenue and heat revenue. The former includes the cost of gas fuel, electricity sales revenue, and electricity purchase cost, while the latter only represents the profit obtained by the microgrid operator through selling heat to users. Therefore, the daily benefits of a microgrid operator can be expressed as: , in, For the utility set of microgrid operators, For electricity revenue, For thermal benefits.

[0045] The formula for electricity revenue is as follows: , In the formula For fuel costs, Indicates power generation efficiency. The calorific value of natural gas. For microgrid operators' electricity sales price, The output power of the micro-turbine on the microgrid operator side can be sold to the user side. The electrical power (excluding the micro-turbine output) is purchased for the user side, therefore This refers to the electricity sales revenue of iMoment microgrid operators. The electricity price sold by the power grid, therefore The electricity purchase cost for i-moment microgrid operators.

[0046] The formula for thermal gain is: , in, The price at which microgrid operators sell heat to users. Heat provided to users by microgrid operators.

[0047] To maximize efficiency, microgrid operators will use electricity and heat pricing as strategies, which must satisfy the following: , In the formula, and These are the grid purchase price and the retail price of electricity, as well as the retail price of electricity for microgrid operators, which are generally time-of-use prices. and To set the upper and lower limits for heat prices respectively.

[0048] User-side electrical loads can be divided into two categories: rigid and flexible. Rigid loads must be supplied with electricity during fixed periods; flexible loads, on the other hand, can adjust their supply time according to electricity price signals, thereby achieving demand-side response. It should be noted that the operation of user-owned electric heating equipment will generate additional power demand.

[0049] The objective function for the user aggregator is: , in, A collection of utility for user aggregators. This refers to the portion of electricity revenue generated by user aggregators within a single day. This indicates the revenue generated from heat.

[0050] Specifically, The electricity revenue of a user aggregator within a day, including electricity sales revenue, electricity purchase cost, energy storage fee, and heat reduction cost, is expressed as: , in The purchase price of electricity from the grid. Electricity sold to the grid by users The rental fee for users to use energy storage, and To facilitate the charging and discharging power of shared energy storage for users. In order to reduce the heat load, This refers to the penalty cost incurred by users for a decrease in comfort caused by reducing heat load.

[0051] and The revenue from heat is represented by the following formula: .

[0052] It should be noted that the revenue of shared energy storage providers is calculated independently, and the optimization objective is not based on a master-slave game. , in, To reduce the cost of energy storage charging and discharging, This refers to subsidies for energy storage. It should be noted that shared energy storage in the model is primarily represented by constraints. The revenue objective function of shared energy storage providers exhibits a dynamic characteristic; there is no independent decision-making strategy, and its value is influenced by both their own operational constraints and the strategies of the two main game players.

[0053] The embodiments of this application can construct reasonable revenue objective functions for microgrid operators, user aggregators, and shared energy storage operators based on the actual operating characteristics and cost structure of various entities. This provides a target orientation that fits the actual operating scenario for subsequent game optimization, thereby realizing multi-entity economic optimization and ensuring that the final generated scheduling strategy has stronger feasibility and rationality.

[0054] In step S102, the constraints of the integrated energy microgrid system are determined based on the multi-energy flow coupling characteristics of the integrated energy microgrid system.

[0055] It is understood that the multi-energy flow coupling characteristics in the embodiments of this application can be understood as the operational characteristics of the integrated energy microgrid in which energy forms such as electricity and heat are converted into each other and supply and demand are linked through conversion equipment such as gas turbines and heat pumps.

[0056] In practical implementation, embodiments of this application can construct energy balance constraints, establishing supply and demand balance equations for electrical and thermal energy respectively to satisfy the law of conservation of energy. To ensure basic electricity rights and thermal comfort, and to maintain participation enthusiasm, load management constraints need to be set to limit the total transferable electricity and the total reducible heat. Furthermore, embodiments of this application can construct equipment boundary constraints, setting upper and lower limits for rated output and ramp rate limits for equipment such as micro-turbines and electric heating equipment to ensure that the equipment always operates within its rated operating range. Embodiments of this application can also combine the operating characteristics of shared energy storage to establish energy storage constraints, including but not limited to state of charge constraints, closed-loop cycles, and charge / discharge mutual exclusion.

[0057] The embodiments of this application can construct a constraint system for integrated energy microgrids by combining the multi-energy flow coupling characteristics of the system, thereby ensuring the physical feasibility of system operation, providing reliable boundary conditions for subsequent game optimization, avoiding the problem of infeasible optimization results due to lack of constraints, and ensuring the safety, stability and compliance of system operation.

[0058] Optionally, in one embodiment of this application, determining the constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system includes: establishing energy balance constraints, load management constraints, and equipment boundary constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system; establishing energy storage constraints of the integrated energy microgrid system based on the operational constraints of the shared energy storage operator; and determining the constraints of the integrated energy microgrid system based on the energy balance constraints, load management constraints, equipment boundary constraints, and energy storage constraints.

[0059] It is understood that the energy balance constraint in the embodiments of this application can be understood as the constraint that the input and output of various energy forms in the system must be balanced at each time period; the load management constraint can be the restriction that the total amount of transferable electricity and the total amount of heat that can be reduced can be used to ensure basic electricity rights and thermal comfort, and maintain participation enthusiasm; the equipment boundary constraint can be understood as the constraint that the operating parameters of various energy equipment must be within the technically permissible range; the energy storage constraint can be understood as the operating rules constraint of shared energy storage equipment.

[0060] In actual implementation, the embodiments of this application can construct the constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system, mainly including: energy balance constraints, load management constraints and equipment boundary constraints.

[0061] The energy balance constraints specifically include electrical balance constraints and thermal balance constraints, and their specific calculation formulas are as follows: , , in, For the optimized user-side electrical load, For transferable electrical loads, Provide heat to user-side electric heating equipment. For users' overall electric heating efficiency, For user-side photovoltaic output, For user heat load.

[0062] To safeguard basic electricity rights and thermal comfort, and to maintain participation, load management constraints need to be set to limit the total amount of transferable electricity and the total amount of heat that can be reduced. The calculation formula is as follows: , , In the formula, and These are the user's electrical and thermal loads before optimization. and This represents the maximum adjustable ratio of the electric heating load.

[0063] All device outputs must meet upper and lower boundary constraints, such as micro-turbines, electric heating equipment, etc. The specific calculation formulas for device boundary constraints are as follows: , .

[0064] Furthermore, energy storage constraints include: state of charge constraints, cycle closure, and charge / discharge mutual exclusion.

[0065] The formula for the charge state constraint is as follows: , and These refer to the charging efficiency and discharging efficiency of the shared energy storage system.

[0066] Clearly, the state of charge of shared energy storage should meet upper and lower limit constraints: , In the formula, For storage batteries Upper and lower limits of the state of charge during a given time period.

[0067] To ensure the shared energy storage system can continuously provide services, it is necessary to guarantee that the energy storage capacity returns to its initial value upon termination. The closed-loop formula is as follows: , Assuming that energy storage cannot be charged and discharged simultaneously, the charging and discharging power of the energy storage must meet certain conditions. The mutual exclusion formula for charging and discharging is as follows: .

[0068] It should be noted that the revenue of shared energy storage providers is calculated independently and is not the optimization objective of the master-slave game. In the subsequent master-slave game optimization model, shared energy storage is mainly reflected in the form of constraints.

[0069] This application embodiment can combine the multi-energy flow coupling characteristics of integrated energy microgrid systems to establish energy balance constraints, load management constraints, and equipment boundary constraints. At the same time, it incorporates the operating rules of shared energy storage operators to form energy storage constraints. It delineates the safety boundary of system operation from multiple dimensions such as energy supply and demand, load regulation, equipment operating limits, and energy storage operating rules, ensuring that the scheduling scheme conforms to the law of energy conservation and the physical characteristics of equipment. This provides a reliable physical basis for game theory optimization, effectively avoids infeasible scheduling schemes, and ensures the engineering practicality of the scheduling strategy.

[0070] In step S103, a master-slave game optimization model for the integrated energy microgrid system is constructed based on the objective function of revenue and constraints, and the master-slave game optimization model is solved to generate the scheduling strategy for the integrated energy microgrid system.

[0071] It is understood that the master-slave game optimization model in this application embodiment refers to a two-layer game model consisting of a dominant leader and subordinate followers. In this application embodiment, the microgrid operator sets the price as the dominant party, while the user aggregator and the shared energy storage operator adjust their energy consumption and storage strategies as followers.

[0072] It should be noted that the revenue of shared energy storage providers is calculated independently and is not the optimization objective of the master-slave game. In the master-slave game optimization model, shared energy storage is mainly reflected in the form of constraints.

[0073] In practical implementation, this embodiment places the microgrid operator in a leadership position, with its decision variables being the electricity and heat prices over a 24-hour period; and the user aggregator in a follower position, dynamically adjusting its electricity and heat load demand for each time period based on the pricing scheme and shared energy storage service rental fees. Key variables in the interaction between the two parties include electricity and heat prices, total electricity purchases, and total heat purchases. If the operator's pricing deviates from a reasonable range, the user aggregator will dynamically change its energy procurement behavior; correspondingly, the operator will also adjust its pricing strategy based on changes in user purchase volumes, maximizing utility through multiple optimizations.

[0074] This application embodiment can construct and solve a master-slave game optimization model for an integrated energy microgrid system under a hub architecture, and generate a system scheduling strategy. This model can effectively adapt to distributed operation scenarios where multiple stakeholders make independent decisions. Based on the electricity and heat sales pricing strategies set by the microgrid operator, it comprehensively considers the multi-energy demand characteristics of the user side, realizes win-win cooperation and maximizes the utility of multiple stakeholders in integrated energy, and provides theoretical guidance for practical engineering applications.

[0075] Optionally, in one embodiment of this application, a master-slave game optimization model for the integrated energy microgrid system is constructed based on the revenue objective function and constraints, and the master-slave game optimization model is solved to generate a scheduling strategy for the integrated energy microgrid system. This includes: determining the microgrid operator's pricing strategy set based on the revenue objective function and constraints, and determining the user optimization strategy set for the user aggregator based on the pricing strategy set; constructing a master-slave game optimization model based on the pricing strategy set and the user optimization strategy set; and using a preset algorithm to solve the microgrid operator's electricity pricing strategy based on the master-slave game optimization model, and calling a preset solver to solve the user aggregator's optimization strategy, so as to generate a scheduling strategy for the integrated energy microgrid system based on the electricity pricing strategy and the optimization strategy.

[0076] It is understood that the pricing strategy set formulated by the microgrid operator in this embodiment may include electricity price and heat price; the user optimization strategy set may include a flexible electrical load strategy set, a heat energy reduction strategy set, and a shared energy storage service usage strategy set. The preset algorithm may be a genetic algorithm, or other intelligent optimization methods may be used instead, to optimize complex nonlinear systems. The preset algorithm can be set by those skilled in the art according to actual conditions, and no specific restrictions are imposed here. The preset solver may be a CPLEX solver, and the preset solver can be set by those skilled in the art according to actual conditions, and no specific restrictions are imposed here.

[0077] In practical implementation, this application embodiment can determine the microgrid operator's electricity and heat procurement price strategy set for 24 hours. Based on this, the user aggregator, in conjunction with the pricing scheme and shared energy storage service rental fees, dynamically adjusts the electricity and heat load demand for each time period and optimizes the allocation scheme of shared energy storage resources. The key variables in the interaction between the two parties include electricity sales price, heat sales price, total electricity purchase volume, and total heat purchase volume. If the operator's pricing deviates from a reasonable range, the user aggregator will dynamically change its energy procurement behavior; correspondingly, the operator will also adjust its pricing strategy according to changes in user procurement volume, maximizing utility through multiple optimizations.

[0078] Given the differing interests and decision-making sequences of the two parties, their interaction can be modeled as a master-slave game. The microgrid operator, as the upper-level decision-maker, aims to maximize daily operating revenue, requiring the determination of the optimal electricity and heat prices for the day. For the lower-level user aggregator, the objective is also to maximize daily revenue, necessitating optimization of the flexible load allocation scheme, reduced heat load, and power dispatch strategies for participating in shared energy storage services.

[0079] For example, in terms of solution methods, this embodiment of the application can use a genetic algorithm to initialize and iteratively update the electricity pricing strategy of the upper-layer microgrid operator, while the optimization problem of the lower-layer user aggregator is solved efficiently by directly calling the CPLEX solver. To verify the effectiveness of the proposed optimization model, simulation experiments were conducted on the MATLAB platform, and the results are as follows. Figure 3 and Figure 4 As shown, microgrid operators, shared energy storage operators, and user aggregators have all achieved corresponding improvements in efficiency.

[0080] This application embodiment can determine the pricing strategy set of microgrid operators based on the revenue objective function and constraints, and derive the user optimization strategy set of user aggregators accordingly to build a complete master-slave game optimization model. It clarifies the decision space and interaction mechanism of each subject, uses a preset algorithm to solve the electricity pricing strategy of microgrid operators and calls a preset solver to solve the optimization strategy of user aggregators, which can efficiently obtain equilibrium solutions, facilitate actual engineering deployment, and the generated scheduling strategy can effectively balance the overall system benefits and individual user interests, achieving the dual goals of multi-energy collaborative optimization and resource sharing.

[0081] Optionally, in one embodiment of this application, the expression for the master-slave game optimization model is: , in, For microgrid operators, For user aggregator, For a set of pricing strategies, Optimize the strategy set for users. For the utility set of microgrid operators, A collection of utility for user aggregators.

[0082] It is understood that, in the embodiments of this application, the utility set refers to the range of benefits that the corresponding subject can obtain under different combinations of strategies.

[0083] In actual implementation, the master-slave game optimization model G in this application embodiment is expressed as follows:

[0084] Among them, microgrid operators As the leading party, the user aggregator To follow, This refers to the set of pricing strategies formulated by microgrid operators, including electricity and heat prices. This represents the user's optimization strategy set, including the flexible electrical load strategy set, the heat reduction strategy set, and the shared energy storage service usage strategy set. For the collection of microgrid operator benefits, It is a collection of user-side aggregated commercial utility.

[0085] In this model: microgrid operators As the leading party, the user aggregator To follow; This refers to the set of pricing strategies formulated by microgrid operators, including electricity and heat prices. This represents the user optimization strategy set, including the flexible electrical load strategy set, the heat reduction strategy set, and the shared energy storage service usage strategy set; For the collection of microgrid operator benefits, It is a collection of user-side aggregated commercial utility.

[0086] The embodiments of this application can construct a master-slave game optimization model for an integrated energy microgrid system under a hub architecture. This model provides a clear objective and boundary for solving the problem, clearly depicts the master-slave game relationship between the microgrid operator and the user aggregator, thereby improving the rigor and reproducibility of the model, providing a unified mathematical benchmark for subsequent solutions, and ensuring the standardization of the optimization process and the accuracy of the results.

[0087] The master-slave game optimization operation method for integrated energy microgrid systems proposed in this application can construct the profit objective functions of microgrid operators, user aggregators, and shared energy storage operators. Combined with the multi-energy flow coupling characteristics of the system, operational constraints are clarified. Based on the profit objective functions and constraints, a master-slave game optimization model for the integrated energy microgrid system is constructed, and a system scheduling strategy is generated. This constructs a refined integrated energy microgrid system under an energy hub architecture, breaking through the limitations of static or single-energy models. It improves the system's adaptability and robustness in the face of different user compositions and market conditions, while enhancing the microgrid's acceptance of renewable energy and overall energy utilization efficiency. It also helps promote the collaborative operation and intelligent scheduling of distributed energy, thus laying a solid technical foundation for building a new type of power system. This solves the problems in related technologies, such as the inability to fully utilize system flexibility and achieve efficient collaboration due to focusing only on a single type of flexible electrical load in the system, the inability to fully integrate shared energy storage mechanisms to reflect the complex interaction characteristics of multiple users, the difficulty in fairly and efficiently allocating energy storage resources, the inability to simultaneously guarantee the overall benefits of the system and the interests of individual users, or the low overall energy efficiency of the system due to the game theory model ignoring the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity.

[0088] Next, referring to the accompanying drawings, the integrated energy microgrid system master-slave game optimization operation device proposed according to the embodiments of this application is described.

[0089] Figure 5 This is a schematic diagram of the structure of the master-slave game optimization operation device for the integrated energy microgrid system according to an embodiment of this application.

[0090] like Figure 5 As shown, the master-slave game optimization operation device 10 of the integrated energy microgrid system includes: a first determining module 100, a second determining module 200 and a generating module 300.

[0091] The first determining module 100 is used to determine the daily operating revenue and electricity load of the integrated energy microgrid system based on the energy hub architecture, and to determine the revenue objective functions of the microgrid operator, user aggregator and shared energy storage operator respectively.

[0092] The second determining module 200 is used to determine the constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system.

[0093] The generation module 300 is used to construct a master-slave game optimization model for the integrated energy microgrid system based on the profit objective function and constraints, and to solve the master-slave game optimization model to generate the scheduling strategy of the integrated energy microgrid system.

[0094] Optionally, in one embodiment of this application, the first determining module 100 includes: a first determining unit, a second determining unit, and a third determining unit.

[0095] The first determining unit is used to obtain the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales based on the daily operating revenue and electricity load, so as to determine the microgrid operator's revenue objective function.

[0096] The second determining unit is used to obtain the user aggregator's electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and heat purchase cost based on the daily operating revenue and electricity load, so as to determine the user aggregator's revenue objective function.

[0097] The third determining unit is used to determine the revenue objective function of the shared energy storage operator based on the energy storage charging and discharging costs and energy storage subsidy fees of the shared energy storage operator.

[0098] Optionally, in one embodiment of this application, the second determining module 200 includes: a first establishing unit, a second establishing unit, and a fourth determining unit.

[0099] The first establishment unit is used to establish the energy balance constraints, load management constraints, and equipment boundary constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system.

[0100] The second establishment unit is used to establish the energy storage constraints of the integrated energy microgrid system based on the operational constraints of the shared energy storage operator.

[0101] The fourth determining unit is used to determine the constraints of the integrated energy microgrid system based on energy balance constraints, load management constraints, equipment boundary constraints, and energy storage constraints.

[0102] Optionally, in one embodiment of this application, the generation module 300 includes: a fifth determining unit, a construction unit, and a solving unit.

[0103] The fifth determining unit is used to determine the pricing strategy set of the microgrid operator based on the revenue objective function and constraints, and to determine the user optimization strategy set of the user aggregator based on the pricing strategy set.

[0104] The building unit is used to construct a master-slave game optimization model based on the price strategy set and the user optimization strategy set.

[0105] The solution unit is used to solve the electricity pricing strategy of the microgrid operator based on the master-slave game optimization model and a preset algorithm, and to call a preset solver to solve the optimization strategy of the user aggregator, so as to generate the scheduling strategy of the integrated energy microgrid system according to the electricity pricing strategy and the optimization strategy.

[0106] Optionally, in one embodiment of this application, the expression for the master-slave game optimization model is: , in, For microgrid operators, For user aggregator, For a set of pricing strategies, Optimize the strategy set for users. For the utility set of microgrid operators, A collection of utility for user aggregators.

[0107] It should be noted that the foregoing explanation of the embodiment of the master-slave game optimization operation method for integrated energy microgrid system also applies to the master-slave game optimization operation device for integrated energy microgrid system in this embodiment, and will not be repeated here.

[0108] The integrated energy microgrid system master-slave game optimization operation device proposed in the embodiments of this application can construct the profit objective function of microgrid operators, user aggregators, and shared energy storage operators, and clarify the operation constraints in combination with the multi-energy flow coupling characteristics of the system. Based on the profit objective function and constraints, a master-slave game optimization model of the integrated energy microgrid system is constructed, and a system scheduling strategy is generated. This constructs an integrated energy microgrid system under a refined energy hub architecture, breaking through the limitations of static or single energy models, improving the system's adaptability and robustness in the face of different user compositions and changes in market conditions, enhancing the microgrid's acceptance of renewable energy and overall energy utilization efficiency, and also helping to promote the collaborative operation and intelligent scheduling of distributed energy, thereby laying a solid technical foundation for the construction of a new type of power system. This solves the problems in related technologies, such as the inability to fully utilize system flexibility and achieve efficient collaboration due to focusing only on a single type of flexible electrical load in the system, the inability to fully integrate shared energy storage mechanisms to reflect the complex interaction characteristics of multiple users, the difficulty in fairly and efficiently allocating energy storage resources, the inability to simultaneously guarantee the overall benefits of the system and the interests of individual users, or the low overall energy efficiency of the system due to the game theory model ignoring the coupling and mutual conversion capabilities between multiple energy flows such as heat and electricity.

[0109] Figure 6A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0110] When the processor 602 executes the program, it implements the master-slave game optimization operation method for the integrated energy microgrid system provided in the above embodiments.

[0111] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0112] The memory 601 is used to store computer programs that can run on the processor 602.

[0113] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0114] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0115] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0116] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0117] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described integrated energy microgrid system master-slave game optimization operation method.

[0118] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described integrated energy microgrid system master-slave game optimization operation method.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0123] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0124] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A master-slave game optimization operation method for an integrated energy microgrid system, characterized in that, Includes the following steps: Based on the daily operating revenue and electricity load of the integrated energy microgrid system under the energy hub architecture, the revenue objective functions of the microgrid operator, user aggregator and shared energy storage operator are determined respectively. The constraints of the integrated energy microgrid system are determined based on the multi-energy flow coupling characteristics of the integrated energy microgrid system. Based on the objective function and the constraints, a master-slave game optimization model for the integrated energy microgrid system is constructed, and the master-slave game optimization model is solved to generate the scheduling strategy for the integrated energy microgrid system.

2. The method according to claim 1, characterized in that, The daily operating revenue and electricity load of the integrated energy microgrid system based on the energy hub architecture are used to determine the revenue objective functions for the microgrid operator, user aggregator, and shared energy storage operator, respectively, including: Based on the daily operating revenue and the electricity load, the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales are obtained to determine the microgrid operator's revenue objective function. Based on the daily operating revenue and the electricity load, the electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and heat purchase cost of the user aggregator are obtained, so as to determine the revenue objective function of the user aggregator. Based on the energy storage charging and discharging costs and energy storage subsidies of the shared energy storage operator, the revenue objective function of the shared energy storage operator is determined.

3. The method according to claim 1, characterized in that, The determination of the constraints of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system includes: Based on the multi-energy flow coupling characteristics of the integrated energy microgrid system, energy balance constraints, load management constraints, and equipment boundary constraints of the integrated energy microgrid system are established. Based on the operational constraints of the shared energy storage operator, the energy storage constraints of the integrated energy microgrid system are established. Based on the energy balance constraints, load management constraints, equipment boundary constraints, and energy storage constraints, the constraints of the integrated energy microgrid system are determined.

4. The method according to claim 1, characterized in that, The process of constructing a master-slave game optimization model for the integrated energy microgrid system based on the objective function and the constraints, and solving the master-slave game optimization model to generate the scheduling strategy for the integrated energy microgrid system includes: Based on the revenue objective function and the constraints, the pricing strategy set of the microgrid operator is determined, and the user optimization strategy set of the user aggregator is determined based on the pricing strategy set. Based on the price strategy set and the user optimization strategy set, the master-slave game optimization model is constructed. Based on the master-slave game optimization model, a preset algorithm is used to solve the electricity pricing strategy of the microgrid operator, and a preset solver is called to solve the optimization strategy of the user aggregator, so as to generate the scheduling strategy of the integrated energy microgrid system according to the electricity pricing strategy and the optimization strategy.

5. The method according to claim 4, characterized in that, The expression for the master-slave game optimization model is: ， in, For the microgrid operator, For the user aggregator, For the set of pricing strategies, Optimize the strategy set for the user. For the utility set of the microgrid operator, This refers to the utility set of the user aggregator.

6. A master-slave game optimization operation device for an integrated energy microgrid system, characterized in that, include: The first determining module is used to determine the daily operating revenue and electricity load of the integrated energy microgrid system based on the energy hub architecture, and to determine the revenue objective functions of the microgrid operator, user aggregator and shared energy storage operator respectively. The second determining module is used to determine the constraint conditions of the integrated energy microgrid system based on the multi-energy flow coupling characteristics of the integrated energy microgrid system. The generation module is used to construct a master-slave game optimization model of the integrated energy microgrid system based on the objective function of revenue and the constraints, and solve the master-slave game optimization model to generate the scheduling strategy of the integrated energy microgrid system.

7. The apparatus according to claim 6, characterized in that, The first determining module includes: The first determining unit is used to obtain the microgrid operator's gas fuel cost, electricity sales revenue, electricity purchase cost, and profit from heat sales based on the daily operating revenue and the electricity load, so as to determine the microgrid operator's revenue objective function. The second determining unit is used to obtain the electricity sales revenue, electricity purchase cost, energy storage fee, heat reduction cost, and heat purchase cost of the user aggregator based on the daily operating revenue and the electricity load, so as to determine the revenue objective function of the user aggregator. The third determining unit is used to determine the revenue objective function of the shared energy storage operator based on the energy storage charging and discharging costs and energy storage subsidy fees of the shared energy storage operator.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the master-slave game optimization operation method for an integrated energy microgrid system as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the master-slave game optimization operation method for the integrated energy microgrid system as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the master-slave game optimization operation method for the integrated energy microgrid system as described in any one of claims 1-5.

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