Micro-grid multi-agent collaborative optimization method and device

Abstract

The application relates to the technical field of micro-grid scheduling, in particular to a micro-grid multi-subject collaborative optimization method and device, wherein the method comprises the following steps: obtaining distributed energy power generation data, load demand and market electricity price of a micro-grid to generate multi-source information; based on the multi-source information, real-time purchase and sale strategies between users and energy storage are generated, and an optimization scheduling framework of the micro-grid is constructed in combination with system power balance constraint conditions and energy storage physical constraint conditions; based on the optimization scheduling framework, a linearization constraint and a large M method are used to process nonlinear problems in scheduling, and a collaborative optimization strategy of multi-subjects in the micro-grid is generated. Therefore, the problems that the related art usually only considers single user demand or relies on static data, so that it is difficult to fully reflect the coupling influence between multi-user behaviors, thereby causing large user energy demand prediction deviation, unreasonable sharing of energy storage resources and the like are solved.

Description

Technical Field

[0001] This application relates to the field of microgrid dispatching technology, and in particular to a method and apparatus for multi-entity collaborative optimization of microgrids. Background Technology

[0002] Among related technologies, the SES (Shared Energy Storage) model has gradually gained attention in order to improve the utilization efficiency of microgrid energy storage resources. Shared energy storage achieves the sharing of energy storage capacity and power resources by centrally constructing energy storage resources and providing energy storage services to multiple microgrid users, thereby reducing overall investment costs and improving energy storage utilization.

[0003] However, in related technologies, microgrid dispatching methods usually only consider the needs of a single user or rely on static data, which makes it difficult to fully reflect the coupling effects between the behaviors of multiple users. This leads to large deviations in the prediction of user energy demand, unreasonable allocation of shared energy storage resources, and mismatch between charging and discharging strategies and actual operating conditions. As a result, the utilization rate of shared energy storage decreases, the capacity for new energy consumption is limited, the energy costs of users increase, and the overall economic efficiency and stability of microgrid operation are reduced. Summary of the Invention

[0004] This application provides a multi-entity collaborative optimization method and device for microgrids to solve the problems in related technologies, where scheduling methods usually only consider the needs of a single user or rely on static data, making it difficult to fully reflect the coupling effects between the behaviors of multiple users, resulting in large deviations in user energy demand prediction, unreasonable allocation of shared energy storage resources, and mismatch between charging and discharging strategies and actual operating conditions.

[0005] The first aspect of this application provides a multi-agent collaborative optimization method for microgrids, comprising the following steps: acquiring distributed energy generation data, load demand, and market electricity prices of the microgrid to generate multi-source information; generating real-time purchase and sale strategies between users and energy storage based on the multi-source information, and constructing an optimized scheduling framework for the microgrid by combining system power balance constraints and energy storage physical constraints; and generating a multi-agent collaborative optimization strategy for the microgrid based on the optimized scheduling framework by using linearization constraints and the Big M method to handle nonlinear problems in scheduling.

[0006] Using the above technical means, based on the microgrid's optimal scheduling framework, linearization constraints, and the Big M method, user electricity purchase and sale behavior and shared energy storage charging and discharging behavior can be jointly modeled. Under the microgrid's optimal scheduling framework, linearization constraints and the Big M method are introduced to equivalently transform nonlinear constraints, thereby reducing the model's solution complexity, improving the solvability and convergence efficiency of the problem, and realizing the coordinated optimal configuration of energy flows from multiple entities.

[0007] Optionally, in one embodiment of this application, before generating a real-time purchase and sale strategy between users and energy storage, the method further includes: constructing a multi-master microgrid supply and demand model based on power balance constraints, capacity and power constraints, with the goal of minimizing the energy cost for microgrid users, and establishing a shared energy storage model; and establishing a multi-master microgrid supply and demand optimization model based on the multi-master microgrid supply and demand model and the shared energy storage model.

[0008] By employing the above technical means, a multi-entity microgrid supply and demand optimization model can be established based on the multi-entity microgrid supply and demand model and the shared energy storage model. This model can achieve a unified description of the supply and demand relationship of multiple microgrids and the shared energy storage regulation mechanism, enhance the modeling accuracy of energy interaction among multiple entities, improve the rationality and coordination of system scheduling decisions, and provide a feasible mathematical model basis for subsequent optimization solutions.

[0009] Optionally, in one embodiment of this application, generating a real-time purchase and sale strategy between users and energy storage includes: inputting multi-source information into a microgrid supply and demand optimization model, optimizing the operating cost of the microgrid, and planning the capacity and maximum power corresponding to the shared energy storage mechanism, so as to output a real-time purchase and sale strategy.

[0010] By using the above technical means, the operating cost of microgrids can be optimized, and the capacity and maximum power corresponding to the shared energy storage mechanism can be planned, so as to achieve the rational allocation and efficient utilization of shared energy storage resources. Furthermore, based on the optimization results, the energy interaction relationship of each entity in different scheduling periods can be determined, and the real-time purchase and sale strategy between users, microgrids and shared energy storage can be obtained.

[0011] Optionally, in one embodiment of this application, the expression for the system power balance constraint is: , in, For users exist Electricity purchased from the power grid during a given period. and users respectively exist Electricity purchased and sold from shared energy storage during specific time periods. , , Representing households exist Wind power, photovoltaic power and load power during the time period.

[0012] By using the above technical means, based on the electricity purchased and sold by users, the power of distributed energy sources, and the charging and discharging status of shared energy storage, the system power balance constraints are determined, and the energy conservation relationship of the microgrid at various time scales is characterized, thereby ensuring the feasibility of system supply and demand balance and optimized scheduling results.

[0013] Optionally, in one embodiment of this application, the calculation formula for the nonlinear problem is: , in, and These are reflections of shared energy storage Boolean variables representing the charging and discharging states during the time period. , These represent shared energy storage. Discharge power and charging power during the time period.

[0014] By using the above techniques to transform nonlinear problems into linear problems, the complexity of the optimization model can be effectively reduced, the difficulty of solving nonconvex problems can be avoided, the solution efficiency can be improved, and the microgrid multi-agent collaborative optimization problem can obtain an approximate optimal solution within an acceptable computation time.

[0015] A second aspect of this application provides a microgrid multi-entity collaborative optimization device, comprising: an acquisition module for acquiring distributed energy generation data, load demand, and market electricity prices of the microgrid to generate multi-source information; a construction module for generating real-time purchase and sale strategies between users and energy storage based on the multi-source information, and constructing an optimized scheduling framework for the microgrid by combining system power balance constraints and energy storage physical constraints; and an optimization module for generating a multi-entity collaborative optimization strategy for the microgrid by using linearization constraints and the Big M method to handle nonlinear problems in scheduling based on the optimized scheduling framework.

[0016] Optionally, in one embodiment of this application, the microgrid multi-entity collaborative optimization device further includes: a first establishment module, used to construct a multi-entity microgrid supply and demand model based on power balance constraints, capacity and power constraints with the goal of minimizing the energy cost of microgrid users, and to establish a shared energy storage model; and a second establishment module, used to establish a multi-entity microgrid supply and demand optimization model based on the multi-entity microgrid supply and demand model and the shared energy storage model.

[0017] Optionally, in one embodiment of this application, the construction module includes: an output unit, used to input multi-source information into the microgrid supply and demand optimization model, optimize the operating cost of the microgrid, and plan the capacity and maximum power corresponding to the shared energy storage mechanism, so as to output a real-time purchase and sale strategy.

[0018] Optionally, in one embodiment of this application, the expression for the system power balance constraint is: , in, For users exist Electricity purchased from the power grid during a given period. and users respectively exist Electricity purchased and sold from shared energy storage during specific time periods. , , Representing households exist Wind power, photovoltaic power and load power during the time period.

[0019] Optionally, in one embodiment of this application, the calculation formula for the nonlinear problem is: , in, and These are reflections of shared energy storage Boolean variables representing the charging and discharging states during the time period. , These represent shared energy storage. Discharge power and charging power during the time period.

[0020] 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 microgrid multi-agent collaborative optimization method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described microgrid multi-agent collaborative optimization method.

[0022] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the microgrid multi-agent collaborative optimization method described above.

[0023] 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

[0024] 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 schematic diagram of the structure of a microgrid multi-agent system according to an embodiment of this application; Figure 2 This is a flowchart of a multi-agent collaborative optimization method for microgrids provided according to an embodiment of this application; Figure 3 This is a flowchart of a microgrid multi-agent collaborative optimization method according to an embodiment of this application; Figure 4 This is a schematic diagram of the power load balance of a user according to an embodiment of this application; Figure 5 This is a schematic diagram of the optimized power and state of charge of an embodiment of the SES according to this application. Figure 6 This is a block diagram of a microgrid multi-entity collaborative optimization device according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0025] Figure label: 10-Microgrid multi-entity collaborative optimization device; 100-Acquisition module, 200-Construction module, 300-Optimization module; 701-Memory, 702-Processor, 703-Communication interface. Detailed Implementation

[0026] 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.

[0027] The following description, with reference to the accompanying drawings, describes a microgrid multi-entity collaborative optimization method and apparatus according to embodiments of this application.

[0028] Before describing the microgrid multi-entity collaborative optimization method of the embodiments of this application, the technical issues and system architecture involved in this application will be explained first.

[0029] Understandably, while research on microgrid dispatch optimization has yielded some results, significant shortcomings remain in introducing multi-user behavior interaction mechanisms and SES models. Firstly, most related methods focus on individual users as the modeling subject, lacking characterization of supply and demand coordination among users and failing to reflect the complementarity and synergy of multiple users in energy flow and cost optimization. Especially in situations with shared resources, how to fairly and efficiently allocate energy storage resources while balancing overall system benefits with individual user interests is a problem generally avoided by related technologies.

[0030] Secondly, in real-world scenarios involving fluctuating electricity prices and market price variations, relevant optimization models generally rely on static data and fail to establish a real-time response dispatch mechanism. The inability to promptly reflect market dynamics to inform user behavior decisions results in inflexible user dispatch strategies, leading to high system operating costs, insufficient economic efficiency, and difficulty in achieving peak shaving and load balancing objectives. Furthermore, related studies often treat energy storage system planning as an independent problem, failing to couple it with user-side energy consumption behavior, electricity purchase and sale decisions, and system operational goals, thus limiting the regulation potential and utilization efficiency of energy storage.

[0031] Furthermore, while some related technologies consider the application of optimization algorithms in microgrid dispatching, their engineering deployment and practical operation are difficult to achieve due to limitations in algorithm design complexity or convergence performance. Meanwhile, the current application of multi-agent frameworks remains at the theoretical level, lacking a systematic strategy that organically integrates user behavior modeling, system goal optimization, and energy market mechanisms.

[0032] To address at least one of the technical problems mentioned above, this application provides a multi-entity collaborative optimization method for microgrids, aiming to minimize user electricity costs, establish a fair, dynamic, and efficient energy sharing mechanism, enhance the system's adaptability and robustness under different user combinations and market environments, improve the absorption capacity and energy utilization efficiency of renewable energy in microgrids, promote the collaborative operation and intelligent dispatch of distributed energy resources, and provide a solid technical foundation for the construction of new power systems.

[0033] As a concrete example, such as Figure 1 As shown, a microgrid multi-entity system considering SES can include three parts: power grid 101, SES 102, and users 103.

[0034] Grid 101 is used to realize the collection and dispatch management of local energy, and is responsible for the aggregation of distributed energy output, load demand coordination, and power interaction with shared energy storage 102. SES102, as a shared energy storage unit, is used to centrally store and release redundant electrical energy within multiple users 103 and grid 101, and to perform charging and discharging regulation according to the system operating status to achieve peak shaving and valley filling and energy time shifting. User 103, as the main body of energy consumption and production, determines the purchase and sale of electricity and load response strategies according to its own load demand, distributed energy output and market electricity price information, and interacts with grid 101 and SES102 to form a multi-entity collaborative microgrid operation system.

[0035] Based on the above system architecture, the microgrid multi-entity collaborative optimization method of this application embodiment can be implemented. The microgrid multi-entity collaborative optimization method will be described in detail below.

[0036] Specifically, Figure 2This is a flowchart illustrating a multi-entity collaborative optimization method for microgrids provided in an embodiment of this application.

[0037] like Figure 2 As shown, the multi-agent collaborative optimization method for microgrids includes the following steps: In step S101, distributed energy generation data, load demand, and market electricity price of the microgrid are acquired to generate multi-source information.

[0038] Among them, distributed energy generation data includes, but is not limited to, historical output data, real-time output data, predicted output data, and environmental parameter data that affect power generation; load demand data includes, but is not limited to, historical load data, real-time load data, predicted load data, and demand response data, load regulation capacity data, and electricity preference data that characterize user electricity consumption behavior.

[0039] This application embodiment generates multi-source information through distributed energy generation data, load demand, and market electricity prices of microgrids. This information can comprehensively characterize the energy supply capacity, energy demand, and market environment changes of microgrids, providing data support for user electricity purchase and sale behavior analysis, shared energy storage resource allocation, and system scheduling optimization.

[0040] In step S202, based on multi-source information, a real-time purchase and sale strategy between users and energy storage is generated, and combined with the system power balance constraints and energy storage physical constraints, an optimized scheduling framework for the microgrid is constructed.

[0041] For example, in a real-time electricity purchase and sale strategy between users and energy storage, users can dynamically adjust their electricity purchase and sale behavior based on market electricity prices, distributed energy generation status, and their own load demand, with the goal of minimizing their own energy costs and improving energy efficiency. Energy storage, on the other hand, aims for optimal overall system operation, comprehensively considering the energy storage's state of charge, charging and discharging power constraints, battery degradation costs, and multi-user service needs. It dynamically determines the energy storage's charging and discharging plan and capacity allocation strategy, aiming to maximize energy storage operating revenue, improve energy storage utilization, and minimize system operating costs while meeting the energy needs of multiple users.

[0042] System power balance constraints can be that the sum of distributed energy generation, electricity purchased by users from the grid, and electricity purchased by users from SES (Supply Storage Equipment) equals the electricity demanded by users and the electricity sold by users; energy storage physical constraints can be upper and lower limits of the state of charge of shared energy storage, upper and lower limits of charging and discharging power, etc. Among them, the system power balance constraints and energy storage physical constraints can be specifically limited according to the actual situation.

[0043] The optimal scheduling framework for microgrids can take distributed energy generation data, load demand data, market electricity price data, and shared energy storage operation status data as inputs. By constructing user behavior models, shared energy storage models, and system operation constraint models, it can jointly optimize user electricity purchase and sale strategies, shared energy storage charging and discharging strategies, and microgrid energy allocation strategies, and output the overall scheduling architecture of the target scheduling scheme.

[0044] This application embodiment, through the microgrid's optimized scheduling framework, can integrate user-side energy demand, distributed energy supply capacity, and shared energy storage regulation capacity to establish a unified energy management and optimized scheduling mechanism, thereby improving the utilization rate of shared energy storage resources and the overall economic efficiency of microgrid operation.

[0045] Optionally, in one embodiment of this application, before generating a real-time purchase and sale strategy between users and energy storage, the method further includes: constructing a multi-master microgrid supply and demand model based on power balance constraints, capacity and power constraints, with the goal of minimizing the energy cost for microgrid users, and establishing a shared energy storage model; and establishing a multi-master microgrid supply and demand optimization model based on the multi-master microgrid supply and demand model and the shared energy storage model.

[0046] Among them, the multi-master microgrid supply and demand model is used to characterize the energy supply and demand relationship and power interaction relationship between multiple microgrid entities and between them and the main grid; the shared energy storage model is used to characterize the charging and discharging behavior, state of charge change and operating constraints of the shared energy storage system in different scheduling periods.

[0047] As a specific example, embodiments of this application can construct a multi-entity microgrid supply and demand optimization model based on a multi-entity microgrid supply and demand model and a shared energy storage model, coordinating user electricity purchase and sale behavior with shared energy storage charging and discharging behavior.

[0048] Specifically, the common goal of microgrid users is to minimize their energy costs; therefore, the embodiments of this application can... Defined as the total cost of purchasing electricity from the grid, then , in, For the number of microgrid users, To optimize the total scheduling time, The price at which electricity is purchased from the grid, in the context of peak-valley pricing, is typically a piecewise function. For users exist Electricity purchased from the power grid during a given period.

[0049] For users in microgrids For example, the power balance constraint conditions can be as follows: , When the equation is true, it means that there was exactly no power wastage during this period.

[0050] Secondly, SES can act as an energy buffer through appropriate charging and discharging strategies. To construct an effective SES model, capacity and power constraints should be met: , in, yes State of Charge (Soc) during a given time period. It is the self-discharge rate of SES. and These are the charging efficiency and discharging efficiency of SES, respectively.

[0051] Considering the dual constraints of power and capacity, the embodiments of this application should satisfy: , in, and These are the maximum and minimum states of charge, respectively. and These are reflections of SES in Boolean variables representing the charging and discharging states during the time period. This is the maximum charge and discharge power of the SES.

[0052] Subsequently, embodiments of this application can introduce SES into a multi-agent microgrid system, establish a multi-entity microgrid supply and demand optimization model, and allow the microgrid user side to adjust its power purchase and sale strategy, optimize operating costs, and plan the capacity and maximum power of the SES.

[0053] In actual implementation, a real-time purchase and sale strategy between users and energy storage is generated, including: inputting multi-source information into the microgrid supply and demand optimization model, optimizing the operating cost of the microgrid, and planning the capacity and maximum power corresponding to the shared energy storage mechanism, so as to output the real-time purchase and sale strategy.

[0054] In the embodiments of this application, operating costs It can be represented as: , in, This is the electricity fee paid by microgrid users to SES. Therefore , in, and These are the prices for purchasing electricity from and selling it to SES, respectively. and users respectively exist Electricity purchased and sold from SES during the specified time period.

[0055] To avoid power curtailment and maximize the utilization of integrated energy, this application embodiment may only consider the case where the equation holds true under power balance constraints.

[0056] In some cases, the system power balance constraints can be expressed as: , in, For users exist Electricity purchased from the power grid during a given period. and users respectively exist Electricity purchased and sold from shared energy storage during specific time periods. , , Representing households exist Wind power, photovoltaic power and load power during the time period.

[0057] user The purchase and sale of electricity to SES is also subject to user... Maximum purchase and sale power Limitations: , in, and These are reflections of users exist The time period affects the purchase and sales status variables of SES. Due to user... Buying and selling cannot occur simultaneously. Therefore, , In summary, the embodiments of this application establish an optimized scheduling framework, wherein the objective function of the multi-entity supply and demand optimization problem of the microgrid considering SES is the operating cost, and the decision variables include the electricity purchased by users from the grid, the electricity purchased and sold by users to SES, the maximum capacity of SES, the maximum charging and discharging power, the Soc state, and the charging and discharging power of SES.

[0058] In step S203, based on the optimization scheduling framework, linearization constraints and the Big M method are used to handle nonlinear problems in scheduling, and a collaborative optimization strategy for multiple agents in the microgrid is generated.

[0059] Because the aforementioned optimization problem involves nonlinear constraints, the microgrid optimization scheduling problem exhibits nonconvexity and strong coupling characteristics, increasing the difficulty of solving it and making it difficult to directly obtain the global optimum. Therefore, the embodiments of this application can use the Big-M method to linearize the nonlinear constraints, thereby solving the microgrid optimization problem.

[0060] This application uses SES charge / discharge power constraint as an example. The calculation formula for the nonlinear problem can be rewritten as follows: , in, and These are reflections of shared energy storage Boolean variables representing the charging and discharging states during the time period. , These represent shared energy storage. Discharge power and charging power during the time period.

[0061] The embodiments of this application employ linearization constraints and the Big M method to handle nonlinear problems in scheduling, which can improve solution efficiency and model engineering feasibility.

[0062] The following is a specific example, such as Figure 3 The following diagram further illustrates the multi-agent cooperative optimization method for microgrids according to embodiments of this application. Embodiments of this application may include the following steps: Step S301: Construct a multi-master microgrid supply and demand model: The multi-master microgrid supply and demand model may include the distributed energy output model, load demand model and power interaction model between each microgrid and the main grid, so as to describe the basic supply and demand structure of the multi-microgrid system.

[0063] Step S302, construct a shared energy storage model: The shared energy storage model is used to describe the operating characteristics of the shared energy storage system, including the dynamic change model of the energy storage state of charge, the charging and discharging power model, the charging and discharging efficiency model, and the capacity constraint model, so as to reflect the energy storage and release process of shared energy storage in different scheduling periods.

[0064] Step S303: Construct a multi-entity microgrid supply and demand model considering shared energy storage: Based on the multi-entity microgrid supply and demand model and the shared energy storage model, construct a multi-entity microgrid supply and demand model considering shared energy storage to describe the energy interaction and coupling relationships between the microgrid, users and shared energy storage, and realize unified modeling of energy flow and information flow among multiple entities.

[0065] Step S304, construct the objective function and nonlinear constraints: Based on the multi-agent microgrid supply and demand model, construct the optimization objective function and constraints. The objective function can be used to characterize the minimization of system operating costs. The constraints include power balance constraints, energy storage physical constraints, and user-side power purchase and sale constraints. Some of the constraints are nonlinear constraints to describe energy storage efficiency, SOC dynamics, and power coupling relationships.

[0066] Step S305: The Big M method is used to linearize the model and output the optimization results: For nonlinear constraints, the Big M method is introduced to perform equivalent transformation, transforming the nonlinear constraints into linear constraints, thereby transforming the original nonlinear optimization problem into a solvable mixed integer optimization problem; at the same time, the optimization solution algorithm is used to solve the processed model to obtain the optimal scheduling result.

[0067] Furthermore, to demonstrate the effectiveness of the multi-stakeholder collaborative optimization method for microgrids, this application's embodiments are based on the MATLAB platform, conducting experiments on a microgrid system with four users. The power load balance curves of the four users are shown below. Figure 4 As shown, the power and Soc curves of SES are as follows: Figure 5 As shown in the figure. The results show that the optimal capacity planning value of SES is 7.3445 kWh, and the maximum charging and discharging power is 2 kW. The microgrid multi-entity collaborative optimization method proposed in this application can meet the power balance, avoid power curtailment, and effectively reduce the electricity cost for microgrid users. At the same time, SES can recover to the initial state after one cycle, ensuring the normal operation of the system in the next cycle.

[0068] The microgrid multi-agent collaborative optimization method proposed in this application generates real-time purchase and sale strategies between users and shared energy storage based on multi-source information of the microgrid, enabling refined modeling of dynamic supply and demand relationships. Furthermore, by combining an optimized scheduling framework, linearized constraints, and the Big M method, the nonlinear, multi-constraint coupled optimization problem is transformed into a solvable mixed-integer optimization problem, effectively reducing solution complexity and improving model solvability and convergence. This method enhances the system's adaptability to renewable energy fluctuations and load uncertainties, improves the utilization efficiency and scheduling accuracy of shared energy storage, achieves collaborative optimization scheduling among multiple agents, reduces system operating costs, and improves overall operational stability and economy.

[0069] Next, referring to the accompanying drawings, a microgrid multi-entity collaborative optimization device according to an embodiment of this application is described.

[0070] Figure 6 This is a block diagram of a microgrid multi-entity collaborative optimization device according to an embodiment of this application.

[0071] like Figure 6As shown, the microgrid multi-entity collaborative optimization device 10 includes: an acquisition module 100, a construction module 200, and an optimization module 300.

[0072] The acquisition module 100 is used to acquire distributed energy generation data, load demand and market electricity price of the microgrid to generate multi-source information.

[0073] Module 200 is used to generate real-time purchase and sale strategies between users and energy storage based on multi-source information, and to build an optimized scheduling framework for the microgrid by combining system power balance constraints and energy storage physical constraints.

[0074] The optimization module 300 is used to generate a collaborative optimization strategy for multiple agents in a microgrid by using linearization constraints and the Big M method to handle nonlinear problems in scheduling based on the optimization scheduling framework.

[0075] Optionally, in one embodiment of this application, the microgrid multi-entity collaborative optimization device 10 further includes: a first establishment module and a second establishment module.

[0076] The first module is used to construct a multi-master microgrid supply and demand model based on power balance constraints, capacity and power constraints, with the goal of minimizing the energy cost for microgrid users, and to establish a shared energy storage model.

[0077] The second module is used to establish a multi-entity microgrid supply and demand optimization model based on the multi-master microgrid supply and demand model and the shared energy storage model.

[0078] Optionally, in one embodiment of this application, the construction module 200 includes an output unit.

[0079] The output unit is used to input multi-source information into the microgrid supply and demand optimization model, optimize the operating cost of the microgrid, and plan the capacity and maximum power corresponding to the shared energy storage mechanism to output a real-time purchase and sale strategy.

[0080] Optionally, in one embodiment of this application, the expression for the system power balance constraint is: , in, For users exist Electricity purchased from the power grid during a given period. and users respectively exist Electricity purchased and sold from shared energy storage during specific time periods. , , Representing households exist Wind power, photovoltaic power and load power during the time period.

[0081] Optionally, in one embodiment of this application, the calculation formula for the nonlinear problem is: , in, and These are reflections of shared energy storage Boolean variables representing the charging and discharging states during the time period. , These represent shared energy storage. Discharge power and charging power during the time period.

[0082] It should be noted that the foregoing explanation of the microgrid multi-entity collaborative optimization method embodiment also applies to the microgrid multi-entity collaborative optimization device of this embodiment, and will not be repeated here.

[0083] The microgrid multi-entity collaborative optimization device proposed in this application generates real-time purchase and sale strategies between users and shared energy storage based on multi-source information of the microgrid, enabling refined modeling of dynamic supply and demand relationships. Furthermore, by combining an optimized scheduling framework, linearized constraints, and the Big M method, the nonlinear, multi-constraint coupled optimization problem is transformed into a solvable mixed-integer optimization problem, effectively reducing solution complexity and improving model solvability and convergence. This device enhances the system's adaptability to renewable energy fluctuations and load uncertainties, improves the utilization efficiency and scheduling accuracy of shared energy storage, achieves collaborative optimization scheduling among multiple entities, reduces system operating costs, and improves overall operational stability and economy.

[0084] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and capable of running on the processor 702.

[0085] When the processor 702 executes the program, it implements the microgrid multi-entity collaborative optimization method provided in the above embodiments.

[0086] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0087] The memory 701 is used to store computer programs that can run on the processor 702.

[0088] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0089] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 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. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 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.

[0090] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0091] The processor 702 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.

[0092] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described microgrid multi-agent collaborative optimization method.

[0093] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the microgrid multi-entity collaborative optimization method provided in this application.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 multi-entity collaborative optimization method for microgrids, characterized in that, Includes the following steps: Acquire distributed energy generation data, load demand, and market electricity prices of microgrids to generate multi-source information; Based on the multi-source information, a real-time purchase and sale strategy between users and energy storage is generated, and an optimized scheduling framework for the microgrid is constructed by combining the system power balance constraints and energy storage physical constraints. Based on the aforementioned optimization scheduling framework, linearization constraints and the Big M method are used to handle nonlinear problems in scheduling, thereby generating a collaborative optimization strategy for multiple stakeholders in the microgrid.

2. The method according to claim 1, characterized in that, Before generating the real-time purchase and sale strategy between the user and energy storage, the method further includes: With the goal of minimizing energy costs for microgrid users, a multi-master microgrid supply and demand model is constructed based on power balance constraints, capacity and power constraints, and a shared energy storage model is established. Based on the multi-master microgrid supply and demand model and the shared energy storage model, a multi-master microgrid supply and demand optimization model is established.

3. The method according to claim 2, characterized in that, The real-time purchase and sale strategy between users and energy storage includes: The multi-source information is input into the microgrid supply and demand optimization model to optimize the operating cost of the microgrid and plan the capacity and maximum power corresponding to the shared energy storage mechanism, so as to output the real-time purchase and sale strategy.

4. The method according to claim 1, characterized in that, The expression for the system power balance constraint is: ， in, For users exist Electricity purchased from the power grid during a given period. and users respectively exist Electricity purchased and sold from shared energy storage during specific time periods. , , Representing households exist Wind power, photovoltaic power and load power during the time period.

5. The method according to any one of claims 1-4, characterized in that, The calculation formula for the nonlinear problem is as follows: ， in, and These are reflections of shared energy storage Boolean variables representing the charging and discharging states during the time period. , These represent shared energy storage. Discharge power and charging power during the time period.

6. A microgrid multi-entity collaborative optimization device, characterized in that, include: The acquisition module is used to acquire distributed energy generation data, load demand, and market electricity prices of the microgrid to generate multi-source information; The module is used to generate a real-time purchase and sale strategy between users and energy storage based on the multi-source information, and to construct an optimized scheduling framework for the microgrid by combining the system power balance constraints and energy storage physical constraints. The optimization module is used to handle nonlinear problems in scheduling based on the optimization scheduling framework, using linearization constraints and the Big M method, and to generate a collaborative optimization strategy for multiple agents in the microgrid.

7. The apparatus according to claim 6, characterized in that, Also includes: The first module is used to construct a multi-master microgrid supply and demand model based on power balance constraints, capacity and power constraints, with the goal of minimizing the energy cost for microgrid users, and to establish a shared energy storage model. The second module is used to establish a multi-entity microgrid supply and demand optimization model based on the multi-master microgrid supply and demand model and the shared energy storage model.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the microgrid multi-agent collaborative optimization method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the microgrid multi-agent collaborative optimization method 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 microgrid multi-agent collaborative optimization method as described in any one of claims 1-5.

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