Community distributed resource optimal configuration method, device and equipment based on regional autonomous balance and sharing concept and medium

Abstract

The invention relates to the technical field of energy configuration, in particular to a community distributed resource optimal configuration method and device based on regional autonomous balance and sharing ideas, equipment and a medium. According to the scheme provided by the invention, based on the target energy storage system investment cost, the photovoltaic system investment cost, the wind power system investment cost, the power grid electricity purchasing cost and the power grid electricity selling income, a target function is constructed by taking the minimum total operation cost as a target; determining the configuration capacity of the energy storage system of the target community and the configuration capacity of the clean energy power station through a commercial solver based on the target function and a preset constraint condition; according to the method, interaction between clean energy and a power grid is reduced, regional autonomous balance of the community is realized, the economic benefit, the technical benefit and the environmental benefit of the model are evaluated, and the economic benefit, the technical benefit and the environmental benefit of the model are evaluated, so that the economic benefit, the technical benefit and the environmental benefit of the community are improved. And the economy and feasibility of the provided model are verified.

Description

Technical Field

[0001] The present application relates to the field of energy configuration technology, and in particular to a method, device, equipment and medium for optimizing configuration of community distributed resources based on the concept of regional autonomous balance and sharing. Background Art

[0002] The greenhouse gas emissions caused by fossil fuel power generation are becoming increasingly serious. In order to cope with climate change, countries around the world have successively formulated policies and measures to achieve carbon neutrality. As one of the main end-use energy scenarios, strengthening energy conservation and efficiency improvement and electrification substitution in buildings is an important countermeasure to achieve low-carbon transformation. With the rapid development of distributed renewable energy, communities composed of building clusters are expected to play a more important role in carbon emission reduction actions in energy systems. The intermittent and volatile nature of distributed renewable energy poses challenges to its application at the building and community levels, and the development of energy storage technology is conducive to the consumption of distributed renewable energy and maximizes the economic and environmental benefits of distributed renewable energy.

[0003] At present, the research on shared energy storage focuses on the exploration of business models for shared energy storage, as well as economic dispatch and optimal configuration based on shared energy storage models. However, there are fewer studies on the application of clean energy sharing and energy storage sharing at the community and building levels, and there is no corresponding method to evaluate the models constructed by clean energy sharing and energy storage sharing.

[0004] Therefore, how to apply the sharing concept to the community and building levels, design a method for optimizing the allocation of community distributed resources, reduce the interaction between clean energy and the power grid, and achieve regional autonomous balance in the community has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a method, device, equipment and medium for optimizing the configuration of community distributed resources based on the concept of regional autonomous balance and sharing. By using the concept of sharing, a method for optimizing the configuration of community distributed resources is designed to reduce the interaction between clean energy and the power grid, achieve regional autonomous balance in the community, and evaluate the economic benefits, technical benefits and environmental benefits of the model, verifying the economy and feasibility of the method for optimizing the configuration of community distributed resources.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing the configuration of community distributed resources based on the concept of regional autonomous balance and sharing, the method comprising:

[0007] Based on the target community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid power purchase cost, and grid power sales revenue, the objective function is constructed with the goal of minimizing the total operating cost.

[0008] Based on the objective function and preset constraints, determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community through a commercial solver, wherein the clean energy power station includes a photovoltaic system and a wind power system, and the preset constraints are used to limit the power interaction between the target community and the power grid and the power status of the distributed resources and buildings in the target community;

[0009] Based on the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station, an evaluation is performed through technical indicators and economic indicators to determine the regional autonomous balance and distributed resource optimization configuration strategy of the target community.

[0010] In a possible implementation, the objective function is:

[0011] min C total =C BES +C PV +C WT +C buy -C sell

[0012] Where minC total The total operating cost of the target community is minimized, C BES is the investment cost of the energy storage system, C PV is the investment cost of the photovoltaic system, C WT is the investment cost of the wind power system, C buy is the power purchase cost of the power grid, C sell The income from electricity sales of the power grid.

[0013] In a possible implementation, the technical indicators include a clean energy self-consumption rate and a load self-supply rate, and the clean energy self-consumption rate is:

[0014]

[0015] In the formula, α RE is the clean energy self-consumption rate, E RE Electricity generated by clean energy, Electricity exported to the grid for clean energy;

[0016] The load self-supply rate is:

[0017]

[0018] In the formula, β L is the load self-supply rate, E L is the electrical energy required by the load, Represents the electrical energy purchased from the grid to meet load demand.

[0019] In a possible implementation, the economic indicators include a static investment payback period and a static CO2 investment payback period, and the static investment payback period is:

[0020]

[0021] Where PBT cost is the static investment payback period; C total,BES is the total investment of the energy storage system, C total,RE is the total investment of the clean energy power station; C saving The electricity purchase cost saved by replacing the electricity purchased from the power grid with the electricity generated by the clean energy power station;

[0022] The static CO2 investment payback period is:

[0023]

[0024] In the formula, is the static CO2 investment payback period; is the carbon emission of the energy storage system, is the carbon emission of the photovoltaic system, is the carbon emission of the wind power system, The carbon emissions avoided by using the electricity generated by the clean energy power station instead of purchasing electricity from the power grid.

[0025] In a possible implementation, determining the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community by a commercial solver based on the objective function and the preset constraints includes:

[0026] The nonlinear term of the multiplication of the constant variable and the 0-1 variable in the preset constraint condition is linearized by the Big-M method, and the bilinear term in the preset constraint condition is linearized by the McCormick relaxation method to obtain the linear constraint condition corresponding to the preset constraint condition;

[0027] Based on the linear constraints and the objective function, the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station are determined through a commercial solver.

[0028] In a possible implementation, in the preset constraint condition, the formula containing the bilinear term is:

[0029]

[0030] In the formula, SOC(t) is the state of charge of the energy storage system at time t, SOC max is the maximum state of charge of the energy storage system, SOC minis the minimum state of charge of the energy storage system, μ cha is the charging efficiency of the energy storage system, μ dis is the discharge efficiency of the energy storage system, P cha (t) is the charge capacity of the energy storage system in time period t, P dis (t) is the discharge capacity of the energy storage system in time period t, A 0-1 variable indicating whether the energy storage system has reached the maximum state of charge during time period t. When it is 1, it means that the energy storage system has reached the maximum state of charge, otherwise it is 0; is a 0-1 variable indicating whether the energy storage system has reached the minimum state of charge during time period t. When it is 1, it means that the energy storage system has reached the minimum state of charge, otherwise it is 0. is the remaining power of the building complex in the target community during period t, is the power shortfall of the building complex in period t, E BES is the configuration capacity of the energy storage system, and Δt is the duration of a unit scheduling period.

[0031] In a possible implementation, the linearizing the bilinear terms in the preset constraint condition by using the McCormick relaxation method includes:

[0032] Define auxiliary variable formula:

[0033] E(t)=SOC(t)·E BES

[0034] The McCormick relaxation method is applied to transform the auxiliary variable formula into a constraint formula:

[0035]

[0036] Based on the constraint formula, the formula containing bilinear terms in the preset constraint condition is converted into a linearized formula.

[0037] In a second aspect, an embodiment of the present application provides a community distributed resource optimization configuration device based on the concept of regional autonomous balance and sharing, the device comprising:

[0038] A construction module is used to construct an objective function based on the target community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid power purchase cost, and grid power sales revenue, with the goal of minimizing the total operating cost;

[0039] A determination module, configured to determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community through a commercial solver based on the objective function and preset constraints, wherein the clean energy power station includes a photovoltaic system and a wind power system, and the preset constraints are used to limit the power interaction between the target community and the power grid and the power status of the distributed resources and buildings in the target community;

[0040] An evaluation module is used to evaluate the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station through technical indicators and economic indicators to determine the regional autonomous balance and distributed resource optimization configuration strategy of the target community.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0043] The solution provided in the embodiments of the present application has the following beneficial effects compared with the prior art:

[0044] (1) Based on the community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid electricity purchase cost and grid electricity sales revenue, the objective function is constructed with the goal of minimizing the total operating cost. Then, based on the objective function and preset constraints, community autonomy is achieved and the energy interaction between the community and the grid is reduced. Furthermore, the objective function and preset constraints are used to determine the configuration capacity of the community's energy storage system and the configuration capacity of the clean energy power station through a commercial solver, thereby improving the utilization rate of clean energy resources and energy storage resources, shortening the investment payback period, achieving higher economic benefits and reducing carbon emissions. Finally, the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station are evaluated through technical indicators and economic indicators, and the regional autonomous balance of energy and the optimal configuration of distributed resources are determined.

[0045] (2) This application uses technical indicators: clean energy self-consumption rate and load self-supply rate for evaluation, which can quantify the interaction between the community and the power grid, and determine that the community’s distributed resource optimization configuration has achieved community energy self-sufficiency; through economic indicators: static investment payback period and static CO2 investment payback period for evaluation, it can be determined that the community’s distributed resource optimization configuration plan avoids a large amount of carbon emissions, has obvious emission reduction benefits, has significant environmental benefits, and is conducive to the achievement of dual carbon goals.

[0046] (3) This application proposes an operation model for sharing community clean energy and energy storage. Based on the community energy management strategy of the sharing model, an objective function with the minimum total community operating cost is established and combined with constraints. The Big-M method and McCormick relaxation method are applied to linearize the MINLP into MILP for linear solution. Economic, technical, and environmental evaluation indicators are given, and the optimization configuration results are analyzed based on the evaluation indicators. The example results verify the effectiveness and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 Shared operational architecture diagram for clean energy and energy storage;

[0049] Figure 2 A community energy management strategy diagram based on clean energy and energy storage sharing;

[0050] Figure 3 A flowchart of a method for optimizing the configuration of community distributed resources based on the concept of regional autonomous balance and sharing provided in an embodiment of the present application;

[0051] Figure 4 A typical daily unit clean energy generation coefficient diagram for each building’s clean energy configuration;

[0052] Figure 5 is the typical daily load demand diagram for each building;

[0053] Figure 6 The price of electricity purchased from the grid and the price of electricity sold to the grid on a typical day;

[0054] Figure 7 A structural block diagram of a community distributed resource optimization configuration device based on the concept of regional autonomous balance and sharing provided in an embodiment of the present application;

[0055] Figure 8 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0057] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0058] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0062] The greenhouse gas emissions caused by fossil fuel power generation are becoming increasingly serious. In order to cope with climate change, countries around the world have successively formulated policies and measures to achieve carbon neutrality. As one of the main end-use energy scenarios, strengthening energy conservation and efficiency improvement and electrification substitution in buildings is an important countermeasure to achieve low-carbon transformation. With the rapid development of distributed renewable energy, communities composed of building clusters are expected to play a more important role in carbon emission reduction actions in energy systems. The intermittent and volatile nature of distributed renewable energy poses challenges to its application at the building and community levels, and the development of energy storage technology is conducive to the consumption of distributed renewable energy and maximizes the economic and environmental benefits of distributed renewable energy.

[0063] In recent years, the emergence of sharing models has promoted the efficient use of distributed renewable energy at the community level. Some scholars have proposed a sharing mechanism for clean electricity in the community and established a community electricity day-ahead dispatch model including an energy storage system. The example shows that the electricity sharing model is conducive to improving the economy of community electricity consumption and the absorption rate of clean energy. Some scholars have studied the joint optimization method of photovoltaic power among multiple users. The results show that the interaction of electricity between users can effectively improve the absorption of photovoltaic power generation. However, the above research did not consider the sharing of energy storage.

[0064] Energy storage systems can store excess electricity from distributed renewable energy and release it when there is demand. However, the high investment cost of energy storage limits its widespread application. The concept of shared energy storage and related models have emerged. Some scholars have proposed a shared energy storage mechanism on the power generation side and established a shared energy storage planning model based on cooperative game. Some scholars have proposed a wind power shared energy storage collaborative operation mode to promote wind power consumption on the power generation side.

[0065] The application of shared energy storage on the user side has been studied more deeply. Some scholars have proposed a user-day-ahead optimized economic dispatch method based on shared energy storage power station services. The results show that the introduction of shared energy storage power station services can significantly reduce the operating costs of the user group. Some scholars have proposed an optimal configuration model for shared energy storage among multiple power sales companies, and case analysis shows the economic feasibility of shared energy storage. Some scholars have proposed a user collaborative optimization model for community integrated energy systems including shared energy storage. The results show that the shared energy storage model can effectively reduce user energy costs.

[0066] Based on the above research, this application proposes a community clean energy and energy storage sharing operation model community, Figure 1 This is a diagram of the clean energy and energy storage shared operation architecture. Figure 1 As shown in the figure, the building itself is equipped with clean energy power stations such as photovoltaic systems and wind power systems. When the building power is insufficient or excessive, the community energy management system will coordinate and prioritize clean energy sharing with other buildings. When the buildings in the community no longer need excess electricity, it will be used to charge the community's energy storage system, and finally the excess electricity will be exported to the grid; when there is a power gap demand for buildings in the community, priority will be given to using the community's energy storage system to discharge, and finally consider purchasing electricity from the grid to meet the load demand. Because the main purpose of configuring the energy storage system is to improve the utilization rate of the community's clean energy and reduce the power interaction between the community and the grid, the power interaction between the community energy storage system and the grid is not considered.

[0067] Figure 2 This is a community energy management strategy diagram based on clean energy and energy storage sharing. Figure 2 As shown, when there is excess electricity in a building, the community energy management system first checks whether other buildings have insufficient electricity. If other buildings have insufficient electricity, the building will supply the excess electricity to other buildings. If there is still excess electricity after supplying other buildings, the charge state of the energy storage system will be checked. If other buildings do not have insufficient electricity, the community energy storage system will be checked to see whether it has reached the maximum charge state. If the energy storage system has reached the maximum charge state, the building will sell the excess electricity. Otherwise, the excess electricity will be used first to charge the community energy storage system.

[0068] When there is a shortage of electricity in a building, the community energy management system will first check whether other buildings have excess electricity. If other buildings have excess electricity, the other buildings will supply the excess electricity to the building. If the building still has insufficient electricity after the supply from other buildings, the charge state of the energy storage system will be checked. If other buildings do not have excess electricity, the community energy storage system will be checked to see whether it has reached the minimum charge state. If the energy storage system has reached the minimum charge state, the building will purchase electricity from the power grid to supplement the electricity supply. Otherwise, the insufficient electricity will be supplied by the community energy storage system first.

[0069] Based on the above-mentioned sharing concept, the present application further proposes a community distributed resource optimization configuration method based on regional autonomous balance and sharing concepts. For ease of understanding, the technical solution of the present application will be introduced in detail below in conjunction with the accompanying drawings.

[0070] Figure 3 A flowchart of a community distributed resource optimization configuration method based on the concept of regional autonomous balance and sharing provided in an embodiment of the present application is provided. For the sake of convenience, only the part related to the present embodiment is shown. The method provided in the present embodiment includes the following steps:

[0071] S110, based on the target community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid electricity purchase cost, and grid electricity sales revenue, with the goal of minimizing the total operating cost, construct an objective function.

[0072] In one possible implementation, the objective function is:

[0073] minC total =C BES +C PV +C WT +C buy -C sell (1)

[0074] Where minC total The total operating cost of the target community is minimized, C BES is the investment cost of energy storage system, C PV is the investment cost of the photovoltaic system, C WT is the wind power system investment cost, C buy is the power purchase cost of the power grid, C sell Revenue from electricity sales to the power grid.

[0075] The investment cost of energy storage system is:

[0076]

[0077] In the formula, r is the discount rate, n BES is the life cycle of the energy storage system, is the one-time investment cost of the unit energy storage system, is the annual operation and maintenance cost of the unit energy storage system, E BES The configured capacity of the energy storage system.

[0078] The investment cost of a photovoltaic system is:

[0079]

[0080] Where n PV is the life cycle of the photovoltaic system, is the one-time investment cost of a unit photovoltaic system, is the annual operation and maintenance cost of the unit photovoltaic system, P PV The configured capacity of the photovoltaic system.

[0081] The investment cost of wind power system is:

[0082]

[0083] Where n WT The life cycle of the wind power system, is the primary investment cost of a unit wind power system, is the annual operation and maintenance cost of the unit wind power system, P WT The configuration capacity of the wind power system.

[0084] The cost of purchasing electricity from the power grid is:

[0085]

[0086] Where λre(t) is the power purchase price of the power grid in period t, P buy (t) is the amount of electricity purchased by the target community from the power grid during period t, and Δt is the duration of the unit dispatch period.

[0087] The revenue from electricity sales of the power grid is:

[0088]

[0089] In the formula, λ fit (t) is the on-grid electricity price during period t, P sell (t) is the amount of electricity sold by the target community to the grid during period t.

[0090] S120, based on the objective function and preset constraints, determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community through a commercial solver.

[0091] Exemplarily, the clean energy power station includes a photovoltaic system and a wind power system, and the preset constraints are used to limit the power interaction between the target community and the power grid and the power status of distributed resources and buildings in the target community.

[0092] It can be understood that the preset constraints are constructed based on the community energy management strategy of clean energy and energy storage sharing, which is used to limit the power interaction between the target community and the power grid, as well as the power status of distributed resources and buildings in the target community, improve the stability and reliability of the power system, and reduce the impact on the power quality of the power grid.

[0093] Among them, distributed resources may include clean energy power stations and energy storage systems distributed in the target community. An optimization configuration model for clean energy power stations and energy storage systems may be constructed based on the objective function and preset constraints; the preset constraints include: building power state constraints, community power state constraints, community power purchase and sales constraints, and energy storage system charge state continuity constraints.

[0094] Exemplarily, the building power state constraint is:

[0095]

[0096] In the formula, is the remaining power in building n during period t, is the power shortfall in building n during period t, P PV,n is the photovoltaic configuration capacity of building n, P WT,n is the wind power configuration capacity of building n, P L,n (t) is the load demand of building n at time t, η PV,n (t) and η WT,n (t) are the unit photovoltaic and unit wind power generation of building n at time t; A 0-1 variable representing the power surplus state of building n during period t. It takes 1 if there is power surplus in building n, otherwise it takes 0. The 0-1 variable represents the power shortage state of building n in period t. When it is 1, it means that there is power shortage in building n, otherwise it is 0. Constraints (7) and (8) respectively represent the calculation methods of the power surplus and shortage of building n in period t. Constraint (9) indicates that the two states of power surplus and power shortage cannot appear at the same time. Constraint (10) represents the variable and All are positive values.

[0097] The community power state constraint is:

[0098]

[0099] Where N is the total number of buildings in the community building complex of the target community, is the remaining power of the community building complex during period t, is the power gap of the community buildings in period t, A 0-1 variable representing the power surplus state in the target community during period t. When it is 1, it means there is power surplus in the community building complex, otherwise it is 0; The 0-1 variable represents the power shortage state in the target community during period t. When it is 1, it means that there is a power shortage in building n, otherwise it is 0. Constraints (11) and (12) respectively represent the calculation methods of the power surplus and shortage of the community buildings during period t. Constraint (13) indicates that the two states of power surplus and power shortage cannot appear at the same time. Constraint (14) represents the variable and All are positive values.

[0100] The constraints on community electricity purchase and sales are:

[0101]

[0102] In the formula, A 0-1 variable indicating whether the energy storage system of the target community has reached the maximum state of charge during period t. When it is 1, it means that the energy storage system has reached the maximum state of charge, otherwise it is 0; is a 0-1 variable indicating whether the energy storage system of the target community has reached the minimum state of charge in period t. When it is 1, it means that the energy storage system has reached the minimum state of charge, otherwise it is 0; (17) and (18) represent the state constraints of the maximum state of charge and the minimum state of charge of the energy storage system, respectively; P cha (t) is the charge capacity of the energy storage system during period t, P dis (t) is the discharge amount of the energy storage system during period t, and its calculation method is the same as (19) and (20).

[0103]

[0104] In the formula, SOC(t) is the state of charge of the energy storage system of the target community at time t, SOC max is the maximum state of charge of the energy storage system, SOC min is the minimum state of charge of the energy storage system, μ cha is the charging efficiency of the energy storage system, μ dis is the discharge efficiency of the energy storage system.

[0105] The continuity constraint of the state of charge of the energy storage system is:

[0106]

[0107] SOC(t=0)=SOC(t=last) (22)

[0108] SOC min ≤SOC(t)≤SOC max (twenty three)

[0109] Formula (21) represents the calculation formula of the state of charge, Formula (22) represents the energy conservation of the initial and final states of the energy storage system of the target community, and Formula (23) indicates that the state of charge at any time is between the maximum and minimum states of charge.

[0110] In one possible implementation, based on the objective function and preset constraints, the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community are determined through a commercial solver, including: linearizing the nonlinear term of the multiplication of the constant variable and the 0-1 variable in the preset constraints by the Big-M method, and linearizing the bilinear terms in the preset constraints by the McCormick relaxation method to obtain linear constraints corresponding to the preset constraints; based on the linear constraints and the objective function, the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community are determined through a commercial solver.

[0111] For example, in the optimization configuration model of clean energy power stations and energy storage systems established with (1) as the objective function and equations (7)-(23) as constraints, there are nonlinear terms in equations (7), (8), (11), (12), (15), (16), (19), (20), and (21). The model is a mixed integer nonlinear programming problem and cannot be solved directly using a commercial solver. The nonlinear terms in constraints (7), (8), (11), (12), (15), and (16) are all constant variables multiplied by 0-1 variables. They can be directly linearized using the Big-M method, where M is a constant with a large value. There are bilinear terms in equations (19), (20), and (21), which cannot be converted using the Big-M method. Therefore, the McCormick relaxation method is used to process the bilinear terms.

[0112] For the bilinear terms in (19), (20), and (21), the auxiliary variables are defined as follows:

[0113] E(t)=SOC(t)·E BES (twenty four)

[0114] Using the McCormick relaxation method, the auxiliary variable formula (24) is transformed into the constraint formulas shown in (25)-(28).

[0115]

[0116] Based on the constraint formula, the formula containing bilinear terms in the preset constraint conditions is converted into a linearized formula.

[0117] In actual implementation, the constraint formulas (25)-(28) of the auxiliary variable formula (24) are used to perform equivalent substitutions on the formulas (19)-(21) containing bilinear terms, and the formulas (19)-(21) are converted into linear formulas.

[0118] Furthermore, after the mixed integer nonlinear programming (MINLP) problem is converted into a mixed integer linear programming problem using the Big-M method and the McCormick relaxation method, the commercial solver CPLEX or GUROBI can be used for solving it. In this embodiment, CPLEX is called by the YALMIP toolbox on the MATLAB 2018a platform to solve the established model and determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station. The computer parameters are: Inter Core i7-8750H CPU@2.20GHz, memory 8GB.

[0119] S130, based on the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station, the regional autonomous balance and distributed resource optimization configuration strategy of the target community are determined through evaluation through technical indicators and economic indicators.

[0120] For example, in order to quantitatively evaluate the optimization effect of the configuration model, evaluation indicators such as clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period are provided from the perspectives of energy utilization, economic return, and environmental benefits.

[0121] The self-consumption rate of clean energy is:

[0122]

[0123] In the formula, α RE is the clean energy self-consumption rate, E RE Electricity generated by clean energy, Electricity that is exported to the grid as clean energy.

[0124] The load self-supply rate is:

[0125]

[0126] In the formula, β L is the load self-supply rate, E L is the electrical energy required by the load, Represents the electrical energy purchased from the grid to meet load demand.

[0127] The static payback period is:

[0128]

[0129] Where PBT cost is the static investment payback period; C total,BES is the total investment of the energy storage system, C total,RE is the total investment of the clean energy power station, which mainly includes the initial investment cost and the operation and maintenance cost during the life cycle; C saving The cost of purchasing electricity is saved by replacing electricity purchased from the power grid with electricity generated by clean energy power stations.

[0130] The static CO2 investment payback period is:

[0131]

[0132] In the formula, is the static CO2 investment payback period; For the carbon emissions of energy storage systems, is the carbon emission of the photovoltaic system, Carbon emissions from wind power systems, Carbon emissions avoided by replacing electricity purchased from the grid with electricity generated by clean energy power stations; among them, carbon emissions from energy storage systems, wind power and photovoltaics are mainly concentrated in the manufacturing process.

[0133] This embodiment constructs a community clean energy and energy storage sharing operation model based on the sharing concept, and on this basis proposes a unified community energy management strategy, and establishes a community clean energy and energy storage optimization configuration model based on this strategy. Among them, the model contains a large number of nonlinear constraints, and the Big-M method and McCormick relaxation method are used to linearize the nonlinear constraints in the constraints, so that the optimization configuration model is transformed from MINLP to a mixed integer linear programming problem (Mixed integer linear programming, MILP), and it is linearized and solved. Evaluation indicators such as clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period are given to conduct a comprehensive evaluation of the optimization configuration results in terms of economy, technology, environment, etc., verifying the economy and feasibility of the proposed model.

[0134] Furthermore, this application evaluates the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station determined by the model through case analysis. The specific contents are as follows:

[0135] In order to evaluate the comprehensive benefits brought by clean energy sharing and community energy storage sharing, this embodiment considers three scenarios based on whether sharing is considered and whether an energy storage system is configured. The definitions of various scenarios are as follows.

[0136] Scenario 1: The community building investment entity only configures a clean energy power station, does not configure an energy storage system, and the clean electricity is not shared within the community.

[0137] Scenario 2: The community building investment entity configures a clean energy power station, invests in and owns the energy storage system, and the clean electricity is not shared within the community.

[0138] Scenario 3: The community building investment entity configures a clean energy power station, the community energy operation entity invests in and owns the community energy storage system, and the clean electricity and community energy storage are shared within the community.

[0139] For the sake of simplicity, the typical curves of clean energy generation and electric load are used as the planning input data for the example. In the example analysis, the number of buildings in the community is set to 3. It is assumed that buildings 1 and 3 are equipped with photovoltaic power for clean energy, and building 2 is equipped with wind power for clean energy. The typical daily unit clean energy generation coefficient of each building is as follows: Figure 4 The typical daily load demand of each building is shown in Figure 5 The electricity purchase price from the grid and the electricity price sold to the grid on a typical day are shown in Figure 6 The scheduling period is 1 hour long.

[0140] According to the actual project, it is assumed that the one-time investment cost of the energy storage system is 1,500 yuan / kWh, the one-time investment cost of the photovoltaic system is 3,500 yuan / kWp, and the one-time investment cost of the wind power system is 7,500 yuan / kW. The annual operation and maintenance costs of general clean energy and energy storage projects account for 1%-5% of the one-time investment cost. According to the actual project situation, this embodiment assumes that the annual operation and maintenance costs are both 2% of the one-time investment cost; in addition, this embodiment assumes that the life cycle of clean energy and energy storage projects is 20 years, and the discount rate is 5%. The charge and discharge efficiency of the energy storage system is set to 88%, the minimum state of charge is 0.1, and the maximum state of charge is 0.9. At the same time, it is assumed that the capacity-power ratio of the energy storage battery is small enough to fully charge or discharge the energy storage system between two consecutive scheduling intervals.

[0141] The low-carbon effect of clean energy in the power generation process is the main reason for its vigorous development. It is even more important to analyze the environmental benefits of clean energy construction under the dual carbon goals. Many literatures have analyzed the carbon emissions of clean energy and energy storage systems from a full life cycle perspective. This embodiment assumes that the carbon emission coefficient of the power grid is 0.673kg / kWh, the life cycle CO2 emission coefficient of the unit energy storage system is 370kg / kWh, the life cycle CO2 emission coefficient of the unit photovoltaic system is 1650kg / kWh, and the life cycle CO2 emission coefficient of the unit wind power system is 1100kg / kWh.

[0142] Analysis of the results of optimized configuration of clean energy and energy storage system:

[0143] The configuration results of clean energy and energy storage systems under the three scenarios are shown in Table 1. The configuration of the energy storage system in scenario 3 is 1500kWh, and the total energy storage system configuration of the community in scenario 2 is 2811kWh. It can be seen that in the shared energy storage system scenario, the energy storage configuration capacity of the target community is saved by 46.6%; the total configuration capacity of clean energy in scenario 2 is 1870kW, and the total configuration capacity of clean energy in scenario 3 is 1475kW. It can be seen that in scenario 3 where clean energy and energy storage are shared, the community’s clean energy configuration is saved by 21.1% compared to scenario 2 where it operates alone.

[0144] Table 1 Clean energy and energy storage system configuration results

[0145]

[0146] Analysis of clean energy self-consumption rate and load self-supply rate:

[0147] The results of clean energy self-consumption rate and load self-supply rate under the three scenarios are shown in Table 2. The clean energy self-consumption rate under each scenario is relatively high, which minimizes the interaction between clean energy and the power grid and avoids the waste of clean energy. This shows that the proposed optimization configuration model can effectively solve the configuration capacity of clean energy power stations required by buildings and communities. In scenario 1, only clean energy is configured, and the load self-supply rates of buildings 1 and 3 that are only configured with photovoltaics are 42.79% and 46.13% respectively. This is because photovoltaics do not generate electricity at night, so building 1 needs to purchase electricity from the power grid to meet its own load needs.

[0148] At the same time, it can be seen from Table 1 that the total configuration capacity of clean energy in Scenario 1 is 1066kW. Without energy storage, it cannot achieve the maximum supply of clean energy. Therefore, the total configuration capacity of clean energy in Scenario 1 is also the lowest. In Scenario 2, energy storage is used to effectively improve the load self-supply rate of each building. Building 2 achieves 100% load self-supply and does not need to obtain electricity from the power grid. In Scenario 3, the clean energy self-consumption rate of Building 2 is only 48.18%, while the load self-supply rates of Building 1 and Building 3 are only 42.90% and 47.57% respectively. This shows that Building 1 and Building 3 are mainly supplied by wind power from Building 2 at night when photovoltaic power generation is not available. By utilizing the complementary characteristics of this clean energy, the clean energy self-consumption rate and load self-supply rate of the community interest community reached 90.78% and 95.79% respectively, basically achieving community energy self-sufficiency.

[0149] Table 2 Clean energy self-consumption rate and load self-supply rate

[0150]

[0151] Economic and environmental benefit analysis: The one-time investment cost, static investment payback period and static CO2 investment payback period under the three scenarios are shown in Table 3. The one-time investment cost in Scenario 1 is the lowest, which is 8.446 million yuan. The static investment payback period and static CO2 investment payback period of each building are also short. However, it can be seen from Table 2 that the self-sufficiency rate of the building is relatively low without energy storage, and the community cannot achieve effective self-sufficiency. The one-time investment cost in Scenario 2 is the highest, which is 20.195 million yuan. Its energy utilization efficiency and self-sufficiency are also strong, but each building invests in the construction of energy storage systems separately and does not share clean energy, which greatly prolongs its static investment payback period and static CO2 investment payback period. In Scenario 3, the sharing of clean energy and energy storage systems is considered. As can be seen from Table 2, the community in Scenario 3 achieves a higher self-consumption rate of clean energy and self-supply rate of load. At the same time, the one-time investment cost is between Scenario 1 and Scenario 2, which is 16.743 million yuan, saving 17.1% of the one-time investment cost compared with Scenario 2, and shortening the static investment payback period. Under scenario 3, the sharing of clean energy in the community can effectively achieve self-sufficiency of the entire community, with little interaction with the power grid, thereby avoiding a large amount of carbon emissions and having significant environmental benefits. Therefore, the static CO2 investment payback period is only 1.2 years, which shows that the sharing of clean energy and energy storage systems in the community can effectively reduce the overall carbon emissions of the community.

[0152] Table 3 Static investment payback period and static CO2 investment payback period

[0153]

[0154] From the above comparative analysis, it can be seen that the investment cost of only configuring clean energy is relatively low, but due to its spatiotemporal characteristics, especially the fact that photovoltaics do not generate electricity at night, the self-supply rate of the load is not high, and the community and buildings still have a large amount of electricity interaction with the power grid, and the environmental benefits are not significant. The use of clean energy with energy storage can effectively improve the self-supply rate of the load of community buildings and avoid the interaction of electricity between community buildings and the power grid, but the disadvantages are high investment costs, long investment recovery period, and separate configuration of energy storage may lead to waste of energy storage resources. Under the operation strategy of sharing community clean energy and energy storage systems mentioned in this article, the community, as a community of interests, uniformly optimizes the configuration of clean energy and community energy storage systems, and utilizes the complementary characteristics of clean energy. While maximizing the self-consumption rate of clean energy and the self-supply rate of load, it saves the configuration capacity of clean energy and energy storage systems, saves economic investment costs, and has a short static investment recovery period, with considerable profit margins. Sharing community clean energy and energy storage systems can also effectively reduce the overall carbon emissions of the community, and the static CO2 investment recovery period is only 1.2 years, which has significant environmental benefits and helps achieve the dual carbon goals.

[0155] It is understandable that the solution corresponding to scenario 3 is used as a regional autonomous balance and distributed resource optimization configuration strategy.

[0156] This embodiment takes the buildings in the community as the research object, and constructs a community clean energy and energy storage sharing operation model based on the sharing concept. On this basis, a unified community energy management strategy is proposed, and a community clean energy and energy storage optimization configuration model based on this strategy is established. Among them, the model contains a large number of nonlinear constraints. The Big-M method and McCormick relaxation method are used to linearize the nonlinear constraints in the constraints, so that the optimization configuration model is converted from MINLP to MILP, and then linearized and solved. Evaluation indicators such as clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period are given to conduct a comprehensive evaluation of the optimization configuration results in multiple aspects such as economy, technology, and environment. By calculating and analyzing the configuration results and evaluation indicators under three scenarios, the economy and feasibility of the proposed model are verified, and the case analysis draws the following conclusions:

[0157] 1) This embodiment proposes a community clean energy and energy storage optimization configuration model based on the sharing concept, which can be used for the optimization planning of community shared energy storage capacity and community clean energy power station scale. The established evaluation indicators can be used to evaluate the economic benefits, technical benefits and environmental benefits of community planning.

[0158] 2) Compared with the scenario of separately configuring energy storage and not sharing clean energy, community clean energy and energy storage sharing can save 46.6% of energy storage configuration capacity and 21.1% of clean energy configuration capacity, effectively improving the utilization rate of clean energy resources and energy storage resources, reducing the overall one-time investment cost by 15.6%, and shortening the static investment payback period to 6.9 years, with significant economic benefits.

[0159] 3) Under the community energy management strategy of sharing clean energy and energy storage, by utilizing the complementary characteristics of wind power and photovoltaic power and combining the community's energy storage sharing services, the clean energy self-consumption rate and load self-supply rate of the community interest group reached 91.75% and 96.80% respectively, effectively reducing the power interaction between the community and the power grid, and basically achieving community energy self-sufficiency.

[0160] 4) The static CO2 investment payback period under community clean energy and energy storage sharing is only 1.2 years. Under this model, the community as a whole reduces the purchase of electricity from the grid, thereby avoiding a large amount of carbon emissions. The emission reduction benefits are obvious, with significant environmental benefits, which is conducive to the achievement of dual carbon goals.

[0161] Figure 7This is a structural block diagram of a community distributed resource optimization configuration device based on the concept of regional autonomous balance and sharing provided in an embodiment of the present application. For the sake of convenience, only the part related to the embodiment of the present application is shown. Figure 7 The evaluation device 700 may include a construction module 701 , a determination module 702 , and an evaluation module 703 .

[0162] In one implementation, the apparatus 700 may be used to implement the above Figure 3 For example, the construction module 701 is used to implement S110, the determination module 702 is used to implement S120, and the evaluation module 703 is used to implement S130.

[0163] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0164] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0165] Figure 8 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 8 As shown, the electronic device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the figure), a memory 81, and a computer program 82 stored in the memory 81 and executable on at least one processor 80, the processor 80 executes the computer program 82 to implement the above Figure 3 The steps in the method embodiment, or the implementation of the above Figure 8 Functions of each module / unit in the device embodiment.

[0166] The electronic device 8 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 8 may include but is not limited to a processor 80 and a memory 81. Those skilled in the art will appreciate that Figure 8 It is only an example of the electronic device 8 and does not constitute a limitation on the electronic device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0167] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0168] In some embodiments, the memory 81 may be an internal storage unit of the electronic device 8, such as a hard disk or memory of the electronic device 8. In other embodiments, the memory 81 may also be an external storage device of the electronic device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 8. Further, the memory 81 may also include both an internal storage unit of the electronic device 8 and an external storage device. The memory 81 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of a computer program, etc. The memory 81 may also be used to temporarily store data that has been output or is to be output.

[0169] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0170] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include at least: any entity or device capable of carrying the computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0171] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned community distributed resource optimization configuration method based on the concept of regional autonomous balance and sharing.

[0172] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0173] A computer program product provided in an embodiment of the present application has the same beneficial effects as the above-mentioned community distributed resource optimization configuration method based on the concept of regional autonomous balance and sharing.

[0174] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0176] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A community distributed resource optimization configuration method based on regional autonomous balance and sharing concepts, characterized in that: The method comprises: Based on the target community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid power purchase cost, and grid power sales revenue, the objective function is constructed with the goal of minimizing the total operating cost. Based on the objective function and preset constraints, determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community through a commercial solver, wherein the clean energy power station includes a photovoltaic system and a wind power system, and the preset constraints are used to limit the power interaction between the target community and the power grid and the power status of the distributed resources and buildings in the target community; Based on the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station, an evaluation is performed through technical indicators and economic indicators to determine the regional autonomous balance and distributed resource optimization configuration strategy of the target community.

2. The method according to claim 1, characterized in that The objective function is: min C total =C BES +C PV +C WT +C buy -C sell Where minC total The total operating cost of the target community is minimized, C BES is the investment cost of the energy storage system, C PV is the investment cost of the photovoltaic system, C WT is the investment cost of the wind power system, C buy is the power purchase cost of the power grid, C sell The income from electricity sales of the power grid.

3. The method according to claim 1, characterized in that The technical indicators include clean energy self-consumption rate and load self-supply rate. The clean energy self-consumption rate is: In the formula, α RE is the clean energy self-consumption rate, E RE Electricity generated by clean energy, Electricity exported to the grid for clean energy; The load self-supply rate is: In the formula, β L is the load self-supply rate, E L is the electrical energy required by the load, Represents the electrical energy purchased from the grid to meet load demand.

4. The method according to claim 1, characterized in that: The economic indicators include a static investment payback period and a static CO2 investment payback period, wherein the static investment payback period is: Where PBT cost is the static investment payback period; C total,BES is the total investment of the energy storage system, C total,RE is the total investment of the clean energy power station; C saving The electricity purchase cost saved by replacing the electricity purchased from the power grid with the electricity generated by the clean energy power station; The static CO2 investment payback period is: In the formula, is the static CO2 investment payback period; is the carbon emission of the energy storage system, is the carbon emission of the photovoltaic system, is the carbon emission of the wind power system, The carbon emissions avoided by using the electricity generated by the clean energy power station instead of purchasing electricity from the power grid.

5. The method according to claim 1, characterized in that The determining of the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community by a commercial solver based on the objective function and the preset constraints includes: The nonlinear term of the multiplication of the constant variable and the 0-1 variable in the preset constraint condition is linearized by the Big-M method, and the bilinear term in the preset constraint condition is linearized by the McCormick relaxation method to obtain the linear constraint condition corresponding to the preset constraint condition; Based on the linear constraints and the objective function, the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station are determined through a commercial solver.

6. The method according to claim 5, characterized in that Among the preset constraints, the formula containing the bilinear term is: In the formula, SOC(t) is the state of charge of the energy storage system at time t, SOC max is the maximum state of charge of the energy storage system, SOC min is the minimum state of charge of the energy storage system, μ cha is the charging efficiency of the energy storage system, μ dis is the discharge efficiency of the energy storage system, P cha (t) is the charge capacity of the energy storage system in time period t, P dis (t) is the discharge capacity of the energy storage system in time period t, A 0-1 variable indicating whether the energy storage system has reached the maximum state of charge during time period t. When it is 1, it means that the energy storage system has reached the maximum state of charge, otherwise it is 0; is a 0-1 variable indicating whether the energy storage system has reached the minimum state of charge during time period t. When it is 1, it means that the energy storage system has reached the minimum state of charge, otherwise it is 0. is the remaining power of the building complex in the target community during period t, is the power shortfall of the building complex in period t, E BES is the configuration capacity of the energy storage system, and Δt is the duration of a unit scheduling period.

7. The method according to claim 6, characterized in that The linearization process of the bilinear terms in the preset constraint conditions using the McCormick relaxation method includes: Define auxiliary variable formula: E(t)=SOC(t)·E BES The McCormick relaxation method is applied to transform the auxiliary variable formula into a constraint formula: Based on the constraint formula, the formula containing bilinear terms in the preset constraint condition is converted into a linearized formula.

8. A community distributed resource optimization configuration device based on the concept of regional autonomy balance and sharing, characterized in that: The device comprises: A construction module is used to construct an objective function based on the target community's energy storage system investment cost, photovoltaic system investment cost, wind power system investment cost, grid power purchase cost, and grid power sales revenue, with the goal of minimizing the total operating cost; A determination module, configured to determine the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station of the target community through a commercial solver based on the objective function and preset constraints, wherein the clean energy power station includes a photovoltaic system and a wind power system, and the preset constraints are used to limit the power interaction between the target community and the power grid and the power status of the distributed resources and buildings in the target community; An evaluation module is used to evaluate the configuration capacity of the energy storage system and the configuration capacity of the clean energy power station through technical indicators and economic indicators to determine the regional autonomous balance and distributed resource optimization configuration strategy of the target community.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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