Energy storage day initial state of charge optimization method and device facing scene differentiation requirement, and equipment

Abstract

This invention provides a method, apparatus, and device for optimizing the initial state of charge (SOC) of energy storage based on scenario-specific needs. The method includes: constructing a load probability fuzzy set based on Wasserstein distance using historical load data; constructing a dynamic economic objective function and a resilience metric objective function using the load probability fuzzy set; generating a comprehensive objective function for the sub-bars based on the dynamic economic objective function, the resilience metric objective function, and the load probability fuzzy set, wherein the resilience metric objective function is used to quantify energy storage demand under fault scenarios; generating multiple constraints applicable to various scenarios using grid parameters, wherein the multiple constraints involve capacity, power, voltage, power flow, and line disconnection amount under different scenarios; solving the comprehensive objective function and multiple constraints; and optimizing the SOC of energy storage based on the solution results.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power distribution system operation technology, and in particular to a method, apparatus and equipment for optimizing the initial state of charge of energy storage for different scenarios. Background Technology

[0002] With the continuous development of power systems, energy storage technology is being applied more and more widely. Energy storage systems can not only balance power supply and demand, but also improve the stability and reliability of the power grid. Especially with the integration of a high proportion of renewable energy, energy storage systems play an important role in coping with load fluctuations and extreme events. However, the performance of energy storage systems under normal-fault scenarios largely depends on the optimized configuration of their initial state of charge (SOC).

[0003] Traditional energy storage system optimization methods primarily focus on economic efficiency under normal operating conditions, often neglecting the resilience required during fault scenarios. In actual operation, power systems may face various uncertainties, such as load fluctuations and equipment failures. These uncertainties can lead to drastic changes in the system's operating state, thereby affecting the performance of energy storage systems. Therefore, optimizing the initial state of charge (SOC) of energy storage while considering load uncertainties and fault scenarios has become a pressing issue.

[0004] Most existing optimization methods employ stochastic or robust optimization approaches, but these methods have limitations when dealing with complex uncertainties. Stochastic optimization methods rely on precise probability distribution assumptions, while the probability distributions of load fluctuations and failure scenarios are often difficult to obtain accurately in practice. Robust optimization methods, although capable of handling uncertainty, are typically overly conservative, potentially leading to excessively high system operating costs. Summary of the Invention

[0005] To address the above problems, embodiments of the present invention provide a method for optimizing the initial state of charge (SPC) of energy storage based on scenario-specific needs, comprising: Construct a load probability fuzzy set based on Wasserstein distance using historical load data; A dynamic economic objective function and an elasticity measurement objective function are constructed by combining the load probability fuzzy set. Based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set, a comprehensive objective function for the sub-bar is generated. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. Multiple constraints are generated by combining power grid parameters and applied to various scenarios. These constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. Solve the comprehensive objective function and multiple constraints, and optimize the initial state of charge of the energy storage based on the solution results.

[0006] In one embodiment, constructing a load probability fuzzy set based on Wasserstein distance based on historical load data includes: The predicted daily load time series value from the historical load data with normalized random error added is determined as the actual value of the daily load; The empirical distribution of the historical load data is determined by combining the actual values ​​of the daily load. A load probability fuzzy set based on the Wasserstein distance metric is constructed by combining the empirical distribution.

[0007] In one embodiment, constructing a dynamic economic objective function by combining the load probability fuzzy set includes: Based on the aforementioned load probability fuzzy set, the following dynamic economic objective function is constructed:

[0008] in, For probabilistic fuzzy sets constructed based on Wasserstein distance, For nodes in the power grid i Time period t The cost of purchasing electricity from the power grid; For nodes in the power grid i Time period t Electricity purchased from the power grid; Cost of power grid transmission losses; For nodes in the power grid i The penalty cost of the initial energy storage capacity; For nodes in the power grid i The total remaining energy storage capacity at the initial moment.

[0009] In one embodiment, constructing an elasticity metric objective function by combining the load probability fuzzy set includes: Based on the aforementioned load probability fuzzy set, the following elasticity metric objective function is constructed:

[0010] in, It is a set of preset fault scenarios, each scenario describing a fault in power grid equipment or an abnormal operating state; For the scene s The weighting coefficients satisfy This reflects the relative importance or historical probability of each scenario; For the scene s For nodes in the power grid i The influence coefficient of power supply capacity; For the scene s Nodes in the power grid iTime period t The emergency discharge capacity of fixed and mobile energy storage.

[0011] In one embodiment, the comprehensive objective function for generating the sub-bar is based on the dynamic economic objective function, the elasticity metric objective function, and the load probability fuzzy set, including: Based on the aforementioned dynamic economic objective function, elasticity measurement objective function, and load probability fuzzy set, the comprehensive objective function for generating the sub-bulb bar is as follows:

[0012] in, For dynamic economic objectives; For the elasticity measurement target, This is a load power probabilistic fuzzy set constructed based on Wasserstein distance.

[0013] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Generate capacity and power constraints applicable to various scenarios by combining power grid parameters; The capacity constraints include:

[0014]

[0015] The charging and discharging efficiency of energy storage; This represents the charge / discharge level under normal conditions. For fault scenarios s Lower charge / discharge level; The power constraint includes: ; In both normal and fault scenarios, the initial energy storage of any node is the same:

[0016] This represents the remaining energy storage capacity at time 0 under normal conditions. This represents the remaining energy storage capacity at time 0 under fault scenarios.

[0017] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, power flow constraints, node power balance constraints, and voltage safety constraints are generated for application under normal scenarios. The power flow constraints include:

[0018]

[0019]

[0020] The node power balance constraints include:

[0021] in, For nodes i exist t Total power generation output during the period; The voltage safety constraints include: .

[0022] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, line disconnection constraints, corrected power flow constraints, emergency power balance constraints, energy storage emergency support constraints, and voltage safety constraints are generated for various fault scenarios: The line disconnection constraint includes: ,

[0023] The modified power flow constraints include:

[0024]

[0025] , ; Emergency power balance constraints include:

[0026] , For nodes m The total emergency discharge power of the stored energy; The energy storage emergency support constraints include:

[0027] For fault recovery time Inside, node m Total energy storage and discharge required For nodes m The maximum available energy storage capacity; The voltage safety constraints include: .

[0028] In one embodiment, solving the integrated objective function and multiple constraints includes: The comprehensive objective function and multiple constraints are solved iteratively based on the column and constraint generation algorithm.

[0029] Another embodiment of the present invention also provides a daily initial state of charge optimization device for energy storage to meet the differentiated needs of different scenarios, comprising: The first building module is used to construct a fuzzy set of load probabilities based on Wasserstein distance based on historical load data; The second construction module is used to construct a dynamic economic objective function and an elasticity measurement objective function by combining the load probability fuzzy set, and to generate a comprehensive objective function for the sub-bulb based on the dynamic economic objective function, the elasticity measurement objective function and the load probability fuzzy set. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. The generation module is used to generate multiple constraints applicable to various scenarios by combining power grid parameters. These multiple constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. The optimization module is used to solve the comprehensive objective function and multiple constraints based on the column and constraint generation algorithm, and to optimize the initial state of charge of the energy storage based on the solution results.

[0030] Another embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage day initial state of charge optimization method for scenario-differentiated needs as described above.

[0031] Based on the above, the solution proposed in this application provides a method for optimizing the initial state of charge (SOC) of energy storage to meet the differentiated needs of normal and fault scenarios. This method significantly improves the resilience of the power distribution system while ensuring its economic efficiency. By optimizing the SOC of energy storage, it is possible to effectively cope with load fluctuations and extreme events, reduce system operating costs, and enhance the stability and emergency response capabilities of the power grid.

[0032] Other features and advantages of this application will be set forth in the following description. The objectives and other advantages of this application can be realized and obtained through the structures particularly pointed out in the written description and drawings.

[0033] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the method for optimizing the initial state of charge of energy storage based on scenario-specific needs in an embodiment of the present invention.

[0036] Figure 2 This is a flowchart illustrating a method for optimizing the initial state of charge of energy storage based on scenario-specific needs, as described in another embodiment of the present invention.

[0037] Figure 3 This is a structural block diagram of an energy storage daily initial state of charge optimization device for scenario-differentiated needs in an embodiment of the present invention. Detailed Implementation

[0038] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but these are not intended to limit the scope of the invention.

[0039] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope of this disclosure will be apparent to those skilled in the art.

[0040] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.

[0041] These and other features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0042] It should also be understood that although the invention has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0043] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0044] Specific embodiments of the present disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the present disclosure. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of substantially any suitable detailed structures.

[0045] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.

[0046] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0047] like Figure 1 As shown, this embodiment of the invention provides a method for optimizing the initial state of charge of energy storage based on different scenario requirements, including: S1: Construct a load probability fuzzy set based on Wasserstein distance based on historical load data; S2: Construct a dynamic economic objective function and an elasticity measurement objective function by combining the load probability fuzzy set, and generate a comprehensive objective function for the sub-bulb based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. S3: Combine grid parameters to generate multiple constraints applicable to various scenarios. These constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. S4: Solve the comprehensive objective function and multiple constraints, and optimize the initial state of charge of the energy storage based on the solution results.

[0048] Solving the comprehensive objective function and multiple constraints includes: S401: The comprehensive objective function and multiple constraints are solved iteratively based on the column and constraint generation algorithm.

[0049] Based on the above, the solution in this embodiment utilizes the probabilistic fuzzy set of the Wasserstein distance to measure load power, designs a multi-objective optimization function encompassing both economy and resilience, and solves the sub-Bruker optimization model using the Column and Constraint Generation Algorithm (C&CG), thereby obtaining the optimal solution for the robust initial state of charge of the energy storage. The advantage of this method is that it not only considers the economy under normal operating conditions but also fully considers the resilience requirements under fault conditions, enabling the method to achieve a balance between economy and resilience in different scenarios by optimizing the initial state of charge of the energy storage. Therefore, the method described in this embodiment can effectively cope with load fluctuations and extreme events, reduce system operating costs, and improve the stability and emergency response capabilities of the power grid.

[0050] For example, in one embodiment, the method may include the following steps: Step 1: Data Collection and Organization and Uncertainty Set Modeling Based on historical load data, a dataset containing time-series characteristics is established, and a load probability fuzzy set based on Wasserstein distance is constructed. This process involves determining the distribution characteristics of random errors through historical data analysis, setting the confidence level and radius parameters of the fuzzy set, and generating a set of extreme event scenarios by combining historical information and distribution network topology.

[0051] Step 2: Construct an energy storage operation model with load uncertainty The design encompasses both economic efficiency and resilience, employing a multi-objective optimization function that separately considers normal operating costs and resilience metrics. Furthermore, by comprehensively considering factors such as grid purchase costs, transmission losses, and energy storage capacity costs, multiple constraints are generated to ensure a balance between economic efficiency and resilience under different scenarios.

[0052] Step 3: Solving the Bruker Bar Optimization Model The column and constraint generation algorithm (C&CG) is used to handle various energy storage scenarios under uncertainty, decomposing the original problem into a main problem and sub-problems for iterative solution. This optimizes the initial state of charge of the energy storage, improves the system's reliability and emergency response capabilities, and effectively copes with load fluctuations and extreme events.

[0053] Specifically, such as Figure 2 As shown, the construction of a load probability fuzzy set based on Wasserstein distance based on historical load data includes: S101: The predicted daily load time series value from the historical load data with normalized random error added is determined as the actual value of the daily load; S102: Determine the empirical distribution of the historical load data by combining the actual values ​​of the daily load; S103: Construct a load probability fuzzy set based on the Wasserstein distance metric by combining the empirical distribution.

[0054] For example, load random fluctuations: This invention incorporates normalized random error into the time-series forecast of typical daily load to represent the actual load value:

[0055] In the formula: i Number the nodes. t For time period numbering, To account for uncertain load values, This is a time-series forecast of typical daily load. To normalize random error, This is the set of power grid nodes (power grid equipment, such as energy storage devices) to be studied.

[0056] In data-driven approaches, it is assumed that the amount of historical data for random errors is... S A data set can be represented as: .

[0057] The empirical distribution of historical data can be defined as: In the formula: This represents the Dirac function.

[0058] Next, based on the above data, a fuzzy set based on the Wasserstein distance metric is constructed:

[0059] In the formula: p Let be the order of the norm. It is a random variable and The joint distribution and , The edge distribution is and .

[0060] Fuzzy sets are defined using the Wasserstein sphere as follows:

[0061] Confidence level and radius r Relationship guarantee:

[0062] and

[0063] constant D By optimizing the problem, we obtain:

[0064] when In this case, the method degenerates into a scenario-based stochastic optimization method.

[0065] In another embodiment, a dynamic economic objective function is constructed by combining the load probability fuzzy set, including: S201: Construct the following dynamic economic objective function based on the aforementioned load probability fuzzy set:

[0066] in, For probabilistic fuzzy sets constructed based on Wasserstein distance, For nodes in the power grid i Time period t The cost of purchasing electricity from the grid (yuan / kWh); For nodes in the power grid i Time period t Electricity purchased from the grid (kWh); Cost of power grid transmission losses; For nodes in the power grid i The penalty cost of the initial energy storage capacity (yuan / kWh) is to ensure that there is no overcharging and waste in the day-to-day period; For nodes in the power grid i Initial remaining energy storage capacity (kWh).

[0067] Furthermore, the elasticity metric objective function is constructed by combining the aforementioned load probability fuzzy set, including: S202: Construct the following elasticity metric objective function based on the aforementioned load probability fuzzy set:

[0068] in, It is a set of preset fault scenarios, each scenario describing a fault in power grid equipment or an abnormal operating state; For the scene s The weighting coefficients satisfy This reflects the relative importance or historical probability of each scenario; For the scene s For nodes in the power grid i The influence coefficient of power supply capacity can be obtained through power grid topology analysis and fault simulation calculation. For the scene s Nodes in the power grid i Time period t The emergency discharge capacity of fixed and mobile energy storage (kWh).

[0069] Furthermore, based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set, a comprehensive objective function for generating the sub-bar is generated, including: S203: Based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set, the comprehensive objective function for generating the sub-bulb bar is as follows:

[0070] in, For dynamic economic objectives; For the elasticity measurement target, This is a load power probabilistic fuzzy set constructed based on Wasserstein distance.

[0071] Next, the system will perform energy storage modeling, including: Constructing capacity constraints: Employing multi-time energy conservation equations

[0072]

[0073] In the formula: The charging and discharging efficiency of energy storage; This represents the charge / discharge level under normal conditions. For fault scenarios s Charge / discharge level.

[0074] The initial energy storage configuration is the same for all normal and fault scenarios:

[0075] In the formula: This represents the remaining energy storage capacity at time 0 under normal conditions. This represents the remaining energy storage capacity at time 0 under fault scenarios.

[0076] The power constraints involved include: .

[0077] During power grid operation, the generation of multiple constraints based on power grid parameters is applied to various scenarios, including: S301: Combine the power grid parameters to generate power flow constraints, node power balance constraints, and voltage safety constraints applicable to normal scenarios; That is, normal scenario ( The constraints include power flow constraints, node power balance constraints, and voltage safety constraints.

[0078] The power flow constraints include:

[0079]

[0080]

[0081] The node power balance constraints include:

[0082] in, For nodes i exist t Total power generation output during the period; The voltage safety constraints include: .

[0083] Furthermore, the generation of multiple constraints based on power grid parameters for application in various scenarios includes: S302: Based on the aforementioned power grid parameters, generate line disconnection constraints, corrected power flow constraints, emergency power balance constraints, energy storage emergency support constraints, and voltage safety constraints applicable to various fault scenarios: That is, the failure scenario ( The constraints include line disconnection constraints, corrected power flow constraints, emergency power balance constraints, energy storage emergency support constraints, and voltage safety constraints.

[0084] The line disconnection constraint includes: ,

[0085] The modified power flow constraints include:

[0086]

[0087] , ; Emergency power balance constraints include:

[0088] , For nodes m The total emergency discharge power of the stored energy; The energy storage emergency support constraints include:

[0089] For fault recovery time Inside, node m Total energy storage and discharge required For nodes mThe maximum available energy storage capacity; The voltage safety constraints include: .

[0090] By applying the daily initial state of charge optimization methods for energy storage, as described in the above embodiments and tailored to the differentiated needs of normal-fault scenarios, in real-world applications, the system's resilience can be significantly improved while ensuring its economic efficiency. Furthermore, optimizing the daily initial state of charge of energy storage can effectively address load fluctuations and extreme events, reduce system operating costs, and enhance grid stability and emergency response capabilities.

[0091] like Figure 3 As shown, another embodiment of the present invention also provides a daily initial state of charge optimization device for energy storage to meet the differentiated needs of different scenarios, including: The first building module is used to construct a fuzzy set of load probabilities based on Wasserstein distance based on historical load data; The second construction module is used to construct a dynamic economic objective function and an elasticity measurement objective function by combining the load probability fuzzy set, and to generate a comprehensive objective function for the sub-bulb based on the dynamic economic objective function, the elasticity measurement objective function and the load probability fuzzy set. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. The generation module is used to generate multiple constraints applicable to various scenarios by combining power grid parameters. These multiple constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. The optimization module is used to solve the comprehensive objective function and multiple constraints based on the column and constraint generation algorithm, and to optimize the initial state of charge of the energy storage based on the solution results.

[0092] In one embodiment, constructing a load probability fuzzy set based on Wasserstein distance based on historical load data includes: The predicted daily load time series value from the historical load data with normalized random error added is determined as the actual value of the daily load; The empirical distribution of the historical load data is determined by combining the actual values ​​of the daily load. A load probability fuzzy set based on the Wasserstein distance metric is constructed by combining the empirical distribution.

[0093] In one embodiment, constructing a dynamic economic objective function by combining the load probability fuzzy set includes: Based on the aforementioned load probability fuzzy set, the following dynamic economic objective function is constructed:

[0094] in, For probabilistic fuzzy sets constructed based on Wasserstein distance, For nodes in the power grid i Time period t The cost of purchasing electricity from the power grid; For nodes in the power grid i Time period t Electricity purchased from the power grid; Cost of power grid transmission losses; For nodes in the power grid i The penalty cost of the initial energy storage capacity; For nodes in the power grid i The total remaining energy storage capacity at the initial moment.

[0095] In one embodiment, constructing an elasticity metric objective function by combining the load probability fuzzy set includes: Based on the aforementioned load probability fuzzy set, the following elasticity metric objective function is constructed:

[0096] in, It is a set of preset fault scenarios, each scenario describing a fault in power grid equipment or an abnormal operating state; For the scene s The weighting coefficients satisfy This reflects the relative importance or historical probability of each scenario; For the scene s For nodes in the power grid i The influence coefficient of power supply capacity; For the scene s Nodes in the power grid i Time period t The emergency discharge capacity of fixed and mobile energy storage.

[0097] In one embodiment, the comprehensive objective function for generating the sub-bar is based on the dynamic economic objective function, the elasticity metric objective function, and the load probability fuzzy set, including: Based on the aforementioned dynamic economic objective function, elasticity measurement objective function, and load probability fuzzy set, the comprehensive objective function for generating the sub-bulb bar is as follows:

[0098] in, For dynamic economic objectives; For the elasticity measurement target, This is a load power probabilistic fuzzy set constructed based on Wasserstein distance.

[0099] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Generate capacity and power constraints applicable to various scenarios by combining power grid parameters; The capacity constraints include:

[0100]

[0101] The charging and discharging efficiency of energy storage; This represents the charge / discharge level under normal conditions. For fault scenarios s Lower charge / discharge level; The power constraint includes: ; In both normal and fault scenarios, the initial energy storage of any node is the same:

[0102] This represents the remaining energy storage capacity at time 0 under normal conditions. This represents the remaining energy storage capacity at time 0 under fault scenarios.

[0103] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, power flow constraints, node power balance constraints, and voltage safety constraints are generated for application under normal scenarios. The power flow constraints include:

[0104]

[0105]

[0106] The node power balance constraints include:

[0107] in, For nodes i exist t Total power generation output during the period; The voltage safety constraints include: .

[0108] In one embodiment, the step of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, line disconnection constraints, corrected power flow constraints, emergency power balance constraints, energy storage emergency support constraints, and voltage safety constraints are generated for various fault scenarios: The line disconnection constraint includes: ,

[0109] The modified power flow constraints include:

[0110]

[0111] , ; Emergency power balance constraints include:

[0112] , For nodes m The total emergency discharge power of the stored energy; The energy storage emergency support constraints include:

[0113] For fault recovery time Inside, node m Total energy storage and discharge required For nodes m The maximum available energy storage capacity; The voltage safety constraints include: .

[0114] In one embodiment, solving the integrated objective function and multiple constraints includes: The comprehensive objective function and multiple constraints are solved iteratively based on the column and constraint generation algorithm.

[0115] Furthermore, one embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage day initial state of charge optimization method for scenario-differentiated needs as described above.

[0116] Furthermore, one embodiment of the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the energy storage daily initial state of charge optimization method described above, tailored to scenario-specific needs. It should be understood that the various solutions in this embodiment have the corresponding technical effects described in the above method embodiments, and will not be repeated here.

[0117] Furthermore, embodiments of the present invention also provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, which, when executed, cause at least one processor to perform a method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs, such as the embodiment described above.

[0118] It should be noted that the computer storage medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.

[0119] Furthermore, those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0120] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

Claims

1. A method for optimizing the initial state of charge (SPC) of energy storage based on scenario-specific needs, characterized in that, include: Construct a load probability fuzzy set based on Wasserstein distance using historical load data; A dynamic economic objective function and an elasticity measurement objective function are constructed by combining the load probability fuzzy set. Based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set, a comprehensive objective function for the sub-bar is generated. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. Multiple constraints are generated by combining power grid parameters and applied to various scenarios. These constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. Solve the comprehensive objective function and multiple constraints, and optimize the initial state of charge of the energy storage based on the solution results.

2. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in claim 1, characterized in that, The construction of a load probability fuzzy set based on Wasserstein distance using historical load data includes: The predicted daily load time series value from the historical load data with normalized random error added is determined as the actual value of the daily load; The empirical distribution of the historical load data is determined by combining the actual values ​​of the daily load. A load probability fuzzy set based on the Wasserstein distance metric is constructed by combining the empirical distribution.

3. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in claim 1, characterized in that, Constructing a dynamic economic objective function by combining the aforementioned load probability fuzzy set includes: Based on the aforementioned load probability fuzzy set, the following dynamic economic objective function is constructed: in, For probabilistic fuzzy sets constructed based on Wasserstein distance, For nodes in the power grid i Time period t The cost of purchasing electricity from the power grid; For nodes in the power grid i Time period t Electricity purchased from the power grid; Cost of power grid transmission losses; For nodes in the power grid i The penalty cost of the initial energy storage capacity; For nodes in the power grid i The initial remaining energy storage capacity; The elasticity metric objective function is constructed by combining the aforementioned load probability fuzzy set, including: Based on the aforementioned load probability fuzzy set, the following elasticity metric objective function is constructed: in, It is a set of preset fault scenarios, each scenario describing a fault in power grid equipment or an abnormal operating state; For the scene s The weighting coefficients satisfy This reflects the relative importance or historical probability of each scenario; For the scene s For nodes in the power grid i The influence coefficient of power supply capacity; For the scene s Nodes in the power grid i Time period t The emergency discharge capacity of fixed and mobile energy storage.

4. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in claim 3, characterized in that, The comprehensive objective function for generating the sub-bars based on the dynamic economic objective function, the elasticity measurement objective function, and the load probability fuzzy set includes: Based on the aforementioned dynamic economic objective function, elasticity measurement objective function, and load probability fuzzy set, the comprehensive objective function for generating the sub-bulb bar is as follows: in, For dynamic economic objectives; For the elasticity measurement target, This is a load power probabilistic fuzzy set constructed based on Wasserstein distance.

5. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs according to claim 1, characterized in that, The process of generating multiple constraints based on power grid parameters for application in various scenarios includes: Generate capacity and power constraints applicable to various scenarios by combining power grid parameters; The capacity constraints include: The charging and discharging efficiency of energy storage; This represents the charge / discharge level under normal conditions. For fault scenarios s Lower charge / discharge level; The power constraint includes: ; In both normal and fault scenarios, the initial energy storage of any node is the same: This represents the remaining energy storage capacity at time 0 under normal conditions. This represents the remaining energy storage capacity at time 0 under fault scenarios.

6. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in claim 1, characterized in that, The process of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, power flow constraints, node power balance constraints, and voltage safety constraints are generated for application under normal scenarios. The power flow constraints include: The node power balance constraints include: in, For nodes i exist t Total power generation output during the period; The voltage safety constraints include: 。 7. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in claim 1, characterized in that, The process of generating multiple constraints based on power grid parameters for application in various scenarios includes: Based on the aforementioned power grid parameters, line disconnection constraints, corrected power flow constraints, emergency power balance constraints, energy storage emergency support constraints, and voltage safety constraints are generated for various fault scenarios: The line disconnection constraint includes: ， The modified power flow constraints include: ， ； Emergency power balance constraints include: , For nodes m The total emergency discharge power of the stored energy; The energy storage emergency support constraints include: For fault recovery time Inside, node m Total energy storage and discharge required For nodes m The maximum available energy storage capacity; The voltage safety constraints include: 。 8. The method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs according to claim 1, characterized in that, Solving the comprehensive objective function and multiple constraints includes: The comprehensive objective function and multiple constraints are solved iteratively based on the column and constraint generation algorithm.

9. A device for optimizing the initial state of charge of energy storage based on different scenario requirements, characterized in that, include: The first building module is used to construct a fuzzy set of load probabilities based on Wasserstein distance based on historical load data; The second construction module is used to construct a dynamic economic objective function and an elasticity measurement objective function by combining the load probability fuzzy set, and to generate a comprehensive objective function for the sub-bulb based on the dynamic economic objective function, the elasticity measurement objective function and the load probability fuzzy set. The elasticity measurement objective function is used to quantify the energy storage demand under fault scenarios. The generation module is used to generate multiple constraints applicable to various scenarios by combining power grid parameters. These multiple constraints involve capacity, power, voltage, power flow, and line breakage under different scenarios. The optimization module is used to solve the comprehensive objective function and multiple constraints based on the column and constraint generation algorithm, and to optimize the initial state of charge of the energy storage based on the solution results.

10. An electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for optimizing the initial state of charge of energy storage based on scenario-differentiated needs as described in any one of claims 1-8.

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