Charging and discharging plan determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a charging and discharging plan determination method and device, electronic equipment and a storage medium. The method comprises the steps that equipment information and historical operation data of an energy storage system in a power grid are acquired; determining a charging and discharging plan of the energy storage system according to the historical operation data, the equipment information and pre-acquired demand information; wherein the charging and discharging plan comprises at least one charging and discharging operation; performing a simulation experiment on the charging and discharging plan according to a pre-established joint simulation model and the equipment information to obtain an experiment result of the charging and discharging plan; and determining a target charging and discharging plan according to the experimental result of each charging and discharging operation, and sending the target charging and discharging plan to the energy storage system, so that the energy storage system operates based on the target charging and discharging plan. According to the method, the charging and discharging plan can be accurately made, it is ensured that the energy storage system can operate in the optimal state in different scenes, and the operation efficiency and economical efficiency of the whole energy storage system are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of energy storage system analysis, and in particular to a method, device, electronic device and storage medium for determining a charge and discharge plan. Background Art

[0002] In modern power systems, energy storage systems, through their charging and discharging operations, are able to store energy when there is an oversupply of energy, and release energy when demand peaks or energy shortages occur, thereby balancing the load on the power grid, improving energy efficiency and system stability. With the rapid growth of renewable energy sources such as wind and solar energy, the intermittent and uncontrollable nature of these energy sources has brought huge challenges to power grid dispatching. The effective charging and discharging plan of the energy storage system has become the key to solving this problem. Through precise charging and discharging strategies, energy storage can not only smooth the fluctuations in renewable energy output, but also provide the necessary system backup capacity and frequency regulation, and enhance the robustness of the power grid to external shocks. In addition, a reasonable charging and discharging plan can also extend the service life of energy storage equipment, reduce operation and maintenance costs, and improve overall economic benefits. Therefore, the development and implementation of efficient and reliable energy storage charging and discharging plans are crucial to ensuring the stable operation of the power system and promoting the deep integration of renewable energy.

[0003] The current method of determining charging and discharging plans mostly directly outputs a charging and discharging plan based on historical data through models such as machine learning. However, this method cannot ensure that the output charging and discharging plan is the most appropriate charging and discharging plan, resulting in inaccurate charging and discharging plans. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for determining a charge and discharge plan, which can perform simulation experiments on different charge and discharge operations in different charge and discharge plans, accurately determine the optimal charge and discharge plan based on the simulation experiment results, and improve the accuracy of determining the charge and discharge plan.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a charge and discharge plan, comprising:

[0006] Obtain equipment information and historical operation data of energy storage systems in the power grid;

[0007] Determining a charge and discharge plan of the energy storage system according to the historical operation data, the device information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation;

[0008] Performing a simulation experiment on the charging and discharging plan according to the pre-established joint simulation model and the device information to obtain an experimental result of the charging and discharging plan;

[0009] A target charge and discharge plan is determined according to the experimental results of each charge and discharge operation, and the target charge and discharge plan is sent to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

[0010] In a second aspect, an embodiment of the present invention provides a device for determining a charge and discharge plan, the device comprising:

[0011] A data acquisition module is used to obtain equipment information and historical operation data of the energy storage system in the power grid;

[0012] A plan determination module, used to determine a charge and discharge plan of the energy storage system according to the historical operation data, the device information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation;

[0013] A simulation experiment module, used to perform simulation experiments on each charging and discharging operation of the charging and discharging plan according to a pre-established joint simulation model and the device information, and obtain experimental results of each charging and discharging operation;

[0014] The target plan determination module is used to determine a target charge and discharge plan according to the experimental results of each charge and discharge operation, and send the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

[0015] In a third aspect, an embodiment of the present invention further 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 program, a method for determining a charge and discharge plan as described in any one of the embodiments of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for determining a charge and discharge plan as described in any one of the embodiments of the present invention.

[0017] In an embodiment of the present invention, the equipment information and historical operation data of the energy storage system in the power grid are obtained; the charge and discharge plan of the energy storage system is determined according to the historical operation data, the equipment information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; the charge and discharge plan is simulated according to the pre-established joint simulation model and the equipment information to obtain the experimental results of the charge and discharge plan; the target charge and discharge plan is determined according to the experimental results of each charge and discharge operation, and the target charge and discharge plan is sent to the energy storage system so that the energy storage system operates based on the target charge and discharge plan. That is, in an embodiment of the present invention, the charge and discharge plan can be accurately formulated according to the historical operation data and the equipment information, and the charge and discharge plan can be experimented by the joint simulation model, so that the performance of different charge and discharge strategies can be quickly and effectively evaluated, avoiding time-consuming and costly trial and error experiments in the physical system. And the optimal charge and discharge strategy can be determined through simulation experiments to ensure that the energy storage system can operate in the best state in different scenarios, thereby improving the operating efficiency and economy of the entire energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A first flow chart of a method for determining a charge and discharge plan provided by an embodiment of the present invention;

[0020] Figure 2 A second flow chart of a method for determining a charge and discharge plan provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of a device for determining a charge and discharge plan provided by an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0024] Figure 1The first flow chart of a method for determining a charge and discharge plan provided in an embodiment of the present invention. The method of the embodiment of the present invention can simulate different charge and discharge operations in different charge and discharge plans, accurately determine the optimal charge and discharge plan based on the simulation experiment results, and improve the accuracy of the charge and discharge plan determination. The method can be executed by a charge and discharge plan determination device provided in an embodiment of the present invention, and the device can be implemented in software and / or hardware. The following embodiments will be described by taking the device integrated in an electronic device as an example, and the electronic device can be a computer device or a server. Reference Figure 1 , the method may specifically include the following steps:

[0025] Step 101: Obtain equipment information and historical operation data of an energy storage system in a power grid.

[0026] Among them, the equipment information is the basic information, configuration and layout information, and status information of the energy storage equipment in the energy storage system. The basic information of the equipment includes the model, rated power, capacity, voltage level, and maximum charge and discharge rate of the energy storage equipment. These parameters determine the performance and operating range of the energy storage system. The configuration and layout information includes the connection method, geographical location, and network topology of the energy storage equipment in the power grid. The status includes the health status of the lipstick equipment, such as the temperature, pressure, and number of charge and discharge cycles of the energy storage equipment. Historical operation data includes load data, charge and discharge records, and event and abnormal records. Load data includes historical power load data, such as peak load, valley load, and average load. Charge and discharge records include the time, power, duration, and power of all previous charge and discharge. These data can be used to analyze the operating mode and efficiency of the system, as well as to perform fault diagnosis. Event and abnormal records include records of faults, accidents, or unconventional operations, such as equipment failures and sudden power outages.

[0027] Specifically, when it is necessary to determine the charging and discharging plan of the energy storage system, the server can obtain device information and historical operation data through the connection interface with the energy storage system, or directly receive device information and historical operation data sent by the user, so as to subsequently determine the charging and discharging plan based on the device information and historical operation data.

[0028] Step 102: Determine a charge and discharge plan for the energy storage system based on historical operation data, equipment information, and pre-acquired demand information.

[0029] Among them, the charging and discharging plan includes at least one charging and discharging operation, and the historical operation data also includes the historical monthly load data of the energy storage system. Demand information includes load demand information, environmental information (such as climate information and geographic location information, etc.), and network service demand information, etc. The charging and discharging plan is a set of data determined by the server based on equipment information and historical operation data, which is used to guide when and how the energy storage system should charge and discharge. A suitable charging and discharging plan can regulate the power usage at the user end and optimize the overall operating efficiency of the power grid. The charging and discharging plan includes a charging plan and a discharging plan. The charging plan includes charging rate, charging time, charging operation and monitoring parameters, etc. The discharging plan includes peak and valley optimization such as discharge rate, discharge depth, and discharge time (using the difference in electricity prices to transfer loads through the energy storage system to reduce the overall electricity cost).

[0030] In an optional implementation, after obtaining the historical operation data, equipment information, and demand information of the energy storage system, the server may first divide the energy storage system into clusters, and divide different types of energy storage devices into different clusters according to the characteristics of each energy storage device in the energy storage system, to obtain each energy storage cluster corresponding to the energy storage system. The historical operation data of each energy storage cluster is determined from the historical operation data of the energy storage system, and each energy storage cluster is configured and optimized according to the historical operation data and demand information of each cluster, to obtain the optimal energy storage configuration of each energy storage cluster. After obtaining the optimal energy storage configuration of each energy storage cluster, a pre-month load forecast is performed on each energy storage cluster according to the optimal energy storage configuration of each energy storage cluster and the historical monthly load data of the energy storage system, to obtain the monthly demand defense value of each energy storage cluster. Furthermore, the charging and discharging plan of the energy storage system is determined according to the monthly demand defense value and the optimal configuration of all energy storage clusters.

[0031] Step 103 : Perform simulation experiments on each charge and discharge operation of the charge and discharge plan according to the pre-established joint simulation model and equipment information to obtain experimental results of each charge and discharge operation.

[0032] Among them, the joint simulation model includes power module, communication module and planning control module. The power module group is responsible for simulating the power generation unit, transmission line, distribution network structure and all equipment with energy storage function in the energy storage system. The power module can show the real-time dynamic characteristics of electrical nodes, simulate the current and voltage changes in active and passive distribution networks, and thus predict the performance of the system under various operating conditions. The communication module is mainly used to simulate the data transmission and communication network inside the energy storage system, including parameters such as network topology, communication protocol and network delay, to ensure that information is accurately transmitted between various parts of the system. The communication module can be used to evaluate the impact of communication delay on the control and monitoring of the energy storage system, especially in remote or widely distributed energy storage systems. The planning control module is mainly responsible for the execution of the charging and discharging plan and the scheduling of the energy storage system. The planning control module sets the simulation parameters according to the actual energy storage data, such as generator output power, energy storage equipment capacity, charging and discharging efficiency and rate. The planning control module adjusts the voltage and power supply current according to the charging and discharging plan through the automatic voltage simulation control component and the power generation control component to simulate the behavior of the system under different operating conditions.

[0033] In an optional implementation, after obtaining a charging plan including each charging operation, the charging plan corresponding to each charging operation is input into the joint simulation model, and the simulation data corresponding to the charging and discharging plan is determined based on the charging and discharging plan and the equipment information through the plan control module, and the simulation data includes simulation model parameters and scenario parameters. For each charging and discharging operation of the charging and discharging plan, the number of experiments of the current charging and discharging operation is determined based on the scenario parameters; based on the simulation data, the power module and the communication module, the energy storage system under the current charging and discharging operation is simulated for the number of experiments to obtain the experimental results of each current charging and discharging operation; the experimental results include dynamic characteristic data and communication data of the energy storage system.

[0034] Step 104: determine a target charge and discharge plan according to the experimental results of each charge and discharge operation, and send the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

[0035] Among them, the target charge and discharge plan is a charge and discharge plan corresponding to the optimal charging operation determined by the server according to the results of the simulation experiment. Specifically, different charge and discharge operations and different scenarios will have different degrees of risk impact (i.e., the probability of risk) on the use of the charge and discharge plan by the energy storage system. According to the experimental results of each charge and discharge operation, the risk impact degree of the charge and discharge plan corresponding to the different charge and discharge operations can be determined, and the target charge and discharge plan can be determined according to the different risk impact degrees. After determining the target charge and discharge plan, the target charge and discharge plan can be sent to the energy storage system. After receiving the target charge and discharge plan, the energy storage system can parse the target charge and discharge plan, determine the parameter configuration information of the target charge and discharge plan, and configure the charging and discharging equipment according to the parameters in the plan.

[0036] In an optional implementation, after obtaining the experimental results of the charge and discharge plans corresponding to each charging operation, determine whether the experimental results of each simulation experiment of the current charge and discharge operation have risks according to the preset experimental standard values; if the experimental results of all simulation experiments do not have risks, determine the charge and discharge plan corresponding to the current charge and discharge operation as the first candidate plan; if there is a risk in the experimental results of at least one simulation experiment, determine the charge and discharge plan corresponding to the current charge and discharge operation as the second candidate plan; if there is at least one first candidate plan, determine the target charge and discharge plan according to the at least one first candidate plan; if there is no first candidate plan, determine the risk impact value of each second candidate plan according to the experimental results and the number of experiments of each second candidate plan, if there is at least one second candidate plan with a risk impact value less than a preset threshold, determine the target charge and discharge plan from at least one second candidate plan less than the preset threshold; if the risk impact values ​​of all second candidate plans are greater than or equal to the preset threshold, calculate the energy storage benefit correction value of the second candidate plans whose risk impact values ​​are greater than or equal to the preset threshold according to the preset energy storage benefit correction formula, and determine the target charge and discharge plan according to the energy storage benefit correction value of each second candidate charge and discharge plan.

[0037] The technical solution of this embodiment obtains the equipment information and historical operation data of the energy storage system in the power grid; determines the charge and discharge plan of the energy storage system according to the historical operation data, equipment information and pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; performs simulation experiments on the charge and discharge plan according to the pre-established joint simulation model and equipment information to obtain the experimental results of the charge and discharge plan; determines the target charge and discharge plan according to the experimental results of each charge and discharge operation, and sends the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan. The technical solution of this embodiment can accurately formulate a charge and discharge plan based on historical operation data and equipment information, and conduct experiments on the charge and discharge plan through a joint simulation model, which can quickly and effectively evaluate the performance of different charge and discharge strategies, avoiding time-consuming and costly trial and error experiments in physical systems. And through simulation experiments, the optimal charge and discharge strategy can be determined to ensure that the energy storage system can operate in the best state in different scenarios, and improve the operating efficiency and economy of the entire energy storage system.

[0038] Figure 2 This is a second flow chart of a method for determining a charge and discharge plan provided in an embodiment of the present invention. This embodiment is a refinement of the above embodiment. The specific method can be as follows Figure 2 As shown, the method may include the following steps:

[0039] Step 201: Obtain equipment information and historical operation data of an energy storage system in a power grid.

[0040] Step 202: Determine a cluster division index of the energy storage system according to the device information and a predetermined cluster division index.

[0041] Among them, the cluster division index is a standard or criterion used to determine how to group multiple energy storage devices to achieve specific goals. In distributed energy storage systems, reasonable cluster division can optimize resource allocation and improve system performance and efficiency. The equipment information of different energy storage systems can reflect the characteristics of different energy storage systems. The cluster division index of the energy storage system can be determined based on the equipment information of the energy storage system.

[0042] Specifically, the device information includes the transfer impedance between devices and the self-impedance of each device. In this solution, nodes represent devices, and edges represent the connection relationship between devices. The node edge weight is calculated according to the predefined electrical distance formula, and the electrical distance modularity index t1 is obtained through the node edge weight. The electrical distance d is calculated through the node impedance matrix Z. ij :

[0043]

[0044] Among them, Z ij represents the transfer impedance from node i to node j, Z ii , Z jj Represents the self-impedance of nodes i and j respectively. The Z matrix is ​​calculated from the component parameters in the structure and equipment parameters of the power grid system. For the edge between nodes i and j, the electrical distance d between them can be used. ij Define edge weight, that is, the edge weight of nodes i and j is equal to the electrical distance d between them ij .

[0045] The specific calculation formula of the electrical distance modularity index is as follows:

[0046]

[0047] Among them, m is the sum of the edge weights of all nodes, k i represents the sum of the edge weights connected to node i, k j It represents the sum of the edge weights connected to node j. δ(i, j) is 1 when nodes i and j are in the same cluster and 0 otherwise.

[0048] The calculation formula of active power balance degree t2 is as follows:

[0049]

[0050] Where C(i) represents the capacity of the i-th energy storage device; η(i) is the charge and discharge power of the i-th energy storage device; n is the total number of energy storage devices in the cluster, and ω is the predicted or actual active power demand in the cluster. The closer the t2 value is to 1, the stronger the active power balancing ability in the cluster.

[0051] Establish the cluster partition index t of comprehensive performance:

[0052] t=α1t1+α2t2;

[0053] α1 and α2 represent the weights of the corresponding indicators, and α1+α2=1. Furthermore, the energy storage systems are clustered according to the clustering index t of the comprehensive performance.

[0054] Step 203: cluster the energy storage system according to the cluster division index to obtain energy storage clusters corresponding to the energy storage system.

[0055] After obtaining the cluster division index, the genetic algorithm can be used to divide the distribution network energy storage cluster. In the genetic algorithm, each individual represents a possible solution. For the distribution network energy storage cluster division problem, the solution can be which energy storage devices should be combined together to form a cluster. Set the fitness function in the genetic algorithm according to the cluster division index. The opening and closing conditions of each branch are represented by binary coding, and the genetic algorithm solution is used to optimize the solution. The fitness function is used to evaluate the fitness of each individual in the genetic algorithm, so as to guide the search process of the genetic algorithm through fitness. According to the fitness value of the individual, algebraic updates are continuously performed until the optimal individual that meets the requirements or reaches the maximum number of iterations appears, and the cluster division scheme is obtained, that is, the energy storage clusters corresponding to the energy storage system are obtained.

[0056] Step 204: determine the historical operation data of each energy storage cluster based on the historical operation data of the energy storage system, and obtain a charge and discharge plan of the energy storage system based on the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system, and demand information.

[0057] Specifically, after determining each energy storage cluster, determine the historical operation data of each energy storage cluster and the historical monthly load data of the energy storage system from the historical operation data of the energy storage system. Obtain the charge and discharge plan of the energy storage system based on the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system, and the demand information. In this solution, obtaining the charge and discharge plan of the energy storage system based on the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system, and the demand information includes the following steps A1-A2:

[0058] Step A1: For each energy storage cluster, the configuration of the current energy storage cluster is optimized according to the pre-acquired demand information and the historical operation data of the current energy storage cluster to obtain the optimal energy storage configuration of the current energy storage cluster.

[0059] Among them, energy storage configuration refers to the selection, adjustment and optimization of various parameters and components in the energy storage system according to the needs and conditions. In this scheme, an objective function can be established based on the demand information to determine the voltage regulation effect index and constraints of the energy storage system, and finally obtain the optimal energy storage configuration that can meet the constraints.

[0060] For example, the objective function is established: maxF = SF v -F f ; Among them, F v is the energy storage benefit, F f is the energy storage cost, and S is the energy storage stability. Energy storage revenue refers to the revenue obtained by the distributed energy storage system through charging during valley hours and discharging during peak hours through the electricity price difference. The specific formula is as follows:

[0061] Among them, e l , e2 represent the peak period and valley period electricity prices respectively, P c (i) P a (i) represents the discharge and charging power at time i, Q represents the charging power, and n represents the number of time periods in a day. The energy storage cost is the cost of the distributed energy storage system, and the specific formula is as follows:

[0062]

[0063] Where Y represents the service life of the distributed energy storage system in days, m is the total number of distributed energy storage units connected to the distribution network, C(k) represents the power cost of the kth distributed energy storage unit, I(k) represents the total power generation of the kth distributed energy storage unit, H(k) represents the capacity cost of the kth distributed energy storage unit, and E(k) represents the rated power capacity of the kth distributed energy storage unit. Represents the operation and maintenance cost of the distributed energy storage system.

[0064] The energy storage stability is determined by the ratio of the stable working time of the distributed energy storage system to the service life time: Among them, A refers to the number of stable working days of the distributed energy storage system. The voltage regulation effect index is determined as: Where T represents the system period, G t Represents the degree of deviation between the system voltage and the rated voltage at time t. The specific formula is as follows: Among them, λ represents the number of system nodes, u a Indicates the rated voltage fluctuation range, ut (j) represents the voltage fluctuation range of node j at time t. The specific formula of the rated voltage fluctuation range is as follows:

[0065] Among them, u max and u min Respectively represent the maximum and minimum values ​​allowed for voltage. Represents the rated voltage value. The specific formula for the voltage fluctuation range of node j at time t is as follows: in, Represents the voltage value of node j at time t. According to the above formula, when the G index is close to 0, it means that the system offset rate is low and the voltage regulation effect is good. With the goal of maximizing energy storage benefits, determine the optimal action power of energy storage, perform voltage regulation based on cluster division, optimize energy storage scheduling, maximize energy storage benefits, reduce the flow of unnecessary power, and reduce resource loss.

[0066] In this scheme, the constraints include power balance constraint, peak load power constraint, rated power capacity constraint and voltage constraint. The power balance constraint is determined as: P o (i)+P w (i) = P l (i)+P c (i)+P a (i); where P o (i) refers to the output power at time i, P w (i) refers to the grid-connected power at time i, P l (i) refers to the load power at time i, P c (i) P a (i) represents the discharge and charge power at time i. The peak load power constraint is: Where P min Indicates the minimum power value allowed to access the system, P max Indicates the maximum power value allowed to access the system, σP max represents the upper power limit of the expected fluctuation on the distribution network side, σ is the energy storage peak regulation coefficient, P1 is the lower power limit of the expected fluctuation on the distribution network side, P c (i) P a (i) represents the discharge and charge power at time i. The rated power capacity constraint is: E min ≤E(k)≤E max ; Among them, E min and E max They represent the minimum and maximum power capacities of distributed energy storage that can be connected to the nodes in the distribution network, respectively, and E(k) represents the rated power capacity of the kth distributed energy storage unit. The voltage constraint is: in represents the voltage value of node j at time t, u max and u min Furthermore, according to the objective function, the constraints and the equipment information of the energy storage system, a configuration scheme that meets the constraints and makes the objective function the optimal solution is obtained, and the configuration scheme is determined as the optimal energy storage configuration.

[0067] Step A2: Perform a pre-month load forecast for the current energy storage cluster based on the optimal energy storage configuration and the historical monthly load data of the energy storage system to obtain the monthly demand defense value of the current energy storage cluster, and determine the charging and discharging plan of the energy storage system based on the monthly demand defense values ​​and optimal configuration of all energy storage clusters.

[0068] Among them, the monthly demand defense value refers to the maximum one-time indication value of the average power of the user's electricity consumption within a certain settlement period (usually one month). The monthly demand defense value can reflect the user's highest electricity demand in a specific time period. Therefore, according to the monthly demand defense value, a charging and discharging plan that can meet the user's electricity demand can be formulated. In this solution, a pre-defined prediction algorithm, such as a long-term and short-term memory model, can be used to predict the pre-month load based on historical load data. Specifically, based on the optimal energy storage configuration and historical monthly load data, the monthly demand defense benefit is determined as: F R (x) = e[L(x) - R(x)]; where F R (x) is the monthly demand defense benefit in month x, e is the average electricity price, L(x) is the user's monthly maximum load value when no energy storage configuration is installed in month x, and R(x) is the user's monthly demand defense value when energy storage configuration is installed in month x. Furthermore, the monthly demand defense benefit calculation formula is iteratively calculated according to a predetermined iterative algorithm to obtain the monthly demand defense value that satisfies the constraint conditions and maximizes the monthly demand defense benefit, that is, the monthly demand defense value of the current energy storage cluster. After obtaining the monthly demand defense values ​​of all energy storage clusters, a charge and discharge plan is specified based on the monthly demand defense values ​​of each energy storage cluster to ensure that at any given moment, the charge and discharge behavior of the energy storage system will not cause the monthly demand defense value to be exceeded.

[0069] In the above steps, energy storage devices with different performance can be clustered by dividing indicators. According to the characteristics of each cluster, the optimal energy storage configuration can be obtained more accurately. According to the optimal energy storage configuration and the historical monthly load data of the energy storage system, the maximum power demand of the energy storage system in a specific time period can be accurately predicted, which helps to formulate a more accurate and efficient charging and discharging plan.

[0070] Step 205: Determine simulation data corresponding to the charge and discharge plan based on the charge and discharge plan and the equipment information through the plan control module.

[0071] Among them, the simulation data includes simulation model parameters and scenario parameters. The simulation model parameters include the output power of the generator, the capacity of the energy storage battery, the charging and discharging efficiency, and the charging and discharging rate. The charging and discharging plan includes at least one charging and discharging operation. Different charging and discharging operations have different characteristics and applicable scenarios in the operation of the energy storage system. For example, in the constant current charging mode, the charging current remains constant, and the voltage of the energy storage device of the energy storage system gradually increases with the charging process. In the constant voltage charging mode, the charging voltage remains constant, and the charging current gradually decreases with the increase of the state of charge of the energy storage device. Multi-stage charging combines multiple stages such as constant current charging, constant voltage charging, and floating charging, and automatically switches according to the state of the energy storage device. In the constant power charging and discharging mode, the charging and discharging power remains constant, and the voltage and current are adjusted according to the state of the energy storage device. Variable power charging and discharging is to adjust the charging and discharging power according to real-time demand and grid conditions. Different charging and discharging operations not only affect the performance and life of the energy storage system, but are also directly related to the economic benefits of the system. In this solution, simulation experiments can be carried out for different charging and discharging operations through a pre-established joint simulation model, and the optimal charging and discharging operation can be determined according to the simulation experiment results.

[0072] The joint simulation model includes a power module, a communication module and a plan control module. Specifically, the plan control module has the control execution function of the charge and discharge plan and the scheduling function of the energy storage system. The simulation components of the plan control module include: an automatic voltage simulation control component and an automatic power generation control component, which are used to change the voltage and power supply current according to the changes in the charge and discharge plan. The plan control module automatically sets the simulation model parameters based on the data of the real energy storage system, such as the output power of the generator, the capacity of the energy storage battery, the charge and discharge efficiency and the charge and discharge rate, so that the simulation experiment is closer to the actual situation.

[0073] Step 206: for each charge and discharge operation of the charge and discharge plan, determine the number of experiments of the current charge and discharge operation based on the scenario parameters; perform a number of simulation experiments on the energy storage system under the current charge and discharge operation based on the simulation data, the power module and the communication module to obtain the experimental results of each time of the current charge and discharge operation.

[0074] The scenario parameters include load change parameters and actual operation scenario parameters of power grid faults, etc. The experimental results include dynamic characteristic data and communication data of the energy storage system. Specifically, different scenarios will have different effects on the experimental results of the simulation operation. Therefore, it is necessary to simulate each charging and discharging operation in each scenario. There are as many simulation experiments as there are scenarios. After obtaining the simulation data, the number of experiments corresponding to each charging and discharging operation is determined according to the scenario parameters.

[0075] Specifically, after determining the number of experiments, simulation experiments are conducted on the charge and discharge operations in different scenarios through the power module, communication module and plan control module. At the beginning of the simulation experiment, the power module calculates the electrical response of the energy storage system, such as changes in parameters such as voltage, current, and frequency, according to the charge and discharge plan and the set scenario. The power module also simulates the state changes of the energy storage device, such as the charge and discharge state, temperature, and health state of the battery. In the simulation experiment, the communication module is responsible for transmitting control commands and status information. The communication module can ensure that the charge and discharge commands issued by the plan control module can be accurately conveyed to the power module and provide real-time feedback on the operating status of the energy storage system. The communication module also simulates potential problems of the communication network, such as delay, packet loss, and interference, to evaluate the impact of these problems on the performance of the energy storage system. The plan control module generates control commands and adjusts the operation of the energy storage system based on the charge and discharge plan and real-time status information. The plan control module receives data from the power module and the communication module, such as the battery on-chip system, load demand, market price, etc., and calculates the optimal charge and discharge strategy based on these data. The planning and control module also sends control commands to the power module to adjust the generator output power, the charging and discharging rate of the energy storage device, etc., in order to implement the charging and discharging plan. During the simulation experiment, the power module, communication module and planning and control module continuously exchange data and information to ensure that the simulation experiment can accurately simulate the dynamic behavior of the energy storage system in actual operation.

[0076] Step 207: Determine whether there is a risk in the experimental results of each simulation experiment of the current charge and discharge operation according to each preset experimental standard value.

[0077] Among them, the experimental standard value is pre-set and is used to determine whether an abnormality occurs during the experiment. For example, the experimental standard value may include the maximum charging current, the highest / lowest operating voltage, the maximum temperature of the battery, and the maximum tolerance value of the communication delay. After each simulation experiment is completed, the planning control module can collect the dynamic characteristic data and communication data during the experiment, and compare them with the experimental standard values, and check whether the electrical parameters such as voltage, current and frequency exceed the preset range. If all monitored parameters are within the normal range, it is judged that there is no risk in this simulation experiment. If any one or more key parameters exceed the experimental standard value, or abnormal behavior occurs (such as communication interruption, data loss, etc.), it is judged that a risk has occurred.

[0078] Step 208: If the experimental results of all simulation experiments do not show any risk, determine the charge and discharge plan corresponding to the current charge and discharge operation as the first candidate plan; if the experimental results of at least one simulation experiment show risk, determine the charge and discharge plan corresponding to the current charge and discharge operation as the second candidate plan.

[0079] If all experimental results of a charge-discharge operation show no risk, it indicates that the charge-discharge operation can be used directly. Further, the charge-discharge plan corresponding to the charge-discharge operation is determined as the first candidate plan. If there is a risk in the experimental results of at least one simulation experiment of a charge-discharge operation, it indicates that the charge-discharge operation cannot be used and needs to be adjusted, and the charge-discharge plan corresponding to the current charge-discharge operation is determined as the second candidate plan.

[0080] Step 209: determine a target charge and discharge plan based on the first candidate plan and the second candidate plan, and send the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

[0081] Specifically, after all the first candidate plans and the second candidate plans are obtained, the target charge and discharge plan can be determined according to the first candidate plans and the second candidate plans. In this solution, optionally, determining the target charge and discharge plan based on the first candidate plan and the second candidate plan includes the following steps B1-B2:

[0082] Step B1: If there is at least one first candidate plan, determine the target charge and discharge plan according to the at least one first candidate plan; if there is no first candidate plan, determine the risk impact value of each second candidate plan according to the experimental results and the number of experiments of each second candidate plan.

[0083] The first candidate plan is a charge and discharge plan corresponding to the charge and discharge operation that can be used directly. Therefore, as long as there is at least one first candidate plan, the target charge and discharge plan is determined from the first candidate plan first. If there are multiple first candidate plans, the results of all experiments of each first candidate plan can be analyzed, and the charge and discharge plan corresponding to the optimal charge and discharge operation can be selected and determined as the target charge and discharge plan. If there is no first candidate plan, it is necessary to determine the target charge and discharge plan from each second candidate plan.

[0084] Specifically, since there are risks in each second candidate plan, its risk impact value can be determined first, so as to determine the target charging and discharging plan according to its risk impact value. n represents the number of experiments, and the risk impact value is calculated as: Among them, k represents the kth charging and discharging operation of the charging and discharging plan, R represents the risk impact value, and L represents the number of risks that occur in the simulation experiment. Represents the average value loss caused by simulation experiments in which risks occur.

[0085] Step B2: If there is at least one second candidate plan whose risk impact value is less than a preset threshold, determine the target charge and discharge plan from at least one second candidate plan whose risk impact value is less than the preset threshold; if the risk impact values ​​of all second candidate plans are greater than or equal to the preset threshold, calculate the energy storage benefit correction value of each second candidate plan whose risk impact value is greater than or equal to the preset threshold according to a preset energy storage benefit correction formula, and determine the target charge and discharge plan according to the energy storage benefit correction value of each second candidate charge and discharge plan.

[0086] Among them, the preset threshold is used to determine the degree of influence of the risk impact value. If the risk impact value is greater than or equal to the preset threshold, it means that the charge and discharge plan needs to be revised. If the risk impact value is less than the preset threshold, it means that the degree of influence is small and the charge and discharge plan does not need to be revised. Therefore, if there is at least one second candidate plan with a risk impact value less than the preset threshold, the charge and discharge plan with the smallest risk impact value can be preferentially determined from at least one second candidate plan with a risk impact value less than the preset threshold, and it can be determined as the target charge and discharge plan.

[0087] If the risk impact values ​​of all second candidate plans are greater than or equal to the preset threshold, they need to be corrected. The energy storage benefit correction value can reflect the economic loss and time loss of the correction process of the charge and discharge plan corresponding to different charge and discharge operations, and is in inverse proportion to it. Therefore, the charge and discharge plan with the highest energy storage benefit correction value can be selected as the target charge and discharge plan.

[0088] In an optional implementation, a risk level threshold Q is set. When R(k) is greater than or equal to Q, the kth charging and discharging operation is judged to have a high risk. The charging and discharging power and the amount of electricity of the kth operation are adjusted to reduce the risk impact value of k until R(k) is less than Q and the adjustment is stopped. In this solution, the energy storage benefit correction value of each charging and discharging plan is obtained by correcting the charging and discharging operations of each charging and discharging plan and determining the economic loss and time loss of the correction process. The energy storage benefit correction value is calculated as follows:

[0089] Where u represents the charge and discharge plan u corresponding to the charge and discharge operation, Ma(u) represents the energy storage benefit correction value of plan u, ma(u) represents the actual energy storage value of plan u, and k u Indicates that the kth time the risk level value in plan u exceeds the risk level threshold u charging and discharging steps, X represents the number of all charging and discharging steps in plan u where the risk level exceeds the risk level threshold, m(k u ) indicates adjusting the kth u The economic cost required to reduce the risk impact value to below the risk level threshold is t(k u ) indicates adjusting the kth uThe time required for the risk impact value of each second candidate charge and discharge plan to drop below the risk level threshold is calculated. Further, the target charge and discharge plan is determined according to the energy storage benefit correction value of each second candidate charge and discharge plan.

[0090] In the above steps, the safe and stable operation of the energy storage system is ensured by giving priority to the first candidate plan without risk. If there are multiple first candidate plans, further analysis and selection of the optimal plan can improve the overall performance of the system while ensuring safety. When there is no first candidate plan, by calculating the risk impact value of the second candidate plan, the risk can be quantified and managed to ensure that the charging and discharging plan is selected within an acceptable risk range. A risk level threshold is set, and the risk of exceeding the threshold is reduced by adjusting the charging and discharging operations, so that the system can flexibly respond to high-risk situations and ensure the stable operation of the system. The selection mechanism based on the risk impact value and the energy storage benefit correction value enables the energy storage system to better adapt to different operating conditions and market environments, and enhance the adaptability and robustness of the system.

[0091] In the technical solution of this embodiment, the equipment information and historical operation data of the energy storage system in the power grid are obtained. The cluster division index of the energy storage system is determined according to the equipment information and the predetermined cluster division index. The energy storage system is clustered according to the cluster division index to obtain each energy storage cluster corresponding to the energy storage system. The historical operation data of each energy storage cluster is determined according to the historical operation data of the energy storage system, and the charge and discharge plan of the energy storage system is obtained according to the historical operation data of the energy storage system, and the charge and discharge plan of the energy storage system is obtained according to the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system and the demand information. Through the plan control module, the simulation data corresponding to the charge and discharge plan is determined based on the charge and discharge plan and the equipment information; wherein the simulation data includes simulation model parameters and scenario parameters. For each charge and discharge operation of the charge and discharge plan, the number of experiments of the current charge and discharge operation is determined based on the scenario parameters; based on the simulation data, the power module and the communication module, the energy storage system under the current charge and discharge operation is subjected to the number of simulation experiments to obtain the results of each experiment of the current charge and discharge operation; wherein the experimental results include the dynamic characteristic data and communication data of the energy storage system. According to the pre-set experimental standard values, it is determined whether the experimental results of each simulation experiment of the current charge and discharge operation have risks. If the experimental results of all simulation experiments do not show any risk, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the first candidate plan; if there is a risk in the experimental results of at least one simulation experiment, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the second candidate plan. A target charge and discharge plan is determined based on the first candidate plan and the second candidate plan, and the target charge and discharge plan is sent to the energy storage system so that the energy storage system operates based on the target charge and discharge plan. In the technical solution of this embodiment, by experimenting with the charge and discharge plan through a joint simulation model, the performance of different charge and discharge strategies can be quickly and effectively evaluated, avoiding time-consuming and costly trial and error experiments in physical systems. In addition, the optimal charge and discharge strategy can be determined through simulation experiments to ensure that the energy storage system can operate in the best state in different scenarios, thereby improving the operating efficiency and economy of the entire energy storage system.

[0092] Figure 3 The structure diagram of a charging and discharging plan determination device provided by an embodiment of the present invention is suitable for executing a charging and discharging plan determination method provided by an embodiment of the present invention. Figure 3 As shown, the device may specifically include:

[0093] The data acquisition module 301 is used to acquire the equipment information and historical operation data of the energy storage system in the power grid;

[0094] A plan determination module 302, configured to determine a charge and discharge plan of the energy storage system according to the historical operation data, the device information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation;

[0095] A simulation experiment module 303 is used to perform a simulation experiment on each charging and discharging operation of the charging and discharging plan according to a pre-established joint simulation model and the device information to obtain an experimental result of each charging and discharging operation;

[0096] The target plan determination module 304 is used to determine a target charge and discharge plan according to the experimental results of each charge and discharge operation, and send the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

[0097] Optionally, the plan determination module 302 is specifically used to: determine a cluster division index of the energy storage system according to the device information and a predetermined cluster division index;

[0098] Clustering the energy storage system according to the cluster division index to obtain energy storage clusters corresponding to the energy storage system;

[0099] The historical operation data of each energy storage cluster is determined according to the historical operation data of the energy storage system, and the charging and discharging plan of the energy storage system is obtained according to the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system and the demand information.

[0100] Optionally, the plan determination module 302 is further used to: for each energy storage cluster, optimize the configuration of the current energy storage cluster according to the pre-acquired demand information and the historical operation data of the current energy storage cluster to obtain the optimal energy storage configuration of the current energy storage cluster;

[0101] Performing a pre-month load forecast on the current energy storage cluster according to the optimal energy storage configuration and the historical monthly load data of the energy storage system to obtain a monthly demand defense value of the current energy storage cluster;

[0102] The charging and discharging plan of the energy storage system is determined according to the monthly demand defense value and optimal configuration of all energy storage clusters.

[0103] Optionally, the target plan determination module 304 is specifically used to: determine the simulation data corresponding to the charging and discharging plan based on the charging and discharging plan and the equipment information through the plan control module; wherein the simulation data includes simulation model parameters and scenario parameters;

[0104] For each charging and discharging operation of the charging and discharging plan, determining the number of experiments of the current charging and discharging operation based on the scenario parameters;

[0105] Based on the simulation data, the power module and the communication module, the number of simulation experiments are performed on the energy storage system under the current charge and discharge operation to obtain the experimental results of each current charge and discharge operation; wherein the experimental results include dynamic characteristic data and communication data of the energy storage system.

[0106] Optionally, the target plan determination module 304 is further used to: determine whether the experimental results of each simulation experiment of the current charging and discharging operation have risks according to each preset experimental standard value;

[0107] If the experimental results of all simulation experiments show no risk, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the first candidate plan; if the experimental results of at least one simulation experiment show risk, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the second candidate plan;

[0108] The target charge and discharge plan is determined based on the first candidate plan and the second candidate plan.

[0109] Optionally, the target plan determination module 304 is further configured to: if there is at least one first candidate plan, determine the target charging and discharging plan according to the at least one first candidate plan;

[0110] If the first candidate plan does not exist, the risk impact value of each second candidate plan is determined according to the experimental results of each second candidate plan and the number of experiments, and the target charging and discharging plan is determined from each second candidate plan based on the risk impact value.

[0111] Optionally, the target plan determination module 304 is further configured to: if there is at least one second candidate plan whose risk impact value is less than a preset threshold, determine the target charging and discharging plan from the at least one second candidate plan whose risk impact value is less than the preset threshold;

[0112] If the risk impact values ​​of all second candidate plans are greater than or equal to the preset threshold, the energy storage benefit correction value of the second candidate plans whose risk impact values ​​are greater than or equal to the preset threshold is calculated according to a preset energy storage benefit correction formula, and the target charging and discharging plan is determined according to the energy storage benefit correction value of each second candidate charging and discharging plan.

[0113] The charging and discharging plan determination device provided in the embodiment of the present invention can execute the charging and discharging plan determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. The contents not described in detail in this embodiment can refer to the description in any method embodiment of the present invention.

[0114] Figure 4A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, referring to Figure 4 , Figure 4 The electronic device 12 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0115] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0116] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0117] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0118] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0119] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0120] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, a method for determining a charge and discharge plan provided in an embodiment of the present invention is implemented: obtaining equipment information and historical operation data of an energy storage system in a power grid; determining a charge and discharge plan of the energy storage system according to the historical operation data, the equipment information and pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; performing simulation experiments on each charge and discharge operation of the charge and discharge plan according to a pre-established joint simulation model and the equipment information, and obtaining experimental results of each charge and discharge operation; determining a target charge and discharge plan according to the experimental results of each charge and discharge operation, and sending the target charge and discharge plan to the energy storage system, so that the energy storage system operates based on the target charge and discharge plan.

[0121] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, a method for determining a charge and discharge plan as provided in all the embodiments of the present invention is implemented: obtaining equipment information and historical operation data of an energy storage system in a power grid; determining a charge and discharge plan of the energy storage system according to the historical operation data, the equipment information and pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; performing simulation experiments on each charge and discharge operation of the charge and discharge plan according to a pre-established joint simulation model and the equipment information, and obtaining the experimental results of each charge and discharge operation; determining a target charge and discharge plan according to the experimental results of each charge and discharge operation, and sending the target charge and discharge plan to the energy storage system, so that the energy storage system operates based on the target charge and discharge plan. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic device, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0122] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0123] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0124] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining a charge and discharge plan, characterized in that: The method comprises: Obtain equipment information and historical operation data of energy storage systems in the power grid; Determining a charge and discharge plan of the energy storage system according to the historical operation data, the device information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; Performing simulation experiments on each charging and discharging operation of the charging and discharging plan according to the pre-established joint simulation model and the device information to obtain experimental results of each charging and discharging operation; A target charge and discharge plan is determined according to the experimental results of each charge and discharge operation, and the target charge and discharge plan is sent to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

2. The method according to claim 1, characterized in that The historical operation data includes historical monthly load data of the energy storage system. The charge and discharge plan of the energy storage system is determined according to the historical operation data, the device information and the pre-acquired demand information, including: Determining a cluster division index of the energy storage system according to the device information and a predetermined cluster division index; Clustering the energy storage system according to the cluster division index to obtain energy storage clusters corresponding to the energy storage system; The historical operation data of each energy storage cluster is determined according to the historical operation data of the energy storage system, and the charging and discharging plan of the energy storage system is obtained according to the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system and the demand information.

3. The method according to claim 2, characterized in that The charging and discharging plan of the energy storage system is obtained according to the historical operation data of each energy storage cluster, the historical monthly load data of the energy storage system and the demand information, including: For each energy storage cluster, optimize the configuration of the current energy storage cluster according to the pre-acquired demand information and the historical operation data of the current energy storage cluster to obtain the optimal energy storage configuration of the current energy storage cluster; Performing a pre-month load forecast on the current energy storage cluster according to the optimal energy storage configuration and the historical monthly load data of the energy storage system to obtain a monthly demand defense value of the current energy storage cluster; The charging and discharging plan of the energy storage system is determined according to the monthly demand defense value and optimal configuration of all energy storage clusters.

4. The method according to claim 1, characterized in that: The joint simulation model includes a power module, a communication module and a plan control module; a simulation experiment is performed on each charging and discharging operation of the charging and discharging plan according to the pre-established joint simulation model and the device information to obtain the experimental results of each charging and discharging operation, including: Determining simulation data corresponding to the charging and discharging plan based on the charging and discharging plan and the device information through the plan control module; wherein the simulation data includes simulation model parameters and scenario parameters; For each charging and discharging operation of the charging and discharging plan, determining the number of experiments of the current charging and discharging operation based on the scenario parameters; Based on the simulation data, the power module and the communication module, the number of simulation experiments are performed on the energy storage system under the current charge and discharge operation to obtain the experimental results of each current charge and discharge operation; wherein the experimental results include dynamic characteristic data and communication data of the energy storage system.

5. The method according to claim 4, characterized in that Determining a target charge and discharge plan according to the experimental results of each charge and discharge operation includes: Determining whether there is a risk in the experimental results of each simulation experiment of the current charge and discharge operation according to each pre-set experimental standard value; If the experimental results of all simulation experiments show no risk, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the first candidate plan; if the experimental results of at least one simulation experiment show risk, the charge and discharge plan corresponding to the current charge and discharge operation is determined to be the second candidate plan; The target charge and discharge plan is determined based on the first candidate plan and the second candidate plan.

6. The method according to claim 5, characterized in that Determining the target charging and discharging plan based on the first candidate plan and the second candidate plan includes: If there is at least one first candidate plan, determining the target charging and discharging plan according to the at least one first candidate plan; If the first candidate plan does not exist, the risk impact value of each second candidate plan is determined according to the experimental results of each second candidate plan and the number of experiments, and the target charging and discharging plan is determined from each second candidate plan based on the risk impact value.

7. The method according to claim 6, characterized in that Determining the target charging and discharging plan from each second candidate plan based on the risk impact value includes: If there is at least one second candidate plan whose risk impact value is less than a preset threshold, determining the target charging and discharging plan from the at least one second candidate plan whose risk impact value is less than the preset threshold; If the risk impact values ​​of all second candidate plans are greater than or equal to the preset threshold, the energy storage benefit correction value of the second candidate plans whose risk impact values ​​are greater than or equal to the preset threshold is calculated according to a preset energy storage benefit correction formula, and the target charging and discharging plan is determined according to the energy storage benefit correction value of each second candidate charging and discharging plan.

8. A device for determining a charge and discharge plan, characterized in that: include: A data acquisition module is used to obtain equipment information and historical operation data of the energy storage system in the power grid; A plan determination module, configured to determine a charge and discharge plan of the energy storage system according to the historical operation data, the device information and the pre-acquired demand information; wherein the charge and discharge plan includes at least one charge and discharge operation; A simulation experiment module, used to perform simulation experiments on each charging and discharging operation of the charging and discharging plan according to a pre-established joint simulation model and the device information, and obtain experimental results of each charging and discharging operation; The target plan determination module is used to determine a target charge and discharge plan according to the experimental results of each charge and discharge operation, and send the target charge and discharge plan to the energy storage system so that the energy storage system operates based on the target charge and discharge plan.

9. 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 program, a charging and discharging plan determination method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for determining a charge and discharge plan as described in any one of claims 1 to 7 is implemented.

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