Regional energy storage dynamic regulation and control method and system based on simulation graph model

Abstract

The invention provides a regional energy storage dynamic regulation and control method and system based on a simulation graph model, and the method comprises the steps: carrying out the calculation of the dynamic regulation and control of the regional energy storage based on the historical data of equipment technical parameters, the historical data and real-time data of loads, the historical data of energy storage system specifications, and the historical data and real-time data of distributed photovoltaic power generation in each region; constructing a simulation graph model according to weather historical data and real-time data; on the basis of a simulation graph model, whether reverse heavy overload occurs in each transformer area or not and whether the transformer area is currently in a distributed photovoltaic power generation peak period or not are monitored in real time, and according to the trigger condition type and the energy storage condition of each transformer area, a particle swarm optimization algorithm is utilized to correspondingly generate an optimal charging strategy of the energy storage equipment; according to the generated optimal charging strategy of the energy storage equipment, energy storage dynamic scheduling is carried out on the energy storage equipment of each transformer area, and through accurate simulation of a simulation graph model and the efficient search capability of a particle swarm optimization algorithm, the regulation and control cost is effectively saved, and the reliability of energy storage regulation and control is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation, and in particular to a method and system for dynamic control of regional energy storage based on simulation graphics. Background Art

[0002] With the large-scale and high-proportion access to the power grid of new energy sources such as wind power and photovoltaics, their inherent intermittency and volatility have posed new challenges to the stability of the power system. As an effective means to smooth the fluctuations of these renewable energy sources, energy storage technology is crucial to maintaining the stability of the power grid and improving operational efficiency. However, at present, the dispatching and operation mechanism of new energy storage is still incomplete, and there are phenomena of "built but not used" and "built but not adjusted", resulting in low utilization. Therefore, it is necessary to develop a new type of energy storage dynamic control system.

[0003] In related technologies, dynamic regulation of energy storage generally sets regulation thresholds based on line operation conditions or time periods, without considering factors such as reverse overload and photovoltaic power generation, or monitoring grid operation conditions through simulation models, resulting in low reliability and economy of energy storage regulation.

[0004] In view of this problem, the present invention provides a method and system for dynamic control of regional energy storage based on simulation model to solve the above problem. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention innovatively proposes a regional energy storage dynamic control method and system based on simulation model, which effectively solves the problems of low reliability and economy of energy storage control caused by the prior art, effectively saves the control cost, and improves the reliability of energy storage control.

[0006] A first aspect of the present invention provides a method for dynamic control of regional energy storage based on a simulation model, comprising:

[0007] Obtain historical data on equipment technical parameters, historical and real-time data on loads, historical data on energy storage system specifications, historical and real-time data on distributed photovoltaic power generation, and historical and real-time data on weather in each substation;

[0008] Build simulation models based on historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area;

[0009] Based on the simulation model, real-time monitoring is performed to determine whether each substation has a reverse overload and whether the substation is currently in the peak period of distributed photovoltaic power generation. If the current substation meets the preset algorithm scheduling trigger conditions, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device according to the trigger condition type and the energy storage situation of each substation; wherein the preset algorithm scheduling trigger conditions include a first trigger condition and a second trigger condition, wherein the first trigger condition is that a first preset number of substations in the region have a reverse overload, and the first preset number of substations in the region are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the region and the number of substations in the peak period of distributed photovoltaic power generation is a second preset number, wherein the first preset number is less than the second preset number;

[0010] According to the generated optimal charging strategy for energy storage equipment, the energy storage equipment in each substation is dynamically scheduled to eliminate reverse heavy overloads in the substation.

[0011] Optionally, based on the historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation, a simulation model is constructed including:

[0012] Based on the historical data of equipment technical parameters and energy storage system specifications in each substation, a power grid model and an equipment electrical model are established. The power grid model includes a topological structure model of the power grid in different substations, which is used to represent the connection relationship between each device node in different substations, display the topological structure and electrical characteristics of the distribution network, and provide an operating environment for other models. The power flow is calculated according to the data predicted by the distributed photovoltaic power generation prediction model, and the results are fed back to the equipment model and the load model; the equipment electrical model includes a distribution transformer model, a circuit breaker model, and a distributed power supply model, which are used to represent the electrical parameters of each device in different substations, determine the role of the device in the power system according to the location information in the power grid model, display the relevant electrical data of the device, and adjust the status or output of the device according to the calculation results of the power grid model;

[0013] Based on the historical load data and real-time data, weather historical data and real-time data in each substation, a load model based on LSTM neural network is constructed to predict the load curve of each substation according to the historical load data and real-time data, weather historical data and real-time data, and adjust the load curve according to the calculation results of the power grid model to adapt to different operating conditions;

[0014] Based on the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data in each substation, a distributed photovoltaic power generation prediction model based on LSTM neural network is constructed to predict the distributed photovoltaic power generation of each substation according to the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data, and pass the prediction results to the power grid model in the form of files.

[0015] Furthermore, based on the simulation model, real-time monitoring is performed to determine whether each substation has a reverse overload and whether the substation is currently in the peak period of distributed photovoltaic power generation:

[0016] When the current in the substation flows from the user to the power grid, and the transformer load rate is higher than the first preset value, the substation has a reverse heavy overload; wherein the transformer load rate is calculated as follows:

[0017]

[0018] Among them, β 反向 is the transformer load factor; S 反向 is the reverse apparent power of the total meter in the area; S e is the rated capacity of the transformer;

[0019] When the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts that the rate of change of the power generation curve obtained during the current period of expected sufficient sunlight exceeds the preset rate of change threshold, then the substation is currently in the peak period of distributed photovoltaic power generation.

[0020] Optionally, according to the type of trigger condition and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device, which specifically includes:

[0021] According to the trigger condition type and the energy storage situation of each substation, determine the objective function and constraint conditions of the regulation. The objective function is the sum of the overload of the power grid and the charging and discharging cost of the energy storage equipment. The constraint condition is that each substation does not have a reverse heavy overload.

[0022] A particle swarm optimization algorithm with constraints is introduced to generate the optimal charging strategy for energy storage equipment.

[0023] Furthermore, when the trigger condition type is the first trigger condition, the objective function is:

[0024]

[0025] Wherein, Objective 1 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition occurs; N 1is the number of areas with reverse heavy overload and at the peak of distributed photovoltaic power generation; Q 1 is the number of areas that have not experienced reverse overload and are not in the peak period of distributed photovoltaic power generation; M is the number of energy storage devices in the area; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; It is allocated to the area where there is no reverse overload and is not in the peak period of distributed photovoltaic power generation. 1 The power of the internal energy storage device j; Cost Allocate,αj It is allocated to the area where there is no reverse overload and is not in the peak period of distributed photovoltaic power generation. 1 The total cost of internal energy storage device j, which includes the cost of charge and discharge loss and the cost of equipment depreciation;

[0026] When the trigger condition type is the second trigger condition, the objective function is:

[0027]

[0028] Among them, Objective2 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition is triggered; N 2 is the number of areas experiencing reverse heavy overload or at the peak of distributed photovoltaic power generation; Q 2 is the number of substations without reverse overload; M is the number of energy storage devices in the substation; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; It is allocated to the station area α where no reverse heavy overload occurs 2 The power of internal energy storage device j; It is allocated to the station area α where no reverse heavy overload occurs 2 The total cost of internal energy storage device j, the total cost includes charging and discharging loss cost and equipment depreciation cost.

[0029] Furthermore, when the trigger condition type is the first trigger condition, the constraint conditions include the grid carrying capacity constraint and the energy storage device capacity constraint, wherein the grid carrying capacity constraint is:

[0030]

[0031] Among them, L t Represents the grid carrying capacity at time t, TotalLoad t represents the total load of the power grid at time t;

[0032] The capacity constraint of energy storage equipment is:

[0033]

[0034] Among them, C max,j is the maximum capacity that can be stored by the jth energy storage device;

[0035] When the trigger condition type is the second trigger condition, the constraints include the grid carrying capacity constraint and the energy storage device capacity constraint, where the grid carrying capacity constraint is:

[0036]

[0037] The capacity constraint of energy storage equipment is:

[0038]

[0039] Furthermore, if there is a violation of the constraint, the objective function is adjusted through the penalty function;

[0040] Among them, the penalty function includes the equipment capacity penalty function Penalty capacity,αj and the power grid capacity penalty function Penalty load,t , when the trigger condition type is the first trigger condition, the device capacity penalty function The expression is:

[0041]

[0042] When the trigger condition type is the second trigger condition, the device capacity penalty function The expression is:

[0043]

[0044] When the trigger condition type is the first trigger condition, the power grid capacity penalty function Penalty load,t,1 The expression is:

[0045]

[0046] When the trigger condition type is the second trigger condition, the power grid capacity penalty function Penalty load,t,2 The expression is:

[0047]

[0048] When the trigger condition type is the first trigger condition, the adjusted new objective function is:

[0049]

[0050] Among them, λ is the penalty function coefficient, which is used to control the impact of the penalty function on the objective function;

[0051] When the trigger condition type is the second trigger condition, the adjusted new objective function is:

[0052]

[0053] Among them, λ is the penalty function coefficient, which is used to control the impact of the penalty function on the objective function.

[0054] Optionally, a particle swarm optimization algorithm with constraints is introduced to generate an optimal charging strategy for energy storage devices, specifically including:

[0055] Initialize the particle position parameters. The position of each particle can be represented as an M×N matrix, where each element Representative assigned to station area α 1 The proportion or absolute value of the power of energy storage device j in the Representative assigned to station area α 2 The proportion or absolute value of the power of energy storage device j;

[0056] Calculate the fitness value, where the fitness value is the objective function value;

[0057] Determine whether a particle violates the constraint condition. If a particle violates the constraint condition, the penalty function is applied to adjust the objective function, and the fitness value of each particle is recalculated according to the adjusted objective function.

[0058] According to the individual extreme value and global extreme value of each particle, the particle position and velocity are updated, where the update expressions of the particle position and velocity are:

[0059] v mn (t+1)=a(t)v mn (t)+b 1 r 1 (t)(p mn (t)-y mn (t))+b 2 r 2 (t)(p gn (t)-y mn (t)),

[0060] y mn (t+1)=y mn (t)+v mn (t+1),

[0061] Among them, v mn (t), y mn (t), p mn (t) represent the velocity, position and individual extreme value of the mth particle in dimension n at the tth iteration; p gn(t) represents the global extreme value in dimension n at the tth iteration; a(t) represents the inertia weight at the tth iteration; b 1 , b 2 is the learning factor; r 1 (t), r 2 (t) is a random number in the interval (0,1);

[0062] The position and speed of each particle are iteratively updated until a preset stop condition is met, wherein the preset stop condition includes reaching a maximum number of iterations or a fitness value greater than a preset fitness threshold.

[0063] Optionally, before dynamically scheduling the energy storage equipment in each substation according to the generated optimal charging strategy for the energy storage equipment, the method further includes:

[0064] The generated optimal charging strategy for the energy storage device is simulated and verified in the simulation model. If the reverse heavy overload phenomenon disappears in each substation, the optimal charging strategy for the energy storage device is verified successfully; if the reverse heavy overload phenomenon does not disappear in a certain substation, the optimal charging strategy for the energy storage device is verified failed.

[0065] A second aspect of the present invention provides a regional energy storage dynamic control system based on a simulation model, comprising:

[0066] The acquisition module obtains the historical data of equipment technical parameters, load history data, energy storage system specification history data, distributed photovoltaic power generation history data, and weather history data in each substation;

[0067] The construction module builds a simulation model based on the historical data of equipment technical parameters, historical and real-time data of load, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area;

[0068] The monitoring and generation module, based on the simulation graph model, monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed photovoltaic power generation. If the current substation meets the preset algorithm scheduling trigger condition, according to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device; wherein, the preset algorithm scheduling trigger condition includes a first trigger condition and a second trigger condition, the first trigger condition is that the first preset number of substations in the area have a reverse heavy overload, and the first preset number of substations in the area are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the area and the number of substations in the peak period of distributed photovoltaic power generation is the second preset number, wherein the first preset number is less than the second preset number;

[0069] The scheduling module dynamically schedules the energy storage equipment in each substation according to the generated optimal charging strategy for the energy storage equipment to eliminate the reverse heavy overload in the substation.

[0070] The technical solution adopted by the present invention includes the following technical effects:

[0071] 1. The present invention constructs a simulation model based on the historical data of equipment technical parameters, historical load data and real-time data, historical data of energy storage system specifications, historical data of distributed photovoltaic power generation and real-time data, and historical weather data and real-time data in each substation; based on the simulation model, it monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed photovoltaic power generation, and generates the optimal charging strategy for energy storage equipment according to the type of trigger conditions and the energy storage situation of each substation using the particle swarm optimization algorithm; according to the generated optimal charging strategy for energy storage equipment, the energy storage equipment in each substation is dynamically scheduled to eliminate the reverse heavy overload in the substation, and by combining the precise simulation of the simulation model and the efficient search capability of the particle swarm optimization algorithm, it realizes precise control and optimal scheduling of regional power grid energy storage equipment, effectively solving the problems of low reliability and economy of energy storage regulation caused by the existing technology, effectively saving regulation costs, and improving the reliability of energy storage regulation.

[0072] 2. The simulation model in the technical solution of the present invention includes a power grid model, an equipment electrical model, a load model, and a distributed photovoltaic power generation prediction model. The various models are interconnected and work together to respond to changes in the power grid load in real time, intelligently allocate and adjust energy storage resources, effectively alleviate the peak-to-valley difference problem of the power grid, and improve the operating efficiency and economy of the power grid.

[0073] 3. In the technical solution of the present invention, real-time monitoring is performed based on the simulation model. When the current in the substation flows from the user to the power grid and the transformer load rate is higher than the first preset value, the substation will experience a reverse heavy overload. Not only the power grid model in the simulation model is considered, but also the electrical parameters in the equipment electrical model are combined to organically combine efficiency and accuracy. When the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts a change rate of the power generation curve in the current period of expected sufficient sunlight, which exceeds the preset change rate threshold, the substation is currently in the peak period of distributed photovoltaic power generation. Not only the time period of the substation in the simulation model is considered, but also the output prediction results of the distributed photovoltaic power generation prediction model are combined to organically combine efficiency and accuracy.

[0074] 4. In the technical solution of the present invention, the objective function and constraint conditions of the regulation are determined according to the trigger condition type and the energy storage situation of each substation. The objective function is the sum of the grid overload and the charging and discharging cost of the energy storage equipment under different trigger conditions, and the constraint condition is that each substation does not have a reverse heavy overload. A particle swarm optimization algorithm with constraint conditions is introduced to generate the optimal charging strategy for the energy storage equipment. If the constraint conditions are violated, the objective function is adjusted through a penalty function. Not only is the objective function different according to the trigger condition type, but the penalty function is also used to adjust the objective function when the constraint conditions are violated, thereby improving the adaptability of energy storage regulation. Moreover, by combining the precise simulation of the simulation model and the efficient search capability of the particle swarm optimization algorithm, precise control and optimal scheduling of regional power grid energy storage equipment are achieved. The adaptability to fluctuations in renewable energy and the anti-interference ability of the power grid are enhanced, energy waste caused by imbalance between supply and demand is reduced, and innovative solutions are provided for the intelligent management and sustainable development of the power grid.

[0075] 5. In the technical solution of the present invention, before the energy storage equipment in each substation is dynamically scheduled according to the generated optimal charging strategy for energy storage equipment, it also includes: simulating and verifying the generated optimal charging strategy for energy storage equipment in a simulation model. If the reverse heavy overload phenomenon disappears in each substation, the optimal charging strategy for energy storage equipment is verified successfully; if the reverse heavy overload phenomenon does not disappear in a certain substation, the optimal charging strategy for energy storage equipment is not verified successfully, thereby ensuring the effectiveness of the implementation of the optimal charging strategy for energy storage equipment and improving the economy of energy storage regulation.

[0076] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0078] Figure 1 A schematic diagram of a process of a method in Embodiment 1 of the present invention;

[0079] Figure 2 This is a schematic diagram of the implementation process of the method of Example 1 in the solution of the present invention;

[0080] Figure 3 Another schematic diagram of the process of the method of Embodiment 1 in the scheme of the present invention;

[0081] Figure 4 It is a schematic diagram of the structure of the system of Example 2 in the solution of the present invention. DETAILED DESCRIPTION

[0082] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits the description of known components and processing techniques and processes to avoid unnecessary limitations on the present invention.

[0083] Embodiment 1

[0084] like Figure 1-Figure 2 As shown, the present invention provides a method for dynamic regulation of regional energy storage based on simulation model, comprising:

[0085] Step S1, obtaining historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation;

[0086] Step S3, constructing a simulation model based on the historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area;

[0087] Step S5, based on the simulation graph, real-time monitoring of whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed photovoltaic power generation. If the current substation meets the preset algorithm scheduling trigger condition, according to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy of the energy storage device; wherein, the preset algorithm scheduling trigger condition includes a first trigger condition and a second trigger condition, the first trigger condition is that the first preset number of substations in the region have a reverse heavy overload, and the first preset number of substations in the region are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the region and the number of substations in the peak period of distributed photovoltaic power generation is a second preset number, wherein the first preset number is less than the second preset number;

[0088] Step S7, according to the generated optimal charging strategy for the energy storage equipment, the energy storage equipment in each substation is dynamically scheduled to eliminate the reverse heavy overload in the substation.

[0089] In step S1, data collection may include:

[0090] Power grid data: collect data of all relevant equipment in the substation, including technical parameters of transformers, lines, switches, etc.;

[0091] Load data: collect historical load data and real-time load data for training and verification of load forecasting models;

[0092] Energy storage system data: Obtain the specifications of the energy storage system, including rated capacity, current power stored in the device, charge and discharge rate, efficiency, etc.

[0093] Distributed photovoltaic data: collect historical and real-time distributed photovoltaic power generation data;

[0094] Weather Data: Collect weather forecast data.

[0095] In step S3, the simulation graph model (simulation graph model) first constructs a detailed power grid model, including a load model and a distributed photovoltaic power generation model, to simulate the power grid operation status under various working conditions. Then, through real-time power generation, the load data is calculated to see whether the preset algorithm scheduling trigger conditions are met. When the preset algorithm scheduling trigger conditions are met, the particle swarm optimization algorithm is used to find the best energy storage device charging strategy. Use simulation technology to establish a regional power grid map, including photovoltaic power generation equipment, energy storage equipment, power grid structure, load characteristics, etc. under each substation, and collect real-time power grid operation data, load data, meteorological data, etc., and display them in real time on a map.

[0096] Specifically, based on the historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation, the simulation model is constructed including:

[0097] Based on the historical data of equipment technical parameters and energy storage system specifications in each substation, a power grid model and an equipment electrical model are established. The power grid model includes a topological structure model of the power grid in different substations, which is used to represent the connection relationship between each equipment node (such as transformers, lines, switches, etc.) in different substations, display the topological structure and electrical characteristics of the distribution network, and provide an operating environment for other models. The power flow is calculated according to the data predicted by the distributed photovoltaic power generation prediction model, and the results are fed back to the equipment model and the load model; the equipment electrical model includes a distribution transformer model, a circuit breaker model, and a distributed power supply model (such as a photovoltaic panel), which are used to represent the electrical parameters of each device in different substations, determine the role of the device in the power system according to the location information in the power grid model, display the relevant electrical data of the device, and adjust the state or output of the device according to the calculation results of the power grid model, such as the charging adjustment of the energy storage equipment;

[0098] Based on the historical load data and real-time data, weather historical data and real-time data in each substation, a load model based on LSTM neural network is constructed (reflecting the user's demand for electricity). It is used to predict the load curve of each substation (forecasting the next few hours in a day) according to the historical load data and real-time data, weather historical data (such as light intensity, temperature, etc.) and real-time data, and adjust the load curve according to the calculation results of the power grid model to adapt to different operating conditions;

[0099] Based on the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data in each substation, a distributed photovoltaic power generation prediction model based on LSTM neural network is constructed to predict the distributed photovoltaic power generation of each substation according to the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data, and pass the prediction results to the power grid model in the form of files.

[0100] Through the simulation of various models, the performance of the power grid under different conditions can be evaluated to help optimize the operation strategy. For example, if the distributed photovoltaic power generation prediction model of a certain area predicts that there will be a higher sunshine intensity tomorrow, the photovoltaic system will generate more electricity. In this case, the power grid model needs to recalculate the power flow distribution of the power grid to ensure voltage stability and avoid overload. At the same time, the load model may predict higher electricity demand, because more electricity is available, which may encourage the use of more electricity. The energy storage system in the equipment model may be dispatched to store excess electricity.

[0101] The simulation model is used to simulate the operation of the entire regional power system, including simulating the working status of various electrical equipment (such as transformers, lines, switches, etc.) in each substation; predicting the changes in voltage, current, power and other factors under different operating conditions; analyzing the impact of the substation on the upstream power grid, and the impact of the upstream power grid on the substation; predicting the load demand of the substation based on historical data and real-time data, and analyzing the load characteristics at different time points (such as peak hours and valley hours). Predict the distributed photovoltaic power generation of the substation based on historical data and real-time data, analyze the power generation at different time points, and plan energy storage in combination with the load.

[0102] In an embodiment of the present invention, the functions of the simulation model are: 1. Providing the initial environment configuration for the particle swarm optimization algorithm, and displaying the voltage, current, and power conditions of each substation in real time. 2. Setting the parameters in the simulation model, such as the maximum charge and discharge power of the energy storage system, the energy storage capacity, etc. 3. Define the operating restrictions of the energy storage system and other power system components to ensure that the solution generated by the particle swarm optimization algorithm meets the constraints of actual operation. 4. Based on the scheduling plan proposed by the particle swarm optimization algorithm, evaluate its effect in the simulation environment, and calculate the corresponding cost or benefit indicators as feedback information in the optimization process to guide the particle swarm optimization algorithm to conduct further search.

[0103] Among them, in step S5, heavy overload: the phenomenon that the current or power carried by the components (wires, transformers, etc.) in the power system exceeds its rated value. In some cases, the load in the power system can become a power source. For example, users with renewable energy power generation systems may transmit electricity to the power grid when all the generated electricity is not needed. The embodiment of the present invention only considers the photovoltaic power generation equipment in the area. After the photovoltaic power generation meets the user's own electricity consumption, it will be transmitted back to the power grid.

[0104] Based on the simulation model, real-time monitoring is performed to determine whether each substation has a reverse overload and whether the substation is currently in the peak period of distributed photovoltaic power generation:

[0105] When the current in the substation flows from the user to the power grid, and the transformer load rate is higher than a first preset value (e.g., 85%), the substation is reversely overloaded; wherein the transformer load rate is calculated as follows:

[0106]

[0107] Among them, β 反向 is the transformer load factor; S 反向 is the reverse apparent power of the total meter in the area (kVA); S e is the rated capacity of the transformer (kVA);

[0108] The simulation diagram displays the current, voltage and power of each component and line in real time. When the current in the substation flows in the forward direction, the current flows from the upper transformer through the node along the line to the load end; when the photovoltaic power generation in the substation is in excess, the current flows in the reverse direction to the grid, and the simulation diagram shows the current flow in the reverse direction. The system automatically calculates according to the formula. When a reverse overload occurs, the reverse current is marked in red on the diagram, indicating a reverse overload.

[0109] When the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts that the rate of change of the power generation curve obtained during the current period of expected sufficient sunlight exceeds the preset rate of change threshold, then the substation is currently in the peak period of distributed photovoltaic power generation.

[0110] The embodiment of the present invention constructs a distributed photovoltaic power generation prediction model in the simulation model, which is trained based on historical power generation data and meteorological data, and predicts the photovoltaic power generation in the subsequent time period in combination with the real-time meteorological conditions of the day. By analyzing the predicted power generation curve, when the expected sunlight is sufficient (for example, between 10 am and 2 pm), and the photovoltaic power generation prediction value output by the distributed photovoltaic power generation prediction model is expected to increase significantly (that is, the change rate of the predicted power generation curve exceeds the preset change rate threshold), it may cause an impact on the power grid. In this case, the system can issue an instruction to start the energy storage device to store electricity, prevent the power grid from reverse heavy overload, and ensure the normal operation of the power grid.

[0111] In step S5, according to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device, which specifically includes:

[0112] According to the trigger condition type and the energy storage situation of each substation, the objective function and constraint conditions of the regulation are determined. The objective function is the sum of the overload of the power grid and the charging and discharging cost of the energy storage equipment. The constraint condition is that each substation does not have a reverse heavy overload or is not currently in the peak period of distributed photovoltaic power generation;

[0113] A particle swarm optimization algorithm with constraints is introduced to generate the optimal charging strategy for energy storage equipment.

[0114] Among them, when the trigger condition type is the first trigger condition (for example, 30% of the areas in the region have reverse heavy overloads, and 30% of the areas in the region are in the peak period of distributed photovoltaic power generation), the objective function is:

[0115]

[0116] Wherein, Objective1 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition occurs; N 1 is the number of areas with reverse heavy overload and at the peak of distributed photovoltaic power generation; Q 1 is the number of areas that have not experienced reverse overload and are not in the peak period of distributed photovoltaic power generation; M is the number of energy storage devices in the area; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; It is allocated to the area where there is no reverse overload and is not in the peak period of distributed photovoltaic power generation. 1 The power of the internal energy storage device j; Cost Allocate,αj It is allocated to the area where there is no reverse overload and is not in the peak period of distributed photovoltaic power generation. 1 The total cost of internal energy storage device j, which includes the cost of charge and discharge loss and the cost of equipment depreciation;

[0117] When the trigger condition type is the second trigger condition (for example, 60% of the areas in the region have reverse heavy overload, or 60% of the areas in the region are in the peak period of distributed photovoltaic power generation), the objective function is:

[0118]

[0119] Among them, Objective2 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition is triggered; N 2 is the number of areas experiencing reverse heavy overload or at the peak of distributed photovoltaic power generation; Q 2 is the number of stations without reverse heavy overload (Q 2 Greater than Q 1 , that is, when allocating, the area at the peak of distributed photovoltaic power generation can be added, as long as there is no reverse overload); M is the number of energy storage devices in the area; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; It is allocated to the station area α where no reverse heavy overload occurs 2 The power of internal energy storage device j; It is allocated to the station area α where no reverse heavy overload occurs 2 The total cost of internal energy storage device j, the total cost includes charging and discharging loss cost and equipment depreciation cost.

[0120] When the trigger condition type is the first trigger condition or the second trigger condition, it will affect the entire area of ​​the substation or the upstream area of ​​the substation. According to the energy storage conditions of each substation, the particle swarm optimization algorithm will be used to dynamically dispatch energy storage to ensure the normal operation of the entire regional power grid.

[0121] Furthermore, when the trigger condition type is the first trigger condition, the constraint conditions include the grid carrying capacity constraint and the energy storage device capacity constraint, wherein the grid carrying capacity constraint is:

[0122]

[0123] Among them, L t Represents the grid carrying capacity at time t, TotalLoad t represents the total load of the power grid at time t;

[0124] The capacity constraint of energy storage equipment is:

[0125]

[0126] Among them, C max,j is the maximum capacity that can be stored by the jth energy storage device;

[0127] When the trigger condition type is the second trigger condition, the constraints include the grid carrying capacity constraint and the energy storage device capacity constraint, where the grid carrying capacity constraint is:

[0128]

[0129] The capacity constraint of energy storage equipment is:

[0130]

[0131] Preferably, if a constraint violation occurs, the objective function is adjusted by a penalty function;

[0132] Among them, the penalty function includes the equipment capacity penalty function Penalty capacity,αj and the power grid capacity penalty function Penalty load,t , when the trigger condition type is the first trigger condition, the device capacity penalty function Penalty capacity,α1j The expression is:

[0133]

[0134] When the trigger condition type is the second trigger condition, the device capacity penalty function The expression is:

[0135]

[0136] When the trigger condition type is the first trigger condition, the power grid capacity penalty function Penalty load,t,1 The expression is:

[0137]

[0138] When the trigger condition type is the second trigger condition, the power grid capacity penalty function Penalty load,t,2 The expression is:

[0139]

[0140] When the trigger condition type is the first trigger condition, the adjusted new objective function is:

[0141]

[0142] Among them, λ is the penalty function coefficient, which is used to control the impact of the penalty function on the objective function;

[0143] When the trigger condition type is the second trigger condition, the adjusted new objective function is:

[0144]

[0145] Among them, λ is the penalty function coefficient, which is used to control the impact of the penalty function on the objective function.

[0146] Specifically, the particle swarm optimization algorithm with constraints is introduced to generate the optimal charging strategy for energy storage equipment, which includes:

[0147] Initialize the particle position parameters. The position of each particle can be represented as an M×N matrix, where each element Representative assigned to station area α 1 The proportion or absolute value of the power of energy storage device j in the Representative assigned to station area α 2 The proportion or absolute value of the power of energy storage device j;

[0148] Calculate the fitness value, where the fitness value is the objective function value;

[0149] Determine whether a particle violates the constraint condition. If a particle violates the constraint condition, the penalty function is applied to adjust the objective function, and the fitness value of each particle is recalculated according to the adjusted objective function.

[0150] According to the individual extreme value and global extreme value of each particle, the particle position and velocity are updated, where the update expressions of the particle position and velocity are:

[0151] v mn (t+1)=a(t)v mn (t)+b 1 r 1 (t)(p mn (t)-y mn (t))+b 2 r 2 (t)(p gn (t)-y mn (t)),

[0152] y mn (t+1)=y mn (t)+v mn (t+1),

[0153] Among them, v mn (t), y mn (t), pmn (t) represent the velocity, position and individual extreme value of the mth particle in dimension n at the tth iteration; p gn (t) represents the global extreme value in dimension n at the tth iteration; a(t) represents the inertia weight at the tth iteration; b 1 , b 2 is the learning factor; r 1 (t), r 2 (t) is a random number in the interval (0, 1];

[0154] The position and speed of each particle are iteratively updated until a preset stop condition is met, wherein the preset stop condition includes reaching a maximum number of iterations or a fitness value greater than a preset fitness threshold.

[0155] The particle swarm optimization algorithm is used to optimize the charging and discharging strategy of the energy storage system, so as to maximize the effective allocation of excess photovoltaic power generation to the energy storage device, eliminate the grid overload phenomenon as a comprehensive consideration goal, minimize the grid overload and the charging and discharging cost of the energy storage device as the objective function, introduce the particle swarm optimization algorithm with constraints, and obtain the best solution.

[0156] The amount of electricity allocated is mainly based on the storable energy of each energy storage device and the state of the energy storage device (charging, discharging). When the energy storage device is in the process of discharging, this device is not considered. The storable energy is constrained as a constraint.

[0157] Among them, a(t)=a(t) max -t(a(t) max -a(t) min ) / t max , a(t) represents the inertia weight of the tth iteration, which can be adjusted, t max is the maximum number of iterations,

[0158] like Figure 3 As shown, the technical solution of the present invention also provides a regional energy storage dynamic control system based on a simulation model, which includes:

[0159] S6, the generated optimal charging strategy for the energy storage device is simulated and verified in the simulation model. If the reverse heavy overload phenomenon in each substation disappears, the optimal charging strategy for the energy storage device is verified successfully, and step S7 is executed; if the reverse heavy overload phenomenon in a certain substation does not disappear, the optimal charging strategy for the energy storage device is verified failed, and step S5 is executed.

[0160] The simulation model is used to verify whether the allocation method is appropriate, that is, the scheduling plan (optimal charging strategy for energy storage equipment) is simulated in the simulation model. If the reverse heavy overload phenomenon of each substation disappears, the scheduling plan can be implemented.

[0161] The present invention constructs a simulation model based on the historical data of technical parameters of equipment in each substation, historical data and real-time data of load, historical data of energy storage system specifications, historical data and real-time data of distributed photovoltaic power generation, and historical data and real-time data of weather in each substation; based on the simulation model, it monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed distributed photovoltaic power generation, and generates an optimal charging strategy for energy storage equipment according to the type of trigger conditions and the energy storage situation of each substation using a particle swarm optimization algorithm; according to the generated optimal charging strategy for energy storage equipment, the energy storage equipment in each substation is dynamically scheduled for energy storage to eliminate the reverse heavy overload in the substation, and by combining the precise simulation of the simulation model with the efficient search capability of the particle swarm optimization algorithm, it realizes precise control and optimal scheduling of regional power grid energy storage equipment, effectively solving the problems of low reliability and economy of energy storage regulation caused by the prior art, effectively saving regulation costs, and improving the reliability of energy storage regulation.

[0162] The simulation model in the technical solution of the present invention includes a power grid model, an equipment electrical model, a load model, and a distributed photovoltaic power generation prediction model. The various models are interconnected and work together to respond to changes in the power grid load in real time, intelligently allocate and adjust energy storage resources, effectively alleviate the peak-to-valley difference problem of the power grid, and improve the operating efficiency and economy of the power grid.

[0163] In the technical solution of the present invention, real-time monitoring is based on the simulation model. When the current in the substation flows from the user to the power grid and the transformer load rate is higher than the first preset value, the substation will experience a reverse heavy overload. Not only the power grid model in the simulation model is considered, but also the electrical parameters in the equipment electrical model are combined to organically combine efficiency and accuracy; when the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts a change rate of the power generation curve in the current period of expected sufficient sunlight, which exceeds the preset change rate threshold, the substation is currently in the peak period of distributed photovoltaic power generation; not only the time period of the substation in the simulation model is considered, but also the output prediction results of the distributed photovoltaic power generation prediction model are combined to organically combine efficiency and accuracy.

[0164] In the technical solution of the present invention, the objective function and constraint conditions of the regulation are determined according to the trigger condition type and the energy storage situation of each substation. The objective function is the sum of the overload of the power grid and the charging and discharging cost of the energy storage equipment, and the constraint condition is that each substation does not have a reverse heavy overload; a particle swarm optimization algorithm with constraint conditions is introduced to generate the optimal charging strategy for the energy storage equipment, and if the constraint conditions are violated, the objective function is adjusted through a penalty function; not only the objective function is different according to the trigger condition type, but also the objective function is adjusted through a penalty function when the constraint conditions are violated, thereby improving the adaptability of energy storage regulation; and by combining the precise simulation of the simulation model and the efficient search capability of the particle swarm optimization algorithm, precise control and optimal scheduling of regional power grid energy storage equipment are achieved; the adaptability to fluctuations in renewable energy and the anti-interference ability of the power grid are enhanced, the energy waste caused by imbalance between supply and demand is reduced, and innovative solutions are provided for the intelligent management and sustainable development of the power grid.

[0165] According to the generated optimal charging strategy for energy storage equipment, the technical solution of the present invention also includes: verifying the generated optimal charging strategy for energy storage equipment in a simulation diagram before dynamically scheduling the energy storage equipment in each substation. If the reverse heavy overload phenomenon disappears in each substation, the verification of the optimal charging strategy for energy storage equipment is passed; if the reverse heavy overload phenomenon does not disappear in a certain substation, the verification of the optimal charging strategy for energy storage equipment is not passed, thereby ensuring the effectiveness of the implementation of the optimal charging strategy for energy storage equipment and improving the economy of energy storage regulation.

[0166] Embodiment 2

[0167] like Figure 4 As shown, the technical solution of the present invention also provides a regional energy storage dynamic control system based on a simulation model, comprising:

[0168] Acquisition module 101, acquires historical data of equipment technical parameters, historical data of load, historical data of energy storage system specifications, historical data of distributed photovoltaic power generation, and historical data of weather in each substation;

[0169] A construction module 102 constructs a simulation model based on the historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area;

[0170] The monitoring and generating module 103 monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed photovoltaic power generation based on the simulation graph. If the current substation meets the preset algorithm scheduling trigger condition, according to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy of the energy storage device; wherein, the preset algorithm scheduling trigger condition includes a first trigger condition and a second trigger condition, the first trigger condition is that the first preset number of substations in the region have a reverse heavy overload, and the first preset number of substations in the region are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the region and the number of substations in the peak period of distributed photovoltaic power generation is a second preset number, wherein the first preset number is less than the second preset number;

[0171] The scheduling module 104 performs dynamic energy storage scheduling on the energy storage equipment in each substation according to the generated optimal charging strategy for the energy storage equipment, so as to eliminate the reverse heavy overload in the substation.

[0172] The acquisition module 101, the construction module 102, the monitoring and generation module 103, and the scheduling module 104 in the technical solution of the present invention correspond to the steps in the embodiment, and will not be described in detail in this embodiment.

[0173] The present invention constructs a simulation model based on the historical data of technical parameters of equipment in each substation, historical data and real-time data of load, historical data of energy storage system specifications, historical data and real-time data of distributed photovoltaic power generation, and historical data and real-time data of weather in each substation; based on the simulation model, it monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed distributed photovoltaic power generation, and generates an optimal charging strategy for energy storage equipment according to the type of trigger conditions and the energy storage situation of each substation using a particle swarm optimization algorithm; according to the generated optimal charging strategy for energy storage equipment, the energy storage equipment in each substation is dynamically scheduled for energy storage to eliminate the reverse heavy overload in the substation, and by combining the precise simulation of the simulation model with the efficient search capability of the particle swarm optimization algorithm, it realizes precise control and optimal scheduling of regional power grid energy storage equipment, effectively solving the problems of low reliability and economy of energy storage regulation caused by the prior art, effectively saving regulation costs, and improving the reliability of energy storage regulation.

[0174] The simulation model in the technical solution of the present invention includes a power grid model, an equipment electrical model, a load model, and a distributed photovoltaic power generation prediction model. The various models are interconnected and work together to respond to changes in the power grid load in real time, intelligently allocate and adjust energy storage resources, effectively alleviate the peak-to-valley difference problem of the power grid, and improve the operating efficiency and economy of the power grid.

[0175] In the technical solution of the present invention, real-time monitoring is based on the simulation model. When the current in the substation flows from the user to the power grid and the transformer load rate is higher than the first preset value, the substation will experience a reverse heavy overload. Not only the power grid model in the simulation model is considered, but also the electrical parameters in the equipment electrical model are combined to organically combine efficiency and accuracy; when the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts a change rate of the power generation curve in the current period of expected sufficient sunlight, which exceeds the preset change rate threshold, the substation is currently in the peak period of distributed photovoltaic power generation; not only the time period of the substation in the simulation model is considered, but also the output prediction results of the distributed photovoltaic power generation prediction model are combined to organically combine efficiency and accuracy.

[0176] In the technical solution of the present invention, the objective function and constraint conditions of the regulation are determined according to the trigger condition type and the energy storage situation of each substation. The objective function is the sum of the overload of the power grid and the charging and discharging cost of the energy storage equipment, and the constraint condition is that each substation does not have a reverse heavy overload; a particle swarm optimization algorithm with constraint conditions is introduced to generate the optimal charging strategy for the energy storage equipment, and if the constraint conditions are violated, the objective function is adjusted through a penalty function; not only the objective function is different according to the trigger condition type, but also the objective function is adjusted through a penalty function when the constraint conditions are violated, thereby improving the adaptability of energy storage regulation; and by combining the precise simulation of the simulation model and the efficient search capability of the particle swarm optimization algorithm, precise control and optimal scheduling of regional power grid energy storage equipment are achieved; the adaptability to fluctuations in renewable energy and the anti-interference ability of the power grid are enhanced, the energy waste caused by imbalance between supply and demand is reduced, and innovative solutions are provided for the intelligent management and sustainable development of the power grid.

[0177] According to the generated optimal charging strategy for energy storage equipment, the technical solution of the present invention also includes: verifying the generated optimal charging strategy for energy storage equipment in a simulation diagram before dynamically scheduling the energy storage equipment in each substation. If the reverse heavy overload phenomenon disappears in each substation, the verification of the optimal charging strategy for energy storage equipment is passed; if the reverse heavy overload phenomenon does not disappear in a certain substation, the verification of the optimal charging strategy for energy storage equipment is not passed, thereby ensuring the effectiveness of the implementation of the optimal charging strategy for energy storage equipment and improving the economy of energy storage regulation.

[0178] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for dynamic control of regional energy storage based on simulation model, characterized in that: include: Obtain historical data on equipment technical parameters, historical and real-time data on loads, historical data on energy storage system specifications, historical and real-time data on distributed photovoltaic power generation, and historical and real-time data on weather in each substation; Build simulation models based on historical data of equipment technical parameters, historical and real-time data of loads, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area; Based on the simulation graph model, real-time monitoring is performed to determine whether each substation has a reverse overload and whether the substation is currently in the peak period of distributed photovoltaic power generation. If the current substation meets the preset algorithm scheduling trigger conditions, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device according to the trigger condition type and the energy storage situation of each substation; wherein the preset algorithm scheduling trigger conditions include a first trigger condition and a second trigger condition, wherein the first trigger condition is that a first preset number of substations in the region have a reverse overload, and the first preset number of substations in the region are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the region with reverse overload and the number of substations in the peak period of distributed photovoltaic power generation is a second preset number, wherein the first preset number is less than the second preset number; According to the generated optimal charging strategy for energy storage equipment, the energy storage equipment in each substation is dynamically scheduled to eliminate reverse heavy overloads in the substation.

2. According to claim 1, a method for dynamic control of regional energy storage based on simulation model is characterized in that: Based on the historical data of equipment technical parameters, historical and real-time data of load, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, historical and real-time data of weather in each substation, the simulation model is constructed including: Based on the historical data of equipment technical parameters and energy storage system specifications in each substation, a power grid model and an equipment electrical model are established. The power grid model includes a topological structure model of the power grid in different substations, which is used to represent the connection relationship between each device node in different substations, display the topological structure and electrical characteristics of the distribution network, and provide an operating environment for other models. The power flow is calculated according to the data predicted by the distributed photovoltaic power generation prediction model, and the results are fed back to the equipment model and the load model; the equipment electrical model includes a distribution transformer model, a circuit breaker model, and a distributed power supply model, which are used to represent the electrical parameters of each device in different substations, determine the role of the device in the power system according to the location information in the power grid model, display the relevant electrical data of the device, and adjust the status or output of the device according to the calculation results of the power grid model; Based on the historical load data and real-time data, weather historical data and real-time data in each substation, a load model based on LSTM neural network is constructed to predict the load curve of each substation according to the historical load data and real-time data, weather historical data and real-time data, and adjust the load curve according to the calculation results of the power grid model to adapt to different operating conditions; Based on the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data in each substation, a distributed photovoltaic power generation prediction model based on LSTM neural network is constructed to predict the distributed photovoltaic power generation of each substation according to the historical data and real-time data of distributed photovoltaic power generation, historical weather data and real-time data, and pass the prediction results to the power grid model in the form of files.

3. According to claim 2, a method for dynamic control of regional energy storage based on simulation model is characterized in that: Based on the simulation model, real-time monitoring is performed to determine whether each substation has a reverse overload and whether the substation is currently in the peak period of distributed photovoltaic power generation: When the current in the substation flows from the user to the power grid, and the transformer load rate is higher than the first preset value, the substation has a reverse heavy overload; wherein the transformer load rate is calculated as follows: Among them, β 反向 is the transformer load factor; S 反向 is the reverse apparent power of the total meter in the area; S e is the rated capacity of the transformer; When the substation is in a period of expected sufficient sunlight, and the distributed photovoltaic power generation prediction model corresponding to the substation predicts that the rate of change of the power generation curve obtained during the current period of expected sufficient sunlight exceeds the preset rate of change threshold, then the substation is currently in the peak period of distributed photovoltaic power generation.

4. The method for dynamic control of regional energy storage based on simulation model according to claim 1 is characterized in that: According to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy for energy storage equipment, including: According to the trigger condition type and the energy storage situation of each substation, determine the objective function and constraint conditions of the regulation. The objective function is the sum of the overload of the power grid and the charging and discharging cost of the energy storage equipment. The constraint condition is that each substation does not have a reverse heavy overload. A particle swarm optimization algorithm with constraints is introduced to generate the optimal charging strategy for energy storage equipment.

5. According to claim 4, a method for dynamic control of regional energy storage based on simulation model is characterized in that: When the trigger condition type is the first trigger condition, the objective function is: Among them, Objective 1 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition occurs; N1 is the number of substations that have reverse heavy overload and are at the peak of distributed photovoltaic power generation; Q1 is the number of substations that have no reverse heavy overload and are not at the peak of distributed photovoltaic power generation; M is the number of energy storage devices in the substation; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; Cost is the amount of electricity allocated to the energy storage device j in the area α1 that has not experienced reverse overload and is not in the peak period of distributed photovoltaic power generation; Cost Allocate,αj is the total cost of energy storage device j allocated to the station area α1 that has not experienced reverse overload and is not in the peak period of distributed photovoltaic power generation. The total cost includes the charging and discharging loss cost and the equipment depreciation cost; When the trigger condition type is the second trigger condition, the objective function is: Among them, Objective2 is the minimum value of the sum of the grid overload and the charging and discharging cost of the energy storage device when the first trigger condition occurs; N2 is the number of substations with reverse heavy overload or at the peak of distributed photovoltaic power generation; Q2 is the number of substations without reverse heavy overload; M is the number of energy storage devices in the substation; PV excess,i is the excess photovoltaic power generation of the station area i that has reverse heavy overload and is at the peak period of distributed photovoltaic power generation; is the amount of electricity allocated to the energy storage device j in the station area α2 where no reverse heavy overload occurs; It is the total cost allocated to the energy storage device j in the station area α2 where no reverse heavy overload occurs. The total cost includes the charging and discharging loss cost and the equipment depreciation cost.

6. The method for dynamic control of regional energy storage based on simulation model according to claim 5 is characterized in that: When the trigger condition type is the first trigger condition, the constraints include the grid carrying capacity constraint and the energy storage device capacity constraint, where the grid carrying capacity constraint is: Among them, L t Represents the grid carrying capacity at time t, TotalLoad t represents the total load of the power grid at time t; The capacity constraint of energy storage equipment is: Among them, C max,j is the maximum capacity that can be stored by the jth energy storage device; When the trigger condition type is the second trigger condition, the constraints include the grid carrying capacity constraint and the energy storage device capacity constraint, where the grid carrying capacity constraint is: The capacity constraint of energy storage equipment is:

7. The method for dynamic control of regional energy storage based on simulation model according to claim 6 is characterized in that: If the constraint conditions are violated, the objective function is adjusted through the penalty function; Among them, the penalty function includes the equipment capacity penalty function Penalty capacity,αj and the power grid capacity penalty function Penalty load,t , when the trigger condition type is the first trigger condition, the device capacity penalty function The expression is: When the trigger condition type is the second trigger condition, the device capacity penalty function The expression is: When the trigger condition type is the first trigger condition, the power grid capacity penalty function Penalty load,t,1 The expression is: When the trigger condition type is the second trigger condition, the power grid capacity penalty function Penalty load,t,2 The expression is: When the trigger condition type is the first trigger condition, the adjusted new objective function is: Among them, λ is the penalty function coefficient, which is used to control the impact of the penalty function on the objective function; When the trigger condition type is the second trigger condition, the adjusted new objective function is:

8. According to the method of claim 4, the method is characterized by introducing The particle swarm optimization algorithm with constraints is added to generate the optimal charging strategy for energy storage equipment, which includes: Initialize the particle position parameters. The position of each particle can be represented as an M×N matrix, where each element Represents the proportion or absolute value of the power allocated to the energy storage device j in the substation α1, or, each element Represents the proportion or absolute value of the power allocated to the energy storage device j in the substation α2; Calculate the fitness value, where the fitness value is the objective function value; Determine whether a particle violates the constraint condition. If a particle violates the constraint condition, the penalty function is applied to adjust the objective function, and the fitness value of each particle is recalculated according to the adjusted objective function. According to the individual extreme value and global extreme value of each particle, the particle position and velocity are updated, where the update expressions of the particle position and velocity are: v mn (t+1)=a(t)v mn (t)+b1r1(t)(p mn (t)-y mn (t))+ b2r2(t)(p gn (t)-y mn (t)), y mn (t+1)=y mn (t)+v mn (t+1), Among them, v mn (t), y mn (t), p mn (t) represent the velocity, position and individual extreme value of the mth particle in dimension n at the tth iteration; p gn (t) represents the global extreme value in dimension n at the tth iteration; a(t) represents the inertia weight at the tth iteration; b1 and b2 are learning factors; r1(t) and r2(t) are random numbers in the interval (0,1]; The position and speed of each particle are iteratively updated until a preset stop condition is met, wherein the preset stop condition includes reaching a maximum number of iterations or a fitness value greater than a preset fitness threshold.

9. A regional energy storage dynamic control system based on simulation model according to any one of claims 1 to 8, characterized in that: According to the generated optimal charging strategy for energy storage equipment, before the energy storage equipment in each substation is dynamically scheduled, the following steps are also required: The generated optimal charging strategy for the energy storage device is simulated and verified in the simulation model. If the reverse heavy overload phenomenon disappears in each substation, the optimal charging strategy for the energy storage device is verified successfully; if the reverse heavy overload phenomenon does not disappear in a certain substation, the optimal charging strategy for the energy storage device is verified failed.

10. A regional energy storage dynamic control system based on simulation model, characterized in that: include: The acquisition module obtains the historical data of equipment technical parameters, load history data, energy storage system specification history data, distributed photovoltaic power generation history data, and weather history data in each substation; The construction module builds a simulation model based on the historical data of equipment technical parameters, historical and real-time data of load, historical data of energy storage system specifications, historical and real-time data of distributed photovoltaic power generation, and historical and real-time data of weather in each substation area; The monitoring and generation module, based on the simulation graph model, monitors in real time whether each substation has a reverse heavy overload and whether the substation is currently in the peak period of distributed photovoltaic power generation. If the current substation meets the preset algorithm scheduling trigger condition, according to the trigger condition type and the energy storage situation of each substation, the particle swarm optimization algorithm is used to generate the optimal charging strategy for the energy storage device; wherein, the preset algorithm scheduling trigger condition includes a first trigger condition and a second trigger condition, the first trigger condition is that the first preset number of substations in the area have a reverse heavy overload, and the first preset number of substations in the area are in the peak period of distributed photovoltaic power generation; the second trigger condition is that the sum of the number of substations in the area and the number of substations in the peak period of distributed photovoltaic power generation is the second preset number, wherein the first preset number is less than the second preset number; The scheduling module dynamically schedules the energy storage equipment in each substation according to the generated optimal charging strategy for the energy storage equipment to eliminate the reverse heavy overload in the substation.

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