Optimal control method of grid hybrid energy storage based on source-grid-load-storage

Abstract

The present invention discloses a hybrid energy storage optimization and control method for power grids based on source, grid, load and storage, which relates to the field of smart grid technology, including: obtaining a power grid energy storage control cloud platform, obtaining energy generation data sets on the power generation energy side, power grid operation data sets on the power grid side, and load power data sets on the load demand side; performing response characteristic analysis and twin simulation fusion to build a digital virtual model of power grid operation; obtaining multiple energy storage device characteristic data sets of energy storage modules, and collaboratively modeling the digital virtual model of power grid operation based on multiple energy storage device characteristic data sets to generate a virtual model of power grid energy storage collaborative control; real-time monitoring to obtain a set of state parameter information on multiple sides of the power grid, performing control strategy analysis, obtaining an energy storage optimization control strategy, and performing hybrid energy storage control on the energy storage module. The present invention solves the technical problem of insufficient real-time control capability of power grids in the prior art, and achieves the technical effect of improving the real-time control capability of power grids.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a method for optimizing and controlling hybrid energy storage in a power grid based on source, grid, load and storage. Background Art

[0002] With the continuous growth of global energy demand and the widespread use of renewable energy, the power grid system is facing increasingly complex challenges. Traditional power grid management methods mainly rely on static scheduling and control strategies, which are usually difficult to cope with dynamically changing load demand and power generation fluctuations. These traditional methods have difficulties in integrating data from different power generation energy sources, power grid operation status, load demand and energy storage equipment, resulting in insufficient real-time control capabilities. Summary of the invention

[0003] The present application provides a hybrid energy storage optimization and control method for a power grid based on source, grid, load and storage, which is used to solve the technical problem of insufficient real-time control capability of the power grid in the prior art.

[0004] In view of the above problems, the present application provides a grid hybrid energy storage optimization and control method based on source-grid-load-storage.

[0005] The present application provides a method for optimizing and controlling hybrid energy storage in a power grid based on source-grid-load-storage, the method comprising:

[0006] A power grid energy storage control cloud platform is obtained, and the power grid energy storage control cloud platform includes a power generation energy side, a power grid side, a load demand side, an energy storage module and a control center. The energy generation data set of the power generation energy side, the power grid operation data set of the power grid side and the load power data set of the load demand side are obtained through the power grid energy storage control cloud platform; the response characteristics of the energy generation data set, the power grid operation data set and the load power data set are analyzed and fused by twin simulation through the control center to construct a power grid operation digital virtual model; multiple energy storage device characteristic data sets of the energy storage module are obtained, and the power grid operation digital virtual model is collaboratively modeled based on the multiple energy storage device characteristic data sets to generate a power grid energy storage collaborative control virtual model; a set of power grid multi-side state parameter information is obtained through real-time monitoring of the power grid energy storage control cloud platform, and a control strategy analysis is performed on the power grid multi-side state parameter information set based on the power grid energy storage collaborative control virtual model to obtain an energy storage optimization control strategy, and the energy storage module is controlled for power grid hybrid energy storage based on the energy storage optimization control strategy.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application obtains a power grid energy storage control cloud platform, which includes a power generation energy side, a power grid side, a load demand side, an energy storage module and a control center. The power grid energy storage control cloud platform obtains energy generation data sets on the power generation energy side, power grid operation data sets on the power grid side and load power data sets on the load demand side; the control center performs response characteristic analysis and twin simulation fusion on the energy generation data sets, the power grid operation data sets and the load power data sets to build a digital virtual model of power grid operation; obtains multiple energy storage device characteristic data sets of the energy storage module, and collaboratively models the power grid operation digital virtual model based on the multiple energy storage device characteristic data sets to generate a power grid energy storage collaborative control virtual model; obtains a set of power grid multi-side state parameter information sets through real-time monitoring on the power grid energy storage control cloud platform, performs control strategy analysis on the power grid multi-side state parameter information sets based on the power grid energy storage collaborative control virtual model, obtains an energy storage optimization control strategy, and performs power grid hybrid energy storage control on the energy storage module based on the energy storage optimization control strategy. The present invention solves the technical problem of insufficient real-time control capability of power grids in the prior art. Through the power grid energy storage control cloud platform, the data of the power generation energy side, the power grid side, the load demand side and the energy storage module are integrated to construct a digital virtual model of power grid operation, and collaborative modeling is performed to generate a virtual model of power grid energy storage collaborative control, monitor power grid state parameters in real time, use the virtual model to analyze the control strategy, obtain the energy storage optimization control strategy, and achieve the technical effect of improving the real-time control capability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for optimizing and controlling hybrid energy storage in a power grid based on source, grid, load and storage provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the process of constructing a digital virtual model of power grid operation in the power grid hybrid energy storage optimization and control method based on source-grid-load-storage provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a hybrid energy storage optimization and control method for power grids based on source, grid, load and storage, which is used to solve the technical problem of insufficient real-time control capability of power grids in the prior art. Through the power grid energy storage control cloud platform, the data of the power generation energy side, the power grid side, the load demand side and the energy storage module are integrated to build a digital virtual model of power grid operation, and conduct collaborative modeling to generate a virtual model of power grid energy storage collaborative control, monitor power grid state parameters in real time, use the virtual model to analyze the control strategy, obtain the energy storage optimization control strategy, and achieve the technical effect of improving the real-time control capability of the power grid.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the present application provides a method for optimizing and controlling hybrid energy storage of a power grid based on source-grid-load-storage, the method comprising:

[0016] Step S100: Obtain a power grid energy storage control cloud platform, which includes a power generation energy side, a power grid side, a load demand side, an energy storage module and a control center. The power grid energy storage control cloud platform obtains an energy generation data set of the power generation energy side, a power grid operation data set of the power grid side and a load power data set of the load demand side.

[0017] In an embodiment of the present application, the power grid energy storage control cloud platform includes a power generation energy side, a power grid side, a load demand side, an energy storage module, and a control center. The power generation energy side covers equipment such as solar panels and wind turbines, and uses a data acquisition system to monitor power generation and equipment status in real time; the power grid side includes substations, transmission lines, and distribution networks, and the data acquisition system monitors parameters such as power flow, voltage, current, and power factor; the load demand side involves industrial, commercial, and household users, and the data acquisition system records information such as power consumption, power consumption patterns, and peak power consumption periods; the energy storage module is composed of a battery energy storage system, a flywheel energy storage system, etc., and the data acquisition system monitors the energy storage status, charging and discharging efficiency, and remaining capacity. As the brain of the platform, the control center communicates with the data acquisition systems of the above-mentioned sides through a wired or wireless data transmission network to obtain real-time operation data.

[0018] In the process of building a grid energy storage control cloud platform, various data acquisition devices, such as sensors and metering instruments, are deployed on the power generation energy side. These devices can monitor the power generation, power generation efficiency and equipment status of power generation equipment such as solar panels, wind turbines, and hydroelectric power stations in real time. All these data are uploaded to the control center through the data transmission network to form an energy generation data set. Data acquisition systems, including voltage sensors, current sensors, and power meters, are deployed on the grid side to monitor key operating parameters such as power flow, voltage, current, and power factor in substations, transmission lines, and distribution networks. These data are transmitted to the control center through the same data transmission network and summarized into a grid operation data set. On the load demand side, smart meters and load monitoring devices are installed at industrial, commercial, and household user ends. These devices can record information such as user power consumption, power consumption patterns, and peak power consumption periods. All collected data are uploaded to the control center through the data transmission network to form a load power data set.

[0019] Step S200: The control center performs response characteristic analysis and twin simulation fusion on the energy generation data set, the power grid operation data set and the load power data set to construct a digital virtual model of power grid operation.

[0020] In an embodiment of the present application, first, data sets from different data sources, namely, energy generation data sets, power grid operation data sets, and load power data sets, are collected through a control center. Next, response characteristic analysis is performed on these data sets. Response characteristic analysis refers to identifying and extracting characteristic indicators of the power generation side, power grid side, and load side by analyzing the relationships and mutual influences between different data sets. Then, twin simulation fusion is performed, and by modeling each characteristic indicator, a power generation side response characteristic model, a power grid side response characteristic model, and a load side response characteristic model are constructed. Next, these models are fused to form a comprehensive digital virtual model of power grid operation.

[0021] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the construction of a digital virtual model of power grid operation includes:

[0022] The control center arranges and aligns the energy generation data set, the power grid operation data set and the load power data set in time series to obtain the energy generation time series data set, the power grid operation time series data set and the load power time series data set; extracts characteristic indicators from the energy generation time series data set, the power grid operation time series data set and the load power time series data set respectively to obtain an energy generation characteristic indicator set, a power grid operation characteristic indicator set and a load power characteristic indicator set; based on the energy generation characteristic indicator set, the power grid operation characteristic indicator set and the load power characteristic indicator set, analyzes the response characteristics of the energy generation time series data set, the power grid operation time series data set and the load power time series data set to obtain a generation side response characteristic model, a power grid side response characteristic model and a load side response characteristic model; performs twin simulation fusion on the generation side response characteristic model, the power grid side response characteristic model and the load side response characteristic model to construct the power grid operation digital virtual model.

[0023] In an embodiment of the present application, when the energy generation data set, the power grid operation data set, and the load power data set are time-series arranged and aligned through the control center, the data at different time points are processed using time series analysis and data alignment algorithms to ensure the time series consistency of the data. Specifically, the missing data is supplemented using data interpolation methods to ensure data integrity; the sampling frequencies of different data sources are unified through resampling technology, such as converting minute-level data into hour-level data; the data is sorted using timestamps to ensure that the data is arranged in chronological order; the data from different data sources are aligned using cross-correlation technology to ensure data synchronization at the same time point. The energy generation time series data set, the power grid operation time series data set, and the load power time series data set are obtained through the above process.

[0024] Next, characteristic indicators are extracted from these time series data sets. For the energy generation time series data set, basic indicators such as power generation and power generation efficiency are extracted, the equipment response time is calculated, and the daily, weekly, and monthly trends of power generation are statistically analyzed to form a set of energy generation characteristic indicators; for the power grid operation time series data set, basic operating parameters such as voltage, current, and power factor are extracted, the transmission capacity is calculated, and the voltage stability is evaluated to form a set of power grid operation characteristic indicators; for the load power time series data set, the daily load curve is extracted, seasonal changes are statistically analyzed, and regional differences are analyzed to form a set of load power characteristic indicators.

[0025] Based on the acquired characteristic index set, the response characteristic analysis is performed. First, for the response characteristic analysis on the power generation side, different types of power generation equipment, such as wind power, photovoltaic power, thermal power, etc., are classified. By recording the response time of the power generation equipment after receiving the dispatch instruction, the response speed is determined by time delay analysis, and the average response time and standard deviation of the equipment are calculated to evaluate the stability of the equipment. At the same time, by calculating the response rate of the equipment under different load conditions, its performance under high load and low load conditions is evaluated, so as to establish a power generation side response characteristic model.

[0026] For the analysis of load side response characteristics, load data from different regions and time periods are collected, and cluster analysis is used to classify the load data to determine the temporal and spatial distribution patterns of the load, such as daily load curves and seasonal changes. Then, regression analysis is used to evaluate the response of the load to electricity price signals and incentives, such as the impact of electricity price changes on electricity consumption. Then, load response elasticity and sensitivity analysis are performed. Specifically, first, load data under different electricity price levels and incentives are collected, including peak electricity prices, valley electricity prices, and the implementation of various incentives (such as demand response plans, power saving rewards, etc.). Then the collected data is cleaned and standardized to ensure that the dimensions of different variables are consistent. Next, a linear regression or nonlinear regression model is used to analyze the impact of electricity prices and incentives on the load. The regression model is used to calculate the change in load before and after the electricity price changes and incentives are implemented, so as to determine the degree of response of the load to electricity prices and incentives. After that, the price elasticity of the load to electricity price changes is calculated to indicate the sensitivity of the load to electricity price changes. Among them, ΔQ is the load change, Q is the benchmark load, ΔP is the electricity price change, and P is the benchmark electricity price. Finally, the load response sensitivity to the incentive measures is evaluated. By comparing the load changes before and after the implementation of different incentive measures, the effect of each incentive measure is calculated to obtain the sensitivity coefficient. When performing the calculation, Where ΔQ 激励 is the load change after the incentive measures are implemented, ΔQ 无激励 is the load change when there is no incentive. Through regression analysis and elasticity and sensitivity calculation, the load side response characteristic model is established.

[0027] For the grid-side operation characteristic analysis, the topological structure data and operation parameter data of the grid are collected, and the grid topology diagram is drawn using network analysis tools to evaluate the node connectivity and key nodes of the grid. Then the maximum transmission capacity and actual transmission capacity of each transmission line are calculated, and the transmission bottleneck and load distribution are evaluated. Specifically, the load rate and transmission efficiency of the line are calculated using the power flow analysis method. Next, the voltage stability of the grid under different load conditions is calculated using the steady-state analysis method, and the voltage fluctuation range and frequency are evaluated. The specific steps include performing power flow calculations to determine the voltage distribution of the grid under different operating conditions; evaluating voltage offsets and voltage fluctuations, and calculating voltage stability indicators such as voltage offset rate and fluctuation frequency. Through network analysis and steady-state analysis, a grid-side response characteristic model is established.

[0028] After obtaining the response characteristic model of the power generation side, the power grid side and the load side, digital twin technology and simulation modeling are used to perform twin simulation fusion on these models. Specifically, simulation software such as MATLAB Simulink is used to simulate and verify each model to ensure its accuracy. Then, multi-model fusion technology is used to integrate the power generation side, power grid side and load side models to form a comprehensive digital virtual model of power grid operation.

[0029] Furthermore, the method provided in the application embodiment includes:

[0030] Define model evaluation dimensions to perform multidimensional performance evaluation on the digital virtual model of power grid operation, and obtain multidimensional evaluation parameters of model performance; perform impact analysis on the digital virtual model of power grid operation based on the multidimensional evaluation parameters of model performance, and determine model performance influencing variables; determine variable selection thresholds according to the model performance influencing variables, and perform sensitivity analysis on the digital virtual model of power grid operation based on the variable selection thresholds to obtain variable parameter sensitivity coefficients; perform performance tuning on the digital virtual model of power grid operation based on the variable parameter sensitivity coefficients and the model performance influencing variables to obtain a power grid operation optimized virtual model.

[0031] In an embodiment of the present application, model evaluation dimensions are first defined to comprehensively evaluate model performance, including accuracy evaluation, stability evaluation, response speed evaluation, and reliability evaluation. Accuracy evaluation measures the accuracy of the model by calculating the error between the model prediction results and the actual data, such as the root mean square error and the mean absolute error. Stability evaluation analyzes the consistency of the model under different conditions, usually by calculating the standard deviation of the model output. Response speed evaluation measures the reaction time of the model to input changes, usually by simulating input changes and recording the response time of the model. Reliability evaluation involves the analysis of performance and failure rate in long-term operation, which is completed by comparing long-term simulation and actual operation data.

[0032] Then, the multidimensional evaluation parameters of the model performance are obtained through the above evaluation results, including all error indicators, stability metrics, response time and other data. These parameters provide a basis for the comprehensive evaluation of model performance. In the impact analysis stage, the correlation coefficient is first calculated to identify the key influencing variables of model performance. Specifically, regression analysis or correlation analysis is used to calculate the correlation coefficient between each variable (such as model parameters, input data) and model performance. This coefficient reflects the degree of linear relationship between the variable and model performance. Then, based on the correlation coefficient, the variable selection threshold is determined. These thresholds are reasonable ranges set according to the actual situation of the model and the variability of the data. Use standard deviations and confidence intervals, such as 95% confidence intervals, to define these ranges to ensure that the changes in variables will not be too drastic within the set interval.

[0033] Then, a sensitivity analysis is performed to gradually adjust the value of each influencing variable within the set threshold range and record the changes in the model output. The local sensitivity coefficient is calculated to measure the impact of each variable on the model performance. The calculation formula is:

[0034]

[0035] Among them, ΔModel Output and ΔVariable represent the changes of model output and variable respectively.

[0036] After obtaining the sensitivity coefficients of variable parameters, the steps to optimize the digital virtual model of power grid operation include adjusting key parameters, improving model structure, and introducing new data sources. First, identify key parameters for the variables that have the greatest impact on model performance, which usually have high sensitivity coefficients. Next, set the adjustment range of these key parameters, and technical experts will formulate reasonable parameter adjustment ranges based on historical data and model requirements. Then, through optimization algorithms such as grid search or random search, adjust the values ​​of these key parameters within the set range, use cross-validation to evaluate the model performance of different parameter configurations, and select the best parameter combination.

[0037] Secondly, improve the model structure. First, analyze the structure of the existing model to determine whether there is room for improvement, such as the number of layers in the model, the number of nodes in each layer, etc. Design improvement plans may include increasing the number of network layers, increasing the number of nodes in each layer, introducing new network layers, or improving the connection method of the model. After implementing these structural improvements, re-evaluate the model performance to ensure that the improvement measures have improved performance indicators such as accuracy and robustness.

[0038] Finally, introduce new data sources to enhance the model input. First, identify the new data types or sources that are needed, such as more real-time monitoring data or high-resolution data. Collect and preprocess this new data, including removing noise, filling missing values, and normalizing data. Integrate the new data into the model and update the model's input layer.

[0039] Through the above process, a virtual model of power grid operation optimization is obtained.

[0040] Step S300: Acquire multiple energy storage device characteristic data sets of the energy storage module, collaboratively model the power grid operation digital virtual model based on the multiple energy storage device characteristic data sets, and generate a power grid energy storage collaborative control virtual model.

[0041] In the embodiment of the present application, firstly, by connecting with the interface or data acquisition system of the energy storage module, characteristic data sets of multiple energy storage devices are obtained, including parameters such as capacity, charge and discharge efficiency, response time, charge and discharge curves of each energy storage device. Next, data preprocessing is performed on these energy storage device characteristic data sets, including removing outliers, filling missing data, and standardizing data, etc., to ensure the quality and consistency of the data.

[0042] After data preprocessing is completed, these energy storage device characteristic data sets are integrated into the digital virtual model of power grid operation for collaborative modeling. Specifically, the characteristic parameters of the energy storage device are first integrated into the digital virtual model of power grid operation. For example, the energy storage capacity and charging and discharging curves are used as inputs to define the response mode of the device under different power load and power generation conditions. Then, multivariate analysis methods or system identification techniques are used to model the interaction between energy storage devices and power grid operation. This may include linear or nonlinear modeling methods, such as dynamic system modeling, to reflect the behavior of energy storage devices under different power load and power generation conditions.

[0043] Through the above process, a virtual model of grid energy storage collaborative control is generated.

[0044] Furthermore, in the method provided in the embodiment of the application, the generating of the grid energy storage collaborative control virtual model further includes:

[0045] Perform equipment performance evaluation on the multiple energy storage device characteristic data sets to obtain multiple energy storage device performance parameter information; set energy storage device control constraints based on the multiple energy storage device characteristic data sets; define energy storage modeling objectives, perform weight distribution fitting based on the energy storage modeling objectives, and construct an energy storage control objective function; design a collaborative model framework mechanism based on the multiple energy storage device performance parameter information and the energy storage device control constraints; based on the collaborative model framework mechanism and the energy storage control objective function, collaboratively model the power grid operation digital virtual model to generate the power grid energy storage collaborative control virtual model.

[0046] In the embodiment of the present application, the device performance evaluation is first performed on multiple energy storage device characteristic data sets, including collecting and analyzing the performance data of each energy storage device, mainly including device capacity, charge and discharge efficiency, response time, etc. The performance evaluation uses statistical analysis and data mining methods, such as calculating the energy density (energy storage capacity per unit volume or mass), power density (power output capacity per unit volume or mass) and cycle life (the number of effective charge and discharge cycles of the device) of each energy storage device. These performance parameter information is processed and analyzed by data statistical software or programming language to obtain multiple energy storage device performance parameter information.

[0047] Then, according to the performance parameter information of multiple energy storage devices, the control constraints of energy storage devices are set, including identifying the technical limitations of each energy storage device, such as the maximum charge and discharge power, the number of charge and discharge times, and the aging effect of the device. Specifically, the equipment specifications and actual operation data are obtained from the database. Through technical literature and experimental data, the performance boundaries of each device are determined, and these limitations are converted into constraints in the mathematical model. For example, the maximum charging power is set to the rated power of the device, and the number of charge and discharge times is limited to the life cycle limit of the device.

[0048] Subsequently, the energy storage modeling objectives are defined. Modeling objectives include optimizing the charging and discharging scheduling of energy storage devices to balance grid loads, reduce energy costs, or improve system reliability. To this end, an objective function is set, for example, minimizing the total grid cost or maximizing the utilization efficiency of energy storage devices. Use linear programming or nonlinear programming techniques to construct and optimize the objective function. A comprehensive objective function model is constructed by determining the priority and weight of each objective, and these weights are determined by weighted average method or sensitivity analysis.

[0049] After defining the energy storage modeling objectives and establishing the objective function, design the collaborative model framework mechanism. The collaborative model framework mechanism includes how the energy storage device interacts with the rest of the grid. It involves designing control algorithms and dispatch strategies, such as dispatch strategies based on model predictive control, which can dynamically adjust the operation of energy storage devices to meet real-time grid needs. This step also includes selecting appropriate modeling methods, such as dynamic system modeling or optimization algorithms, such as genetic algorithms, to ensure that the model can accurately reflect the collaborative relationship between energy storage devices and the grid.

[0050] Finally, based on the collaborative model framework mechanism and energy storage control objective function, the digital virtual model of power grid operation is collaboratively modeled. This process integrates the performance parameters, control constraints and modeling objectives of energy storage equipment. Specific steps include using computer simulation tools such as MATLAB to implement model construction and operation. By simulating different operating scenarios and scheduling strategies, the comprehensive performance of energy storage equipment in the power grid is evaluated, and a virtual model of grid energy storage collaborative control is generated.

[0051] Furthermore, in the method provided in the embodiment of the application, the design collaboration model framework mechanism includes:

[0052] Acquire energy storage coordination factor information, the energy storage coordination factor information including energy scheduling, energy storage priority allocation and power balancing control; construct an energy storage factor control algorithm list based on the energy storage coordination factor information; based on the performance parameter information of the multiple energy storage devices and the control constraints of the energy storage devices, respectively match them with the energy storage factor control algorithm list to obtain a set of multiple device optional energy storage factor control algorithms; test, evaluate and optimize the set of multiple device optional energy storage factor control algorithms to obtain multiple device target energy storage factor control algorithms, and determine the coordination model framework mechanism based on the multiple device target energy storage factor control algorithms.

[0053] In an embodiment of the present application, in the process of constructing a virtual model of coordinated control of power grid energy storage, firstly, information on energy storage coordination factors is obtained, including energy scheduling, energy storage priority allocation and power balancing control. Energy scheduling formulates charging and discharging strategies for energy storage equipment through scheduling optimization algorithms, such as linear programming or integer programming. These algorithms optimize energy scheduling plans based on grid load demand, energy storage equipment status and energy cost. Energy storage priority allocation uses a priority sorting algorithm, such as a hierarchical analysis method or a weighted sum method, to set priorities based on factors such as the capacity, efficiency and life of the energy storage equipment to ensure that the needs of high-priority energy storage equipment are met first when allocating power. Power balancing control uses a power balancing algorithm, such as a power flow calculation, to ensure that the charging and discharging of energy storage equipment can be balanced on the basis of meeting priority requirements.

[0054] Next, the process of building a list of energy storage factor control algorithms includes designing and selecting control algorithms. Optimization algorithms, such as genetic algorithms or simulated annealing algorithms, are used to optimize and screen energy storage control strategies. Simulation tests are performed using MATLAB to evaluate the performance and stability of different control algorithms in actual operation to determine the optimal algorithm.

[0055] Then the matching and test evaluation steps match the performance parameters of the energy storage device with the control algorithm. First, the information of the device and the algorithm is stored and queried through the database management system to ensure the compatibility of each algorithm with the device performance. Then, simulation tests are performed on each optional control algorithm using simulation platforms such as MATLAB. The effect of the algorithm is evaluated through regression analysis or statistical tests such as t-tests, and the best performing algorithm is selected.

[0056] Finally, the collaborative model framework mechanism is determined. Through the system engineering approach, the selected control algorithms are integrated into a unified framework.

[0057] Step S400: Real-time monitoring is performed through the power grid energy storage control cloud platform to obtain a set of state parameter information on multiple sides of the power grid, and a control strategy analysis is performed on the set of state parameter information on multiple sides of the power grid based on the power grid energy storage collaborative control virtual model to obtain an energy storage optimization control strategy, and power grid hybrid energy storage control is performed on the energy storage module based on the energy storage optimization control strategy.

[0058] In the embodiment of the present application, firstly, the state parameter information set of multiple sides of the power grid is obtained by real-time monitoring through the power grid energy storage control cloud platform. Specifically, the state parameter information is collected from multiple sides of the power grid, such as the power generation side, the transmission side, and the load side, by using real-time data acquisition technology. The state parameter information includes voltage, current, frequency, load demand, and the charging and discharging status of the energy storage device.

[0059] Next, based on the grid energy storage collaborative control virtual model, the control strategy analysis of the state parameter information set on multiple sides of the grid is carried out. Data analysis and modeling techniques, such as state space models and dynamic system simulation, are used here to analyze the collected state parameters. Through model comparison and analysis, the performance of different control strategies under the current grid state is evaluated. Specific methods include optimization algorithms (such as linear programming or nonlinear optimization) and control theories (such as PID control or model predictive control) to determine the control strategy suitable for the current grid state.

[0060] Based on the analysis results, the energy storage optimization control strategy is obtained. By optimizing the charging and discharging cycle of the energy storage equipment, the stability and efficiency of the power grid can be maximized. The strategy optimization process uses genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms to achieve the best control strategy, and verifies it through algorithm simulation to ensure the effectiveness of the strategy.

[0061] Finally, the energy storage module is regulated for grid hybrid energy storage based on the energy storage optimization control strategy. The optimization strategy is applied to actual energy storage equipment and implemented through an automatic control system, such as a PLC control system.

[0062] Furthermore, in the method provided in the embodiment of the application, the step of obtaining the energy storage optimization control strategy includes:

[0063] Based on the grid energy storage collaborative control virtual model, energy storage control analysis is performed on the multi-side state parameter information set of the grid to obtain a threshold for selecting energy storage control parameters; an energy storage parameter solution space is constructed according to the threshold for selecting energy storage control parameters, and particle swarm parameters are initialized, wherein the particle swarm parameters include a particle position vector and a particle velocity vector; the energy storage control objective function is used to perform parameter iterative search and evaluation on the particle swarm parameters in the energy storage parameter solution space to obtain an energy storage parameter fitness set; based on the energy storage parameter fitness set, parameters are iteratively updated until preset iteration conditions are met, and the particle with the largest fitness is determined by searching and optimizing as the energy storage optimization control strategy.

[0064] In an embodiment of the present application, first, a grid energy storage collaborative control virtual model is used to perform energy storage control analysis on a set of state parameter information on multiple sides of the grid. This process uses virtual modeling technology to analyze various control parameters of the energy storage system by simulating the grid operation status and energy storage equipment performance. Here, twin simulation technology is used to create a digital twin of grid energy storage, matching the actual collected data with the model. By simulating different energy storage control effects, the thresholds for selecting energy storage control parameters are determined. These thresholds are set for specific requirements of energy storage system performance, such as charging and discharging power limits, energy efficiency, response time, etc.

[0065] Next, the energy storage parameter solution space is constructed by selecting thresholds based on the energy storage control parameters. Specifically, a parameter space is first defined, which contains all possible combinations of energy storage control parameters. The particle swarm parameters are initialized, including the particle position vector and the particle velocity vector, where the particle position vector represents the current parameter combination and the particle velocity vector defines the update step of the particle in the solution space. The energy storage control objective function is used to perform parameter iterative search and evaluation on the particle swarm parameters in the energy storage parameter solution space. During the fitness calculation process, the energy storage control objective function evaluates the performance of each particle position. Specifically, it includes calculating energy efficiency, evaluating response time, checking power limit, and finally combining these evaluation results into a fitness value. Energy efficiency is calculated by the charging and discharging efficiency of the energy storage system, the response time measures the response speed of the system under different parameter settings, and the power limit check ensures that the parameter combination is within the set range. The comprehensive performance score adopts a weighted sum method to comprehensively score each indicator to form the final fitness value.

[0066] Then, the parameters are iteratively updated based on the energy storage parameter fitness set. In each iteration, the particle swarm optimization algorithm updates the position and velocity of the particles according to the fitness value until the preset iteration conditions are met, such as reaching the maximum number of iterations or the Pareto frontier no longer changes. These conditions ensure the effectiveness of the algorithm and the stability of the results.

[0067] Finally, the particle with the highest fitness will be determined through optimization as the energy storage optimization control strategy.

[0068] Furthermore, the method provided in the application embodiment also includes:

[0069] According to the energy storage parameter fitness set, a Pareto frontier parameter solution is selected to be established; a preset proportion of parameters are screened from the Pareto frontier parameter solution as the parent particle parameter solution, and a parameter crossover probability is set; a random crossover operation is performed on the parent particle parameter solution based on the parameter crossover probability, and the particle group parameters are expanded and iteratively updated according to the crossover operation results until the preset iteration condition is met.

[0070] In an embodiment of the present application, first, a Pareto front parameter solution is selected to be established based on the fitness set of energy storage parameters. The Pareto front is a set of optimal solutions that do not dominate each other on multiple objectives by comprehensively evaluating them. In each optimization iteration, the Pareto front is updated by calculating the fitness value of each particle, such as cost. This method uses the Pareto optimization technique to find a solution that achieves a balance among all optimization objectives. For example, in an energy storage system, it may be necessary to balance the goals of energy efficiency and cost, and the solution on the Pareto front represents the best overall performance in these two aspects.

[0071] Then, a preset proportion of parameters is screened from the Pareto front parameter solutions. This proportion determines what proportion of the Pareto front solutions will be selected as the parent particle parameter solutions. In this application, the preset proportion is pre-set by technical experts, for example, the top 20% of solutions are selected as parents. These parent particles will be used to generate new particles and explore a wider solution space through parameter crossover operations. The crossover operation is a technique in genetic algorithms that generates new daughter particles by randomly combining the parameters of parent particles. The crossover probability of the crossover operation is set between 0.6 and 0.9 to balance the relationship between exploring new solutions and maintaining existing excellent solutions.

[0072] The particle swarm parameters are then expanded and iteratively updated based on the results of the crossover operation. In this phase, new offspring particles are added to the particle swarm and updated together with the existing particles. The particle positions and velocities in the particle swarm optimization algorithm are adjusted to further optimize the solution. The update process is performed through velocity vectors and position vectors, which are used to describe the movement of particles in the solution space. The velocity vector defines the particle update step size, while the position vector represents the current solution state of the particle.

[0073] This optimization process will continue until the preset iteration conditions are met. The preset iteration conditions include reaching the maximum number of iterations or the fitness value of the solution no longer changes significantly. For example, the maximum number of iterations is set to 1000 rounds, or it stops when the improvement of the Pareto frontier is less than a certain threshold.

[0074] Furthermore, the method provided in the application embodiment also includes:

[0075] Based on the parameter crossover probability, a crossover probability selection is performed on the parent particle parameter solution to obtain a plurality of parameter solutions to be crossed; parameter dimension analysis is performed on the plurality of parameter solutions to be crossed to construct a parameter dimension crossover rule, and a crossover operation expansion is performed on the plurality of parameter solutions to be crossed based on the parameter dimension crossover rule.

[0076] In an embodiment of the present application, first, according to a set crossover probability, for example, 0.8, the current particle population is screened to select multiple particle parameter solutions to be crossed. The crossover probability determines the probability of each particle solution being selected. The specific operation is to generate a random number between 0 and 1 and compare it with the crossover probability to determine whether to select a particle solution. If the generated random number is less than the crossover probability, the particle solution is selected for the crossover operation. In this way, by generating and comparing random numbers, it is ensured that the selection of the particle solution meets the predetermined probability and effectively covers the particle space. Multiple parameter solutions to be crossed are obtained through the above process.

[0077] Then, parameter dimension analysis is performed on the selected particle parameter solutions to be crossed. The purpose here is to evaluate the performance of each parameter dimension of the particle solution in the power grid energy storage model. The specific steps include using correlation analysis, principal component analysis and other techniques to calculate the impact of each parameter dimension on the model output. Through analysis, the contribution of each parameter dimension is quantified, and then the dimensions with poor performance are identified. Based on these analysis results, parameter dimension crossing rules are constructed, that is, to clarify which parameter dimensions need to be prioritized in the crossing process in the hope of improving model performance.

[0078] Next, the crossover operation is expanded on the crossover parameter solutions based on the constructed parameter dimension crossover rules. Specifically, the crossover points are selected according to the set crossover rules, and the parameter values ​​are exchanged at these points. For example, in single-point crossover, a crossover point is selected and the parameter values ​​of the two particles are exchanged at this point; in multi-point crossover, multiple crossover points are selected and different parts of the particle solutions are exchanged respectively. This process uses crossover operations to generate new particle solutions, which combine the characteristics of the parent particles and increase the diversity of the particle population.

[0079] Finally, through these steps, new particle solutions are introduced into the particle swarm, expanding the search space of the particle swarm, completing the crossover operation expansion of multiple parameter solutions to be crossed, and improving the effect of model optimization.

[0080] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0081] The present application obtains a power grid energy storage control cloud platform, which includes a power generation energy side, a power grid side, a load demand side, an energy storage module and a control center. The power grid energy storage control cloud platform obtains energy generation data sets on the power generation energy side, power grid operation data sets on the power grid side and load power data sets on the load demand side; the control center performs response characteristic analysis and twin simulation fusion on the energy generation data sets, the power grid operation data sets and the load power data sets to build a digital virtual model of power grid operation; obtains multiple energy storage device characteristic data sets of the energy storage module, and collaboratively models the power grid operation digital virtual model based on the multiple energy storage device characteristic data sets to generate a power grid energy storage collaborative control virtual model; obtains a set of power grid multi-side state parameter information sets through real-time monitoring on the power grid energy storage control cloud platform, performs control strategy analysis on the power grid multi-side state parameter information sets based on the power grid energy storage collaborative control virtual model, obtains an energy storage optimization control strategy, and performs power grid hybrid energy storage control on the energy storage module based on the energy storage optimization control strategy. The present invention solves the technical problem of insufficient real-time control capability of power grids in the prior art. Through the power grid energy storage control cloud platform, the data of the power generation energy side, the power grid side, the load demand side and the energy storage module are integrated to construct a digital virtual model of power grid operation, and collaborative modeling is performed to generate a virtual model of power grid energy storage collaborative control, monitor power grid state parameters in real time, use the virtual model to analyze the control strategy, obtain the energy storage optimization control strategy, and achieve the technical effect of improving the real-time control capability of the power grid.

[0082] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0084] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A grid hybrid energy storage optimization control method based on source, grid, load and storage, characterized in that: The method comprises: The power grid energy storage control cloud platform includes a power generation energy side, a power grid side, a load demand side, an energy storage module and a control center, and obtains the energy generation data set of the power generation energy side, the power grid operation data set of the power grid side and the load power data set of the load demand side through the power grid energy storage control cloud platform; The control center performs response characteristic analysis and twin simulation fusion on the energy generation data set, the power grid operation data set and the load power data set to construct a digital virtual model of power grid operation; Acquire multiple energy storage device characteristic data sets of the energy storage module, perform collaborative modeling on the power grid operation digital virtual model based on the multiple energy storage device characteristic data sets, and generate a power grid energy storage collaborative control virtual model; The grid energy storage control cloud platform is used to monitor and obtain a set of state parameter information on multiple sides of the grid in real time, and a control strategy analysis is performed on the set of state parameter information on multiple sides of the grid based on the grid energy storage collaborative control virtual model to obtain an energy storage optimization control strategy, and the energy storage module is used to control grid hybrid energy storage based on the energy storage optimization control strategy; The construction of a digital virtual model of power grid operation includes: The control center arranges and aligns the energy generation data set, the power grid operation data set and the load power data set in time series to obtain an energy generation time series data set, a power grid operation time series data set and a load power time series data set; Extract characteristic indicators of the energy generation time series data set, the power grid operation time series data set and the load power time series data set respectively to obtain an energy generation characteristic indicator set, a power grid operation characteristic indicator set and a load power characteristic indicator set; Based on the energy generation characteristic index set, the power grid operation characteristic index set and the load power characteristic index set, the response characteristic analysis is performed on the energy generation time series data set, the power grid operation time series data set and the load power time series data set to obtain a generation side response characteristic model, a power grid side response characteristic model and a load side response characteristic model; The power generation side response characteristic model, the power grid side response characteristic model and the load side response characteristic model are subjected to twin simulation fusion to construct a digital virtual model of the power grid operation.

2. The method for optimizing and controlling the hybrid energy storage of a power grid based on source, grid, load and storage as claimed in claim 1, characterized in that: The method comprises: Define model evaluation dimensions to perform multi-dimensional performance evaluation on the power grid operation digital virtual model to obtain multi-dimensional evaluation parameters of model performance; Performing an impact analysis on the power grid operation digital virtual model based on the multi-dimensional evaluation parameters of the model performance to determine model performance influencing variables; Determine a variable selection threshold value according to the model performance influencing variables, perform sensitivity analysis on the power grid operation digital virtual model based on the variable selection threshold value, and obtain variable parameter sensitivity coefficients; Based on the variable parameter sensitivity coefficients and the model performance influencing variables, the performance of the power grid operation digital virtual model is optimized to obtain a power grid operation optimization virtual model.

3. The method for optimizing and controlling the hybrid energy storage of a power grid based on source, grid, load and storage as claimed in claim 1, characterized in that: The generating of the grid energy storage coordinated control virtual model comprises: Performing equipment performance evaluation on the multiple energy storage equipment characteristic data sets to obtain multiple energy storage equipment performance parameter information; Setting energy storage device control constraints according to the plurality of energy storage device characteristic data sets; Defining energy storage modeling objectives, performing weight distribution fitting based on the energy storage modeling objectives, and constructing an energy storage control objective function; Designing a collaborative model framework mechanism based on the performance parameter information of the multiple energy storage devices and the control constraints of the energy storage devices; Based on the collaborative model framework mechanism and the energy storage control objective function, the power grid operation digital virtual model is collaboratively modeled to generate the power grid energy storage collaborative control virtual model.

4. The method for optimizing and controlling the hybrid energy storage of a power grid based on source, grid, load and storage as claimed in claim 3, characterized in that: The design collaboration model framework mechanism includes: Acquiring energy storage coordination factor information, wherein the energy storage coordination factor information includes energy scheduling, energy storage priority allocation, and power balancing control; Constructing a list of energy storage factor control algorithms based on the energy storage synergy factor information; Based on the performance parameter information of the multiple energy storage devices and the control constraints of the energy storage devices, respectively matching with the energy storage factor control algorithm list to obtain a set of multiple optional energy storage factor control algorithms for the devices; The plurality of equipment selectable energy storage factor control algorithm sets are tested and evaluated to obtain a plurality of equipment target energy storage factor control algorithms, and the collaborative model framework mechanism is determined based on the plurality of equipment target energy storage factor control algorithms.

5. The grid hybrid energy storage optimization control method based on source-grid-load-storage as claimed in claim 3 is characterized in that: The energy storage optimization control strategy is obtained, including: Based on the grid energy storage collaborative control virtual model, energy storage control analysis is performed on the grid multi-side state parameter information set to obtain an energy storage control parameter selection threshold; Selecting a threshold value according to the energy storage control parameter to construct an energy storage parameter solution space, and initializing a particle swarm parameter, wherein the particle swarm parameter includes a particle position vector and a particle velocity vector; Using the energy storage control objective function to perform parameter iterative search and evaluation on the particle swarm parameters in the energy storage parameter solution space to obtain an energy storage parameter fitness set; Based on the energy storage parameter fitness set, parameters are iteratively updated until a preset iteration condition is met, and the particle with the largest fitness is determined by searching and optimizing as the energy storage optimization control strategy.

6. The grid hybrid energy storage optimization control method based on source, grid, load and storage as claimed in claim 5 is characterized in that: The method comprises: According to the energy storage parameter fitness set, a Pareto frontier parameter solution is selected and established; Filtering parameters of a preset proportion from the Pareto frontier parameter solution as the parent particle parameter solution, and setting the parameter crossover probability; A random crossover operation is performed on the parent particle parameter solution based on the parameter crossover probability, and the particle swarm parameters are expanded and iteratively updated according to the crossover operation result until the preset iteration condition is met.

7. The method for optimizing and controlling the hybrid energy storage of a power grid based on source, grid, load and storage as claimed in claim 6, characterized in that: The method comprises: Performing crossover probability selection on the parent particle parameter solution based on the parameter crossover probability to obtain a plurality of parameter solutions to be crossed; Parameter dimension analysis is performed on the multiple parameter solutions to be crossed, a parameter dimension crossing rule is constructed, and a crossing operation expansion is performed on the multiple parameter solutions to be crossed based on the parameter dimension crossing rule.

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