Power system energy storage optimization method and system

Abstract

The invention relates to the technical field of power system energy storage, and particularly discloses a power system energy storage optimization method and system, and the method comprises the steps: data collection and preprocessing: obtaining historical output data, meteorological data and power grid load data of a new energy power station, predicting the output power of the new energy power station by using a time sequence analysis and machine learning algorithm to generate a predicted output curve; meanwhile, the running state of the energy storage system is monitored in real time; building a multi-objective optimization model: building the multi-objective optimization model with the goals of power grid stability, energy storage system life maximization and operation cost minimization on the basis of the predicted output curve and the power grid load demand; the layered optimization scheduling strategy comprises a long-term optimization layer, a short-term optimization layer and a real-time control layer. According to the invention, by coordinating and controlling the energy storage system and other adjustment resources, multi-energy complementation and collaborative optimization are realized, and the flexibility and reliability of the whole power system are improved.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and specifically to a power system energy storage optimization method and system. Background Technology

[0002] In power systems, the large-scale integration of new energy sources such as wind and solar power has brought significant challenges to the stable operation of the power grid due to the volatility and intermittency of their output. Energy storage systems, as a key means of solving this problem, can store energy when there is excess output from new energy sources and release energy when there is insufficient output, thereby smoothing out the fluctuations in new energy output and improving the stability and reliability of the power grid.

[0003] However, existing technologies for scheduling and controlling energy storage systems often suffer from the following problems: Firstly, the capacity of energy storage systems is limited, making it difficult to maintain stable grid operation for extended periods when the output of new energy sources deviates significantly from the planned output. Secondly, the charging and discharging strategies of energy storage systems are not optimized enough, resulting in low utilization of energy storage resources and failing to fully leverage the role of the energy storage system. Therefore, there is an urgent need for a method and system capable of optimizing the scheduling and control of energy storage systems based on the output of new energy sources and the status of the energy storage system. Summary of the Invention

[0004] The purpose of this invention is to provide a power system energy storage optimization method and system, to solve the problems existing in the scheduling and control of energy storage systems in the prior art, and to improve the utilization rate of energy storage systems and the stability of the power grid.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for optimizing energy storage in a power system includes the following steps:

[0007] Data Acquisition and Preprocessing: Historical power output data, meteorological data, and grid load data of new energy power plants are acquired. Time series analysis and machine learning algorithms are used to predict the power output of new energy power plants and generate predicted power output curves. Simultaneously, the operating status of the energy storage system is monitored in real time, including parameters such as the remaining capacity, charging and discharging efficiency, and health status of the energy storage system.

[0008] Multi-objective optimization model construction: Based on the predicted power output curve and grid load demand, a multi-objective optimization model is constructed with the objectives of maximizing grid stability, maximizing the lifespan of the energy storage system, and minimizing operating costs. This model considers the charging and discharging power limitations, capacity constraints, efficiency characteristics of the energy storage system, and the uncertainty of renewable energy output.

[0009] Hierarchical optimization scheduling strategy:

[0010] Long-term optimization layer: Based on forecast data, formulate daily scheduling plans for the energy storage system, determine the charging and discharging strategies and power allocation schemes of the energy storage system at different times, so as to balance grid supply and demand and maximize the economic benefits of the energy storage system.

[0011] Short-term optimization layer: Based on real-time monitoring data and prediction errors, the long-term scheduling plan is rolled over and adjusted dynamically to adjust the charging and discharging power of the energy storage system in order to cope with the real-time fluctuations in the output of new energy sources.

[0012] Real-time control layer: Based on a fast response control algorithm, the energy storage system is controlled in real time to ensure that the energy storage system can respond quickly to grid dispatch commands and smooth out short-term fluctuations in the output of new energy sources.

[0013] Uncertainty Handling and Risk Control: Scenario analysis and probabilistic prediction methods are employed to quantitatively analyze the uncertainty of renewable energy output, generating multiple possible output scenarios. For each scenario, the optimal scheduling strategy for the energy storage system is calculated, and the system's operational risks are assessed. By setting risk thresholds, the utilization rate of the energy storage system is maximized while ensuring system safety.

[0014] Coordinated control and energy management: This involves coordinating the energy storage system with other regulating resources in the power grid (such as traditional generators and demand response resources) to achieve multi-energy complementarity and synergistic optimization. By establishing an energy management system, the energy flow of the entire power system is monitored and optimized in real time, ensuring the safe and stable operation of the power grid.

[0015] A power system energy storage optimization system, comprising:

[0016] Data acquisition and monitoring module: responsible for collecting power output data, meteorological data, grid load data, and energy storage system operation status data of new energy power plants, and preprocessing and storing the data.

[0017] Prediction and Analysis Module: Based on historical and real-time data, it performs predictive analysis on renewable energy output and grid load, providing data support for optimized scheduling.

[0018] Optimization Decision Module: Constructs a multi-objective optimization model, adopts a hierarchical optimization strategy, and generates the optimal scheduling scheme for the energy storage system.

[0019] Risk assessment and control module: assesses the uncertainty of new energy output, calculates the operational risks of the system, and formulates corresponding risk control strategies.

[0020] Coordination, Control and Execution Module: Based on the optimization decision results, it coordinates and controls the energy storage system and other regulation resources to achieve energy optimization management of the power system.

[0021] Human-computer interaction and monitoring module: Provides a user-friendly human-computer interaction interface to realize real-time monitoring of the power system's operating status and visualize the dispatching decision.

[0022] The beneficial effects of this invention are as follows: By constructing a multi-objective optimization model and a hierarchical optimization scheduling strategy, this invention comprehensively considers multiple objectives such as grid stability, energy storage system lifespan, and operating costs, effectively improving the utilization rate of energy storage systems and grid stability. Through quantitative analysis and risk control of the uncertainty of new energy output, the economic benefits of energy storage systems can be maximized while ensuring system safety. By coordinating and controlling energy storage systems with other regulatory resources, multi-energy complementarity and synergistic optimization are achieved, improving the flexibility and reliability of the entire power system. Attached Figure Description

[0023] The invention will now be further described with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a power system energy storage optimization method according to the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 As shown, this invention is a power system energy storage optimization method, comprising the following steps:

[0027] Data Acquisition and Preprocessing: Sensors installed in renewable energy power plants, power grids, and energy storage systems collect real-time data on renewable energy output, meteorological data, power grid load, and the operational status of energy storage systems. The collected data is cleaned, filtered, and feature-extracted to remove noise and outliers, extracting useful feature information. Time series analysis, neural networks, and other machine learning algorithms are used to predict renewable energy output, generating predicted output curves.

[0028] Prophet is a forecasting model developed by Facebook for time-series data, particularly well-suited for handling data with seasonality, trends, and holiday effects. Its core principle is to decompose the time series data into a superposition of trend, seasonality, and holiday components.

[0029] y(t) = g(t) + s(t) + h(t) + ∈t;

[0030] in:

[0031] g(t) represents the trend function, used to fit the long-term trend of data;

[0032] s(t) represents a seasonality function used to fit the periodic changes in data;

[0033] h(t) represents the holiday effect, used to fit abnormal changes on a specific date or time period;

[0034] ∈t represents the error term, which follows a normal distribution;

[0035] Using Fourier series to fit seasonal variations:

[0036]

[0037] Where P represents the period (e.g., 365.25 days represents an annual period, and 7 days represents a weekly period), and N represents the order of the Fourier series, which controls the flexibility of seasonal changes.

[0038] Multi-objective optimization model construction: A multi-objective optimization model is constructed with the objectives of minimizing grid frequency deviation, minimizing energy storage system charging and discharging losses, and minimizing operating costs. Considering the charging and discharging power limitations, capacity constraints, efficiency characteristics of the energy storage system, and the uncertainty of new energy output, the model is transformed into a solvable mathematical programming problem.

[0039] The multi-objective optimization model adopts a three-layer architecture of "objective-constraint-solution":

[0040] Objective layer: Clearly define conflicting optimization objectives such as grid stability, energy storage lifetime, and operating costs.

[0041] Constraint layer: Integrates physical constraints, operational constraints, and safety constraints

[0042] Solution layer: Utilizes intelligent algorithms or mathematical programming methods to solve for the Pareto optimal solution set.

[0043] Power grid stability objectives:

[0044] With the core objective of smoothing out fluctuations in renewable energy output and maintaining grid frequency stability, this is achieved by minimizing output deviations and frequency fluctuations.

[0045] Minimize output deviation:

[0046] Among them, P grid (t) represents the actual power output of the power grid at time t, P plan (t) represents the planned output, and T represents the scheduling period.

[0047] Frequency fluctuation suppression:

[0048] Where Δf(t) is the deviation between the grid frequency and the rated frequency (50Hz) at time t;

[0049] Energy storage system lifespan maximization target:

[0050] By reducing the deep charge / discharge cycle count and the number of cycles in the energy storage system, the rate of battery degradation is reduced:

[0051] Deep charge / discharge penalty:

[0052]

[0053] Where SoC(t) represents the energy storage state of charge at time t, SoC nom δ represents the rated state of charge, w(t) is the deep charge / discharge threshold, and Ⅱ() is the indicator function.

[0054] Minimize the number of iterations: f4 = minN cycle ;

[0055] Where, N cycle This refers to the number of complete charge-discharge cycles of energy storage within the scheduling period.

[0056] Operating cost minimization objective:

[0057] Taking into account energy storage charging and discharging losses, electricity purchase costs, and peak-valley electricity price arbitrage profits:

[0058] Total cost function:

[0059] Where: C loss (t)=η-1·P ch (t)·Δt-η·P dis (t)·Δt represents the discharge loss cost, (η represents the energy storage efficiency, P) ch / P dis (Charging / discharging power);

[0060] C buy (t)=p buy (t)·P buy (t)·Δt represents the electricity purchase cost (P) buy (t) represents the electricity purchase price.

[0061] R sell (t)=p sell (t)·P sell (t)·Δt represents the revenue from electricity sales (P) sell (t) represents the electricity sales price.

[0062] The constraints include physical constraints of the energy storage system, grid operation constraints (power balance constraints and node voltage constraints), and safety and reliability constraints (reserve capacity constraints and charge / discharge conversion constraints).

[0063] Regarding the solution process:

[0064] The multi-objective model is transformed into a single objective using a weighted summation method.

[0065]

[0066] Among them, w i The target weights are determined through expert experience or the Analytic Hierarchy Process (AHP).

[0067] Implementation of hierarchical optimization scheduling strategy:

[0068] Long-term optimization layer: Based on predicted data, a model predictive control algorithm is used to formulate a daily scheduling plan for the energy storage system. This plan considers the peak and valley electricity prices of the power grid, the predicted trend of renewable energy output, and the lifespan characteristics of the energy storage system, determining the charging and discharging strategies and power allocation schemes of the energy storage system at different times.

[0069] The core task of the long-term optimization layer:

[0070] Develop a charge / discharge plan:

[0071] Based on weather forecasts, new energy power generation predictions, and grid load trends, the charging and discharging times and power of the energy storage system can be planned in advance. For example, if it is predicted that solar power generation will be high but the load will be low by noon tomorrow, the energy storage system will be charged; if the load peaks in the evening but solar power output decreases, the energy storage system will be discharged.

[0072] Balancing economy and reliability:

[0073] Economic efficiency: By taking advantage of the peak-valley electricity price difference, charging during off-peak hours (when electricity prices are low) and discharging during peak hours (when electricity prices are high), arbitrage profits can be realized;

[0074] Reliability: Reserve sufficient backup capacity to cope with fluctuations in new energy output or sudden load increases, and ensure stable grid operation;

[0075] Extend the lifespan of energy storage devices:

[0076] By optimizing charging and discharging strategies, deep charging and discharging of energy storage batteries can be avoided (e.g., avoiding levels below 20% or above 80%), thus reducing battery wear and extending battery life.

[0077] The workflow of the long-term optimization layer:

[0078] Data collection and prediction;

[0079] Collect historical data, including renewable energy output, grid load, and electricity prices.

[0080] Using machine learning models (such as LSTM and Prophet) to predict changes in renewable energy output and load over the next few days.

[0081] Establish an optimization model;

[0082] Target setting: Simultaneously consider economy (lowest cost), reliability (minimum grid fluctuations), and equipment lifespan (minimum number of charge-discharge cycles).

[0083] Constraints: Energy storage capacity limitations, charging and discharging power limitations, grid security constraints, etc.

[0084] Find the optimal strategy;

[0085] Use intelligent optimization algorithms (such as genetic algorithms and particle swarm optimization) to find the optimal charging and discharging strategy that satisfies all objectives and constraints.

[0086] Generate a scheduling plan;

[0087] The optimization results are converted into specific scheduling instructions, such as "charge at 5MW power from 8:00 to 10:00 tomorrow morning and discharge at 8MW power from 5:00 to 7:00 pm".

[0088] Scrolling optimization and adjustments;

[0089] Every day (or every few hours), the future scheduling plan is re-optimized based on the latest data and forecast results, forming a closed-loop control.

[0090] Short-term optimization layer: Based on real-time monitoring data and prediction errors, a rolling optimization algorithm is used to revise the long-term scheduling plan. This layer mainly focuses on the intraday fluctuations in renewable energy output and mitigates the impact of renewable energy output fluctuations on the power grid by dynamically adjusting the charging and discharging power of the energy storage system.

[0091] In power system energy storage optimization, the short-term optimization layer plays the role of a "tactical adjuster." Its main function is to dynamically adjust the energy storage system's operating strategy based on the latest real-time data, within the framework established by the long-term optimization layer. While the long-term optimization layer provides daily or weekly plans based on forecast data, in actual operation, renewable energy output may deviate from forecasts due to sudden weather changes, and grid load may experience unexpected fluctuations. This is where the short-term optimization layer comes in to "fill in the gaps."

[0092] The short-term optimization layer typically operates on a cycle of several hours to a day, with finer time granularity, generally in 15-minute or 30-minute scheduling periods. It acquires real-time data on the actual output of renewable energy power plants, the current power level of energy storage systems, the real-time load of the grid, and the latest electricity price information. For example, if the long-term plan predicts a photovoltaic output of 50MW at 2 PM today, but the actual output is only 40MW due to thicker cloud cover, the short-term optimization layer calculates the output deviation and adjusts the discharge power of the energy storage system to make up for the 10MW shortfall, ensuring stable power supply to the grid.

[0093] In practical operation, the short-term optimization layer first compares the target value of the long-term plan with the current actual value to calculate the amount of adjustment needed. For example, if it finds that the current state of charge of the energy storage system is lower than the planned value, it will appropriately reduce the discharge amount in the current period or increase the charging amount in the next period to avoid insufficient power later. At the same time, it will also consider the physical limitations of the energy storage system, such as the maximum charging and discharging power and the minimum remaining power, to ensure that the adjusted strategy is feasible in practice.

[0094] The short-term optimization layer also focuses on handling short-term uncertainties, such as sudden peak electricity demand or short-term drastic fluctuations in wind power. In these situations, it activates a rapid response mechanism, potentially increasing the discharge power of energy storage temporarily or quickly charging it during load drops to mitigate the impact of these fluctuations on the grid. Furthermore, it works in conjunction with the real-time control layer. In extreme cases, such as a sudden deviation of the grid frequency from the standard value, the short-term optimization layer issues adjustment instructions, allowing the real-time control layer to execute specific charging and discharging operations to quickly restore grid stability.

[0095] Real-time control layer: Based on a fast-response control algorithm, this layer performs real-time control of the energy storage system. It primarily focuses on short-term fluctuations in renewable energy output, maintaining grid frequency stability by rapidly adjusting the charging and discharging power of the energy storage system.

[0096] In the hierarchical architecture of power system energy storage optimization, the real-time control layer is the lowest-level execution unit that directly interacts with physical equipment. It is like the "nerve endings" of the entire system, responsible for translating the optimization strategies formulated by the upper layers into actual equipment operation instructions. If the long-term optimization layer is the "strategic planner" and the short-term optimization layer is the "tactical adjuster," then the real-time control layer is undoubtedly the "front-line executor."

[0097] The core task of the real-time control layer is to perform precise and rapid control of energy storage devices, typically on a timescale of seconds to minutes, or even milliseconds (e.g., in response to sudden changes in grid frequency). It does not involve complex optimization calculations but focuses on adjusting the charging and discharging states of the energy storage system in real time according to upper-level instructions and preset rules. For example, when the grid frequency is below 50Hz (rated value), the real-time control layer must increase the energy storage discharge power in a very short time to inject additional energy into the grid; conversely, when the frequency is too high, it quickly switches to charging mode to absorb excess energy.

[0098] The real-time control layer relies on high-density data acquisition and high-speed communication networks. It continuously monitors various parameters of the energy storage device, including the battery's state of charge (SoC), temperature, and charging / discharging current, as well as the grid's voltage, frequency, and power flow distribution. Using this real-time data, the control layer can accurately determine the device's status and grid demands, and respond promptly. For example, when the battery temperature exceeds a safe threshold, it will automatically reduce charging / discharging power or activate the cooling system to prevent device damage.

[0099] The control logic at this layer is typically based on preset rules or algorithms. For simple control tasks, a proportional-integral-derivative (PID) controller can be used to calculate the amount of power that needs adjustment based on the deviation value (such as frequency deviation). For complex scenarios, such as the coordinated control of multiple energy storage devices, a distributed control algorithm may be used, allowing each energy storage unit to make autonomous decisions through information exchange. For example, in a microgrid, multiple distributed energy storage systems can communicate in real time and automatically allocate adjustment tasks based on their respective remaining capacity and response capabilities.

[0100] The real-time control layer also features fault detection and protection. Once an abnormality is detected, such as battery overcharging, over-discharging, short circuit, or sudden changes in grid voltage, it immediately takes protective measures, such as cutting off the circuit, limiting power output, or triggering an alarm. These protection mechanisms are the last line of defense to ensure the safe operation of the energy storage system.

[0101] In terms of coordination with the short-term optimization layer, the real-time control layer periodically reports equipment status and execution progress to help the short-term optimization layer evaluate the effectiveness of its strategies and make dynamic adjustments. Simultaneously, it strictly executes the instructions issued by the short-term optimization layer, but in emergency situations (such as equipment failure or power grid accidents), it prioritizes the execution of preset safety control logic and promptly reports any abnormal information.

[0102] The implementation of the real-time control layer relies on advanced hardware and software platforms. Hardware components include energy management systems (EMS), programmable logic controllers (PLCs), and smart sensors; software components require real-time operating systems, communication protocols, and control algorithms. The combination of these technologies enables the real-time control layer to complete the closed-loop process of data acquisition, computational decision-making, and instruction execution within milliseconds.

[0103] In summary, the real-time control layer is the "cornerstone of execution" for energy storage optimization in power systems. Through high-precision real-time control and rapid response capabilities, it ensures the safe and stable operation of energy storage devices, achieving precise support for the power grid. Without an efficient and reliable real-time control layer, upper-level optimization strategies cannot be implemented, and the value of the entire energy storage system cannot be realized.

[0104] Uncertainty handling and risk control: Monte Carlo simulation is used to generate multiple renewable energy output scenarios and assess the system's operational risk in each scenario. By setting risk thresholds, the utilization rate of the energy storage system is maximized while ensuring system safety. When the system's operational risk exceeds the threshold, backup regulation resources are activated to ensure the safe and stable operation of the power grid.

[0105] Coordinated Control and Energy Management: This involves coordinating and controlling energy storage systems with traditional generator sets and demand response resources to achieve multi-energy complementarity and synergistic optimization. By establishing an energy management system, the energy flow of the entire power system is monitored and optimized in real time. Based on the real-time operating status of the power grid, the output of various regulating resources is dynamically adjusted to ensure the supply-demand balance and safe and stable operation of the power grid.

[0106] A power system energy storage optimization system, comprising:

[0107] Data acquisition and monitoring module: Composed of various sensors, data acquisition devices and communication networks, it is responsible for collecting operational data from new energy power plants, power grids and energy storage systems, and transmitting the data to the data processing center for processing and storage.

[0108] The prediction and analysis module includes a data preprocessing unit, a prediction model unit, and an analysis and evaluation unit. The data preprocessing unit cleans and extracts features from the collected data; the prediction model unit uses machine learning algorithms to predict renewable energy output and grid load; and the analysis and evaluation unit analyzes and evaluates the prediction results to provide a basis for optimization decisions.

[0109] The optimization decision-making module includes a model building unit, an optimization solution unit, and a strategy generation unit. The model building unit constructs a multi-objective optimization model; the optimization solution unit solves the model using a hierarchical optimization algorithm; and the strategy generation unit generates scheduling strategies for the energy storage system based on the optimization results.

[0110] The risk assessment and control module includes an uncertainty analysis unit, a risk assessment unit, and a control strategy unit. The uncertainty analysis unit quantitatively analyzes the uncertainty of new energy output; the risk assessment unit assesses the operational risks of the system; and the control strategy unit formulates corresponding risk control strategies based on the risk assessment results.

[0111] Coordination Control and Execution Module: This module includes a coordination control unit, a command issuing unit, and an execution feedback unit. The coordination control unit coordinates and controls the energy storage system and other regulatory resources; the command issuing unit issues control commands to each execution device; and the execution feedback unit monitors the operating status of the execution devices in real time and transmits feedback information to the control center.

[0112] Human-Computer Interaction and Monitoring Module: This module includes a visualization unit, an operation control unit, and a data management unit. The visualization unit presents the system's operating status and scheduling decisions to operators in an intuitive way; the operation control unit provides a human-computer interaction interface, allowing operators to manually intervene in the system; and the data management unit is responsible for managing and maintaining the system's data.

[0113] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for optimizing energy storage in a power system, characterized in that, Includes the following steps: Data acquisition and preprocessing: Acquire historical power output data, meteorological data, and grid load data of new energy power plants, and use time series analysis and machine learning algorithms to predict the power output of new energy power plants and generate predicted power output curves; at the same time, monitor the operating status of energy storage systems in real time, including the remaining capacity, charging and discharging efficiency, and health status parameters of the energy storage systems. Multi-objective optimization model construction: Based on the predicted power output curve and grid load demand, a multi-objective optimization model is constructed with the objectives of grid stability, maximizing the lifespan of the energy storage system, and minimizing operating costs. This model considers the charging and discharging power limitations, capacity constraints, efficiency characteristics, and uncertainties of new energy output of the energy storage system. Hierarchical optimization scheduling strategy: Long-term optimization layer: Based on forecast data, formulate daily scheduling plans for the energy storage system and determine the charging and discharging strategies and power allocation schemes for the energy storage system at different times; Short-term optimization layer: Based on real-time monitoring data and prediction errors, the long-term scheduling plan is rolled out and the charging and discharging power of the energy storage system is dynamically adjusted. Real-time control layer: Based on a fast response control algorithm, the energy storage system is controlled in real time to ensure that the energy storage system responds quickly to grid dispatch commands.

2. The power system energy storage optimization method according to claim 1, characterized in that, Also includes: Uncertainty handling and risk control: Using scenario analysis and probabilistic prediction methods, the uncertainty of new energy output is quantitatively analyzed to generate multiple possible output scenarios; For each scenario, the optimal scheduling strategy for the energy storage system is calculated, and the operational risks of the system are assessed. By setting risk thresholds, the utilization rate of the energy storage system is maximized while ensuring system safety.

3. The power system energy storage optimization method according to claim 1, characterized in that, Also includes: Coordinated control and energy management: Coordinate the control of energy storage systems with other regulation resources in the power grid to achieve multi-energy complementarity and synergistic optimization; By establishing an energy management system, the energy flow of the entire power system can be monitored and optimized in real time.

4. The power system energy storage optimization method according to claim 1, characterized in that, When formulating daily scheduling plans in the long-term optimization layer, the peak and valley electricity prices of the power grid, the predicted trend of new energy output, and the lifespan characteristics of the energy storage system are taken into account.

5. The power system energy storage optimization method according to claim 1, characterized in that, When making rolling corrections to the long-term scheduling plan in the short-term optimization layer, a rolling optimization algorithm is used to pay attention to the intraday fluctuations in the output of new energy sources.

6. The power system energy storage optimization method according to claim 1, characterized in that, When the energy storage system is controlled in real time in the real-time control layer, a fast response control algorithm is adopted to pay attention to the short-term fluctuations in the output of new energy sources and maintain the stability of the grid frequency.

7. The power system energy storage optimization method according to claim 2, characterized in that, In the uncertainty handling and risk control, the Monte Carlo simulation method is used to generate new energy output scenarios, assess the system operation risk, and activate backup regulation resources when the risk exceeds the threshold.

8. A power system energy storage optimization system, characterized in that, include: Data acquisition and monitoring module: Collects power output data, meteorological data, grid load data, and energy storage system operation status data of new energy power plants, and preprocesses and stores the data; Prediction and Analysis Module: Based on historical and real-time data, it performs predictive analysis on renewable energy output and grid load, providing data support for optimized scheduling; Optimization Decision Module: Constructs a multi-objective optimization model, adopts a hierarchical optimization strategy, and generates the optimal scheduling scheme for the energy storage system; Risk assessment and control module: assesses the uncertainty of new energy output, calculates the operational risks of the system, and formulates corresponding risk control strategies; Coordination Control and Execution Module: Based on the optimization decision results, it coordinates and controls the energy storage system and other regulation resources to achieve energy optimization management of the power system; Human-computer interaction and monitoring module: Provides a human-computer interaction interface to realize real-time monitoring of the power system's operating status and visualize the scheduling decision.

Images