A wind storage system optimization configuration method based on source network load storage interaction

Abstract

This invention relates to the field of power system technology, specifically providing a method for optimizing the configuration of wind-storage systems based on source-grid-load-storage interaction. The method includes constructing a data acquisition and situational awareness layer to acquire multi-source data from sources (source, grid, load, and storage) in real time and calculate grid regulation demand and load response potential; constructing a three-layer collaborative optimization architecture consisting of a day-ahead planning layer, an intraday rolling layer, and a real-time control layer, respectively performing capacity planning, rolling scheduling, and real-time control; constructing a bottom-up collaborative feedback loop to feed back real-time execution deviations and the actual operating status of energy storage to the upper-level correction model; and iteratively solving to output the optimal configuration scheme and operating strategy. This invention achieves deep coupling and dynamic correction of sources, grid, load, and storage through a three-layer architecture and feedback loop, solving the problems of poor coordination and low energy storage utilization caused by the fragmentation of each link.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method for optimizing the configuration of a wind-storage system based on the interaction between power generation, grid, load, and storage. Background Technology

[0002] With the continuous expansion of wind power grid connection, the inherent intermittency and volatility affect the safe and stable operation of the power system. To improve wind power absorption capacity and smooth grid-connected power fluctuations, configuring energy storage systems for wind farms has become one of the main technical means. However, energy storage systems are costly, and capacity configuration and operation strategies directly determine the economic efficiency and technical effectiveness of wind-storage combined operation.

[0003] Existing optimization methods for wind-storage systems typically construct a two-layer optimization model. The upper layer focuses on capacity planning with objectives such as annual total revenue or wind curtailment rate, while the lower layer focuses on scheduling optimization with objectives such as daily operating cost or power quality. Intelligent algorithms are then used to solve the problem. These methods often treat the "source" (wind farm) and "storage" (energy storage system) as the core optimization objects, while treating the "grid" (power grid) and "load" (load) as fixed boundary conditions or simple constraints. This approach severs the inherent connections between different components of the system and fails to fully consider the real-time adjustment needs of the power grid and the proactive response capabilities of load-side resources. Consequently, the optimized configuration scheme is difficult to adapt to complex and changing grid conditions in actual operation, failing to achieve globally optimal economy and stability. Traditional single-layer or two-layer optimization models are relatively singular in their time scale, making it difficult to balance long-term capacity planning with short-term power fluctuation suppression, resulting in low utilization of the energy storage system and insufficient overall synergistic efficiency.

[0004] Therefore, existing technologies lack a source-grid-load-storage collaborative optimization configuration system that can sense the dynamic adjustment needs of the power grid and the flexible response potential of the load in real time, and build a two-way feedback mechanism between day-ahead capacity planning, intraday rolling scheduling and real-time power control, so as to solve the problems of poor coordination, low energy storage utilization and insufficient overall economic efficiency and stability caused by the fragmentation of each link. Summary of the Invention

[0005] This invention overcomes the shortcomings of existing technologies and provides a wind-storage system optimization configuration method based on source-grid-load-storage interaction. By constructing a three-layer collaborative optimization architecture including a day-ahead planning layer, an intraday rolling layer, and a real-time control layer, and establishing a bottom-up collaborative feedback loop among the three layers, the actual execution deviation of the real-time control layer and the actual operating status of energy storage are fed back to the intraday rolling layer and the day-ahead planning layer, respectively. This achieves deep coupling and dynamic correction between capacity planning, scheduling strategy, and real-time control, and solves the problems of disconnect between energy storage configuration and operation strategy, poor system coordination, and low energy storage utilization caused by the fragmentation of source, grid, load, and storage links in existing technologies.

[0006] The technical solution adopted by this invention is as follows: This solution provides a method for optimizing the configuration of a wind-storage system based on source-grid-load-storage interaction, the specific steps of which include:

[0007] Step 1: Construct a data acquisition and situational awareness layer to acquire real-time electrical data from the grid connection point, energy storage system status data, grid-side operation data, and load-side response data. The grid connection point electrical data includes voltage signals, current signals, active power signals, reactive power signals, and frequency signals. The data acquisition and situational awareness layer also obtains forecast data from meteorological service systems or grid dispatch systems, including ultra-short-term wind power forecast data and load forecast data, and obtains real-time electricity price signals from the grid dispatch system. The real-time acquired grid connection point electrical data, energy storage system status data, grid-side operation data, and load-side response data are stored in a time series in a local or cloud-based historical database to form historical operation data. Based on the grid-side operation data and load-side response data, the real-time regulation capacity demand of the grid and the flexible adjustability potential of the load are calculated.

[0008] Step 2: Construct a three-layer collaborative optimization architecture: daily planning layer - intraday rolling layer - real-time control layer;

[0009] At the planning level, based on historical and forecast data, and with the overall system cost and renewable energy consumption as optimization objectives, a wind-storage system capacity planning model is constructed, and a preliminary configuration scheme for the energy storage system is output. The wind-storage system capacity planning model includes an energy storage operation and maintenance cost model and a lifetime decay model. The energy storage operation and maintenance cost model calculates the annual operation and maintenance cost of the energy storage system, and the lifetime decay model describes the decay law of the energy storage system's health status with the number of cycles.

[0010] The intraday rolling layer receives the preliminary configuration plan of the energy storage system, and based on the ultra-short-term wind power forecast data and the real-time adjustment capacity demand of the power grid, it constructs a multi-time-scale source-storage-load coordinated scheduling model with the optimization objectives of system operating cost and power fluctuation smoothing effect, and outputs the rolling charging and discharging plan and load adjustment instructions of the energy storage system.

[0011] The real-time control layer receives rolling charge and discharge plans and load adjustment commands, and obtains the actual power of the wind farm grid connection point based on the grid connection point electrical data; with the goal of tracking the plan and suppressing fast timescale fluctuations, it constructs a real-time collaborative control model between the energy storage converter and the flexible load, and generates real-time power adjustment commands and load fast response commands for the energy storage system; among which, the energy storage converter executes the charge and discharge commands of the energy storage system.

[0012] Step 3: Construct a collaborative feedback loop to feed back the deviation between the actual power of the wind farm grid connection point and the rolling charge and discharge plan generated by the real-time control layer during execution, as well as the energy storage system status data acquired in real time by the data acquisition and situational awareness layer, to the intraday rolling layer and the day-ahead planning layer. The intraday rolling layer adjusts the scheduling plan for subsequent periods based on the deviation, and the day-ahead planning layer corrects the wind-storage system capacity planning model for the next optimization cycle based on the energy storage system status data.

[0013] Step 4: Based on the three-layer collaborative optimization architecture and collaborative feedback loop, a multi-objective optimization algorithm is used to iteratively solve the capacity planning model of the wind storage system and the source-storage-load coordination scheduling model at multiple time scales, and output the optimal energy storage system configuration scheme and the corresponding source-grid-load-storage collaborative operation strategy.

[0014] Furthermore, the calculation of the real-time regulation capacity demand of the power grid in step one specifically includes: extracting the operating status characteristics of the power grid based on the operating data of the power grid side; inputting the operating status characteristics into a pre-constructed power grid regulation demand assessment model, which is based on a fuzzy logic algorithm and outputs the real-time upward and downward capacity demand of the power grid.

[0015] Furthermore, step one, calculating the load's flexibility and adjustability potential, specifically includes: classifying the load based on load-side response data to identify different types of flexible loads, including interruptible loads, transferable loads, and price-sensitive loads; constructing response characteristic models for various types of flexible loads, including response capacity constraints, response time constraints, and user comfort constraints; and calculating, based on the response characteristic models and combined with real-time electricity price signals, the upward and downward adjustment response potential that the load side can provide in the current period and in the future preset period.

[0016] Furthermore, in step two, the wind-storage system capacity planning model at the day-ahead planning level has the optimization objective of minimizing the overall system cost and maximizing the renewable energy consumption. The decision variables include the rated power and rated capacity of the energy storage system, and the constraints include the power constraints, state of charge constraints, system power balance constraints, and grid interconnection power constraints of the energy storage system.

[0017] Furthermore, in step two, the source-storage-load coordinated scheduling model of the intraday rolling layer has the following optimization objectives: minimum system operating cost, minimum grid-connected power fluctuation, and maximum system flexibility margin. The decision variables include the charging and discharging power of the energy storage system in each time period and the switching or transfer power of various flexible loads. The constraints include system power balance constraints, energy storage system state of charge and power constraints, flexible load response capability constraints, and grid safety operation constraints.

[0018] Furthermore, the real-time collaborative control model of the real-time control layer in step two specifically includes:

[0019] Step C1: Generate a reference power command for the energy storage system based on the rolling charge and discharge plan;

[0020] Step C2: Detect the power deviation between the actual power at the wind farm's grid connection point and the planned power value for the corresponding period in the rolling charge and discharge plan, as well as the grid frequency deviation;

[0021] Step C3: Input the power deviation and grid frequency deviation into the pre-built virtual synchronous machine control model. The virtual synchronous machine control model simulates the rotor inertia and damping characteristics of the synchronous generator and outputs the inertia support and primary frequency regulation additional power command of the energy storage system.

[0022] Step C4: Superimpose the reference power command and the additional power command to generate the final real-time power adjustment command for the energy storage converter;

[0023] Step C5: When the power deviation or grid frequency deviation exceeds the preset threshold, the real-time control layer triggers a load fast response command to quickly disconnect or connect some interruptible loads within milliseconds.

[0024] Furthermore, step three involves constructing a collaborative feedback loop, specifically including:

[0025] Step D1: Calculate the power deviation between the actual power at the wind farm's grid connection point and the planned power value for the corresponding period in the rolling charge and discharge plan, and use it as the first feedback signal;

[0026] Step D2: Statistically analyze the actual charge / discharge amount, cycle count, and health status of the energy storage system as the second feedback signal;

[0027] Step D3: Statistically analyze the actual response rate and actual response accuracy of the load side to the load adjustment command, and use this as the third feedback signal;

[0028] Step D4: The first feedback signal and the third feedback signal are transmitted to the intraday rolling layer in real time. The source-storage-load coordination scheduling model of the intraday rolling layer corrects the prediction confidence and load response capacity estimate in subsequent scheduling cycles based on the first feedback signal and the third feedback signal.

[0029] Step D5: The second feedback signal is periodically transmitted to the day-ahead planning layer. Based on the second feedback signal, the wind and energy storage system capacity planning model of the day-ahead planning layer corrects the energy storage operation and maintenance cost model and the lifespan decay model in the subsequent optimization cycle.

[0030] Furthermore, in step four, the multi-objective optimization algorithm is a particle swarm optimization algorithm based on population entropy. During the iteration process, the particle swarm optimization algorithm calculates the population diversity entropy value in real time and dynamically adjusts the inertia weight and learning factor of the particle swarm optimization algorithm according to the entropy value, so as to balance the global search and local exploitation capabilities of the particle swarm optimization algorithm.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] (1) By constructing a three-layer collaborative optimization architecture consisting of a day-ahead planning layer, an intraday rolling layer, and a real-time control layer, and forming a complete optimization system together with the data acquisition and situational awareness layer, the isolated state of each link of source, grid, load, and storage in the traditional source-storage optimization is broken. The data acquisition and situational awareness layer acquires the electrical data of the grid connection point, the status data of the energy storage system, the grid-side operation data, and the load-side response data in real time, and calculates the real-time adjustment capacity demand of the grid and the flexible adjustment potential of the load. The above information is input into the models of the intraday rolling layer and the real-time control layer, respectively, realizing the active adaptation of energy storage configuration and operation strategy to the dynamic changes of the grid and the active response capability of the load;

[0033] (2) An innovative bottom-up collaborative feedback loop was designed. The deviation between the actual power of the wind farm grid connection point and the rolling charge and discharge plan generated by the real-time control layer during the execution process, as well as the actual response rate and actual response accuracy of the load side to the load adjustment command, are fed back to the intraday rolling layer in real time to correct the prediction confidence and load response capability estimate in the subsequent scheduling cycle. The actual charge and discharge volume, cycle number and health status of the energy storage system in actual operation are periodically fed back to the day-ahead planning layer to correct the energy storage operation and maintenance cost model and life decay model in the subsequent optimization cycle. This mechanism makes capacity planning no longer a one-time static calculation, but can be dynamically corrected and continuously optimized according to the actual system operation data, realizing a high degree of coupling between planning and operation, and ensuring the economy and applicability of the configuration scheme throughout the entire life cycle.

[0034] (3) In the source-storage-load coordinated scheduling model of the intraday rolling layer, the system flexibility adjustment margin is introduced into the optimization objective, and the relationship between operating cost, power fluctuation smoothing effect and flexibility adjustment capability is balanced by dynamic weight coefficients; the virtual synchronous machine control strategy is integrated into the real-time control layer, so that the energy storage system can simulate the rotor inertia and damping characteristics of the synchronous generator while tracking the rolling charge and discharge plan, and actively participate in the frequency regulation of the grid. This not only smooths the power fluctuation of wind power itself, but also provides inertia support and primary frequency regulation service for the grid, realizing the leap from "source-storage" coordination to "source-grid-load-storage" deep interaction, and improving the overall synergistic efficiency of the system. Attached Figure Description

[0035] Figure 1This is a schematic diagram of the three-layer collaborative optimization architecture and collaborative feedback loop of the present invention;

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Example 1:

[0039] Please see Figure 1 This embodiment presents an optimized configuration method for a wind-storage system based on source-grid-load-storage interaction, aiming to address the problems of low energy storage utilization and poor system economics caused by insufficient coordination among the source, grid, load, and storage links in existing technologies. By constructing a three-layer collaborative architecture and a bidirectional feedback loop, end-to-end collaborative optimization from capacity planning to real-time operation is achieved. Specific steps include:

[0040] Step 1: Construct the data acquisition and situational awareness layer;

[0041] The data acquisition and situational awareness layer acquires in real time the electrical data of the wind storage system and its grid environment at the grid connection point, the status data of the energy storage system, the grid-side operation data, and the load-side response data.

[0042] The electrical data at the grid connection point includes voltage, current, active power, reactive power, and frequency signals, which are used to monitor the wind power output characteristics in real time. Among them, the frequency signal is used for subsequent grid frequency deviation detection.

[0043] Energy storage system status data includes the energy storage system's state of charge, health status, and chargeable / dischargeable power, which are used to assess the energy storage's real-time regulation capabilities.

[0044] The grid-side operation data includes dispatch instructions from the upper-level grid, power exchanged on tie lines, voltage amplitude and phase angle at key nodes, and regional grid frequency and reserve capacity information obtained from the grid dispatch system.

[0045] Load-side response data includes real-time load curves for various users, as well as user agreement information for those participating in demand-side response.

[0046] The data acquisition and situational awareness layer also obtains forecast data from the power grid dispatch system, including ultra-short-term wind power forecast data and load forecast data, and obtains real-time electricity price signals from the power grid dispatch system; it stores the real-time acquired grid connection point electrical data, energy storage system status data, power grid side operation data and load side response data into a local or cloud historical database in time series to form historical operation data;

[0047] Based on grid-side operation data and load-side response data, the data acquisition and situational awareness layer calculates the real-time regulation capacity demand of the power grid and the flexible adjustment potential of the load.

[0048] Calculating the real-time regulation capacity demand of the power grid specifically includes: extracting key characteristic quantities representing the power grid operating state based on power grid side operating data, including power grid frequency deviation, tie-line power deviation, and key node voltage deviation; and inputting the power grid frequency deviation, tie-line power deviation, and voltage deviation into a pre-constructed power grid regulation demand assessment model. In this embodiment, the power grid regulation demand assessment model adopts a fuzzy logic algorithm, and the specific steps include:

[0049] Step A1: Blurring process. This involves blurring the power grid frequency deviation. Power deviation of connecting lines and critical node voltage deviation As input variables, their respective fuzzy sets are defined as {negative large, negative medium, zero, positive medium, positive large}, and the triangular membership function is used to calculate the membership degree of each input variable to each fuzzy set;

[0050] Step A2: Fuzzy Reasoning. Construct a fuzzy rule base, with rules in the form of "IF". is Zhengda AND "The demand for increased capacity is extremely high"; when the grid frequency deviation is large and the tie line power exceeds the limit, it is judged that the grid urgently needs to increase power support; when the voltage deviation is the main problem, it is judged that the grid may need reactive power support or active power reduction.

[0051] Step A3: Defuzzification. The centroid method is used to convert the output fuzzy set obtained from fuzzy inference into precise values, outputting the real-time capacity adjustment demand of the power grid at the current operating point. and real-time reduction of capacity demand .

[0052] The flexibility and adjustability potential of the calculation load specifically includes:

[0053] Step B1: Based on load-side response data, classify the loads using a clustering algorithm. Identify different types of flexible loads, including interruptible loads, transferable loads, and price-sensitive loads. Interruptible loads can be directly disconnected in emergencies; transferable loads can be shifted during certain time periods; and price-sensitive loads are sensitive to electricity price signals.

[0054] Step B2: Construct a response characteristic model for each type of flexible load. For interruptible loads, the response characteristic model includes maximum interruption capacity, allowable interruption duration, recovery characteristics after interruption, and user compensation cost function. For transferable loads, the response characteristic model includes transferable power range, transfer time window, and user comfort constraints. For price-sensitive loads, the response characteristic model includes price elasticity coefficient, response time constant, and response capacity constraints.

[0055] Step B3: Based on the response characteristic model and combined with real-time electricity price signals obtained from the power grid dispatch system, calculate the upward and downward response potential that the load side can provide in the current time period and the preset future time period. Specific calculation formula:

[0056] Increase response potential Calculation formula:

[0057] ;

[0058] Lower response potential Calculation formula:

[0059] ;

[0060] in, , , These are collections of interruptible loads, transferable loads, and price-sensitive loads, respectively. The maximum interrupt capacity of the interruptible load; , , These are the maximum power, minimum power, and actual power of the transferable load, respectively. For power transfer limitations; The price elasticity coefficient of price-sensitive loads; For real-time electricity prices, This is the benchmark electricity price.

[0061] Step 2: Construct a three-layer collaborative optimization architecture: daily planning layer - intraday rolling layer - real-time control layer;

[0062] This step is the core of the invention, achieving tight coupling from long-term planning to short-term control through three optimization layers with different time scales.

[0063] The current planning layer uses a 24-hour optimization cycle (simulating year-round operation using a typical daily scenario method) to construct and solve the capacity planning model for the wind-storage system based on historical and forecast data.

[0064] The specific mathematical form of the capacity planning model is:

[0065] (1) Decision variables: Let the rated power of the energy storage system be... Rated capacity is It should be noted that during the capacity planning phase, these two parameters are decision variables (i.e. unknowns) to be optimized, used to determine the construction scale of the energy storage system; while in the subsequent operation and scheduling phase, their optimization results will be used as inherent parameters of the equipment.

[0066] (2) Objective Function: To address the issue of inconsistent dimensions in multi-objective optimization, a weighted summation method after normalization is used to construct the objective function. Represented as:

[0067] ;

[0068] in, For the overall system cost, For the amount of new energy consumed, , , , These are the extreme values ​​of each objective within the constraint boundary, used for normalization to eliminate the influence of dimensions; , These are the weighting coefficients.

[0069] Overall system cost The calculation model is as follows:

[0070] ;

[0071] in, For initial investment costs, Annual operating and maintenance costs, This refers to the replacement cost caused by the degradation of energy storage life (i.e., the economical representation of the life degradation model).

[0072] The specific optimization method for the lifetime degradation model is as follows: the number of energy storage cycles under typical daily operating conditions is counted using the rainflow counting method, combined with the lifetime degradation constraint throughout the entire life cycle. ,in, This represents the cumulative loss rate. Typically, a value of 1 is used to determine the replacement cost. This translates into a penalty for the initial capacity. If the energy storage charging and discharging strategy is too aggressive during optimization, leading to excessively rapid lifespan degradation, it will significantly increase the penalty. This allows for the automatic suppression of unreasonable configuration schemes through the objective function.

[0073] (3) Constraints: These include power constraints, state of charge constraints, system power balance constraints, power transmission constraints of grid interconnects, and lifetime degradation constraints of the energy storage system. After solving, the preliminary configuration scheme of the energy storage system is finally output, including rated power and rated capacity.

[0074] The intraday rolling layer performs optimizations on a rolling basis with an optimization cycle of 4-6 hours and a time resolution of 15 minutes. The intraday rolling layer receives the preliminary configuration scheme issued by the day-ahead planning layer as the optimization boundary, and at the same time receives wind power ultra-short-term forecast data, real-time grid regulation capacity demand, and load-side upward and downward adjustment response potentials provided by the data acquisition and situational awareness layer.

[0075] The intraday rolling layer constructs and solves a multi-timescale source-storage-load coordinated scheduling model. To address the issue of inconsistent dimensions among multiple objectives, the specific objective function of the source-storage-load coordinated scheduling model is constructed in a normalized form as follows:

[0076] objective function :

[0077] ;

[0078] in, For system operating costs; This is a power fluctuation index (usually the sum of squares of power deviations). To provide a flexible adjustment margin for the system; , , These are the benchmark values ​​for each indicator (benchmark operating cost, maximum allowable fluctuation limit, and total system installed capacity), used to eliminate dimensional differences and transform each indicator into a dimensionless relative value to participate in optimization. , , These are dynamic weighting coefficients used to balance the three optimization objectives. Decision variables include the charging and discharging power of the energy storage system in different time periods and the switching or transfer power of various flexible loads. Constraints include system power balance constraints, energy storage system state of charge and power constraints, flexible load response capability constraints, and grid safety operation constraints. The final solution outputs a refined set of instructions, including the rolling charging and discharging plan of the energy storage system and corresponding load adjustment instructions.

[0079] The real-time control layer operates on a second-level control cycle, receiving rolling charge / discharge plans from the daily rolling layer and directly reading the grid-connected electrical data provided by the data acquisition and situational awareness layer to obtain the actual power and grid frequency signals of the wind farm's grid connection point. The real-time control layer constructs a real-time collaborative control model between the energy storage converter and the flexible load. The energy storage converter is used to execute the charging and discharging commands of the energy storage system.

[0080] The specific implementation steps of the real-time cooperative control model include:

[0081] Step C1: Based on the rolling charge and discharge plan, extract the planned power value corresponding to the current moment as the reference power command for the energy storage system.

[0082] Step C2: Detect the power deviation between the actual power at the wind farm's grid connection point and the planned power value for the corresponding period in the rolling charge and discharge plan, as well as the grid frequency deviation.

[0083] Step C3: Input the power deviation and grid frequency deviation into the pre-built virtual synchronous machine control model. The virtual synchronous machine control model simulates the rotor motion equation and excitation regulation characteristics of a traditional synchronous generator, giving it virtual inertia and damping coefficient, and outputs the inertia support and primary frequency regulation additional power command of the energy storage system.

[0084] Step C4: The baseline power command and the additional power command are superimposed to generate the final real-time power adjustment command for the energy storage converter. This mechanism ensures that the energy storage can spontaneously respond to grid frequency changes while tracking economic dispatch, playing a grid support role similar to that of a synchronous generator.

[0085] Step C5: When the power deviation or grid frequency deviation exceeds a preset threshold, the real-time control layer triggers a load rapid response command, quickly disconnecting or connecting some interruptible loads within milliseconds. This provides emergency power support to the system and prevents the situation from worsening.

[0086] Step 3: Construct a collaborative feedback loop;

[0087] To achieve closed-loop optimization of the three-layer architecture, this invention designs a collaborative feedback loop from the lower layer to the upper layer.

[0088] Step D1: Calculate the power deviation between the actual power at the wind farm's grid connection point and the planned power value for the corresponding period in the rolling charge and discharge plan, and use it as the first feedback signal.

[0089] Step D2: Statistically analyze the actual charge / discharge amount, cycle count, and health status of the energy storage system as the second feedback signal.

[0090] Step D3: Statistically analyze the actual response rate and actual response accuracy of the load side to the load adjustment command, and use this as the third feedback signal.

[0091] Step D4: The first and third feedback signals are transmitted to the intraday rolling layer in real time. Based on the first and third feedback signals, the source-storage-load coordination scheduling model of the intraday rolling layer corrects the predicted confidence level and load response capacity estimates for subsequent scheduling cycles. The specific correction method is as follows:

[0092] Prediction confidence correction: Calculating prediction error If the value exceeds a preset threshold, the weighting coefficient of the predicted value for the next period will be reduced using the following formula: ,in As a correction factor;

[0093] Load response capacity estimate correction: Taking into account the actual response rate in the third feedback signal. Compared with actual response accuracy Establish correction coefficients The available capacity parameter in the load response potential calculation formula is revised as follows:

[0094] ;

[0095] If the actual response accuracy Lower (i.e., larger response bias), even with a lower response rate If the load is high, the corrected available capacity will also decrease accordingly, thus allowing for a more conservative assessment of load-side resources in the next round of rolling optimization, increasing the adjustment margin reserved for energy storage. If the third feedback signal indicates that the response accuracy of a certain type of load is very poor, the source-storage-load coordinated scheduling model will reduce the estimated response capability of that type of load.

[0096] Step D5: The second feedback signal is periodically transmitted to the day-ahead planning layer. Based on the second feedback signal, the wind-storage system capacity planning model at the day-ahead planning layer corrects the energy storage operation and maintenance cost model and lifetime degradation model for subsequent optimization cycles. The specific correction method is as follows:

[0097] The actual number of cycles per unit time. Compared with the theoretical number of cycles deviation Correcting the parameters of the maximum cycle count curve in the lifetime decay model. and : This will allow the life model in the next round of planning to better reflect actual working conditions.

[0098] Step 4: Iterative solution based on three-layer architecture and feedback loop;

[0099] Based on the constructed three-layer collaborative optimization architecture and collaborative feedback loop, a multi-objective optimization algorithm is used to iteratively solve the capacity planning model of the wind storage system and the source-storage-load coordinated scheduling model at multiple time scales, and output the optimal energy storage system configuration scheme and the corresponding source-grid-load-storage coordinated operation strategy.

[0100] In this embodiment, a particle swarm optimization algorithm based on population entropy is used. The specific calculation process is as follows:

[0101] First, calculate the population diversity entropy value. :

[0102] ;

[0103] in, For the first The fitness percentage of each particle in the current population This represents the total number of particles.

[0104] Secondly, the inertia weight is dynamically adjusted based on the entropy value. and learning factors , :

[0105] ;

[0106] When entropy value When the inertia weight is too small (indicating population aggregation), the inertia weight is... Increase the entropy value to enhance global search capabilities; When the inertial weight is too large (indicating population dispersion), the inertial weight... This reduces particle convergence and accelerates it. In this way, the algorithm can effectively balance global search and local exploitation capabilities to find the optimal energy storage configuration.

[0107] Through the above steps, this embodiment ultimately outputs an optimal energy storage system configuration scheme and a corresponding all-weather source-grid-load-storage coordinated operation strategy, realizing a closed-loop optimization of the entire chain from static planning to dynamic operation.

[0108] Example 2:

[0109] This embodiment is based on Embodiment 1 and provides a specific application scenario.

[0110] Suppose a wind farm has an installed capacity of 100MW and plans to configure an energy storage system. The method of this invention is used for optimized configuration.

[0111] Data Acquisition: The system first accesses the historical operating data of the wind farm over the past year, local meteorological data, and the power grid load characteristics and typical flexible load parameters of the area.

[0112] Three-layer collaborative optimization: The method of this invention is implemented. The day-ahead planning layer, after optimization calculations, initially proposes an energy storage configuration scheme of 20MW / 40MWh. The intraday rolling layer, on a typical day with strong winds and high grid peak-shaving pressure, dynamically adjusts the dispatch strategy based on ultra-short-term forecasts and grid demand. This ensures that the energy storage plan charges more before peak load to support the grid during peak discharge, while simultaneously issuing "pause charging" load adjustment commands to electric vehicle charging stations. The real-time control layer, when a sudden drop in wind speed at the minute level causes a sudden change in output, uses virtual synchronous machine control to allow the energy storage to release power instantaneously, smoothing the output curve and effectively suppressing grid frequency fluctuations.

[0113] Feedback and Iteration: After one month of operation, system statistics revealed that the actual number of energy storage cycles was higher than expected, and the health status was declining slightly faster. This second feedback signal was transmitted back to the day-ahead planning layer. During the next optimization cycle, the wind-storage system capacity planning model automatically adjusted the cost parameters, ultimately revising the configuration to 18MW / 45MWh. By appropriately reducing power and increasing capacity, battery degradation was slowed down, the overall system lifespan was extended, and the overall economic efficiency was improved.

[0114] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of a wind-storage system based on source-grid-load-storage interaction, characterized in that, include: Step 1: Construct a data acquisition and situational awareness layer to obtain historical operational data, including grid connection point electrical data, energy storage system status data, grid-side operational data, and load-side response data. Based on the grid-side operational data and load-side response data, calculate the real-time adjustment capacity demand of the power grid and the flexible adjustment potential of the load; also obtain forecast data. Step 2: Construct a three-layer collaborative optimization architecture consisting of a day-ahead planning layer, an intraday rolling layer, and a real-time control layer. The day-ahead planning layer builds a capacity planning model based on historical operating data and forecast data, and outputs a preliminary configuration plan. The intraday rolling layer receives the preliminary configuration plan, constructs a source-storage-load coordinated scheduling model based on forecast data and the real-time adjustment capacity demand of the power grid, and outputs rolling charging and discharging plans and load adjustment instructions; the real-time control layer receives the rolling charging and discharging plans and load adjustment instructions, obtains the actual power of the grid connection point based on the electrical data of the grid connection point, constructs a real-time collaborative control model, and generates real-time power adjustment instructions and load rapid response instructions. Step 3: Construct a collaborative feedback loop, calculate the deviation between the actual power at the grid connection point and the rolling charge and discharge plan, and feed the deviation and energy storage system status data back to the intraday rolling layer and the day-ahead planning layer; Step 4: Use a multi-objective optimization algorithm to iteratively solve the capacity planning model and the source-storage-load coordinated scheduling model, and output the optimal configuration scheme and the source-grid-load-storage coordinated operation strategy.

2. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 1, characterized in that: The calculation of the real-time regulation capacity demand of the power grid specifically includes: extracting operating status characteristics based on the power grid side operating data, inputting the operating status characteristics into a pre-built power grid regulation demand assessment model, and outputting the real-time upward and downward capacity demand of the power grid.

3. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 1, characterized in that: The calculation of load flexibility potential specifically includes: classifying loads based on load-side response data to identify different types of flexible loads, including interruptible loads, transferable loads, and price-sensitive loads; constructing response characteristic models for various types of flexible loads, including response capacity constraints, response time constraints, and user comfort constraints; the data acquisition and situational awareness layer also obtains real-time electricity price signals from the power grid dispatching system; and based on the response characteristic models and real-time electricity price signals, calculating the upward and downward response potential that the load side can provide in the current period and the future preset period.

4. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 1, characterized in that: Energy storage system status data includes state of charge, health status, and charging / discharging power; grid-side operation data includes tie-line transmission power.

5. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 4, characterized in that: The optimization objectives of the capacity planning model are to minimize the overall system cost and maximize the absorption of new energy sources. The decision variables include the rated power and rated capacity of the energy storage system, and the constraints include the power constraints, state of charge constraints, power balance constraints, and power transmission constraints of the grid interconnection line.

6. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 5, characterized in that: The optimization objectives of the source-storage-load coordinated scheduling model include minimizing system operating costs, minimizing grid-connected power fluctuations, and maximizing system flexibility margin. Decision variables include the charging and discharging power of the energy storage system in each time period and the switching power of various flexible loads. Constraints include system power balance constraints, energy storage system state of charge and power constraints, flexible load response capability constraints, and grid safety operation constraints.

7. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 6, characterized in that: The specific working process of the real-time collaborative control model includes: generating a reference power command based on the rolling charge and discharge plan; detecting the power deviation between the actual power at the grid connection point and the planned power value for the corresponding period in the rolling charge and discharge plan, as well as the grid frequency deviation; inputting the power deviation and grid frequency deviation into the pre-built virtual synchronous machine control model and outputting an additional power command; superimposing the reference power command and the additional power command to generate a real-time power adjustment command; and triggering a load fast response command when the power deviation or grid frequency deviation exceeds a preset threshold.

8. The wind-storage system optimization configuration method based on source-grid-load-storage interaction according to claim 7, characterized in that: The specific working process of the collaborative feedback loop includes: using power deviation as the first feedback signal; statistically analyzing the actual charging and discharging capacity, cycle count, and health status of the energy storage system as the second feedback signal; statistically analyzing the actual response rate and accuracy of the load side to load adjustment commands as the third feedback signal; transmitting the first and third feedback signals to the intraday rolling layer, and the source-storage-load coordinated scheduling model correcting the prediction confidence and load response capability estimates in subsequent scheduling cycles; the capacity planning model includes an energy storage operation and maintenance cost model and a lifetime decay model, and the second feedback signal is transmitted to the day-ahead planning layer, where the capacity planning model corrects the energy storage operation and maintenance cost model and the lifetime decay model in subsequent optimization cycles.

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