Fuzzy-logic-control-based coordination method and system for power grid requirement response and energy storage system

Abstract

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

Description

Coordinated method and system for power grid demand response and energy storage system based on fuzzy logic control TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, and particularly relates to a coordinated method and system for power grid demand response and energy storage system based on fuzzy logic control. BACKGROUND

[0002] In the current energy system, the management and dispatch of power grid are facing unprecedented challenges. With the rapid development and wide application of renewable energy such as wind and solar energy, the operation of power grid becomes more complex and unpredictable. The traditional power grid management strategy is not up to the task in dealing with these challenges, especially in maintaining the stability of the power grid and improving the efficiency of energy utilization. This is mainly because the output of renewable energy has high volatility and uncertainty, while the traditional power grid dispatching system is often designed based on stable and predictable energy output. In addition, the rapid changes in the demand side of the power grid also bring additional pressure to the management of the power grid. For example, the popularity of electric vehicles and the increasing load of industry, commerce and residents make the load fluctuation of the power grid more frequent, which poses a challenge to the stable operation of the power grid.

[0003] The existing technology mainly relies on prediction algorithms to predict the load of the power grid and the output of renewable energy, and then makes power grid dispatching and management according to the prediction results. However, these methods often ignore the fuzziness and uncertainty of the power grid data, resulting in that the power grid dispatching decision is difficult to be both efficient and flexible when facing the actual operating conditions. For example, the traditional method is difficult to accurately handle the sudden increase and decrease of renewable energy and the sharp change of power grid load, so when the actual situation does not match the prediction, the response of the power grid system is often delayed and inaccurate, which directly affects the stability of the power grid and the effective use of energy.

[0004] In addition, the existing technology also has shortcomings in the coordinated management of power grid demand response and energy storage system. Although energy storage technology is considered as an effective means to balance the supply and demand of the power grid and improve the utilization rate of renewable energy, in actual application, how to optimize the charging and discharging strategy of the energy storage system and how to effectively coordinate with the power grid demand response measures is still a technical problem. Especially when the power grid encounters emergencies or extreme weather conditions, the existing energy storage system management strategy often cannot be adjusted in time, and cannot fully play the role of the energy storage system in the power grid.

[0005] Therefore, how to provide a coordinated method for power grid demand response and energy storage system based on fuzzy logic control is a problem that those skilled in the art need to solve. SUMMARY

[0006] The purpose of the present application is to propose a power grid demand response and energy storage system coordination method and system based on fuzzy logic control. The present application can effectively handle the uncertainty and fuzziness in the operation of the power grid by using a fuzzy logic controller to process and analyze power grid data in real time, especially for the fluctuation of renewable energy production and the rapid change of power load.

[0007] Technical solution. A power grid demand response and energy storage system coordination method based on fuzzy logic control, comprising the following steps:

[0008] S1, collect real-time data and predicted data of the power grid and construct corresponding real-time data set and predicted data set of the power grid, the real-time data of the power grid including the load change rate of the subdivided area of the power grid, the instantaneous output fluctuation of the distributed energy resource, the frequency fluctuation rate of the power grid and the abnormal event warning signal, the predicted data including the demand elasticity index of the user side;

[0009] S2, process the real-time data set and predicted data set collected in step S1 by a fuzzy logic controller, convert the real-time data set and predicted data set and multi-dimensional renewable energy information into a fuzzy set by using a fuzzy algorithm, and apply fuzzy principles for in-depth analysis to comprehensively evaluate the dynamic supply-demand balance state of the current power grid and the real-time availability of renewable energy;

[0010] S3, based on the in-depth analysis result of step S2, customize the power grid demand response measures and the operation strategy of the energy storage system, the operation strategy including the real-time adjustment strategy of the load response, the optimized charging and discharging time and magnitude of the energy storage system, and the dynamic adjustment scheme for the future prediction and current state;

[0011] S4, execute the strategy formulated in step S3, and convey the demand response and energy storage strategy to each key node of the power grid in real time through communication technology, and implement specific operations, including adjusting the charging and discharging behavior of the energy storage device, dynamically adjusting the demand side load, optimizing the power distribution and consumption mode;

[0012] S5, real-time monitor the effect of the strategy and collect the power load matching degree, the response speed and efficiency of the energy storage device, and the demand response participation degree operation data, and feed the data back to the fuzzy logic controller;

[0013] S6, comprehensively utilize the feedback data to periodically update the decision model of the fuzzy logic controller, including the adjustment of the fuzzy set and the fuzzy principle.

[0014] Optionally, the S1 specifically comprises:

[0015] S11, acquire the load change rate V L,i (t) of the i-th subdivided area of the power grid at time t:

[0016] Among them, the load change rate V L,i (t) represents the change in load in this region per unit time, used to assess load fluctuations in different sub-regions of the power grid, ΔL i (t) represents the change in load in the i-th region within the time interval Δt;

[0017] S12. Collect the instantaneous output volatility F of the distributed energy resources in the i-th grid sub-region at time t. DER,i (t):

[0018] Among them, F DER,i (t) represents the rate of change of output power of distributed energy resources in the region per unit time, used to reflect the stability and prediction difficulty of distributed energy. DER,i,n (t) is the output power of the nth distributed energy source at time t. It is the average value of energy output power, and N is the total number of distributed energy sources;

[0019] S13. Monitor the frequency fluctuation rate F of the i-th power grid at time t. F,i (t):

[0020] Among them, F F,i (t) represents the rate of change of the power grid frequency, used to indicate the overall stability of the power grid operation and potential supply-demand imbalances. i (t) is the grid frequency F i (t) is the derivative of time, representing the rate of change of frequency;

[0021] S14. Receive the early warning signal S for the abnormal event of the i-th power grid at time t. EE,i (t): S EE,i (t)=α·I overload,i (t)+β·I linefault,i (t);

[0022] Among them, S EE,i (t) is used to identify abnormal events that may occur during the operation of the power grid, including overload and line disconnection, providing early warning information for the safe operation of the power grid. overload,i (t) and I linefault,i (t) represent the overload and disconnection indicators, respectively, and α and β are weighting factors used to adjust the influence of the two types of indicators in the total warning signal;

[0023] S15. Estimate the demand elasticity index E of the i-th grid user at time t. DI,i (t):

[0024] Among them, EDI,i (t) is used to describe the degree of response of the user side to changes in electricity prices or other incentives. It is the marginal rate of change of demand with respect to price, P i (t) is the electricity price, D i (t) represents the quantity demanded;

[0025] S16. Based on the data collected in steps S11 to S15, construct the real-time dataset D of the i-th power grid at time t. RT,i (t) and the prediction dataset D P,i (t): D RT,i (t)={V L,i (t),F DER,i (t),F F,i (t),S EE,i (t)}; D P,i (t)={E DI,i (t)}.

[0026] Optionally, S2 specifically includes:

[0027] S21. Real-time dataset D for each sub-region of the power grid RT,i (t) and the prediction dataset D P,i (t), fuzzification is used to transform the numerical data into a fuzzy set. The fuzzification process includes converting the load change rate V... L,i (t) Instantaneous output volatility F of distributed energy resources DER,i (t), grid frequency fluctuation rate F F,i (t), Abnormal event warning signal S EE,i (t) and the demand elasticity index E DI,i (t) is mapped to the corresponding fuzzy set through fuzzy member functions;

[0028] S22. Establish a fuzzy principle to assess the impact of load changes on energy storage demand, formally expressed as: If V L,i (t) is "high" and E DI,i If (t) is "low", then the energy storage demand D S,i (t) represents “very high”, where “high”, “low” and “very high” are items in the fuzzy set, corresponding to different membership functions;

[0029] S23. Introduce the composite fuzzy principle to assess the impact of renewable energy volatility on grid stability. If F DER,i (t) is "highly unstable" and F F,i If (t) represents "significant fluctuations", then the grid stability risk R S,i(t) is "very high", this principle considers the fluctuation of renewable energy output and the change of grid frequency, and evaluates the comprehensive risk to grid stability;

[0030] S24, design fuzzy principles to optimize demand response strategies with abnormal event warning signals, if S EE,i (t) is "warning" and E DI,i (t) is "medium", the demand response emergency level L S,i (t) is "high", the principle uses warning signals from grid monitoring systems and user demand flexibility information to provide priority guidance for demand response measures;

[0031] S25, apply fuzzy logic reasoning mechanism, combine the fuzzy principles defined in steps S22-S24 and the fuzzy sets obtained in step S21, comprehensively evaluate the dynamic supply and demand balance state of the current grid and the real-time availability of renewable energy, determine the optimal choice of grid demand response measures and energy storage system operation strategy, for a given grid area i at time t, the formula of fuzzy logic reasoning is:

[0032] Where, R i (t) represents the comprehensive reasoning result of area i at time t, w j is the weight of the jth fuzzy principle, F j is the function after applying the jth fuzzy principle, involving min, max or other fuzzy operators, depending on the definition of the fuzzy principle, are the membership degrees of load change rate, demand elasticity index and renewable energy output fluctuation at time t, respectively;

[0033] S26, use the maximum membership method to convert the fuzzy logic reasoning result into explicit operation instructions or decision suggestions for guiding the specific operation of grid demand response and energy storage system, and use the centroid method for defuzzification:

[0034] Where, D 操作,i (t) is the specific operation instruction or decision value for grid area i at time t, is the membership function of the fuzzy reasoning result of area i, the membership degree in the operation or decision domain X, x represents the specific operation or decision value, and the optimal operation value is determined by calculating the weighted centroid of the membership function.

[0035] Optionally, the S22 specifically comprises:

[0036] The membership functions of "high" load change rate and "low" demand elasticity index are defined as follows:

[0037] For the rate of load change V L,i (t), the membership function for "high" is expressed as:

[0038] where k V is a parameter controlling the slope of the curve, V0 is a threshold defining the "high" rate of load change;

[0039] For the demand elasticity index E DI,i (t), the membership function for "low" is expressed as:

[0040] where k E is a parameter controlling the slope of the curve, E0 is a threshold defining the "low" demand elasticity index;

[0041] The membership function defining the "very high" energy storage demand is derived by fuzzy principle, according to fuzzy logic reasoning, if V L,i (t) is "high" and E DI,i (t) is "low", the principle that D S,i (t) is "very high" is expressed as: μ 非常高 (D S,i (t)) = min(μ 高 (V L,i (t)), μ 低 (E DI,i (t))) ;

[0042] Using the Min operator in fuzzy logic as the implementation of "and", the membership of the energy storage demand is the minimum of V L,i (t) belonging to "high" and E DI,i (t) belonging to "low", the level of the energy storage demand D S,i (t) will be assessed as "very high" according to its membership.

[0043] Optionally, the S23 specifically comprises:

[0044] The instability of F DER,i (t) is quantified by the standard deviation of its output power , the grid frequency fluctuation rate F F,i (t) is quantified by the rate of change of frequency ΔF i (t), and the evaluation method of the grid stability risk R S,i (t) is expressed by using the quantified indicators, and the instability rating function of F DER,i (t) is defined as: μ 不稳定 (F DER,i (t)) ;

[0045] The rating function of the grid frequency fluctuation rate is: 显著波动 (F F,i (t));

[0046] The grid stability risk R S,i (t) is evaluated by the following formula:

[0047] wherein, represents the "AND" operation in fuzzy logic, which is realized by multiplication:

[0048] wherein, c σ and σ σ are the fuzzy set center and standard deviation of , c ΔF and σ ΔF are the fuzzy set center and standard deviation of ΔF i (t), which are used to adjust the shape of the fuzzy membership function;

[0049] The fuzzy logic evaluation formula of the grid stability risk is:

[0050] Optionally, the S24 specifically comprises:

[0051] The membership functions of the fuzzy sets "warning" and "medium" are defined as follows:

[0052] For the abnormal event early warning signal S EE,i (t), its membership function is represented as:

[0053] wherein, x represents the quantitative value of the early warning signal, and a and b are parameters for determining the "warning" membership degree;

[0054] For the demand elasticity index E DI,i (t), its membership function is represented as:

[0055] wherein, y represents the quantitative value of the demand elasticity index, and c, d, and e are parameters for determining the "medium" membership degree;

[0056] After the specific values of S EE,i (t) and E DI,i (t) are given, the fuzzy principle of the demand response emergency level L R,i (t) is specifically realized by fuzzy AND operation:

[0057] Optionally, the S3 specifically comprises:

[0058] S31, determine the specific content of each power grid sub-area demand response measure according to the depth analysis result of step S2, including real-time adjustment strategy of load response, adjustment strategy adjusts temporary cut-off of non-critical load and optimization scheduling of critical load of user according to current and predicted supply and demand state of power grid:

[0059] Wherein, γ and η are adjustment coefficients, respectively control the sensitivity of load adjustment and the reaction to future renewable energy prediction and load prediction, L max,i is the maximum load limit of area i, L current,i is the actual load at current time t, P RE,i (t+τ) is the predicted output of renewable energy at future time t+τ, D forecast,i (t+τ) is the load prediction at future time t+τ, T is the considered prediction time range;

[0060] S32, determine the optimal charging and discharging time and magnitude of the energy storage system, adjust the charging and discharging behavior of the energy storage system according to the output of the fuzzy logic controller, preferentially consider the changes of power grid load demand and renewable energy supply, the charging and discharging strategy of the energy storage system takes maximizing energy utilization efficiency and supporting power grid stable operation as the goal, the charging time is carried out when the predicted renewable energy supply is surplus or the power grid load is low, and the discharging time is started when the power grid load is at peak or the renewable energy supply is insufficient:

[0061] Wherein, SOC i (t) is the state-of-charge of the energy storage system at time t, C i is the capacity of the energy storage system, P charge,i and P discharge,i are the charging and discharging power at time t, E loss,i is the energy loss per unit time, E i is the energy efficiency ratio, and Δt is the time step;

[0062] S33, for the dynamic adjustment scheme of future prediction and current state, use fuzzy logic controller to comprehensively consider real-time and predicted data of power grid, including load change and renewable energy output volatility, customize adjustment strategy, for upcoming high load demand or low renewable energy output period, relieve the burden of power grid in advance through demand side management and energy storage discharging, or increase the charging operation of energy storage system when high renewable energy output and low load demand period is predicted:

[0063] Wherein, P adjust,i(t) is the total power adjustment amount under the adjustment strategy, ζ and θ are the reaction coefficients to the future prediction error and the state-of-charge change of the energy storage, ΔSOC i (t) is the predicted state-of-charge change amount of the energy storage system;

[0064] S34, formulating the implementation time point, operation steps and expected target of the demand response measure and the energy storage system operation strategy, a monitoring scheme for the implementation effect of the strategy, and an adjustment mechanism if necessary.

[0065] Optionally, the S4 specifically comprises:

[0066] S41, develop and deploy a communication network connecting key nodes of the power grid, including the energy storage facility, the load management center, the renewable energy power station and the user-side equipment, and the network ensures real-time information transmission and execution of the demand response measure and the energy storage system operation strategy;

[0067] S42, through the communication network, convey the demand response measure and the energy storage system operation strategy formulated in step S3 to the key nodes of the power grid in real time, and the conveyed information includes the demand adjustment instruction ΔL adj,i (t), the charge and discharge instruction ΔSOC adjust,i (t) of the energy storage system, and the power distribution optimization instruction P adjust,i (t);

[0068] S43, at the energy storage equipment end, adjust the charging or discharging behavior of the energy storage equipment according to the received charge and discharge instruction ΔSOC adjust,i (t): SOC i (t+1) = SOC i (t) + ΔSOC adjust,i (t);

[0069] Wherein, ΔSOC adjust,i (t) is the change of the charging or discharging amount determined based on the optimization algorithm;

[0070] S44, at the load management center, execute the demand adjustment instruction ΔL adj,i (t), dynamically adjust the power grid load, and optimize the power distribution and consumption mode by increasing or reducing the power supply of non-critical loads or adjusting the operation mode of critical loads;

[0071] S45, at the user-side equipment, automatically adjust the power consumption mode according to the instruction conveyed by the communication network, including adjusting the operation time of smart home equipment and optimizing the energy use of large industrial facilities to adapt to the demand and supply state of the power grid.

[0072] Optionally, the S5 specifically comprises:

[0073] S51, establish a comprehensive monitoring system to monitor the grid operation state, the response behavior of energy storage devices and the participation degree of demand response measures in real time, capture and analyze key performance indicators including grid load matching degree, charging and discharging speed and efficiency of energy storage devices, and demand response participation degree of users;

[0074] S52, grid load matching degree monitoring, by comparing the difference between actual grid load and predicted load, calculate the load matching degree M L,i (t):

[0075] Where, L actual,i (t) is the actual load, L predicted,i (t) is the predicted load, M L,i (t) reflects the accuracy of load forecasting and the effect of real-time regulation;

[0076] S53, energy storage device response speed and efficiency monitoring, according to the time T response,i (t) and energy efficiency ratio E efficiency,i (t) to evaluate: T response,i (t) = t end -t start ;

[0077] Where, t start is the time when the instruction is received, t end is the time when the specified SOC is reached, E output,i (t) and E input,i (t) represent the energy in the discharging and charging process respectively;

[0078] S54, demand response participation degree monitoring, by counting the number of users participating in demand response measures and the total load affected, measure the universality and influence of demand response, the participation degree P DR,i (t) is expressed as:

[0079] Where, N participating,i (t) is the number of users participating in demand response, N total,i is the total number of users in the region;

[0080] S55, feedback the collected monitoring data to the fuzzy logic controller, the data includes load matching degree, energy storage device response speed, energy storage device energy efficiency ratio and demand response participation degree.

[0081] A power grid demand response and energy storage system based on fuzzy logic control, specifically comprising:

[0082] Data collection and preprocessing module: responsible for collecting real-time data and prediction data of the power grid, including the load change rate of the power grid sub-area, the instantaneous output volatility of distributed energy resources, the power frequency fluctuation rate, abnormal event warning signals, and user-side demand elasticity index, constructing real-time data set and prediction data set of the power grid, and providing input data for the fuzzy logic controller;

[0083] Fuzzy logic controller: using fuzzy logic algorithm to process the collected data, converting it into fuzzy set, and applying fuzzy principle for in-depth analysis. Comprehensive evaluation of the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy;

[0084] Strategy formulation and execution module: based on the in-depth analysis results output by the fuzzy logic controller, customizing power grid demand response measures and operation strategies of energy storage system, conveying the strategies to key nodes of the power grid in real time, and guiding specific operation execution, adjusting the charge and discharge behavior of energy storage devices, dynamically adjusting demand-side load, optimizing power distribution and consumption mode.

[0085] Real-time monitoring and feedback module: real-time monitoring of strategy execution effect, and collecting power grid load matching degree, energy storage device response speed and efficiency, demand response participation degree operation data, feeding the monitoring data back to the fuzzy logic controller for adjusting and optimizing control strategy.

[0086] The beneficial effects of the present application are:

[0087] (1) The present application can effectively handle the uncertainty and fuzziness in the operation of the power grid by using the fuzzy logic controller to process and analyze the power grid data in real time, especially for the fluctuation of renewable energy production and the rapid change of power load. Compared with the traditional management method based on accurate prediction, it provides higher flexibility and adaptability, can respond to the changes of power grid state in real time, thereby improving the stability and efficiency of the power grid.

[0088] (2) The present application optimizes the demand response measures and operation strategies of energy storage system by introducing fuzzy logic control technology. This not only includes real-time adjustment of load response, but also optimization of charge and discharge timing and magnitude of energy storage system, as well as dynamic adjustment scheme of future prediction and current state. Through intelligent demand response and coordinated operation of energy storage system, it can effectively reduce energy waste in the power grid and improve the utilization rate of renewable energy. At the same time, it enhances the adaptability of the power grid to the fluctuation of renewable energy supply and the change of load, and improves the overall operation reliability of the power grid.

[0089] (3) The present application not only can real-time monitor the operation state of the power grid and the response effect of the energy storage device, but also can dynamically adjust the demand response measures and energy storage system strategies according to the collected data. BRIEF DESCRIPTION OF DRAWINGS

[0090] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and are intended to serve as an aid for explaining the application, and are intended to provide further understanding of the application, and are included as a part of this detailed description, to explain the application. In the drawings:

[0091] Fig. 1 is a flow chart of the method for coordinating grid demand response and energy storage system based on fuzzy logic control according to the present application. DETAILED DESCRIPTION

[0092] The application will now be described in further detail with reference to the drawings. These drawings show only the essential features of the application and are therefore to be regarded only as a schematic representation of the basic structure of the application. In the drawings:

[0093] Referring to Fig. 1, a method for coordinating grid demand response and energy storage system based on fuzzy logic control includes the following steps:

[0094] S1, collecting real-time data and predicted data of the grid and constructing corresponding real-time data set and predicted data set of the grid, the real-time data of the grid including load change rate of the subdivided area of the grid, instantaneous output fluctuation of distributed energy resources, grid frequency fluctuation rate and abnormal event warning signal, the predicted data including user-side demand elasticity index;

[0095] In this embodiment, S1 specifically includes:

[0096] S11, acquiring the load change rate V L,i (t) of the i-th subdivided area of the grid at time t:

[0097] Wherein, the load change rate V L,i (t) represents the change amount of the load of the area per unit time, which is used to evaluate the load fluctuation of each subdivided area of the grid, ΔL i (t) represents the change amount of the load of the i-th area in the time interval Δt;

[0098] S12, collecting the instantaneous output fluctuation F DER,i (t) of the distributed energy resources of the i-th subdivided area of the grid at time t:

[0099] Wherein, the instantaneous output fluctuation F DER,i (t) represents the change rate of the output power of the distributed energy resources per unit time of the area, which is used to reflect the stability and prediction difficulty of the distributed energy, P DER,i,n (t) is the output power of the n-th distributed energy at time t, is the average value of the energy output power, and N is the total number of distributed energy;

[0100] S13, monitor the frequency fluctuation rate F of the i-th power grid at time t F,i (t):

[0101] wherein the frequency fluctuation rate F F,i (t) represents the rate of change of the grid frequency, indicating the stability of the overall operation of the grid and the possible imbalance between supply and demand, dF i (t) is the grid frequency F i (t) is the derivative of F

[0102] S14, receive the abnormal event warning signal S of the i-th power grid at time t EE,i (t): S EE,i (t) = a·I overload,i (t) + β·I linefault,i (t);

[0103] wherein the abnormal event warning signal S EE,i (t) is used to identify possible abnormal events in the operation of the grid, including overload and disconnection, providing warning information for the safe operation of the grid, I overload,i (t) and I linefault,i (t) represent the indicators of overload and disconnection, respectively, and a and β are weight factors used to adjust the influence of the two types of indicators in the total warning signal.

[0104] S15, estimate the demand elasticity index E of the i-th power grid user side at time t DI,i (t):

[0105] wherein the demand elasticity index E DI,i (t) is used to describe the response of the user side to changes in electricity prices or other incentive measures, is the marginal rate of change of demand with respect to price, P i (t) is the electricity price, D i (t) is the demand;

[0106] S16, based on the data collected in steps S11 to S15, construct the real-time data set D RT,i (t) and the predicted data set D P,i (t) of the i-th power grid at time t: D RT,i (t) = {V L,i (t), F DER,i (t), F F,i (t), S EE,i (t)}; D P,i (t) = {E DI,i (t)}.

[0107] S2, processing the real-time data set and the predicted data set collected in step S1 through a fuzzy logic controller, converting the real-time data set and the predicted data set and multi-dimensional renewable energy information into fuzzy sets using a fuzzy algorithm, and performing in-depth analysis and comprehensive evaluation of the dynamic supply-demand balance state of the current power grid and the real-time availability of renewable energy according to fuzzy principles;

[0108] In this embodiment, S2 specifically includes:

[0109] S21, processing the real-time data set D RT,i (t) and the predicted data set D P,i (t) of each power grid sub-region, converting numerical data into fuzzy sets through fuzzification, and the fuzzification process includes mapping the load change rate V L,i (t), the instantaneous output fluctuation F DER,i (t) of distributed energy resources, the power frequency fluctuation rate F F,i (t), the abnormal event warning signal S EE,i (t), and the demand elasticity index E DI,i (t) to corresponding fuzzy sets through fuzzy membership functions;

[0110] S22, establishing a fuzzy principle to evaluate the impact of load change on energy storage demand, which is formally expressed as: if V L,i (t) is "high" and E DI,i (t) is "low", then the energy storage demand D S,i (t) is "very high", wherein "high", "low", and "very high" are terms in the fuzzy set, corresponding to different membership functions;

[0111] S23, introducing a compound fuzzy principle to evaluate the impact of renewable energy fluctuation on power grid stability, if F DER,i (t) is "very unstable" and F F,i (t) is "significant fluctuation", then the power grid stability risk R S,i (t) is "extremely high", and the compound fuzzy principle takes into account the fluctuation of renewable energy output and the change of power frequency, and evaluates the comprehensive risk to power grid stability;

[0112] S24, designing a fuzzy principle to optimize demand response strategies using abnormal event warning signals, if S EE,i (t) is "warning" and E DI,i (t) is "medium", then the demand response emergency level L S,i (t) is "high", and this fuzzy principle uses warning signals from the power grid monitoring system and user demand elasticity information to provide priority guidance for demand response measures;

[0113] S25, applying a fuzzy logic inference mechanism, combining the fuzzy principles defined in steps S22-S24 and the fuzzy sets obtained in step S21, to comprehensively evaluate the dynamic supply-demand balance state of the current power grid and the real-time availability of renewable energy, determine the optimal selection of power grid demand response measures and energy storage system operation strategies, in combination with the evaluation of fuzzy principles and the use of fuzzy inference matrix, the formula of fuzzy logic inference for a given power grid region i at time t is:

[0114] wherein R i (t) represents the comprehensive inference result of region i at time t, w j is the weight of the jth fuzzy principle, F j is the function after applying the jth fuzzy principle, involving min, max or other fuzzy operators, depending on the definition of the fuzzy principle, are the membership degrees of load change rate, demand elasticity index and renewable energy output volatility at time t, respectively;

[0115] S26, using the maximum membership method process, converting the fuzzy logic inference result into explicit operation instructions or decision suggestions for guiding the specific operation of power grid demand response and energy storage system, and using the centroid method for defuzzification:

[0116] wherein D 操作,i (t) is the specific operation instruction or decision value for power grid region i at time t, is the membership function of the fuzzy inference result of region i, the membership degree in the operation or decision domain X, x represents the specific operation or decision value, and the optimal operation value is determined by calculating the weighted centroid of the membership function.

[0117] In this embodiment, S22 specifically includes:

[0118] The membership functions of "high" load change rate and "low" demand elasticity index are defined as follows:

[0119] For the load change rate V L,i (t), the membership function of "high" is represented as:

[0120] wherein k V is a parameter controlling the slope of the curve, and V0 is the threshold value defining the "high" load change rate;

[0121] For the demand elasticity index E DI,i (t), the membership function of "low" is represented as:

[0122] wherein k Eis a parameter controlling the slope of the curve, E0is a threshold value defining the "low" demand elasticity index;

[0123] a membership function defining "very high" energy storage demand, derived by fuzzy principles, according to fuzzy logic reasoning, if V L,i (t) is "high" and E DI,i (t) is "low", then the energy storage demand D S,i (t) is "very high" is expressed by the principle: μ 非常高 (D S,i (t)) = min(μ 高 (V L,i (t)), μ 低 (E DI,i (t)));

[0124] using the Min operator in fuzzy logic as an implementation of "and", the membership of the energy storage demand is the minimum of V L,i (t) belonging to "high" and E DI,i (t) belonging to "low", the level of the energy storage demand D S,i (t) will be rated as "very high" according to its membership.

[0125] In this embodiment, S23 specifically comprises:

[0126] The instability of F DER,i (t) is quantified by the standard deviation of its output power , the grid frequency fluctuation rate F F,i (t) is quantified by the rate of change of frequency ΔF i (t), and the evaluation method of the grid stability risk R S,i (t) is expressed using the quantification indicators, and the rating function of F DER,i (t) is defined as: μ 不稳定 (F DER,i (t));

[0127] The rating function of the grid frequency fluctuation rate is: μ 显著波动 (F F,i (t));

[0128] The evaluation of the grid stability risk R S,i (t) is expressed by the following formula:

[0129] wherein, represents the "AND" operation in fuzzy logic, which is realized by multiplication:

[0130] wherein, c σ and σσ respectively the fuzzy set center and standard deviation of ΔF and ΔF is the fuzzy set center and standard deviation of i (t) for adjusting the shape of the fuzzy membership function;

[0131] The fuzzy logic evaluation formula of the grid stability risk is expressed as:

[0132] In the embodiment, S24 specifically comprises:

[0133] The membership functions of the fuzzy sets "warning" and "medium" are defined as follows: and "medium"

[0134] For the abnormal event early warning signal S EE,i (t), the membership function thereof is expressed as:

[0135] wherein x represents the quantified value of the early warning signal, and a and b are parameters for determining the "warning" membership degree;

[0136] For the demand elasticity index E DI,i (t), the membership function thereof is expressed as:

[0137] wherein y represents the quantified value of the demand elasticity index, and c, d, and e are parameters for determining the "medium" membership degree;

[0138] After specific values of S EE,i (t) and E DI,i (t) are given, the fuzzy principle of the demand response emergency level L R,i (t) is specifically expressed through fuzzy AND operation as:

[0139] S3, based on the deep analysis result of step S2, customizes the grid demand response measures and the operation strategy of the energy storage system, the operation strategy including the real-time adjustment strategy of the load response, the optimized charging and discharging time and magnitude of the energy storage system, and the dynamic adjustment scheme for the future prediction and the current state;

[0140] In the embodiment, S3 specifically comprises:

[0141] S31, according to the deep analysis result of step S2, determines the specific content of the demand response measures of each grid subdivision area, including the real-time adjustment strategy of the load response, the adjustment strategy dynamically adjusting the temporary cut-off of the non-critical load and the optimized scheduling of the critical load of the user according to the current and predicted supply and demand state of the grid: ​

[0142] where, ΔL adj,i (t) is the demand adjustment instruction, representing the load adjustment amount of the i-th grid subdivision area at time t; γ and η are adjustment coefficients, respectively controlling the sensitivity of load adjustment and the reaction to future renewable energy prediction and load prediction, L max,i is the maximum load limit of area i, L current,i is the actual load at the current time t, P RE,i (t+τ) is the renewable energy predicted output at future time t+τ, D forecast,i (t+τ) is the load prediction at future time t+τ, T is the prediction time range considered;

[0143] S32, determine the optimal charging and discharging time and magnitude of the energy storage system, adjust the charging and discharging behavior of the energy storage system according to the output of the fuzzy logic controller, give priority to the changes of grid load demand and renewable energy supply, and the charging and discharging strategy of the energy storage system takes maximizing energy utilization efficiency and supporting grid stable operation as the goal, the charging time is carried out when the predicted renewable energy supply is surplus or the grid load is low, and the discharging time is started when the grid load is at peak or the renewable energy supply is insufficient:

[0144] where, SOC i (t) is the state-of-charge of the energy storage system at time t, representing the state-of-charge (SOC) of the energy storage system of the i-th grid subdivision area at time t, i.e., representing the power state of the current energy storage system; SOC i (t+1) is the state-of-charge (SOC) of the energy storage system of the i-th grid subdivision area at time t+1, representing the power state of the energy storage system after the next time step;

[0145] C i is the capacity of the energy storage system, P charge,i and P discharge,i are the charging and discharging power at time t, E loss,i is the energy loss per unit time, E i is the energy efficiency ratio, and Δt is the time step;

[0146] S33, for the dynamic adjustment scheme of future prediction and current state, the fuzzy logic controller is used to comprehensively consider the real-time and predicted data of the grid, including load changes and renewable energy output volatility, and a regulation strategy is customized, for the upcoming high load demand or low renewable energy output period, the grid burden is relieved in advance through demand side management and energy storage discharging, or when high renewable energy output and low load demand period is predicted, the charging operation of the energy storage system is increased:

[0147] where P adjust,i (t) is the total power adjustment under the adjustment strategy, ζ and θ are the reaction coefficients to the future prediction error and the state-of-charge change of the energy storage, respectively, and ΔSOC i (t) is the predicted state-of-charge change of the energy storage system;

[0148] S34, formulating the implementation time point, operation steps and expected target of the demand response measures and the energy storage system operation strategy, the monitoring scheme of the strategy implementation effect, and the adjustment mechanism if necessary.

[0149] S4, execute the strategy formulated in step S3, and convey the demand response and the energy storage strategy to each key node of the power grid in real time through communication technology, and implement specific operations, including adjusting the charging and discharging behavior of the energy storage device, dynamically adjusting the demand side load, optimizing the power distribution and consumption mode;

[0150] In this embodiment, S4 specifically includes:

[0151] S41, develop and deploy a communication network, which connects each key node of the power grid, including the energy storage facility, the load management center, the renewable energy power station and the user side device, and ensures the real-time information transmission and execution of the demand response measures and the energy storage system operation strategy;

[0152] S42, through the communication network, convey the demand response measures and the energy storage system operation strategy formulated in step S3 to each key node of the power grid in real time, and the transmitted information includes the demand adjustment instruction ΔL adj,i (t), the charging and discharging instruction SOC adjust,i (t) of the energy storage system and the power distribution optimization instruction P adjust,i (t);

[0153] S43, at the energy storage device end, adjust the charging or discharging behavior of the energy storage device according to the received charging and discharging instruction ΔSOC adjust,i (t): SOC i (t+1) = SOC i (t) + ΔSOC adjust,i (t);

[0154] where ΔSOC adjust,i (t) is the charging or discharging amount change determined based on the optimization algorithm;

[0155] S44, at the load management center, execute the demand adjustment instruction ΔL adj,i (t), dynamically adjust the power grid load, and optimize the power distribution and consumption mode by increasing or reducing the power supply of non-critical loads, or adjusting the operation mode of critical loads;

[0156] S45, at the user-side device, automatically adjust the power consumption mode according to the instructions delivered by the communication network, including adjusting the operation time of smart home devices, optimizing the energy use of large industrial facilities, to adapt to the demand and supply state of the power grid.

[0157] S5, real-time monitoring of the effect of policy implementation and collecting data on grid load matching degree, response speed and efficiency of energy storage devices, and participation degree of demand response, and feeding back the data to the fuzzy logic controller;

[0158] In this embodiment, S5 specifically includes:

[0159] S51, establishing a comprehensive monitoring system to real-time monitor the operation state of the power grid, the response behavior of the energy storage devices, and the participation degree of the demand response measures, capture and analyze key performance indicators, including the grid load matching degree, the charging and discharging speed and efficiency of the energy storage devices, and the participation degree of the demand response at the user side;

[0160] S52, grid load matching degree monitoring, by comparing the difference between the actual grid load and the predicted load, calculating the load matching degree M L,i (t):

[0161] Wherein, L actual,i (t) is the actual load, L predicted,i (t) is the predicted load, M L,i (t) reflects the accuracy of load prediction and the effect of real-time regulation;

[0162] S53, energy storage device response speed and efficiency monitoring, according to the time T response,i (t) and energy efficiency ratio E efficiency,i (t) from receiving charging and discharging instructions to reaching the specified state-of-charge (SOC) of the energy storage device: T response,i (t) = t end -t start ;

[0163] Wherein, t start is the time when the instruction is received, t end is the time when the specified SOC is reached, E output,i (t) and E input,i (t) represent the energy amount in the discharging and charging process respectively;

[0164] S54, demand response participation degree monitoring, by counting the number of users participating in demand response measures and the total load affected, measuring the universality and influence of demand response, and the participation degree P DR,i (t) is expressed as:

[0165] wherein, N participating,i (t) is the number of users participating in demand response, N total,i is the total number of users in the region;

[0166] S55, feedback the collected monitoring data to the fuzzy logic controller, the data including load matching degree, energy storage device response speed, energy storage device energy efficiency ratio and demand response participation degree.

[0167] S6, comprehensively utilize the feedback data to periodically update the decision model of the fuzzy logic controller, including adjustment of fuzzy set and fuzzy principle.

[0168] A coordination system of power grid demand response and energy storage system based on fuzzy logic control, comprising:

[0169] A data collection and preprocessing module: responsible for collecting real-time data and predicted data of the power grid, including load change rate of power grid sub-regions, instantaneous output fluctuation of distributed energy resources, power grid frequency fluctuation rate, abnormal event warning signals and user-side demand elasticity index, constructing real-time data set and predicted data set of the power grid, and providing input data for the fuzzy logic controller;

[0170] A fuzzy logic controller: using fuzzy logic algorithm to process the collected data, converting it into fuzzy set, and applying fuzzy principle for in-depth analysis. Comprehensive evaluation of the dynamic supply-demand balance state of the current power grid and real-time availability of renewable energy;

[0171] A strategy formulation and execution module: based on the in-depth analysis results output by the fuzzy logic controller, customizing power grid demand response measures and operation strategies of the energy storage system, conveying the strategies to each key node of the power grid in real time, and guiding specific operation execution, adjusting charging and discharging behavior of energy storage devices, dynamically adjusting demand-side load, optimizing power distribution and consumption mode.

[0172] A real-time monitoring and feedback module: real-time monitoring of strategy execution effect, and collecting power grid load matching degree, energy storage device response speed and efficiency, demand response participation degree operation data, feeding the monitoring data to the fuzzy logic controller for adjusting and optimizing control strategies.

[0173] Embodiment 1:

[0174] In this embodiment, a sunny medium-sized city is described, and in the summer peak period, how to apply the coordination method of power grid demand response and energy storage system based on fuzzy logic control to solve the problem of power grid demand peak and unstable renewable energy supply.

[0175] Summer is a season of high electricity demand, especially when heatwaves hit, air conditioner usage surges, leading to a sharp rise in grid load. At the same time, although the city has a large number of solar power generation facilities, the output of renewable energy is greatly affected by weather conditions, especially cloud cover or other weather changes can cause solar power generation to fluctuate sharply in a short time. This mismatch between grid demand and supply poses a huge challenge to the stable operation of the grid.

[0176] In this scenario, the grid operator has deployed a grid management system based on fuzzy logic control. The system first collects real-time data and forecast data of the grid, including solar power generation, grid load, electricity price information and weather conditions, etc. Then, the system analyzes these data through the fuzzy logic controller, considering the current demand of the grid, future energy supply situation and load forecast, and formulates targeted demand response measures and energy storage system operation strategies.

[0177] For example, when it is predicted that solar power generation will sharply decrease due to cloud cover in the afternoon period, while the grid load is expected to rise due to increased air conditioner usage due to high temperature weather, the fuzzy logic controller immediately initiates demand response measures, encouraging users to reduce unnecessary power consumption by adjusting price signals, while instructing the energy storage system to charge in advance and discharge when solar power generation decreases to support the grid load.

[0178] In a certain week of August 2023, a medium-sized city experienced consecutive high-temperature weather. Through the application of the invention, the grid operator successfully balanced the supply and demand of the grid, with the following specific results:

[0179] During the peak load period caused by high-temperature weather, the grid demand response measures successfully reduced 15.34% of the unnecessary load through dynamic price adjustment. Through the automatic adjustment of smart home devices and industrial facilities, the power consumption of residents and commercial users was effectively managed.

[0180] The energy storage system was fully charged before the peak and discharged power during the peak demand, helping to balance the grid load. The response speed and efficiency of the energy storage device were significantly improved, with the discharge response time shortened from 5 minutes to 2 minutes, and the energy storage efficiency increased by 20%.

[0181] The invention can effectively handle the uncertainty and fuzziness in grid operation by using a fuzzy logic controller to process and analyze grid data in real time, especially for the fluctuation of renewable energy production and rapid changes in power load. Compared with traditional management methods based on accurate prediction, it provides higher flexibility and adaptability, and can respond to changes in grid state in real time, thereby improving the stability and efficiency of the grid.

[0182] The application optimizes the operation strategy of the power grid demand response measure and the energy storage system by introducing fuzzy logic control technology. This not only includes real-time adjustment of load response, but also optimization of charging and discharging time and magnitude of the energy storage system, as well as dynamic adjustment scheme of future prediction and current state. Through intelligent demand response and coordinated operation of the energy storage system, energy waste in the power grid can be effectively reduced, and the utilization rate of renewable energy can be improved. At the same time, the adaptability of the power grid to renewable energy supply fluctuation and load change is enhanced, and the overall operation reliability of the power grid is improved.

[0183] The application not only can monitor the operation state of the power grid and the response effect of the energy storage device in real time, but also can dynamically adjust the demand response measure and the energy storage system strategy according to the collected data.

[0184] The above is only the preferred specific embodiment of the application, but the protection scope of the application is not limited to this. Any person skilled in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the application within the technical range disclosed by the application, which should be covered in the protection scope of the application.

Claims

1. A method for coordinating grid demand response and energy storage system based on fuzzy logic control, characterized in that, The method comprises the following steps: S1, constructing a real-time power grid data set and a prediction data set based on real-time data and prediction data of the power grid; S2, converting the real-time data set, the prediction data set, and multi-dimensional renewable energy information into fuzzy sets using a fuzzy algorithm, and performing in-depth analysis and comprehensive evaluation of the dynamic supply-demand balance state of the current power grid and the real-time availability of renewable energy by applying fuzzy principles; S3, based on the results of the in-depth analysis in step S2, customizing power grid demand response measures and energy storage system operation strategies; S4, executing the operation strategies customized in step S3, and conveying the demand response measures and energy storage strategies to each key node of the power grid in real time, and implementing specific operations, including adjusting the charging and discharging behavior of energy storage devices, dynamically adjusting demand-side loads, optimizing power distribution and consumption patterns; S5, monitoring the execution effect of the operation strategy in real time, and collecting power grid load matching degree, energy storage device response speed and efficiency, and demand response participation degree operation data for feedback; S6, periodically updating the fuzzy sets and adjusting the fuzzy principles by comprehensively utilizing the feedback data.

2. The method of coordinating demand response and energy storage system based on fuzzy logic control of power grid according to claim 1, characterized in that, The S1 specifically comprises: S11, collect the load change rate V of the i-th power grid subarea at time t L,i (t): where ΔL i (t) represents the change in the load of the i-th region in the time interval Δt; S12, collecting the instantaneous output variability F of the distributed energy resources of the i-th grid subdivision at time t DER,i (t): where P DER,i,n (t) is the output power of the nth distributed energy source at time t, is the average value of energy output power, and N is the total number of distributed energy sources; S13, monitor the frequency fluctuation rate F of the i-th power grid at time t F,i (t): where dF i (t) is the grid frequency F i (t) is the derivative with respect to time, indicating the rate of change of frequency; S14, receiving an abnormal event warning signal S of the i-th power grid at time t EE,i (t): S EE,i (t) = a - I overload,i (t) + β - I linefault,i (t); where I overload,i (t) and I linefault,i (t) are indices of overload and broken wire, respectively, and a and β are weight factors; S15, estimating the demand elasticity index E of the i-th grid consumer side at time t DI,i (t): wherein, is the marginal rate of change of demand with respect to price, P i (t) is the price of electricity, D i (t) is the quantity of demand; S16. Based on the data collected in steps Sll to S15, construct a real-time dataset D for the ith power grid at time t RT,i (t) and the predicted dataset D P,i (t): D RT,i (t) = {V L,i (t), F DER,i (t), F F,i (t), S EE,i (t)}; D P,i (t) = {E DI,i (t)}.

3. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 2, characterized in that, The S2 specifically comprises: S21, real-time data set D for each grid sub-area RT,i (t) and predicted data set D P,i (t), the numerical data is converted into a fuzzy set by fuzzification, the fuzzification process including converting the load change rate V L,i (t), the instantaneous output fluctuation F of the distributed energy resource DER,i (t), the grid frequency fluctuation rate F F,i (t), the abnormal event early warning signal S EE,i (t) and the demand elasticity index E DI,i (t) is mapped into the corresponding fuzzy set by a fuzzy membership function; S22, establish fuzzy principle to evaluate the impact of load change on energy storage demand, formalized as: if V L,i (t) is "high" and E DI,i (t) is "low", then the energy storage demand D S,i (t) is "very high", wherein "high", "low" and "very high" are terms in fuzzy sets, corresponding to different membership functions; S23, the composite fuzzy principle is introduced to evaluate the impact of renewable energy volatility on grid stability; if F DER,i (t) is "very unstable" and F F,i (t) is "significant fluctuations", the grid stability risk R S,i (t) is "extremely high"; S24, design fuzzy principle to optimize demand response strategy with abnormal event early warning signal; if S EE,i (t) is "warning" and E DI,i (t) is "medium", the demand response emergency level L S,i (t) is "high"; S25, applying a fuzzy logic reasoning mechanism, combining the fuzzy principles defined in steps S22-S24 and the fuzzy sets obtained in step S21, performing a comprehensive evaluation of the dynamic supply-demand balance state of the current power grid and the real-time availability of renewable energy, combining the evaluation of the fuzzy principles and using the fuzzy reasoning matrix to determine the optimal selection of power grid demand response measures and the operation strategy of the energy storage system; for a given power grid area i at time t, the formula of fuzzy logic reasoning is represented as: wherein R i (t) represents the integrated inference result of region i at time t, w j is the weight of the jth fuzzy principle, F j is the function after applying the jth fuzzy principle, are the membership degrees of load change rate, demand elasticity index, and renewable energy output fluctuation at time t, respectively; S26, using the maximum member method process, the fuzzy logic inference result is converted into clear operation instruction or decision suggestion, used to guide the specific operation of power grid demand response and energy storage system, centroid method is used for defuzzification: wherein D 操作,i (t) is a specific operation instruction or decision value for grid area i at time t, is the membership function of the fuzzy reasoning result of region i, which is the membership degree on the operation or decision domain X, and x represents a specific operation or decision value. The optimal operation value is determined by calculating the weighted centroid of the membership function.

4. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 3, characterized in that, The S22 specifically comprises: The membership functions of "high" load change rate and "low" demand elasticity index are defined as follows: For the load change rate V L,i The membership function for "high" is expressed as: where k V is a parameter that controls the slope of the curve, and V0is a threshold value that defines the "high" rate of load change. For the demand elasticity index E DI,i The membership function for "low" is represented as: where k E is a parameter that controls the slope of the curve, E0is a threshold value that defines the "low" demand elasticity index; The membership function defining the "very high" energy storage demand is derived by fuzzy principles and, according to fuzzy logic reasoning, if V L,i (t) is "high" and E DI,i (t) is "low", then the energy storage demand D S,i (t) is "very high" is expressed by the principle: μ 非常高 (D S,i (t)) = min(μ 高 (V L,i (t)), μ 低 (E DI,i (t))) The membership of the energy storage demand is V using the Min operator in fuzzy logic as an implementation of "and" L,i (t) membership in "high" and E DI,i (t) minimum value of the membership in "low", level D of the energy storage demand S,i (t) will be rated as "very high" according to its membership.

5. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 3, characterized in that, The S23 specifically comprises: F DER,i instability of (t) by standard deviation of its output power to quantify, the grid frequency fluctuation rate F F,i (t) to quantify, the grid stability risk R i (t) is expressed using a quantification indicator S,i (t) to quantify, the grid stability risk R DER,i (t) is expressed using a quantification indicator μ 不稳定 (F DER,i (t)) The rating function of power grid frequency fluctuation rate is: μ 显著波动 (F F,i (t)) Grid stability risk R S,i The evaluation of (t) is represented by the following formula: wherein, represents the "AND" operation in fuzzy logic, implemented by means of the product: wherein c σ and σ σ are, respectively the fuzzy set center and standard deviation of ΔF ΔF and σ ΔF is the fuzzy set center and standard deviation of ΔF i (t). The fuzzy logic assessment formula for power grid stability risk is expressed as:

6. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 3, wherein, The S24 specifically comprises: Defining the fuzzy set "warning" and "moderate" The membership function of is as follows: For the abnormal event warning signal S EE,i (t), whose membership function is expressed as: where x represents the quantitative value of the warning signal, and a and b are parameters for determining the "warning" membership degree; For the demand elasticity index E DI,i (t), whose membership function is expressed as: where y represents the quantitative value of the demand elasticity index, and c, d, and e are parameters for determining the "medium" membership degree; Given S EE,i (t) and E DI,i (t) after the specific values of S R,i (t) are determined, the demand response emergency level L R,i (t) is specified by a fuzzy AND operation as:

7. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 3, characterized in that, The S3 specifically comprises: S31, according to the depth analysis result of step S2, determine the specific content of each power grid subdivision area demand response measure, including the real-time adjustment strategy of load response, the adjustment strategy adjusts the temporary cut-off of non-critical load and the optimization scheduling of critical load of users according to the current and predicted supply and demand state of the power grid: where ΔL adj,i (t) is the demand adjustment command, γ and η are adjustment coefficients that control the sensitivity of the load adjustment and the reaction to future renewable energy forecasts and load forecasts, respectively, L max,i is the maximum load limit of the region i, L current,i is the actual load at the current time t, P RE,i (t+τ) is the renewable energy forecast output at the future time t+τ, D forecast,i (t+τ) is the load forecast at the future time t+τ, T is the considered prediction time range; S32, determine the optimal charging and discharging time and magnitude of the energy storage system, adjust the charging and discharging behavior of the energy storage system according to the deep analysis result, give priority to the change of power grid load demand and renewable energy supply, and the charging and discharging strategy of the energy storage system takes maximizing energy utilization efficiency and supporting power grid stable operation as the goal, the charging time is when the predicted renewable energy supply is surplus or the power grid load is low, and the discharging time is when the power grid load is high or the renewable energy supply is insufficient: where SOC i (t) is the state-of-charge of the energy storage system at time t, C i is the capacity of the energy storage system, P charge,i and P discharge,i are the charging and discharging power at time t, respectively, E loss,i is the energy loss per unit time, E i is the energy efficiency ratio, and Δt is the time step. S33, for the future prediction and current state of dynamic adjustment scheme, using deep analysis results to consider the real-time and predicted data of power grid, including load change, renewable energy output volatility, customizing adjustment strategy, for the upcoming high load demand or low renewable energy output period, reducing the burden of power grid in advance through demand side management and energy storage discharge, or increasing the charging operation of energy storage system when predicting high renewable energy output and low load demand period: where P adjust,i (t) is the total power adjustment under the adjustment strategy, ζ and θ are the reaction coefficients to the future prediction error and the state-of-charge change of the energy storage, respectively, ΔSOC i (t) is the predicted state-of-charge change of the energy storage system; S34, determining the implementation time point, operation steps, and expected target of the demand response measures and energy storage system operation strategies, monitoring the implementation effect of the strategies, and adjusting the strategies if necessary.

8. The method of coordinating demand response and energy storage system based on fuzzy logic control of the power grid according to claim 7, characterized in that, The S4 specifically comprises: S41, developing and deploying a communication network that connects each key node of the power grid, including energy storage facilities, load management centers, renewable energy power stations, and user-side devices, to ensure real-time information transmission and execution of demand response measures and energy storage system operation strategies; S42, through the communication network, conveying the demand response measures and the operation strategy of the energy storage system formulated in step S3 to each key node of the power grid in real time, the information delivered including demand adjustment instruction AL adj,i (t), charge and discharge instruction SOC of the energy storage system adjust,i (t) and power distribution optimization instruction P adjust,i (t); S43、At the energy storage device end, according to the received charge and discharge instruction ΔSOC adjust,i (t), adjust the charging or discharging behavior of the energy storage device: SOC i (t+1) = SOC i (t) + ΔSOC adjust,i (t); where ΔSOC adjust,i (t) is a charge or discharge amount change determined based on an optimization algorithm; S44, at the load management center, execute demand adjustment instructions ΔL adj,i (t) dynamically adjust grid load by increasing or decreasing supply to non-critical loads, or adjusting the operating mode of critical loads to optimize power distribution and consumption patterns; S45, at the user-side devices, automatically adjusting power consumption patterns according to the instructions transmitted by the communication network, including adjusting the operation time of smart home devices and optimizing the energy use of large industrial facilities to adapt to the demand and supply state of the power grid.

9. The method of coordinating demand response and energy storage system based on fuzzy logic control of an electrical grid according to claim 8, wherein, The S5 specifically comprises: S51, monitoring the power grid operation state, the response behavior of energy storage devices, and the participation degree of demand response measures in real time, capturing and analyzing key performance indicators, including power grid load matching degree, charging and discharging speed and efficiency of energy storage devices, and demand response participation degree of user-side devices; S52, power grid load matching degree monitoring, by comparing the difference between the actual power grid load and the predicted load, calculating the load matching degree M L,i (t): wherein, L actual,i (t) is the actual load, L predicted,i (t) is the predicted load, M L,i (t) reflects the accuracy of load prediction and the effect of real-time regulation. S53, response speed and efficiency monitoring of the energy storage device, according to the time T from receiving the charge-discharge instruction to reaching the specified state-of-charge (SOC) of the energy storage device response,i (t) and energy efficiency ratio E efficiency,i (t) are evaluated: T response,i (t) = t end -t start ; where t start is the instruction reception time, t end is the time to reach the specified SOC, E output,i (t) and E input,i (t) represent the energy during discharging and charging, respectively. S54, demand response participation monitoring, by counting the number of users participating in demand response measures and the total load affected, measure the comprehensiveness and influence of demand response, participation P DR,i (t) is represented as: where N participating,i (t) is the number of users participating in demand response, N total,i is the total number of users in the area; S55, feedback the collected monitoring data, including load matching degree, energy storage device response speed, energy storage device energy efficiency ratio and demand response participation degree.

10. A coordination system for power grid demand response and energy storage system based on fuzzy logic control for the coordination method of any one of claims 1-9, characterized in that, Comprise: a data collection and preprocessing module: responsible for collecting real-time grid data and prediction data, including load change rate of grid subdivision area, instantaneous output volatility of distributed energy resources, grid frequency fluctuation rate, abnormal event warning signal and user-side demand elasticity index, constructing grid real-time data set and prediction data set, providing input data for fuzzy logic controller; a fuzzy logic controller: using fuzzy logic algorithm to process the grid real-time data set and prediction data set, converting them into fuzzy sets, and applying fuzzy principles for in-depth analysis; comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy; a strategy formulation and execution module: based on the in-depth analysis results output by the fuzzy logic controller, customize the grid demand response measures and the operation strategy of the energy storage system, real-time communicate the operation strategy to each key node of the grid, and guide the specific operation execution, adjust the charging and discharging behavior of the energy storage device, dynamically adjust the demand side load, optimize the power distribution and consumption mode; a real-time monitoring and feedback module: real-time monitor the strategy execution effect, and collect the grid load matching degree, energy storage device response speed and efficiency, demand response participation degree operation data, feed back the monitoring data to the fuzzy logic controller for adjusting and optimizing the control strategy.

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