Power grid demand response and energy storage system coordination method and system based on fuzzy logic control

Abstract

The invention discloses a power grid demand response and energy storage system coordination method and system based on fuzzy logic control. The method comprises the steps that S1, power grid real-time data and prediction data are collected, and a corresponding power grid real-time data set and a corresponding prediction data set are constructed; s2, converting the real-time data set, the prediction data set and the multi-dimensional renewable energy information of the power grid into a fuzzy set by using a fuzzy algorithm; s3, customizing power grid demand response measures and an operation strategy of the energy storage system; s4, executing the strategy formulated in the step S3; s5, the strategy execution effect is monitored in real time, and operation data of the power grid load matching degree, the energy storage equipment response speed and efficiency and the demand response participation degree are collected; and S6, periodically updating the decision model of the fuzzy logic controller. According to the invention, the fuzzy logic controller is adopted to process and analyze the power grid data in real time, so that the uncertainty and fuzziness in the operation of the power grid, especially the capacity fluctuation of renewable energy and the rapid change of power load, can be effectively processed.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a coordination method and system for power grid demand response and energy storage system based on fuzzy logic control. Background Art

[0002] In the current energy system, the management and dispatching of power grids face unprecedented challenges. With the rapid development and widespread application of renewable energy, such as wind and solar energy, the operation of power grids has become more complex and unpredictable. Traditional power grid management strategies are unable to cope with these challenges, especially in maintaining power grid stability and improving energy efficiency. This is mainly because the output of renewable energy is highly volatile and uncertain, while traditional power grid dispatching systems are often designed based on stable and predictable energy output. In addition, the rapid changes in the demand side of the power grid have also brought additional pressure to power grid management. For example, the popularity of electric vehicles and the increasing industrial, commercial, and residential loads have made power grid load fluctuations more frequent, posing a challenge to the stable operation of the power grid.

[0003] Existing technologies mainly rely on prediction algorithms to predict grid load and renewable energy output, and then dispatch and manage the grid based on the prediction results. However, these methods often ignore the ambiguity and uncertainty of grid data, making it difficult for grid dispatch decisions to be both efficient and flexible when facing actual operating conditions. For example, traditional methods find it difficult to accurately handle the sudden increase and decrease of renewable energy and the sharp changes in grid load. Therefore, when the actual situation does not match the prediction, the response of the grid system is often delayed and inaccurate, which directly affects the stability of the grid and the effective use of energy.

[0004] In addition, existing technologies also have deficiencies in grid demand response and coordinated management of energy storage systems. Although energy storage technology is seen as an effective means to balance grid supply and demand and improve the utilization rate of renewable energy, in actual applications, how to optimize the charging and discharging strategy of the energy storage system and how to effectively coordinate with grid demand response measures remain technical challenges. In particular, when the grid encounters emergencies or extreme weather conditions, the existing energy storage system management strategy often cannot be adjusted in a timely manner, and the role of the energy storage system in the grid cannot be fully utilized.

[0005] Therefore, how to provide a coordination method between grid demand response and energy storage system based on fuzzy logic control is an urgent problem to be solved by those skilled in the art. Summary of the invention

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

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

[0008] S1. Collect real-time data and forecast data of the power grid and construct corresponding real-time data sets and forecast data sets of the power grid. The real-time data of the power grid includes the load change rate of the subdivided area of ​​the power grid, the instant output volatility of distributed energy resources, the frequency volatility of the power grid and the warning signal of abnormal events. The forecast data includes the user-side demand elasticity index;

[0009] S2, processing the real-time data set and the prediction data set of the power grid collected in step S1 through a fuzzy logic controller, converting the real-time data set and the prediction data set of the power grid and the multi-dimensional renewable energy information into a fuzzy set using a fuzzy algorithm, and applying fuzzy principles to perform in-depth analysis to comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy;

[0010] S3. Based on the in-depth analysis results of step S2, customize the grid demand response measures and the operation strategy of the energy storage system, wherein the operation strategy includes the real-time adjustment strategy of the load response, the optimized charging and discharging timing and magnitude of the energy storage system, and the dynamic adjustment scheme for future predictions and current status;

[0011] S4, executing the strategy formulated in step S3, transmitting the demand response and energy storage strategy to key nodes of the power grid in real time through communication technology, and implementing specific operations, including adjusting the charging and discharging behavior of energy storage equipment, dynamically adjusting the demand side load, and optimizing power distribution and consumption patterns;

[0012] S5, real-time monitoring of the strategy execution effect and collection of grid load matching, energy storage equipment response speed and efficiency, demand response participation operation data, and feedback of the data to the fuzzy logic controller;

[0013] S6. Comprehensively utilize feedback data to periodically update the decision model of the fuzzy logic controller, including the adjustment of fuzzy sets and fuzzy principles.

[0014] Optionally, the S1 specifically includes:

[0015] S11, collect the load change rate V of the i-th grid segment at time t L,i (t):

[0016]

[0017] Among them, the load change rate V L,i (t) represents the load change in the area per unit time, which is used to evaluate the load fluctuation of each subdivision area of ​​the power grid. i (t) represents the change in load of the ith area within the time interval Δt;

[0018] S12, collect the instantaneous output volatility F of distributed energy resources in the i-th grid segment at time t DER,i (t):

[0019]

[0020] Among them, F DER,i (t) represents the rate of change of the output power of distributed energy resources in the region per unit time, which is 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, is the average value of energy output power, N is the total number of distributed energy resources;

[0021] S13, monitoring the frequency fluctuation rate F of the i-th power grid at time t F,i (t):

[0022]

[0023] Among them, F F,i (t) is the rate of change of the grid frequency, which is used to indicate the stability of the overall operation of the grid and possible imbalance between supply and demand. i (t) is the grid frequency F i (t) The derivative with respect to time, which represents the rate of change of frequency;

[0024] S14, receiving the abnormal event warning signal S of the i-th power grid at time t EE,i (t):

[0025] S EE,i (t) = α·I overload,i (t)+β·I linefault,i (t);

[0026] Among them, S EE,i (t) It is used to identify abnormal events that may occur during the operation of the power grid, including overload and disconnection, and provide early warning information for the safe operation of the power grid. overload,i (t) and I linefault,i (t) represents the overload and disconnection indicators respectively, α and β are weight factors used to adjust the influence of the two indicators in the overall warning signal;

[0027] S15. Estimate the demand elasticity index E of the i-th grid user at time t DI,i (t):

[0028]

[0029] Among them, E DI,i (t) is used to describe the degree of user response to changes in electricity prices or other incentives. is the marginal rate of change of demand in response to price, P i (t) is the electricity price, D i (t) is the quantity demanded;

[0030] S16. Based on the data collected in steps S11 to S15, construct a real-time data set D of the i-th power grid at time t. RT,i (t) and prediction dataset D P,i (t):

[0031] D RT,i (t) = {V L,u (t),F DER,i (t),F F,i (t),S EE,i (t)};

[0032] D P,i (t) = {E DI,i (t)}.

[0033] Optionally, the S2 specifically includes:

[0034] S21, real-time data set D for each power grid segment area RT,i (t) and prediction dataset D P,i (t), the numerical data is converted into fuzzy sets by fuzzification processing. The fuzzification process includes converting the load change rate V L,i (t), instant output volatility of distributed energy resources F DER,i (t), grid frequency fluctuation rate F F,i (t), abnormal event warning signal S EE,i (t) and demand elasticity index E DI,i (t) Mapping to the corresponding fuzzy set through fuzzy membership function;

[0035] S22. Establish fuzzy principles to evaluate the impact of load changes on energy storage demand, 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”, where “high”, “low” and “very high” are terms in the fuzzy set, corresponding to different membership functions;

[0036] S23. Introduce the composite fuzzy principle 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 “significantly fluctuating”, then the grid stability risk R S,i (t) is “very high”, which takes into account the volatility of renewable energy output and changes in grid frequency, and assesses the comprehensive risk posed to grid stability;

[0037] S24. Design fuzzy principles 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”, which uses early warning signals from the grid monitoring system and user demand elasticity information to provide priority guidance for demand response measures;

[0038] S25. Apply the fuzzy logic reasoning mechanism, combine the fuzzy principles defined in steps S22-S24 and the fuzzy set obtained in step S21, and comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy. Combine the evaluation of the fuzzy principles and use the fuzzy reasoning matrix to determine the optimal choice of power grid demand response measures and energy storage system operation strategy. For a given power grid area i at time t, the fuzzy logic reasoning formula is expressed as:

[0039]

[0040] Among them, R i (t) represents the comprehensive reasoning 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, They are load change rate, demand elasticity index and membership degree of renewable energy output volatility at time t;

[0041] S26. Use the maximum member method process to convert the fuzzy logic reasoning results into clear operation instructions or decision suggestions to guide the specific operation of the grid demand response and energy storage system, and use the centroid method for defuzzification:

[0042]

[0043] Among them, D 操作,i (t) is the specific operation instruction or decision value for the power grid area i at time t, It is the membership function of the fuzzy reasoning result of region i, and its membership degree on the operation or decision domain X. x represents the specific operation or decision value. The optimal operation value is determined by calculating the weighted centroid of the membership function.

[0044] Optionally, the S22 specifically includes:

[0045] The membership function that defines the load change rate "high" and the demand elasticity index "low" is as follows:

[0046] For the load change rate V L,i (t), the membership function of “high” is expressed as:

[0047]

[0048] Among them, k V is the parameter that controls the slope of the curve, and V0 is the threshold that defines the “high” rate of load change;

[0049] For the demand elasticity index E DI,i (t), the membership function of “low” is expressed as:

[0050]

[0051] Among them, k E is the parameter that controls the slope of the curve, and E0 is the threshold that defines a “low” demand elasticity index;

[0052] Define the membership function of "very high" energy storage demand, and derive it through fuzzy principle. 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 The principle that (t) is "very high" is expressed as:

[0053] μ 非常高 (D S,i (t))=min(μ 高 (V L,i (t)),μ 低 (E DI,i (t)));

[0054] Using the Min operator in fuzzy logic as the implementation of “and”, the membership of energy storage demand is V L,i (t) belongs to "high" and E DI,i (t) Minimum value belonging to “low”, level D of energy storage demand S,i (t) will be rated as “very high” based on its membership.

[0055] Optionally, the S23 specifically includes:

[0056] F DER,i The instability of (t) is determined by the standard deviation of its output power To quantify, the grid frequency fluctuation rate F F,i (t) The rate of change of the frequency ΔF i (t) to quantify and use quantitative indicators to express the grid stability risk R S,i (t) evaluation method, define F DER,i The instability rating function of (t) is:

[0057] μ 不稳定 (F DER,i (t));

[0058] The rating function of grid frequency fluctuation rate is:

[0059] μ 显著波动 (F F,i (t));

[0060] Grid stability risk R S,i The evaluation of (t) is expressed by the following formula:

[0061]

[0062] in, Represents the "AND" operation in fuzzy logic, which is implemented by multiplication:

[0063]

[0064]

[0065] Among them, c σ and σ σ They are The fuzzy set center and standard deviation, c ΔF and σ ΔF is ΔF i The fuzzy set center and standard deviation of (t) are used to adjust the shape of the fuzzy membership function;

[0066] The fuzzy logic evaluation formula for power grid stability risk is expressed as:

[0067]

[0068] Optionally, the S24 specifically includes:

[0069] Defining fuzzy sets "warning" and "Medium" The membership function of is as follows:

[0070] For abnormal event warning signal SEE,i (t), its membership function is expressed as:

[0071]

[0072] Among them, x represents the quantitative value of the warning signal, and a and b are parameters that determine the "warning" membership;

[0073] For the demand elasticity index E DI,i (t), its membership function is expressed as:

[0074]

[0075] Among them, y represents the quantitative value of the demand elasticity index, c, d, and e are parameters that determine the "medium" membership;

[0076] Given S EE,i (t) and E DI,i (t) after the specific value, the demand response emergency level L R,i The fuzzy principle of (t) is embodied through the fuzzy AND operation as follows:

[0077]

[0078] Optionally, the S3 specifically includes:

[0079] S31. According to the in-depth analysis results of step S2, the specific content of the demand response measures for each grid segment area is determined, including the real-time adjustment strategy of load response. The adjustment strategy dynamically adjusts the temporary disconnection of non-critical loads of users and the optimal scheduling of critical loads based on the current and predicted supply and demand status of the grid:

[0080]

[0081] Among them, γ and η are adjustment coefficients, which control the sensitivity of load adjustment and the response to future renewable energy forecast and load forecast respectively. max,i is the maximum load limit of zone i, L current,i is the actual load at the current time t, P RE,i (t+τ) is the predicted output of renewable energy at the future time t+τ, D forecast,i (t+τ) is the load forecast at the future time t+τ, and T is the forecast time range considered;

[0082] S32. Determine the optimal charging and discharging timing and magnitude of the energy storage system. According to the output of the fuzzy logic controller, adjust the charging and discharging behavior of the energy storage system, give priority to the changes in grid load demand and renewable energy supply. The charging and discharging strategy of the energy storage system aims to maximize energy efficiency and support stable grid operation. Charging is carried out when it is predicted that there is an oversupply of renewable energy or a low grid load, and discharging is started when the grid load is at a peak or the renewable energy supply is insufficient:

[0083]

[0084] Among them, 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 powers at time t, E loss,i is the energy loss per unit time, E i is the energy efficiency ratio, Δt is the time step;

[0085] S33. Dynamic regulation scheme for future predictions and current status. Fuzzy logic controller is used to comprehensively consider the real-time and predicted data of the power grid, including load changes and volatility of renewable energy output, to customize the regulation strategy. For the upcoming high-load demand or low renewable energy output period, demand-side management and energy storage discharge are used to reduce the burden on the power grid in advance, or charging operations of the energy storage system are increased when high renewable energy output and low load demand are predicted:

[0086]

[0087] Among them, 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 energy storage state-of-charge change, ΔSOC i (t) is the expected state-of-charge change of the energy storage system;

[0088] S34. Formulate the implementation time points, operation steps and expected goals of demand response measures and energy storage system operation strategies, a monitoring plan for the effectiveness of strategy implementation, and an adjustment mechanism when necessary.

[0089] Optionally, the S4 specifically includes:

[0090] S41. Develop and deploy a communications network that connects key nodes of the power grid, including energy storage facilities, load management centers, renewable energy power plants, and user-side equipment. The network ensures real-time information transmission and execution of demand response measures and energy storage system operation strategies;

[0091] S42: The demand response measures and energy storage system operation strategies formulated in step S3 are transmitted to key nodes of the power grid in real time through the communication network. The transmitted information includes the demand adjustment instruction ΔL adj,i (t), charge and discharge command SOC of energy storage system adjust,i (t) and power distribution optimization instruction P adjust,i (t);

[0092] 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:

[0093] SOC i (t+1) = SOC i (t)+ΔSOC adjust,i (t);

[0094] Among them, ΔSOC adjust,i (t) is the change in charge or discharge amount determined based on the optimization algorithm;

[0095] S44. In the load management center, execute the demand adjustment instruction ΔL adj,i (t), dynamically adjust the grid load to optimize power distribution and consumption patterns by increasing or decreasing the power supply of non-critical loads or adjusting the operating mode of critical loads;

[0096] S45. On the user side, the equipment automatically adjusts the electricity consumption pattern according to the instructions transmitted by the communication network, including adjusting the operating time of smart home devices and optimizing the energy use of large industrial facilities to adapt to the demand and supply status of the power grid.

[0097] Optionally, the S5 specifically includes:

[0098] S51. Establish a comprehensive monitoring system to monitor the grid operation status, the response behavior of energy storage equipment and the degree of participation in demand response measures in real time, and capture and analyze key performance indicators, including grid load matching, charging and discharging speed and efficiency of energy storage equipment, and user-side demand response participation;

[0099] S52, grid load matching monitoring, by comparing the difference between the actual grid load and the predicted load, calculate the load matching degree M L,i (t):

[0100]

[0101] Among them, L actual,i (t) is the actual load, L predicted,i (t) is the predicted load, M L,i(t) reflects the load forecasting accuracy and the effect of real-time regulation;

[0102] S53, monitoring the response speed and efficiency of the energy storage device, based on the time T from when the energy storage device receives the charge and discharge instruction to when it reaches the specified state-of-charge (SOC) response,i (t) and energy efficiency ratio E efficiency,i (t) Carry out assessment:

[0103] T response,i (t) = t end -t start ;

[0104]

[0105] Among them, t start is the time when the command is received, t end is the moment when the specified SOC is reached, E output,i (t) and E input,i (t) represents the energy during discharge and charge, respectively;

[0106] S54, Demand response participation monitoring, by counting the number of users participating in demand response measures and the total load affected, to measure the breadth and influence of demand response, participation P DR,i (t) is expressed as:

[0107]

[0108] Among them, 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;

[0109] S55, feeding back 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.

[0110] A power grid demand response and energy storage system based on fuzzy logic control, specifically comprising:

[0111] Data collection and preprocessing module: responsible for collecting real-time and predicted data of the power grid, including the load change rate of the power grid segment area, the instant output volatility of distributed energy resources, the power grid frequency volatility, abnormal event warning signals and the user-side demand elasticity index, constructing the real-time data set and predicted data set of the power grid, and providing input data for the fuzzy logic controller;

[0112] Fuzzy logic controller: Use fuzzy logic algorithms to process the collected data, convert it into fuzzy sets, and apply fuzzy principles for in-depth analysis. Comprehensively evaluate the dynamic supply and demand balance of the current power grid and the real-time availability of renewable energy;

[0113] Strategy formulation and execution module: Based on the in-depth analysis results output by the fuzzy logic controller, the module customizes grid demand response measures and energy storage system operation strategies, transmits the strategies to key nodes of the grid in real time, and guides specific operation execution, regulates the charging and discharging behavior of energy storage equipment, dynamically adjusts the demand side load, and optimizes power distribution and consumption patterns.

[0114] Real-time monitoring and feedback module: monitors the execution effect of the strategy in real time, collects the operation data of grid load matching, energy storage equipment response speed and efficiency, and demand response participation, and feeds the monitoring data back to the fuzzy logic controller for adjusting and optimizing the control strategy.

[0115] The beneficial effects of the present invention are:

[0116] (1) The present invention can effectively handle the uncertainty and ambiguity in grid operation, especially the fluctuation of renewable energy production capacity and the rapid change of power load, by using fuzzy logic controller to process and analyze grid data in real time. Compared with the traditional management method based on precise prediction, it provides higher flexibility and adaptability, and can respond to the change of grid status in real time, thereby improving the stability and efficiency of the grid.

[0117] (2) The present invention optimizes the grid demand response measures and the operation strategy of the energy storage system by introducing fuzzy logic control technology. This includes not only real-time adjustment of load response, but also optimization of the charging and discharging timing and magnitude of the energy storage system, as well as dynamic adjustment schemes for future predictions and current status. Through intelligent demand response and coordinated operation of the energy storage system, energy waste in the grid can be effectively reduced and the utilization rate of renewable energy can be improved. At the same time, the grid's ability to adapt to fluctuations in renewable energy supply and load changes is enhanced, and the overall operational reliability of the grid is improved.

[0118] (3) The present invention can not only monitor the operation status of the power grid and the response effect of the energy storage equipment in real time, but also dynamically adjust the demand response measures and energy storage system strategies according to the collected data. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0120] Figure 1 This is a flow chart of the coordination method of power grid demand response and energy storage system based on fuzzy logic control proposed by the present invention. DETAILED DESCRIPTION

[0121] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0122] refer to Figure 1 , a coordination method of power grid demand response and energy storage system based on fuzzy logic control, comprising the following steps:

[0123] S1. Collect real-time data and forecast data of the power grid and construct corresponding real-time data sets and forecast data sets of the power grid. The real-time data of the power grid includes the load change rate of the power grid subdivision area, the instant output volatility of distributed energy resources, the power grid frequency volatility and abnormal event warning signals. The forecast data includes the user-side demand elasticity index;

[0124] In this implementation, S1 specifically includes:

[0125] S11, collect the load change rate V of the i-th grid segment at time t L,i (t):

[0126]

[0127] Among them, the load change rate V L,i (t) represents the load change in the area per unit time, which is used to evaluate the load fluctuation of each subdivision area of ​​the power grid. i (t) represents the change in load of the ith area within the time interval Δt;

[0128] S12, collect the instantaneous output volatility F of distributed energy resources in the i-th grid segment at time t DER,i (t):

[0129]

[0130] Among them, the instant output volatility F DER,i (t) represents the rate of change of the output power of distributed energy resources in the region per unit time, which is 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, is the average value of energy output power, N is the total number of distributed energy resources;

[0131] S13, monitoring the frequency fluctuation rate F of the i-th power grid at time t F,i (t):

[0132]

[0133] Among them, the frequency fluctuation rate F F,i (t) represents the rate of change of the grid frequency, which is used to indicate the stability of the overall operation of the grid and possible imbalance between supply and demand. i (t) is the grid frequency F i (t) The derivative with respect to time, which represents the rate of change of frequency;

[0134] S14, receiving the abnormal event warning signal S of the i-th power grid at time t EE,i (t):

[0135] S EE,i (t) = α·I overload,i (t)+β·I linefault,i (t);

[0136] Among them, the abnormal event warning signal S EE,i (t) It is used to identify abnormal events that may occur during the operation of the power grid, including overload and disconnection, and provide early warning information for the safe operation of the power grid. overload,i (t) and I linefault,i (t) represents the overload and disconnection indicators respectively, α and β are weight factors used to adjust the influence of the two indicators in the overall warning signal;

[0137] S15. Estimate the demand elasticity index E of the i-th grid user at time t DI,i (t):

[0138]

[0139] Among them, the demand elasticity index E DI,i (t) is used to describe the degree of user response to changes in electricity prices or other incentives. is the marginal rate of change of demand in response to price, P i (t) is the electricity price, D i (t) is the quantity demanded;

[0140] S16. Based on the data collected in steps S11 to S15, construct a real-time data set D of the i-th power grid at time t. RT,i (t) and prediction dataset D P,i (t):

[0141] D RT,i (t) = {V L,i (t),F DER,i (t),F F,i (t),S EE,i (t)};

[0142] D P,i (t) = {E DI,i (t)}.

[0143] S2, processing the real-time data set and the prediction data set of the power grid collected in step S1 through a fuzzy logic controller, converting the real-time data set and the prediction data set of the power grid and the multi-dimensional renewable energy information into a fuzzy set using a fuzzy algorithm, and applying fuzzy principles to perform in-depth analysis to comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy;

[0144] In this implementation, S2 specifically includes:

[0145] S21, real-time data set D for each power grid segment area RT,i (t) and prediction dataset D P,i (t), the numerical data is converted into fuzzy sets by fuzzification processing. The fuzzification process includes converting the load change rate VL, i (t), instant output volatility of distributed energy resources F DER,i (t), grid frequency fluctuation rate F F,i (t), abnormal event warning signal S EE,i (t) and demand elasticity index E DI,i (t) Mapping to the corresponding fuzzy set through fuzzy membership function;

[0146] S22. Establish fuzzy principles to evaluate the impact of load changes on energy storage demand, 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”, where “high”, “low” and “very high” are terms in the fuzzy set, corresponding to different membership functions;

[0147] S23. Introduce the composite fuzzy principle 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 “significantly fluctuating”, then the grid stability risk R S,i (t) is “very high”, the composite fuzzy principle takes into account the volatility of renewable energy output and the change of grid frequency to assess the comprehensive risk posed to grid stability;

[0148] S24. Design fuzzy principles 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”, the fuzzy principle uses the early warning signals from the power grid monitoring system and the user demand elasticity information to provide priority guidance for demand response measures;

[0149] S25. Apply the fuzzy logic reasoning mechanism, combine the fuzzy principles defined in steps S22-S24 and the fuzzy set obtained in step S21, and comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy. Combine the evaluation of the fuzzy principles and use the fuzzy reasoning matrix to determine the optimal choice of power grid demand response measures and energy storage system operation strategy. For a given power grid area i at time t, the fuzzy logic reasoning formula is expressed as:

[0150]

[0151] Among them, R i (t) represents the comprehensive reasoning 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, They are load change rate, demand elasticity index and membership degree of renewable energy output volatility at time t;

[0152] S26. Use the maximum member method process to convert the fuzzy logic reasoning results into clear operation instructions or decision suggestions to guide the specific operation of the grid demand response and energy storage system, and use the centroid method for defuzzification:

[0153]

[0154] Among them, D 操作,i (t) is the specific operation instruction or decision value for the power grid area i at time t, It is the membership function of the fuzzy reasoning result of region i, and its membership degree on the operation or decision domain X. x represents the specific operation or decision value. The optimal operation value is determined by calculating the weighted centroid of the membership function.

[0155] In this implementation, S22 specifically includes:

[0156] The membership function that defines the load change rate "high" and the demand elasticity index "low" is as follows:

[0157] For the load change rate V L,i (t), the membership function of “high” is expressed as:

[0158]

[0159] Among them, k V is the parameter that controls the slope of the curve, and V0 is the threshold that defines the “high” rate of load change;

[0160] For the demand elasticity index E DI,i (t), the membership function of “low” is expressed as:

[0161]

[0162] Among them, k E is the parameter that controls the slope of the curve, and E0 is the threshold that defines a “low” demand elasticity index;

[0163] Define the membership function of "very high" energy storage demand, and derive it through fuzzy principle. According to fuzzy logic reasoning, if V L,i (t) is "high" and E DI,u (t) is "low", then the energy storage demand D S,i The principle that (t) is "very high" is expressed as:

[0164] μ 非常高 (D S,i (t))=min(μ 高 (V L,i (t)),μ 低 (E DI,i (t)));

[0165] Using the Min operator in fuzzy logic as the implementation of “and”, the membership of energy storage demand is V L,i (t) belongs to "high" and E DI,i (t) Minimum value belonging to “low”, level D of energy storage demand S,i (t) will be rated as “very high” based on its membership.

[0166] In this implementation, S23 specifically includes:

[0167] F DER,i The instability of (t) is determined by the standard deviation of its output power To quantify, the grid frequency fluctuation rate F F,i (t) The rate of change of the frequency ΔF i (t) to quantify and use quantitative indicators to express the grid stability risk R S,i (t) evaluation method, define F DER,i The instability rating function of (t) is:

[0168] μ 不稳定 (F DER,i (t));

[0169] The rating function of grid frequency fluctuation rate is:

[0170] μ 显著波动 (F F,i (t));

[0171] Grid stability risk R S,i The evaluation of (t) is expressed by the following formula:

[0172]

[0173] in, Represents the "AND" operation in fuzzy logic, which is implemented by multiplication:

[0174]

[0175]

[0176] Among them, c σ and σ σ They are The fuzzy set center and standard deviation, c ΔF and σ ΔF is ΔF i The fuzzy set center and standard deviation of (t) are used to adjust the shape of the fuzzy membership function;

[0177] The fuzzy logic evaluation formula for power grid stability risk is expressed as:

[0178]

[0179] In this implementation, S24 specifically includes:

[0180] Defining fuzzy sets "warning" and "Medium" The membership function of is as follows:

[0181] For abnormal event warning signal S EE,i (t), its membership function is expressed as:

[0182]

[0183] Among them, x represents the quantitative value of the warning signal, and a and b are parameters that determine the "warning" membership;

[0184] For the demand elasticity index E DI,i (t), its membership function is expressed as:

[0185]

[0186] Among them, y represents the quantitative value of the demand elasticity index, c, d, and e are parameters that determine the "medium" membership;

[0187] Given S EE,i (t) and E DI,i (t) after the specific value, the demand response emergency level LR,i The fuzzy principle of (t) is embodied through the fuzzy AND operation as follows:

[0188]

[0189] S3. Based on the in-depth analysis results of step S2, customize the grid demand response measures and the operation strategy of the energy storage system. The operation strategy includes the real-time adjustment strategy of load response, the optimized charging and discharging timing and magnitude of the energy storage system, and the dynamic adjustment plan for future predictions and current status;

[0190] In this implementation, S3 specifically includes:

[0191] S31. According to the in-depth analysis results of step S2, the specific content of the demand response measures for each grid segment area is determined, including the real-time adjustment strategy of load response. The adjustment strategy dynamically adjusts the temporary disconnection of non-critical loads of users and the optimal scheduling of critical loads based on the current and predicted supply and demand status of the grid:

[0192]

[0193] Where, ΔL adj,i (t) is the demand adjustment instruction, which indicates the load adjustment amount for the ith grid segment at time t; γ and η are adjustment coefficients, which control the sensitivity of load adjustment and the response to future renewable energy forecasts and load forecasts, respectively. max,i is the maximum load limit of zone i, L current,i is the actual load at the current time t, P RE,i (t+τ) is the predicted output of renewable energy at the future time t+τ, D forecast,i (t+τ) is the load forecast at the future time t+τ, and T is the forecast time range considered;

[0194] S32. Determine the optimal charging and discharging timing and magnitude of the energy storage system. According to the output of the fuzzy logic controller, adjust the charging and discharging behavior of the energy storage system, give priority to the changes in grid load demand and renewable energy supply. The charging and discharging strategy of the energy storage system aims to maximize energy efficiency and support stable grid operation. Charging is carried out when it is predicted that there is an oversupply of renewable energy or a low grid load, and discharging is started when the grid load is at a peak or the renewable energy supply is insufficient:

[0195]

[0196] Among them, SOC i (t) is the state-of-charge of the energy storage system at time t, indicating the state-of-charge (SOC) of the energy storage system in the i-th grid segment at time t, that is, the current state of charge of the energy storage system; SOCi (t+1) is the state-of-charge (SOC) of the energy storage system in the i-th grid segment at time t+1, indicating the charge state of the energy storage system after the next time step;

[0197] C i is the capacity of the energy storage system, P charge,i and P discharge,i are the charging and discharging powers at time t, E loss,i is the energy loss per unit time, E i is the energy efficiency ratio, Δt is the time step;

[0198] S33. Dynamic regulation scheme for future predictions and current status. Fuzzy logic controller is used to comprehensively consider the real-time and predicted data of the power grid, including load changes and volatility of renewable energy output, to customize the regulation strategy. For the upcoming high-load demand or low renewable energy output period, demand-side management and energy storage discharge are used to reduce the burden on the power grid in advance, or charging operations of the energy storage system are increased when high renewable energy output and low load demand are predicted:

[0199]

[0200] Among them, 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 energy storage state-of-charge change, ΔSOC i (t) is the expected state-of-charge change of the energy storage system;

[0201] S34. Formulate the implementation time points, operation steps and expected goals of demand response measures and energy storage system operation strategies, a monitoring plan for the effectiveness of strategy implementation, and an adjustment mechanism when necessary.

[0202] S4, executing the strategy formulated in step S3, transmitting the demand response and energy storage strategy to key nodes of the power grid in real time through communication technology, and implementing specific operations, including adjusting the charging and discharging behavior of energy storage equipment, dynamically adjusting the demand side load, and optimizing power distribution and consumption patterns;

[0203] In this implementation, S4 specifically includes:

[0204] S41. Develop and deploy a communications network that connects key nodes of the power grid, including energy storage facilities, load management centers, renewable energy power plants, and user-side equipment. The network ensures real-time information transmission and execution of demand response measures and energy storage system operation strategies;

[0205] S42: The demand response measures and energy storage system operation strategies formulated in step S3 are transmitted to key nodes of the power grid in real time through the communication network. The transmitted information includes the demand adjustment instruction ΔL adj,i (t), charge and discharge command SOC of energy storage system adjust,i (t) and power distribution optimization instruction P adjust,i (t);

[0206] 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:

[0207] SOC i (t+1) = SOC i (t)+ΔSOC adjust,i (t);

[0208] Among them, ΔSOC adjust,i (t) is the change in charge or discharge amount determined based on the optimization algorithm;

[0209] S44. In the load management center, execute the demand adjustment instruction ΔL adj,i (t), dynamically adjust the grid load to optimize power distribution and consumption patterns by increasing or decreasing the power supply of non-critical loads or adjusting the operating mode of critical loads;

[0210] S45. On the user side, the equipment automatically adjusts the electricity consumption pattern according to the instructions transmitted by the communication network, including adjusting the operating time of smart home devices and optimizing the energy use of large industrial facilities to adapt to the demand and supply status of the power grid.

[0211] S5, real-time monitoring of the strategy execution effect and collection of grid load matching, energy storage equipment response speed and efficiency, demand response participation operation data, and feedback of the data to the fuzzy logic controller;

[0212] In this implementation, S5 specifically includes:

[0213] S51. Establish a comprehensive monitoring system to monitor the grid operation status, the response behavior of energy storage equipment and the degree of participation in demand response measures in real time, and capture and analyze key performance indicators, including grid load matching, charging and discharging speed and efficiency of energy storage equipment, and user-side demand response participation;

[0214] S52, grid load matching monitoring, by comparing the difference between the actual grid load and the predicted load, calculate the load matching degree M L,i (t):

[0215]

[0216] Among them, L actual,i (t) is the actual load, L predicted,i (t) is the predicted load, M L,i (t) reflects the load forecasting accuracy and the effect of real-time regulation;

[0217] S53, monitoring the response speed and efficiency of the energy storage device, based on the time T from when the energy storage device receives the charge and discharge instruction to when it reaches the specified state-of-charge (SOC) response,i (t) and energy efficiency ratio E efficiency,i (t) Carry out assessment:

[0218] T response,i (i) = t end -t start ;

[0219]

[0220] Among them, t start is the time when the command is received, t end is the moment when the specified SOC is reached, E output,i (t) and E input,i (t) represents the amount of energy during discharge and charge, respectively;

[0221] S54, Demand response participation monitoring, by counting the number of users participating in demand response measures and the total load affected, to measure the breadth and influence of demand response, participation P DR,i (t) is expressed as:

[0222]

[0223] Among them, 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;

[0224] S55, feeding back 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.

[0225] S6. Comprehensively utilize feedback data to periodically update the decision model of the fuzzy logic controller, including the adjustment of fuzzy sets and fuzzy principles.

[0226] A coordination system of power grid demand response and energy storage system based on fuzzy logic control, comprising:

[0227] Data collection and preprocessing module: responsible for collecting real-time and predicted data of the power grid, including the load change rate of the power grid segment area, the instant output volatility of distributed energy resources, the power grid frequency volatility, abnormal event warning signals and the user-side demand elasticity index, constructing the real-time data set and predicted data set of the power grid, and providing input data for the fuzzy logic controller;

[0228] Fuzzy logic controller: Use fuzzy logic algorithms to process the collected data, convert it into fuzzy sets, and apply fuzzy principles for in-depth analysis. Comprehensively evaluate the dynamic supply and demand balance of the current power grid and the real-time availability of renewable energy;

[0229] Strategy formulation and execution module: Based on the in-depth analysis results output by the fuzzy logic controller, the module customizes grid demand response measures and energy storage system operation strategies, transmits the strategies to key nodes of the grid in real time, and guides specific operation execution, regulates the charging and discharging behavior of energy storage equipment, dynamically adjusts the demand side load, and optimizes power distribution and consumption patterns.

[0230] Real-time monitoring and feedback module: monitors the execution effect of the strategy in real time, collects the operation data of grid load matching, energy storage equipment response speed and efficiency, and demand response participation, and feeds the monitoring data back to the fuzzy logic controller for adjusting and optimizing the control strategy.

[0231] Embodiment 1:

[0232] In this embodiment, a medium-sized city with abundant sunshine is described, in which a coordination method of grid demand response based on fuzzy logic control and energy storage system is applied to solve the problems of peak grid demand and unstable renewable energy supply during the peak period in summer.

[0233] Summer is the season of peak electricity demand, especially when heat waves hit, the use of air conditioners surges, causing a sharp increase 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 clouds or other weather changes, which may cause solar power generation to fluctuate sharply in a short period of time. This mismatch between grid demand and supply poses a huge challenge to the stable operation of the grid.

[0234] In this scenario, the grid operator deployed a grid management system based on fuzzy logic control. The system first collected real-time and forecast data of the grid, including solar power generation, grid load, electricity price information, and weather conditions. Then, the system analyzed this data through the fuzzy logic controller, comprehensively considered the current demand of the grid, future energy supply and load forecast, and formulated targeted demand response measures and energy storage system operation strategies.

[0235] For example, when it is predicted that solar power generation will drop sharply in the afternoon due to cloud cover, while the grid load is expected to rise due to increased air conditioning usage caused by hot weather, the fuzzy logic controller immediately initiates demand response measures, encouraging users to reduce non-essential electricity consumption by adjusting price signals, while instructing the energy storage system to charge in advance and discharge when solar power generation drops to support the grid load.

[0236] In a certain week in August 2023, a medium-sized city experienced continuous high temperature weather. By applying the present invention, the grid operator successfully balanced the supply and demand of the grid, and the specific results are as follows:

[0237] During peak load periods caused by hot weather, grid demand response measures successfully reduced non-essential loads by 15.34% through dynamic electricity price adjustments. Electricity consumption by residential and commercial users was effectively managed through automatic adjustments of smart home devices and industrial facilities.

[0238] The energy storage system is fully charged before the peak and releases electricity when demand peaks, helping to balance the load on the grid. The response speed and efficiency of the energy storage equipment have been significantly improved, with the discharge response time shortened from 5 minutes to within 2 minutes, and the energy storage efficiency increased by 20%.

[0239] The present invention uses a fuzzy logic controller to process and analyze power grid data in real time, which can effectively handle the uncertainty and ambiguity in power grid operation, especially for the fluctuation of renewable energy production capacity and the rapid change of power load. Compared with the traditional management method based on precise prediction, it provides higher flexibility and adaptability, and can respond to changes in power grid status in real time, thereby improving the stability and efficiency of the power grid.

[0240] The present invention optimizes the grid demand response measures and the operation strategy of the energy storage system by introducing fuzzy logic control technology. This includes not only real-time adjustment of load response, but also optimization of the charging and discharging timing and magnitude of the energy storage system, as well as dynamic adjustment schemes for future predictions and current states. Through intelligent demand response and coordinated operation of the energy storage system, energy waste in the grid can be effectively reduced and the utilization rate of renewable energy can be improved. At the same time, the grid's ability to adapt to fluctuations in renewable energy supply and load changes is enhanced, and the overall operational reliability of the grid is improved.

[0241] The present invention can not only monitor the operation status of the power grid and the response effect of the energy storage equipment in real time, but also dynamically adjust the demand response measures and energy storage system strategies according to the collected data.

[0242] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A coordination method of power grid demand response and energy storage system based on fuzzy logic control, characterized in that: The following steps are involved: S1. Constructing a real-time data set and a prediction data set of a power grid based on the real-time data and the prediction data of the power grid; S2. Use fuzzy algorithms to transform the real-time data set of the power grid, the forecast data set and the multi-dimensional renewable energy information into fuzzy sets, and apply fuzzy principles to conduct in-depth analysis to comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy; S3. Customizing grid demand response measures and energy storage system operation strategies based on the in-depth analysis results of step S2; S4: Execute the operation strategy customized in step S3, transmit the demand response measures and energy storage strategies to key nodes of the power grid in real time, and implement specific operations, including adjusting the charging and discharging behavior of energy storage equipment, dynamically adjusting the demand side load, and optimizing power distribution and consumption patterns; S5. Real-time monitoring of the execution effect of the operation strategy, and collection of grid load matching, energy storage equipment response speed and efficiency, and demand response participation operation data for feedback; S6. Comprehensively utilize feedback data to periodically update fuzzy sets and adjust fuzzy principles.

2. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 1, characterized in that: The S1 specifically includes: S11, collect the load change rate V of the i-th grid segment at time t L，i (t): Where, ΔL i (t) represents the change in load of the ith area within the time interval Δt; S12, collect the instantaneous output volatility F of distributed energy resources in the i-th grid segment at time t DER，i (t): Among them, 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, N is the total number of distributed energy resources; S13, monitoring the frequency fluctuation rate F of the i-th power grid at time t F，i (t): Among them, dF i (t) is the grid frequency F i (t) The derivative with respect to time, which represents the rate of change of frequency; S14, receiving the abnormal event warning signal S 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); Among them, I overload，i (t) and I linefault，i (t) represents the overload and disconnection indexes, respectively, and α and β are weight factors; S15. Estimate the demand elasticity index E of the i-th grid user at time t DI，i (t): in, is the marginal rate of change of demand in response to price, P i (t) is the electricity price, D i (t) is the quantity demanded; S16. Based on the data collected in steps S11 to S15, construct a real-time data set D of the i-th power grid at time t. RT，i (t) and 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)}。 3. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 2 is characterized in that: The S2 specifically includes: S21, real-time data set D for each power grid segment area RT，i (t) and prediction dataset D P，i (t), the numerical data is converted into fuzzy sets by fuzzy processing. The fuzzy processing process includes converting the load change rate V L，i (t), instant output volatility of distributed energy resources F DER，i (t), grid frequency fluctuation rate F F，i (t), abnormal event warning signal S EE，i (t) and demand elasticity index E DI，i (t) Mapping to the corresponding fuzzy set through fuzzy membership function; S22. Establish fuzzy principles to evaluate the impact of load changes on energy storage demand, 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", where "high", "low" and "very high" are items in the fuzzy set, corresponding to different membership functions; S23. Introduce composite fuzzy principle 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 fluctuation", the grid stability risk R S，i (t) is "very high"; S24. Design fuzzy principles 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"; S25, applying the fuzzy logic reasoning mechanism, combining the fuzzy principles defined in steps S22-S24 and the fuzzy set obtained in step S21, comprehensively evaluate the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy, and determine the optimal selection of power grid demand response measures and energy storage system operation strategy by combining the evaluation of the fuzzy principles and using the fuzzy reasoning matrix; for a given power grid area i at time t, the formula of fuzzy logic reasoning is expressed as: Among them, R i (t) represents the comprehensive reasoning 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, They are load change rate, demand elasticity index and membership degree of renewable energy output volatility at time t; S26. Use the maximum member method process to convert the fuzzy logic reasoning results into clear operation instructions or decision suggestions to guide the specific operation of the grid demand response and energy storage system, and use the centroid method for defuzzification: Among them, D 操作，i (t) is the specific operation instruction or decision value for the power grid area i at time t, (x, t) is the membership function of the fuzzy reasoning result of region i, and the membership degree on the operation or decision domain X. x represents the specific operation or decision value. The optimal operation value is determined by calculating the weighted centroid of the membership function.

4. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 3 is characterized in that: The S22 specifically includes: The membership function that defines the load change rate "high" and the demand elasticity index "low" is as follows: For the load change rate V L，i (t), the membership function of "high" is expressed as: Among them, k V is the parameter that controls the slope of the curve, and V0 is the threshold that defines the "high" load change rate; For the demand elasticity index E DI，i (t), the membership function of "low" is expressed as: Among them, k E is the parameter that controls the slope of the curve, and E0 is the threshold that defines a "low" demand elasticity index; Define the membership function of "very high" energy storage demand, derive it through fuzzy principle, 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 The principle that (t) is "very high" is expressed as: m 非常高 (D S，i (t))=min(μ 高 (V L，i (t)), μ 低 (E DI，i (t))); Using the Min operator in fuzzy logic as the implementation of "and", the membership of energy storage demand is V L，i (t) belongs to "High" and E DI，i (t) Minimum value belonging to "low", level D of energy storage demand S，i (t) will be rated as "very high" based on its membership.

5. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 3 is characterized in that: The S23 specifically includes: F DER，i The instability of (t) is determined by the standard deviation of its output power To quantify, the grid frequency fluctuation rate F F，i (t) The rate of change of the frequency ΔF i (t) to quantify and use quantitative indicators to express the grid stability risk R S，i (t) evaluation method, define F DER，i The instability rating function of (t) is: m 不稳定 (F DER，i (t)); The rating function of grid frequency fluctuation rate is: m 显著波动 (F F，i (t)); Grid stability risk R S，i The evaluation of (t) is expressed by the following formula: in, Represents the "AND" operation in fuzzy logic, implemented by multiplication: Among them, c σ and σ σ They are The fuzzy set center and standard deviation, c ΔF and σ ΔF is ΔF i (t) fuzzy set center and standard deviation; The fuzzy logic evaluation formula for power grid stability risk is expressed as:

6. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 3 is characterized in that: The S24 specifically includes: Defining fuzzy sets "Warning" and "medium" The membership function of is as follows: For abnormal event warning signal S EE，i (t), its membership function is expressed as: Among them, x represents the quantitative value of the warning signal, and a and b are parameters that determine the "warning" membership; For the demand elasticity index E DI，i (t), its membership function is expressed as: Among them, y represents the quantitative value of the demand elasticity index, c, d, and e are parameters that determine the "medium" membership; Given S EE，i (t) and E DI，i (t) after the specific value, the demand response emergency level L R，i The fuzzy principle of (t) is embodied through the fuzzy AND operation as follows:

7. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 3 is characterized in that: The S3 specifically includes: S31. According to the in-depth analysis results of step S2, the specific content of the demand response measures for each grid segment area is determined, including the real-time adjustment strategy of load response. The adjustment strategy dynamically adjusts the temporary disconnection of non-critical loads of users and the optimal scheduling of critical loads based on the current and predicted supply and demand status of the grid: Where, ΔL adj，i (t) is the demand adjustment command, γ and η are adjustment coefficients, which control the sensitivity of load adjustment and the response to future renewable energy forecast and load forecast, respectively. max，i is the maximum load limit of zone i, L current,i is the actual load at the current time t, P RE，i (t+τ) is the predicted output of renewable energy at the future time t+τ, D forecast，i (t+τ) is the load forecast at the future time t+τ, and T is the forecast time range considered; S32. Determine the optimal charging and discharging timing and magnitude of the energy storage system. According to the in-depth analysis results, adjust the charging and discharging behavior of the energy storage system, give priority to changes in grid load demand and renewable energy supply, and the charging and discharging strategy of the energy storage system aims to maximize energy efficiency and support stable grid operation. Charging is carried out when it is predicted that there is an oversupply of renewable energy or a low grid load, and discharging is started when the grid load is at a peak or when there is an insufficient supply of renewable energy: Among them, 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 powers at time t, E loss，i is the energy loss per unit time, E i is the energy efficiency ratio, Δt is the time step; S33. Dynamic regulation scheme for future forecasts and current status. The in-depth analysis results are used to comprehensively consider the real-time and forecast data of the power grid, including load changes and volatility of renewable energy output, and customize the regulation strategy. For the upcoming high-load demand or low renewable energy output period, the power grid burden can be reduced in advance through demand-side management and energy storage discharge, or the charging operation of the energy storage system can be increased when high renewable energy output and low load demand are predicted: Among them, 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 energy storage state-of-charge change, ΔSOC i (t) is the expected state-of-charge change of the energy storage system; S34. Formulate the implementation time points, operation steps and expected goals of demand response measures and energy storage system operation strategies, a monitoring plan for the effectiveness of strategy implementation, and an adjustment mechanism when necessary.

8. The coordination method of power grid demand response and energy storage system based on fuzzy logic control according to claim 7, characterized in that: The S4 specifically includes: S41. Develop and deploy a communications network that connects key nodes of the power grid, including energy storage facilities, load management centers, renewable energy power plants, and user-side equipment. The network ensures real-time information transmission and execution of demand response measures and energy storage system operation strategies; S42: The demand response measures and energy storage system operation strategies formulated in step S3 are transmitted to key nodes of the power grid in real time through the communication network. The transmitted information includes the demand adjustment instruction ΔL adj，i (t), charging and discharging instruction SOC of 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); Among them, ΔSOC adjust，i (t) is the change in charge or discharge amount determined based on the optimization algorithm; S44. In the load management center, execute the demand adjustment instruction ΔL adj，i (t), dynamically adjust the grid load to optimize power distribution and consumption patterns by increasing or decreasing the power supply of non-critical loads or adjusting the operating mode of critical loads; S45. On the user side, the equipment automatically adjusts the electricity consumption pattern according to the instructions transmitted by the communication network, including adjusting the operating time of smart home devices and optimizing the energy use of large industrial facilities to adapt to the demand and supply status of the power grid.

9. The method for coordinating power grid demand response and energy storage system based on fuzzy logic control according to claim 8, characterized in that: The S5 specifically includes: S51. Real-time monitoring of the grid operation status, the response behavior of energy storage equipment, and the degree of participation in demand response measures, capturing and analyzing key performance indicators, including grid load matching, the charging and discharging speed and efficiency of energy storage equipment, and the degree of participation in demand response on the user side; S52, grid load matching monitoring, by comparing the difference between the actual grid load and the predicted load, calculate the load matching degree M L，i (t): Among them, L actual，i (t) is the actual load, L predicted，i (t) is the predicted load, M L，i (t) reflects the load forecasting accuracy and the effect of real-time regulation; S53, monitoring the response speed and efficiency of the energy storage device, based on the time T from when the energy storage device receives the charge and discharge instruction to when it reaches the specified state-of-charge (SOC) response，i (t) and energy efficiency ratio E efficiency，i (t) Carry out assessment: T response，i (t)=t end -t start ; Among them, t start is the command receiving time, t end is the moment when the specified SOC is reached, E output，i (t) and E input，i (t) represents the energy during discharge and charge, respectively; S54, Demand response participation monitoring, by counting the number of users participating in demand response measures and the total load affected, to measure the breadth and influence of demand response, participation P DR，i (t) is expressed as: Among them, 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; S55. Feedback the collected monitoring data, which includes 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 according to any one of claims 1 to 9, characterized in that: include: Data collection and preprocessing module: responsible for collecting real-time and predicted data of the power grid, including the load change rate of the power grid segment area, the instant output volatility of distributed energy resources, the power grid frequency volatility, abnormal event warning signals and the user-side demand elasticity index, constructing the real-time data set and predicted data set of the power grid, and providing input data for the fuzzy logic controller; The fuzzy logic controller processes the real-time data set and the forecast data set of the power grid using a fuzzy logic algorithm, converts them into fuzzy sets, and applies fuzzy principles to perform in-depth analysis; comprehensively evaluates the dynamic supply and demand balance state of the current power grid and the real-time availability of renewable energy; Strategy formulation and execution module: Based on the in-depth analysis results output by the fuzzy logic controller, the module customizes grid demand response measures and energy storage system operation strategies, transmits the operation strategies to key nodes of the grid in real time, and guides specific operation execution, regulates the charging and discharging behavior of energy storage equipment, dynamically adjusts the demand side load, and optimizes power distribution and consumption patterns; Real-time monitoring and feedback module: monitors the execution effect of the strategy in real time, collects the operation data of grid load matching, energy storage equipment response speed and efficiency, and demand response participation, and feeds the monitoring data back to the fuzzy logic controller for adjusting and optimizing the control strategy.

Images