Regional power grid flexibility evaluation method based on fuzzy logic theory

Abstract

The invention belongs to the technical field of power grid operation and control, and particularly relates to a regional power grid flexibility evaluation method based on a fuzzy logic theory. According to the method, uncertain factors such as load fluctuation and new energy fluctuation in the regional power grid can be better handled, the problem that uncertain factors are insufficient to process in a traditional method is avoided, and more accurate power grid flexibility evaluation is provided. Comprising the following steps: S1, establishing a regional power grid flexibility supply and demand balance analysis model, and selecting load fluctuation, new energy fluctuation, conventional unit output and energy storage output as input variables; s2, reasoning a fuzzy set of the input variables by formulating a'if-if 'rule; s3, calculating a membership degree of each input variable by applying a fuzzy reasoning method, and converting a reasoning result into a fuzzy set representing a flexible supply and demand matching level; and S4, performing defuzzification on a result obtained by fuzzy reasoning. And S5, dynamically adjusting the fuzzy rule base, and verifying the method.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid operation and control, and in particular relates to a regional power grid flexibility assessment method based on fuzzy logic theory. Background Art

[0002] With the development of the energy sector, more and more renewable energy sources are connected to regional power grids, and their penetration rate is gradually increasing. Renewable energy is gradually becoming an important part of power supply in the power system, which helps to improve the environmental friendliness and economy of the power system.

[0003] However, the integration of new energy sources into the grid has increased the volatility, intermittency and uncertainty of the power system. As the scale of new energy integration continues to expand, the peak load regulation of the power system faces many difficulties and the balance of power and electricity is also challenged, which undoubtedly puts higher demands on the flexibility and regulation capabilities of the power system. Especially in the case of large-scale access to new energy sources, the uncertainty of power grid operation has increased significantly.

[0004] Most existing flexibility calculation methods rely on traditional mathematical models or linear programming methods. These methods lack effective calculation methods when faced with nonlinear and uncertain factors such as grid complexity, load volatility and new energy volatility. They are difficult to accurately evaluate and plan the flexibility of the power system and cannot meet the actual grid operation's needs for accurate flexibility calculation and management.

[0005] In view of this, a new method for calculating the flexibility of regional power grids is urgently needed. Fuzzy logic theory provides a possible solution to this problem. Fuzzy logic theory aims to deal with various uncertain factors in regional power grids, such as load fluctuations, new energy fluctuations, etc. In addition, the balance of supply and demand of flexibility in the power grid is also faced with complex interrelationships. Fuzzy logic can effectively describe and analyze the fuzziness of these factors by constructing fuzzy membership functions and rule bases. Through fuzzy reasoning methods, the system can calculate the matching of flexibility supply and demand, thereby optimizing the dispatching strategy of the power grid. In the context of large-scale new energy access, fuzzy logic can effectively improve the flexibility regulation capability of the power grid, ensure the safe and stable operation of the power grid under uncertain conditions, improve the utilization rate of new energy, and ensure the safety and economy of the regional power grid. Summary of the invention

[0006] The present invention aims at addressing the defects of the prior art and provides a regional power grid flexibility assessment method based on fuzzy logic theory.

[0007] To achieve the above object, the present invention adopts the following technical solution, a method for calculating regional power grid flexibility based on fuzzy logic theory, comprising the steps of:

[0008] S1. Establish a regional power grid flexibility supply and demand balance analysis model, which selects load fluctuation, new energy fluctuation, conventional unit output, and energy storage output as input variables, and constructs corresponding fuzzy membership functions for each input variable to analyze the characteristics of flexibility supply and demand;

[0009] S2. Construct a fuzzy rule base to associate the characteristics of flexibility supply and demand, and formulate "if-then" rules to reason about the fuzzy set of input variables to form a fuzzy state of flexibility supply and demand matching;

[0010] S3, applying fuzzy reasoning methods to calculate the membership of each input variable, activating different flexibility states according to the fuzzy rule base, and converting the reasoning results into fuzzy sets representing the matching level of flexibility supply and demand;

[0011] S4. Defuzzify the results obtained by fuzzy reasoning and quantify the flexibility index of the regional power grid to evaluate the flexibility of the regional power grid.

[0012] It also includes S5, which dynamically adjusts the fuzzy rule base according to historical operation data and scenario analysis results to improve the accuracy and adaptability of the flexibility calculation method and verify the method; verify the practicality and effectiveness under large-scale new energy access.

[0013] Furthermore, in S1, the fuzzy membership function is used to describe the fuzziness of load fluctuation, new energy fluctuation, conventional unit output and energy storage output; the fuzzy membership function is expressed as:

[0014]

[0015] Where i, j and k are the starting point, vertex and end point of the fuzzy membership function respectively, and x is the input variable.

[0016] Furthermore, in S2, the constructed fuzzy rule base includes:

[0017] If the output of conventional units is "high" and the output of energy storage is "high", the flexibility supply is "high";

[0018] If the output of conventional units is “high” and the output of energy storage is “low”, the flexibility supply is “medium”;

[0019] If the output of conventional units is “low” and the output of energy storage is “low”, the flexibility supply is “low”;

[0020] If load fluctuation is “high” and renewable energy fluctuation is “high”, then flexibility demand is “high”;

[0021] If load fluctuation is “high” and renewable energy fluctuation is “low”, the flexibility demand is “medium”;

[0022] If load fluctuation is “low” and renewable energy fluctuation is “low”, then flexibility demand is “low”;

[0023] If flexibility supply is “high” and flexibility demand is “low”, the flexibility status is “adequate”;

[0024] If flexibility supply is “low” and flexibility demand is “high”, the flexibility status is “insufficient”;

[0025] When the output of conventional units and energy storage exceeds 80%, it is "high"; when it is less than 30%, it is "low"; when the load fluctuation and new energy fluctuation are less than the 5% threshold, it is "low"; when it exceeds 20%, it is "high"; when the flexibility supply is higher than the demand, the flexibility status is "sufficient", otherwise it is "insufficient"; these rules combine the input variables (load fluctuation, new energy fluctuation, conventional unit output and energy storage output, etc.) with the flexibility status, and obtain the final flexibility supply and demand matching value through the fuzzy reasoning mechanism.

[0026] Furthermore, in S3, based on the fuzzy reasoning method, multiple fuzzy output results are reasoned and operated, different flexibility states are activated, and fuzzy results of flexibility supply and demand matching are generated; the activation degree β of each rule is calculated:

[0027] β=min(f(x1),f(x2))

[0028] Where: β represents the rule activation, that is, the minimum value of the input variable membership; f(x i ) represents the membership function;

[0029] According to the activation degree of each rule, the output fuzzy set of the rule is scaled, and the outputs of all rules are synthesized to form the final fuzzy set of flexibility supply and demand matching; for N rules, the synthesis of the output fuzzy set B is expressed as:

[0030]

[0031] In the formula, μ B (y) is the fuzzy set of the flexibility supply and demand matching level, indicating the membership degree of different flexibility states; β i is the activation degree of rule i; μ' B (y) is the fuzzy set output by rule i, the result after activation scaling; ∪ represents the rule synthesis operation, which usually takes the maximum value, that is, merging the output fuzzy sets of all rules.

[0032] Furthermore, in S4, defuzzification can convert the fuzzy reasoning result into a certain flexibility index; specifically, the output result is determined by calculating the weighted average of the area under the membership curve. The defuzzification formula is expressed as:

[0033]

[0034] Where: y is the input variable; μ Y (y) is the value of the membership function curve at point x, which is expressed as the ordinate in the coordinate system; y0 is the final flexibility index;

[0035] Furthermore, in S5, the process of dynamically adjusting the fuzzy rule base is based on the historical load data of the regional power grid, the output data of new energy sources, the output data of conventional units, and the operation data of energy storage equipment, and the fuzzy rules are optimized to improve the accuracy of flexibility evaluation; that is:

[0036] The rule function is optimized by dynamically adjusting the rule weights in the rule base. The optimized rule function is expressed as:

[0037]

[0038] Where: Q(x) is the optimization rule; α i is the weight coefficient of the i-th rule, ε i is the error of the ith rule, m is the number of rules;

[0039] ε i The calculation formula is:

[0040] ε i =|y pre,i- y act,i |

[0041] Where: y pre,i is the prediction result of the i-th rule, y act,i is the actual result of the i-th rule;

[0042] Then: weight coefficient α i for:

[0043]

[0044] Furthermore, in S4, the flexibility index includes system flexibility supply and system flexibility demand, and the flexibility supply and demand balance evaluation index calculated by fuzzy logic can quantify the regulation and response capabilities of the regional power grid; specifically, the flexibility supply and demand balance evaluation index F flex The calculation formula is expressed as:

[0045] F flex =f(P supply ,P demand )

[0046] and

[0047] P supply =P g +Psto

[0048] P demand =P load -P renew

[0049] Where: P g Output for conventional units; P sto Contribute to energy storage; P load is the load demand; P renew Contribute to new energy;

[0050] By using fuzzy logic reasoning method and combining the flexibility supply and demand model, the flexibility index F is calculated. flex The fuzzy set of is defuzzified to obtain the flexibility score of the regional power grid; the flexibility score S flex The centroid method is used for defuzzification, and the calculation formula is:

[0051]

[0052] Where: x is the flexibility supply and demand balance evaluation index F flex The value of; μ(x) is the flexibility score membership function corresponding to x; x min and x max They are flexibility evaluation index F flex The minimum and maximum values ​​of .

[0053] Compared with the prior art, the present invention has beneficial effects.

[0054] The present invention adopts fuzzy logic theory to calculate the flexibility of regional power grids, which can better cope with uncertain factors such as load fluctuations and new energy fluctuations in regional power grids, avoids the problem of insufficient handling of uncertain factors by traditional methods, and provides a more accurate assessment of power grid flexibility.

[0055] The present invention can calculate the flexibility supply and demand balance state of the regional power grid in real time, enhance the rapid response capability of the power grid, and improve the accuracy and adaptability of the regional power grid flexibility calculation method in different scenarios by dynamically adjusting the fuzzy rule base.

[0056] Compared with traditional linear programming or complex mathematical modeling methods, the present invention simplifies the calculation process, reduces the dependence on complex mathematical models, improves calculation efficiency, and makes grid flexibility calculation more efficient and convenient through the application of fuzzy reasoning and membership functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. The protection scope of the present invention is not limited to the following description.

[0058] Figure 1It is the overall flow chart of regional power grid flexibility calculation based on fuzzy logic theory.

[0059] Figure 2 It is the structural diagram of the fuzzy logic model of regional power grid flexibility.

[0060] Figure 3 It is the input variable membership function structure diagram.

[0061] Figure 4 It is the structure diagram of flexibility evaluation function.

[0062] Figure 5 It is a functional structure diagram of regional power grid flexibility supply and demand evaluation. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and beneficial effects of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0064] like Figure 1-5 As shown, Figure 1 It is an overall flow chart. From the process in the figure, it can be seen that this method is to calculate the regional power grid flexibility assessment result through the defined fuzzy logic model and rule base, fuzzy reasoning and defuzzification processing, which is the essential difference from other methods.

[0065] The regional power grid flexibility calculation method based on fuzzy logic theory includes the following steps:

[0066] S1. Establish a regional power grid flexibility supply and demand balance analysis model, which selects load fluctuation, new energy fluctuation, conventional unit output, and energy storage output as input variables, and constructs corresponding fuzzy membership functions for each input variable to analyze the characteristics of flexibility supply and demand;

[0067] S2. Construct a fuzzy rule base to associate the characteristics of flexibility supply and demand, and formulate "if-then" rules to reason about the fuzzy set of input variables to form a fuzzy state of flexibility supply and demand matching;

[0068] S3, applying fuzzy reasoning methods to calculate the membership of each input variable, activating different flexibility states according to the fuzzy rule base, and converting the reasoning results into fuzzy sets representing the matching level of flexibility supply and demand;

[0069] S4. Defuzzify the results obtained by fuzzy reasoning and quantify the flexibility index of the regional power grid to evaluate the flexibility of the regional power grid.

[0070] It also includes S5, which dynamically adjusts the fuzzy rule base according to historical operation data and scenario analysis results to improve the accuracy and adaptability of the flexibility calculation method and verify the method; verify the practicality and effectiveness under large-scale new energy access.

[0071] Preferably, in S1, the fuzzy membership function is used to describe the fuzziness of load fluctuation, new energy fluctuation, conventional unit output and energy storage output; the fuzzy membership function is expressed as:

[0072]

[0073] Where i, j and k are the starting point, vertex and end point of the fuzzy membership function respectively, and x is the input variable.

[0074] Preferably, in S2, the constructed fuzzy rule base includes:

[0075] If the output of conventional units is "high" and the output of energy storage is "high", the flexibility supply is "high";

[0076] If the output of conventional units is “high” and the output of energy storage is “low”, the flexibility supply is “medium”;

[0077] If the output of conventional units is “low” and the output of energy storage is “low”, the flexibility supply is “low”;

[0078] If load fluctuation is “high” and renewable energy fluctuation is “high”, then flexibility demand is “high”;

[0079] If load fluctuation is “high” and renewable energy fluctuation is “low”, the flexibility demand is “medium”;

[0080] If load fluctuation is “low” and renewable energy fluctuation is “low”, then flexibility demand is “low”;

[0081] If flexibility supply is “high” and flexibility demand is “low”, the flexibility status is “adequate”;

[0082] If flexibility supply is “low” and flexibility demand is “high”, the flexibility status is “insufficient”;

[0083] When the output of conventional units and energy storage exceeds 80%, it is "high"; when it is less than 30%, it is "low"; when the load fluctuation and new energy fluctuation are less than the 5% threshold, it is "low"; when it exceeds 20%, it is "high"; when the flexibility supply is higher than the demand, the flexibility status is "sufficient", otherwise it is "insufficient"; these rules combine the input variables (load fluctuation, new energy fluctuation, conventional unit output and energy storage output, etc.) with the flexibility status, and obtain the final flexibility supply and demand matching value through the fuzzy reasoning mechanism.

[0084] Preferably, in S3, a fuzzy reasoning method is used to perform reasoning operations on multiple fuzzy output results, activate different flexibility states, and generate fuzzy results of flexibility supply and demand matching; and the activation degree β of each rule is calculated:

[0085] β=min(f(x1),f(x2))

[0086] Where: β represents the rule activation, that is, the minimum value of the input variable membership; f(x i ) represents the membership function;

[0087] According to the activation degree of each rule, the output fuzzy set of the rule is scaled, and the outputs of all rules are synthesized to form the final fuzzy set of flexibility supply and demand matching; for N rules, the synthesis of the output fuzzy set B is expressed as:

[0088]

[0089] In the formula, μ B (y) is the fuzzy set of the flexibility supply and demand matching level, indicating the membership degree of different flexibility states; β i is the activation degree of rule i; μ' B (y) is the fuzzy set output by rule i, the result after activation scaling; ∪ represents the rule synthesis operation, which usually takes the maximum value, that is, merging the output fuzzy sets of all rules.

[0090] Preferably, in S4, defuzzification can convert the fuzzy reasoning result into a determined flexibility index; specifically, the output result is determined by calculating the weighted average of the area under the membership curve, and the defuzzification formula is expressed as:

[0091]

[0092] Where: y is the input variable; μ Y (y) is the value of the membership function curve at point x, which is expressed as the ordinate in the coordinate system; y0 is the final flexibility index;

[0093] Preferably, in S5, the process of dynamically adjusting the fuzzy rule base is based on the historical load data of the regional power grid, the output data of new energy sources, the output data of conventional units and the operation data of energy storage equipment, and the accuracy of the flexibility assessment is improved by optimizing the fuzzy rules; that is:

[0094] The rule function is optimized by dynamically adjusting the rule weights in the rule base. The optimized rule function is expressed as:

[0095]

[0096] Where: Q(x) is the optimization rule; α iis the weight coefficient of the i-th rule, ε i is the error of the ith rule, m is the number of rules;

[0097] ε i The calculation formula is:

[0098] ε i =|y pre,i -y act,i |

[0099] Where: y pre,i is the prediction result of the i-th rule, y act,i is the actual result of the i-th rule;

[0100] Then: weight coefficient α i for:

[0101]

[0102] Preferably, in S4, the flexibility index includes system flexibility supply and system flexibility demand, and the flexibility supply and demand balance evaluation index calculated by fuzzy logic can quantify the regulation capability and response capability of the regional power grid; specifically, the flexibility supply and demand balance evaluation index F flex The calculation formula is expressed as:

[0103] F flex =f(P supply ,P demand )

[0104] and

[0105] P supply =P g +P sto

[0106] P demand =P load -P renew

[0107] Where: P g Output for conventional units; P sto Contribute to energy storage; P load is the load demand; P renew Contribute to new energy;

[0108] By using fuzzy logic reasoning method and combining the flexibility supply and demand model, the flexibility index F is calculated. flex The fuzzy set of is defuzzified to obtain the flexibility score of the regional power grid; the flexibility score S flex The centroid method is used for defuzzification, and the calculation formula is:

[0109]

[0110] Where: x is the flexibility supply and demand balance evaluation index F flex The value of; μ(x) is the flexibility score membership function corresponding to x; x min and x max They are flexibility evaluation index F flex The minimum and maximum values ​​of .

[0111] in addition, Figure 2 This is the structure diagram of the regional power grid flexibility fuzzy logic model, which shows the core structure and components of the fuzzy logic model in this method. The model constitutes a complete flexibility assessment framework by defining input variables, fuzzy membership functions, fuzzy rule bases, and reasoning processes.

[0112] Specifically, the figure first inputs the variable part, including key factors such as load fluctuations, new energy fluctuations, and energy storage output. Each input variable is fuzzified through the corresponding fuzzy membership function to convert specific data into membership. Next, the figure shows the fuzzy rule base, which contains "IF-ZHEN" rules formulated according to the combination of different variables, illustrating the logical relationship of flexibility supply and demand matching. These rules are inferred through the fuzzy inference engine, which activates the corresponding rules and outputs the fuzzy state according to the membership of the input variables. Finally, through the defuzzification module, the inference results are converted into specific flexibility evaluation indicators, and the flexibility score of the regional power grid is output.

[0113] Figure 3 It is a structural diagram of the membership function of input variables. In order to describe the membership functions of load fluctuation, new energy fluctuation, conventional unit output and energy storage output, the fuzzification process of each input variable in this method is demonstrated.

[0114] Membership function of load fluctuation

[0115]

[0116] Membership function of new energy fluctuations

[0117]

[0118] Membership function of conventional unit output

[0119]

[0120] Membership function of energy storage output

[0121]

[0122] Figure 4The structure of the regional power grid flexibility evaluation function based on fuzzy logic theory is shown. The flexibility score of the power grid is calculated by inputting variables (load fluctuation, new energy fluctuation, conventional unit output, energy storage output), where the flexibility score ranges from 0 to 10, indicating different states of power grid flexibility. Figure 4 The flexibility status is divided into four levels: poor, medium, good and excellent, and the membership changes of each level are described by the triangular membership function.

[0123] Figure 5 The flexibility supply and demand evaluation function structure diagram of a regional power grid is shown. The flexibility demand is calculated based on the membership of load fluctuations and renewable energy fluctuations. The grid flexibility demand score is evaluated by fuzzy evaluation of load fluctuations and renewable energy fluctuations. The flexibility supply of the regional power grid is calculated based on the membership of conventional unit output and energy storage output, and the grid flexibility supply score is evaluated.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "preferred embodiments", "specific implementation", or "preferred implementation" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features therein may be replaced by equivalents. Therefore, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for calculating regional power grid flexibility based on fuzzy logic theory, characterized by: Includes steps: S1. Establish a regional power grid flexibility supply and demand balance analysis model, which selects load fluctuation, new energy fluctuation, conventional unit output, and energy storage output as input variables, and constructs corresponding fuzzy membership functions for each input variable to analyze the characteristics of flexibility supply and demand; S2. Construct a fuzzy rule base to associate the characteristics of flexibility supply and demand, and formulate "if-then" rules to reason on the fuzzy set of input variables to form a fuzzy state of flexibility supply and demand matching; S3, applying fuzzy reasoning methods to calculate the membership of each input variable, activating different flexibility states according to the fuzzy rule base, and converting the reasoning results into fuzzy sets representing the matching level of flexibility supply and demand; S4, defuzzifying the results obtained by fuzzy reasoning, quantifying and outputting the flexibility index of the regional power grid, so as to evaluate the flexibility of the regional power grid; S5. Dynamically adjust the fuzzy rule base according to historical operation data and scenario analysis results to improve the accuracy and adaptability of the flexibility calculation method, and verify the method.

2. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1 is characterized in that: In S1, the fuzzy membership function is used to describe the fuzziness of load fluctuation, new energy fluctuation, conventional unit output and energy storage output; the fuzzy membership function is expressed as: Where i, j and k are the starting point, vertex and end point of the fuzzy membership function respectively, and x is the input variable.

3. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1 is characterized in that: S2 includes: If the output of conventional units is "high" and the output of energy storage is "high", the flexibility supply is "high"; If the output of conventional units is "high" and the output of energy storage is "low", the flexibility supply is "medium"; If the output of conventional units is "low" and the output of energy storage is "low", the flexibility supply is "low"; If load fluctuation is "high" and renewable energy fluctuation is "high", flexibility demand is "high"; If the load fluctuation is "high" and the renewable energy fluctuation is "low", the flexibility demand is "medium"; If load fluctuation is "low" and renewable energy fluctuation is "low", flexibility demand is "low"; If flexibility supply is "high" and flexibility demand is "low", the flexibility status is "sufficient"; If flexibility supply is "low" and flexibility demand is "high", the flexibility status is "insufficient"; Conventional unit output and energy storage output exceeding 80% are "high" and below 30% are "low"; load fluctuation and new energy fluctuation below the 5% threshold are "low" and above 20% are "high"; when flexibility supply is higher than demand, the flexibility status is "sufficient", otherwise it is "insufficient"; these rules combine input variables with flexibility status and obtain the final flexibility supply and demand matching value through fuzzy reasoning mechanism.

4. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1, characterized in that: In S3, multiple fuzzy output results are inferred based on the fuzzy reasoning method to activate different flexibility states and generate fuzzy results of flexibility supply and demand matching; the activation degree β of each rule is calculated: β=min(f(x1),f(x2)) Where: β represents the rule activation, that is, the minimum value of the input variable membership; f(x i ) represents the membership function; According to the activation degree of each rule, the output fuzzy set of the rule is scaled, and the outputs of all rules are synthesized to form the final fuzzy set of flexibility supply and demand matching; for N rules, the synthesis of the output fuzzy set B is expressed as: In the formula, μ B (y) is the fuzzy set of the flexibility supply and demand matching level, indicating the membership degree of different flexibility states; β i is the activation degree of rule i; μ' B (y) is the fuzzy set output by rule i, the result after activation scaling; ∪ represents the rule synthesis operation, which usually takes the maximum value, that is, merging the output fuzzy sets of all rules.

5. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1 is characterized in that: In S4, defuzzification can convert the fuzzy reasoning results into a certain flexibility index; specifically, the output result is determined by calculating the weighted average of the area under the membership curve. The defuzzification formula is expressed as: Where: y is the input variable; μ Y (y) is the value of the membership function curve at point x, which is expressed as the vertical coordinate in the coordinate system; y0 is the final flexibility index.

6. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1 is characterized in that: In S5, the process of dynamically adjusting the fuzzy rule base is based on the historical load data of the regional power grid, the output data of new energy sources, the output data of conventional units, and the operating data of energy storage equipment, and the fuzzy rules are optimized to improve the accuracy of flexibility assessment; that is: The rule function is optimized by dynamically adjusting the rule weights in the rule base. The optimized rule function is expressed as: Where: Q(x) is the optimization rule; α i is the weight coefficient of the i-th rule, ε i is the error of the ith rule, m is the number of rules; ε i The calculation formula is: ε i =|and pre,i -and act,i | Where: y pre,i is the prediction result of the i-th rule, y act,i is the actual result of the i-th rule; Then: weight coefficient α i for:

7. The method for calculating regional power grid flexibility based on fuzzy logic theory according to claim 1, characterized in that: In S4, the flexibility index includes system flexibility supply and system flexibility demand, and the flexibility supply and demand balance evaluation index calculated by fuzzy logic can quantify the regulation and response capabilities of the regional power grid; specifically, the flexibility supply and demand balance evaluation index F flex The calculation formula is expressed as: F flex =f(P supply ,P demand ) and P supply =P g +P sto P demand =P load -P renew Where: P g Output for conventional units; P sto Contribute to energy storage; P load is the load demand; P renew Contribute to new energy; By using fuzzy logic reasoning method and combining the flexibility supply and demand model, the flexibility index F is calculated. flex The fuzzy set of is defuzzified to obtain the flexibility score of the regional power grid; the flexibility score S flex The centroid method is used for defuzzification, and the calculation formula is: Where: x is the flexibility supply and demand balance evaluation index F flex The value of; μ(x) is the flexibility score membership function corresponding to x; x min and x max They are flexibility evaluation index F flex The minimum and maximum values ​​of .

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