Regional power grid new energy bearing capacity assessment method giving consideration to safety and insurance supply constraints

Abstract

The invention discloses a regional power grid new energy bearing capacity evaluation method giving consideration to safety and supply guarantee constraints, and relates to the field of new energy power systems, a multi-target time sequence production simulation optimization model is constructed, safety, stability and supply guarantee constraints are embedded, solving is performed through a whale optimization algorithm, and new energy bearing capacities under different power supply configurations are obtained; building a new energy bearing capacity evaluation index system, and quantitatively calculating an index original value; combining an analytic hierarchy process and an improved entropy weight method to obtain a balanced combination weight; an initial comprehensive benefit index is calculated through fusion of an improved TOPSIS method and grey correlation analysis, and a final index is obtained through a key constraint adjustment mechanism; ranking the schemes and classifying grades in combination with statistical characteristics; and testing the robustness of the model through dual sensitivity analysis, and outputting an optimal scheme. According to the method, full-chain evaluation from technical feasibility to comprehensive benefits is realized, and a scientific and accurate decision basis is provided for new energy planning and operation of a regional power grid.

Description

A method for assessing the renewable energy carrying capacity of regional power grids while balancing safety and supply constraints Technical Field

[0001] This invention relates to the field of new energy power system technology, and more specifically to a method for assessing the new energy carrying capacity of regional power grids that takes into account both safety and supply constraints. Background Technology

[0002] Currently, renewable energy generation methods such as wind power and photovoltaic power generally exhibit significant randomness, intermittency, and power fluctuation characteristics. Their large-scale integration complicates the operating characteristics of the power system. The system displays technical characteristics such as low immunity, insufficient inertia support, and reduced short-circuit capacity, posing severe challenges to the system's power balance, stability support, regulation performance, and even overall safety and stability. The core contradiction lies in the insufficient renewable energy carrying capacity of the existing system. Renewable energy carrying capacity refers to the maximum renewable energy capacity that the power grid can accommodate while meeting the hard constraints of power system safety, reliability, and economic operation. Therefore, how to scientifically and accurately assess and improve the renewable energy carrying capacity of the power grid has become a key technical bottleneck restricting the achievement of energy transition goals.

[0003] Power system production simulation is a fundamental tool for power system planning and operation, and can be used to calculate the renewable energy carrying capacity of the power grid. By simulating the power system dispatch process, it predicts the power generation, fuel consumption, and emissions of each generating unit, the utilization hours of the computer group, and determines the overall system operating cost, providing a basis for formulating reasonable power planning schemes or medium- and long-term operation plans. Therefore, time-series production simulation models can be constructed to depict in detail the various states of the system from planning to operation. Efficient solutions for large systems have always been a key focus. Embedding the whale optimization algorithm into time-series production simulation enhances its global exploration capabilities and is more likely to find carrying capacity close to the true limit when solving high-dimensional, nonlinear, and multi-peak carrying capacity optimization problems. Its unique stochastic search and adaptive parameter mechanism can be combined with penalty function methods or constraint-preserving techniques to more effectively guide the search to meet the complex constraint space of safety and supply assurance.

[0004] In comprehensive evaluations involving multiple indicators, the determination of indicator weights often relies on subjective methods such as the analytic hierarchy process (AHP) or purely objective methods such as the entropy weight method, failing to effectively balance expert experience and data information. Furthermore, commonly used multi-attribute decision-making methods (such as traditional TOPSIS) employ static ideal solutions and Euclidean distance, making it difficult to handle correlations between indicators and adapting to the dynamic technological development of different solution sets. Consequently, the robustness and persuasiveness of the evaluation results need improvement.

[0005] Therefore, how to accurately quantify the carrying capacity of new energy sources and scientifically evaluate the comprehensive benefits of multiple schemes, so as to provide a basis for decision-making in power grid planning and operation, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method for assessing the renewable energy carrying capacity of regional power grids that takes into account both safety and supply constraints. This method aims to solve the challenges of coordinated optimization between power grid planning and operation under high-proportion renewable energy integration, as well as the problem of unclear renewable energy carrying capacity. The method constructs a full-chain assessment system from "carrying capacity simulation" to "comprehensive benefit decision-making," providing a complete quantitative analysis tool and decision-making basis for the power grid to accurately improve its renewable energy carrying capacity within strict safety and supply boundaries.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, comprising: S1, constructing a time-series production simulation optimization model with the objective functions of minimizing overall system operating costs, maximizing renewable energy output, and minimizing carbon emissions, embedding safety and stability constraints and power supply constraints, and using the whale optimization algorithm to solve the time-series production simulation optimization model in a time-series manner to obtain the renewable energy carrying capacity of the regional power grid under different power supply capacity configuration schemes; S2, constructing a multi-dimensional renewable energy carrying capacity evaluation index system and quantifying the specific indicators under each dimension to obtain the original values ​​of the indicators for each candidate scheme; S3, obtaining subjective weights using the analytic hierarchy process and objective weights using the improved entropy weight method, treating the subjective and objective weights as game participants, and constructing an optimization objective and combining it with the Lagrange multiplier algorithm. The multiplier method is used to solve for the weighted combination of indicators after equilibrium. S4: A weighted normalized decision matrix is ​​constructed using the weighted combination of indicators. The relative proximity is calculated using the improved TOPSIS method and grey relational analysis, and then fused to obtain the initial comprehensive benefit index. Key constraints such as safety, power supply guarantee, and policy are set, and a key constraint adjustment mechanism is established. Rewards are adjusted for schemes that meet the constraints to obtain the final comprehensive benefit index. S5: Candidate schemes are ranked in descending order of the final comprehensive benefit index. The relative advantage of each scheme is calculated, and the schemes are divided into different levels based on the mean and standard deviation of the comprehensive benefit index. S6: Sensitivity analysis of the weights and sensitivity analysis of the key constraint adjustment coefficients are conducted to test the robustness of the scheme ranking results to changes in the parameters of the time-series production simulation optimization model. Finally, the new energy carrying capacity scheme with the optimal comprehensive benefit is output.

[0008] Optionally, the objective function is specifically: ① Minimize system operating cost.

[0009] In the formula: T is the total optimized running time; The duration of each time period; , , , These represent the unit power generation costs of thermal power, hydropower, wind power, and photovoltaic power units, respectively. , , , These represent the various types of generating units: thermal power, hydropower, wind power, and photovoltaic power. The amount of effort or contribution made during the specified period; , , , , These represent the penalty coefficients for wind curtailment, solar curtailment, load shedding, hydropower curtailment, and unit startup, respectively. , , These represent the unit loss costs for wind curtailment, solar curtailment, and load shedding, respectively. , , These represent the curtailed wind power, curtailed solar power, and load shedding power during time period t, respectively. For thermal power units in A 0-1 variable indicating whether the activity was started within the specified time period; ① The equivalent power output of the hydropower station; ② The largest output from new energy sources.

[0010] In the formula: , These represent the total wind power and solar power that the system can support at time t, respectively.

[0011] ③ Minimum carbon emissions

[0012] The extent of carbon emissions from the power system is measured by calculating the proportion of renewable energy installed capacity and the carbon neutrality rate over a certain period in the region. The proportion of renewable energy installed capacity is used as... The carbon neutrality rate is expressed as... The carbon emission function is as follows:

[0013] In the formula: For system assembly; C NE For the installation of new energy power in the system; This refers to the system's carbon capture capacity; This represents the system's carbon emissions.

[0014] Optionally, the safety and stability constraints include line power flow constraints, branch power constraints, and voltage amplitude constraints; the power supply constraints include power balance, upper and lower limits of new capacity constraints, and energy storage ratio constraints.

[0015] Optionally, the whale optimization algorithm specifically includes: S11. Data initialization: Set the initial installed capacity of new energy, and based on the initial installed capacity of new energy, generate an 8760-hour time-series output curve of new energy using a probability distribution model; integrate the load of each region to form a comprehensive load curve for the entire network; set the solver parameters; S12. Initialize the whale pod: Randomly generate a group of individual whales, each representing a power capacity configuration scheme, and initialize the optimal solution record; S13. Time-series production simulation: Receive the installation capacity configuration schemes of various power sources and energy storage from the upper-level model; according to the objective function, simulate the time-series power generation operation mode throughout the year to obtain the optimal operation scheme with the lowest system cost and the highest new energy output; S14. Safety and stability verification: Perform power flow calculation on the key scenarios obtained from the time-series production simulation; monitor the system power angle, voltage and frequency stability, and evaluate whether the safety and stability requirements are met. Standard; S15. Capacity Iteration Adjustment: If any indicator exceeds the limit, the preset output value of the new energy power station needs to be adjusted, and the process should return to step 3 to re-simulate production; iterate continuously until the maximum installed capacity of new energy that satisfies all safety and stability constraints is found, which is the new energy carrying capacity of the system; S16. Update Optimal Solution: Return the calculated new energy carrying capacity index results to the upper-level model for fitness evaluation; compare and update the individual historical best and the current global best capacity scheme and fitness according to the WOA mechanism; S17. Convergence Judgment: If the maximum number of iterations is reached or the optimal solution is continuously stable, the optimal capacity scheme is output; otherwise, return to S14; if the maximum number of iterations is reached, or the change of the global optimal solution is less than the threshold after multiple consecutive iterations, the iteration is terminated; otherwise, k=k+1, return to S14 to continue iterating, and finally output the new energy carrying capacity and related index data.

[0016] Optionally, the multi-dimensional new energy carrying capacity evaluation index system has three layers: the target layer, the criterion layer, and the index layer. The target layer is the ultimate goal pursued by the comprehensive evaluation, namely, to achieve the optimal comprehensive benefits of the new energy carrying capacity scheme. The criterion layer is the dimension for classifying and integrating evaluation indicators, including safety indicators, economic indicators, reliability indicators, flexibility indicators, and cleanliness indicators. Among them, safety indicators include static safety margin, voltage stability margin, and frequency stability margin; economic indicators include total life cycle cost, investment payback period, and line network loss rate; reliability indicators include power shortage probability, power shortage expected value, and fault recovery capability; flexibility indicators include the proportion of flexible adjustment resources, adjustment capacity adequacy, and reserve capacity adequacy; and cleanliness indicators include carbon emission intensity, pollutant emission intensity, and biological environmental benefits.

[0017] Optionally, the process of determining objective weights using the improved entropy weight method in S3 is as follows: S31, standardize the original values ​​of the indicators to obtain a normalized decision matrix; S32, calculate the entropy value and difference coefficient of each indicator based on the normalized decision matrix; S33, calculate the objective weight of each indicator based on the difference coefficient to obtain the objective weight vector.

[0018] Optionally, in S4, the improved TOPSIS method uses Mahalanobis distance instead of traditional Euclidean distance, and introduces positive and negative adjustment quantities of indicators to determine the dynamic positive and negative ideal solutions, and then calculates the relative closeness; in the key constraint adjustment mechanism, if the scheme violates any key constraint, its comprehensive benefit index is set to 0; if the constraint is met, the reward adjustment is made according to the relative performance of the scheme on the key constraints.

[0019] Optionally, S5 classifies the schemes into four levels: Level A: Comprehensive benefit index ≥ μ + σ, relative advantage ≥ 80%; Level B: μ ≤ Comprehensive benefit index < μ + σ, 60% ≤ Relative advantage < 80%; Level C: μ - σ ≤ Comprehensive benefit index < μ, 40% ≤ Relative advantage < 60%; Level D: Comprehensive benefit index < μ - σ, relative advantage < 40%; where μ is the average of the comprehensive benefit indices of all schemes, and σ is the standard deviation. As can be seen from the above technical solution, compared with the prior art, this invention discloses a method for assessing the renewable energy carrying capacity of regional power grids that balances safety and power supply constraints. First, a time-series production simulation optimization model for renewable energy carrying capacity is established, balancing safety and power supply constraints. Through 8760 hours of simulation, the actual carrying capacity under a specific configuration is accurately quantified, providing basic data for assessment. Next, a comprehensive evaluation index system for renewable energy carrying capacity is constructed, establishing evaluation indicators from five dimensions: safety, economy, reliability, sufficiency, and cleanliness, transforming simulation results into comprehensive evaluation criteria values. Finally, a comprehensive benefit assessment method for renewable energy carrying capacity based on game theory and multi-attribute decision-making is proposed, integrating game theory to balance subjective and objective weights. An improved TOPSIS method combined with grey relational analysis is used to rank multiple candidate planning schemes. Through sensitivity analysis, key factors affecting the decision-making results are identified, ultimately outputting the optimal scheme that combines high carrying capacity and high comprehensive benefits. This invention realizes a systematic solution from technical feasibility analysis to economic comparison, providing a systematic solution for the scientific development of high-proportion renewable energy power grids. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 is a schematic diagram of the method flow provided by the present invention; Figure 2 is a schematic diagram of the comprehensive evaluation index system for new energy carrying capacity provided by the present invention; Figure 3 is a schematic diagram of the overall framework for new energy carrying capacity assessment provided by the present invention; Figure 4 is a schematic diagram of the comprehensive benefit assessment process for new energy carrying capacity schemes provided by the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses a method for evaluating the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as shown in Figure 1. The method includes: S1, constructing a time-series production simulation optimization model with the objective functions of minimizing overall system operating costs, maximizing renewable energy output, and minimizing carbon emissions, embedding safety and stability constraints and supply guarantee constraints, and using the whale optimization algorithm to solve the time-series production simulation optimization model to obtain the renewable energy carrying capacity of the regional power grid under different power supply capacity configuration schemes; S2, constructing a multi-dimensional renewable energy carrying capacity evaluation index system and quantifying the specific indicators under each dimension to obtain the original values ​​of the indicators for each candidate scheme; S3, obtaining subjective weights using the analytic hierarchy process and objective weights using the improved entropy weight method, treating the subjective and objective weights as game participants, and constructing an optimization objective combined with the Lagrange multiplier method. S4. Solve to obtain the weighted combination of indicators after equilibrium; construct a weighted normalized decision matrix by combining the weighted combination of indicators, and calculate the relative closeness using the improved TOPSIS method and grey relational analysis respectively, and merge them to obtain the initial comprehensive benefit index; set key constraints indicators for safety, power supply guarantee, and policy, establish a key constraint adjustment mechanism, and adjust the rewards for schemes that meet the constraints to obtain the final comprehensive benefit index; S5. Rank each candidate scheme in descending order of the final comprehensive benefit index, calculate the relative advantage of each scheme, and classify the schemes into different levels by combining the mean and standard deviation of the comprehensive benefit index; S6. Conduct weight sensitivity analysis and key constraint adjustment coefficient sensitivity analysis respectively to test the robustness of the scheme ranking results to the parameter changes of the time series production simulation optimization model, and finally output the new energy carrying capacity scheme with the best comprehensive benefit.

[0024] The specific related principles are explained as follows: 1. Simulation of the time-series production operation of new energy carrying capacity while taking into account safety and power supply constraints 1.1 Objective function Taking into account factors such as economic benefits, environmental benefits, and safe and stable operation of the system, a multi-objective function is constructed with the goal of minimizing the overall system operating cost, minimizing carbon emissions, and maximizing the new energy absorption value.

[0025] ① Lowest system operating cost

[0026] In the formula: T is the total optimized running time; The duration of each time period; , , , These represent the unit power generation costs of thermal power, hydropower, wind power, and photovoltaic power units, respectively. , , , These represent the various types of generating units: thermal power, hydropower, wind power, and photovoltaic power. The amount of effort or contribution made during the specified period; , , , , These represent the penalty coefficients for wind curtailment, solar curtailment, load shedding, hydropower curtailment, and unit startup, respectively. , , These represent the unit loss costs for wind curtailment, solar curtailment, and load shedding, respectively. , , These represent the curtailed wind power, curtailed solar power, and load shedding power during time period t, respectively. For thermal power units in A 0-1 variable indicating whether the activity was started within the specified time period; ① Represents the converted power output of the hydropower station; ② Maximum output of new energy sources is set to meet the maximum carrying capacity of new energy sources in the regional power grid under certain constraints, and is the maximum output value that new energy sources can reach within the total operating cycle.

[0027] (2)

[0028] In the formula: , These represent the total wind power and solar power that the system can support at time t, respectively.

[0029] ③ Minimum carbon emissions

[0030] The extent of carbon emissions from the power system is measured by calculating the proportion of renewable energy installed capacity and the carbon neutrality rate over a certain period in the region. The proportion of renewable energy installed capacity is used as... The carbon neutrality rate is expressed as... The carbon emission function is as follows: (3) Where: For system assembly; C NE For the installation of new energy power in the system; This refers to the system's carbon capture capacity; This represents the system's carbon emissions. When... and The higher the value, the better the system's environmental benefits and the lower the carbon emissions, and vice versa.

[0031] 1.2 Constraints

[0032] To ensure the safe operation of the equipment and meet the overall system operation requirements, a comprehensive consideration of factors such as technology, economy, reliability, and policy is taken into account. Among these, the constraints on safe and stable operation, power supply, and new energy output are as follows.

[0033] (1) Safety and stability constraints

[0034] Integrating N-1, cross-sectional transmission limits, and voltage safety requirements, these are quantified into line power flow constraints and node voltage amplitude constraints in the optimization model.

[0035] ① Power flow constraints of the line (4) Where: and These represent the active power and reactive power injected into node i at time t, respectively. and These are the real and imaginary parts of the node admittance matrix in row i and column j, respectively. Let be the voltage amplitude at node i at time t; Let be the voltage amplitude at node j at time t; Let be the phase angle difference between the two ends of branch ij at time t; N is the number of nodes in the system.

[0036] ② Branch power constraints (5) In the formula: The branch through which the flow occurs at time t The active power flows from node i to node j. and These represent the upper and lower limits of the active power flowing through branch ij, respectively.

[0037] ③ Voltage amplitude constraint (6) Where: and Let be the minimum and maximum allowable voltage amplitudes at node i at time t, respectively.

[0038] (2) Power supply constraints

[0039] The supply demand is quantified into the following constraints to ensure basic power supply capacity under extreme conditions. These boundary parameters can be dynamically updated according to the system status.

[0040] ① Power balance (7) Where: Let t be the load power of the power grid in this region at time t; Let t be the power transmitted externally; These represent the total output of conventional generating units at time t, which includes the output of thermal power units. and hydropower unit output ; The charging and discharging power of the energy storage device; P w (t) represents the output of the wind turbine at time t; P s (t) represents the output of the photovoltaic unit at time t.

[0041] ② Added upper and lower capacity limits constraints (8) Where: For the first Increased capacity for this type of equipment; Indicates the first The lower limit for the new capacity of this type of equipment; Indicates the first The upper limit for the new capacity of this type of device.

[0042] ③ Energy storage ratio constraints (9) Where: For the additional power capacity of energy storage system s; This represents the newly added energy capacity of energy storage system s; , These are the minimum and maximum energy power ratios of the energy storage system, respectively.

[0043] 1.3 Model Solving

[0044] The whale optimization algorithm is used to solve the time-series production simulation model. The specific operations and main contents of the model are as follows: S11. Data initialization. Set the initial installed capacity of new energy. Based on the initial installed capacity of new energy, generate the 8760-hour time-series output curve of new energy using a probability distribution model; integrate the load of each region to form the comprehensive load curve of the whole network; set the solver parameters.

[0045] S12. Initialize the whale pod. Randomly generate a group of individual whales, each representing a power capacity configuration scheme, and initialize the optimal solution record.

[0046] S13. Time-Series Production Simulation. Receives installation capacity configuration schemes for various power sources and energy storage from the upper-level model. Based on the objective function, simulates the annual time-series power generation operation mode to derive the optimal operation scheme that minimizes system cost and maximizes renewable energy output.

[0047] S14. Safety and Stability Verification. Perform power flow calculations on key scenarios obtained from time-series production simulations. Monitor system power angle, voltage, and frequency stability to assess whether they meet safety and stability standards.

[0048] S15. Capacity Iteration Adjustment. If any indicators exceed the limits, the preset output value of the renewable energy power station needs to be adjusted, and the process should return to S13 to re-simulate production. This process continues iteratively until the maximum renewable energy installed capacity that satisfies all safety and stability constraints is found; this is the system's renewable energy carrying capacity.

[0049] S16. Update the optimal solution. Return the calculated results of indicators such as new energy carrying capacity to the upper-level model for fitness evaluation. Compare and update the historical best and current global best capacity schemes and fitness of individuals based on the WOA mechanism.

[0050] S17. Convergence Check. If the maximum number of iterations is reached or the optimal solution is continuously stable, output the optimal capacity scheme; otherwise, return to S14. If the maximum number of iterations is reached, or the change in the global optimal solution is less than the threshold after multiple consecutive iterations, terminate the iteration; otherwise, k=k+1, return to S14 to continue iterating. Finally, output the new energy carrying capacity and related indicator data.

[0051] 2. Construction of a comprehensive evaluation index system for new energy carrying capacity

[0052] 2.1 Construction of a Multidimensional Indicator System

[0053] The comprehensive evaluation index system for new energy carrying capacity has three layers: the objective layer, the criterion layer, and the indicator layer. Specifically, the objective layer is the ultimate goal pursued by the comprehensive evaluation, namely, to achieve the optimal comprehensive benefits of the new energy carrying capacity scheme; the criterion layer is the dimension for classifying and integrating evaluation indicators, mainly including safety, economy, reliability, flexibility, and cleanliness.

[0054] Based on the current resource allocation and future development trends of new power systems, and referring to research results on the construction of comprehensive evaluation indicators for the carrying capacity of new energy in power grids both domestically and internationally, a scientific and feasible multi-dimensional and multi-level evaluation index system for the carrying capacity of new energy is established. The comprehensive evaluation index system for the carrying capacity of new energy established in this invention is shown in Figure 2.

[0055] 2.2 Quantitative Calculation of Evaluation Indicators

[0056] 2.2.1 Calculation of safety indicators

[0057] ① Static safety margin (N-1 pass rate) (10) Where: This refers to the static safety margin index value. The number of faults that pass the N-1 check refers to the number of fault scenarios in which the system can still meet the safe operation constraints even under the condition of a single component failure; To determine the total number of faults, each critical component included in the verification scope (considering only generators, transformers, and transmission lines) is counted as one preset fault. Numerically equal to the total number of critical components included in the verification.

[0058] ② Voltage stability margin (11) Where: For voltage stability margin; The total system load level corresponding to the voltage collapse critical point is obtained using continuous power flow calculations. Based on the current operating mode, as the total system load is continuously increased, when the Jacobian matrix of the system power flow equation becomes singular at the critical point, the system loses its static voltage stability. The load level at this critical point is recorded as follows: ; This represents the total actual operating load of the system.

[0059] ③ Frequency stability margin (12) Where: For frequency stability margin; This is the absolute value of the maximum allowable frequency deviation of the system; This represents the absolute value of the maximum frequency deviation that may occur during actual operation.

[0060] 2.2.2 Calculation of Reliability Indicators

[0061] ① Loss of Load Probability (LOLP) (13) Where: This represents the probability of insufficient power. The total number of statistical periods (e.g., 8760 hours in a year); This is an indicator function that takes the value 1 when the condition inside the parentheses is true, and 0 otherwise; The available system capacity for time period t; Let t represent the total system load demand during time period t.

[0062] ② Expected Energy Not Supplied (EENS) (14) Where: This represents the expected value of power shortage, typically measured in MWh or kWh. The duration (in hours) of each time period; The function ensures that only the portion of the load exceeding the available capacity is calculated.

[0063] ③ Fault recovery capability (15) Where: For fault recovery capability; This refers to the number of failures recorded. The actual recovery time for the i-th fault; Let be the standard recovery time for the i-th fault. Based on the average repair time guidelines for each device given in the national standard "Specification for Reliability Assessment of Power Supply System", reasonable values ​​are assigned to the recovery time of various typical faults.

[0064] 2.2.3 Calculation of Adequacy Index

[0065] ① Flexibly adjust resource ratio (16) Where: To allow for flexible adjustment of resource allocation; The total power of resources is adjusted to enable system flexibility, including adjustable generating units, energy storage systems, and demand response. This represents the total installed power of the system.

[0066] ② Adjusting capacity adequacy (17) Where: To adjust the capacity adequacy; The available adjustment capacity of the system; This represents the maximum fluctuation range of net load (load minus renewable energy output).

[0067] ③Sufficient reserve capacity (18) Where: Sufficient reserve capacity; This is the available spare capacity for the system; This refers to the backup capacity required according to reliability standards.

[0068] 2.2.4 Calculation of Economic Indicators

[0069] ① Total life cycle cost (19) Where: The total life cycle cost (present value); 1 represents the equipment lifespan or evaluation cycle; Let t be the investment cost in year t; The operating and maintenance cost in year t; The fuel cost in year t; Let t be the environmental cost in year t. is the discount rate.

[0070] ② Investment recovery period (20) Where: Investment payback period (in years); This represents the net income in year t. n is the initial investment cost, and n0 is the total number of years.

[0071] ③ Network loss rate (21) Where: For line network loss rate; To assess the total power loss of the line during the assessment period; To assess the total power generation during the assessment period.

[0072] 2.2.5 Calculation of Cleanliness Indicators

[0073] ① Carbon emission intensity (22) Where: For the new energy consumption rate, It is the total carbon emissions of the system (considering the carbon emissions of all power sources). It is the total power generation of the system.

[0074] ② Pollutant emission intensity (23) Where: It is the pollutant emission intensity (g / kWh). It is the total emissions of pollutants from the system (such as SO2, NOx, dust, etc.). This is the system's total power generation. This indicator reflects the environmental friendliness of the power generation process.

[0075] ③ Biological and environmental benefits (24) Where: The comprehensive value of biological and environmental benefits; For the land resources saved compared to the benchmark scheme; The environmental benefit coefficient per unit of land resources; To save water resources; The environmental benefit coefficient per unit of water resources; To improve air quality indicators; The environmental benefit coefficient for improving air quality per unit.

[0076] 3. Comprehensive Benefit Assessment of New Energy Carrying Capacity Based on Game Theory and Multi-Attribute Decision Making

[0077] 3.1 Overall Framework

[0078] A multi-dimensional, multi-stage comprehensive benefit evaluation model is constructed to evaluate the comprehensive benefits of different new energy carrying schemes, and the optimal scheme is selected based on game theory and multi-attribute decision-making methods. The overall framework is shown in Figure 3.

[0079] 3.2 Comprehensive Benefit Assessment of New Energy Carrying Capacity Based on Game Theory and Multi-Attribute Decision Making

[0080] 3.2.1 Comprehensive Benefit Index

[0081] Because there are complex correlations among the dimensions of new energy carrying capacity assessment, the following should be considered when aggregating them: safety and economy: negative correlation (safety investment increases costs); reliability and adequacy: positive correlation (adequacy improves reliability); policy implementation and economy: complex correlation (policies may increase short-term costs but bring long-term benefits).

[0082] The comprehensive benefit index is a quantitative indicator that measures the overall value of a carrying capacity plan, and is defined as follows: (25) Where: Let be the comprehensive benefit index of scheme i, with a value range of [0,1]. The larger the value, the better the comprehensive benefit. The weights of dimension d, ; Let i be the standardized score of solution i in dimension d; Let i be the actual value of the key constraint indicator c. The maximum value of the key constraint index c among all options; This is an adjustment coefficient for key constraint indicators, reflecting policy preferences.

[0083] 3.2.2 Combinatorial weighting based on game theory

[0084] The core idea of ​​game theory empowerment is to treat different empowerment methods as game participants, considering two participants: subjective empowerment method and objective empowerment method.

[0085] ① Subjective weight

[0086] Construct a judgment matrix using expert judgment. The importance of indicators is compared pairwise using a 1-9 scale, where 1 indicates equal importance and 9 indicates extreme importance.

[0087] (26)

[0088] In the formula: For the total number of indicators, Each element represents the importance scale of the i-th indicator relative to the j-th indicator. Matrix A must satisfy the following conditions: all diagonal elements are 1 and all elements are reciprocals of each other.

[0089] The weights of each indicator are calculated using the square root method: (27) Where: For the first The subjective weight value of each indicator.

[0090] To ensure the logical consistency of expert judgments, a consistency check is required, and the consistency ratio is calculated. (28) Where: The consistency ratio is used. When its value is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable, and the calculated subjective weights are valid. As a consistency index, the larger the value, the higher the degree of inconsistency of the judgment matrix; To determine the largest eigenvalue of matrix A; The average random consistency index can be obtained by querying the average consistency index table.

[0091] After the consistency check passes, the subjective weight vector is obtained as follows: (29) Where: For subjective weight vectors; For the first The subjective weight value of each indicator; n is the total number of indicators.

[0092] ② Objective weighting

[0093] An improved entropy weight method is used to determine the objective weights of the indicators. Assume there are m new energy carrying capacity schemes and n evaluation indicators. The original indicator value matrix after the indicator calculation stage is as follows: Because the indicators have different dimensions and inconsistent directions, they need to be standardized.

[0094] For positive indicators: (30) For contrarian indicators: (31) Where: Let i be the standardized value of scheme i on the j-th positive index; Let be the standardized value of scheme i on the j-th inverse metric; positive metrics are those with larger values, while inverse metrics are those with smaller values, which are better.

[0095] After standardization, a normalized decision matrix is ​​obtained. .

[0096] (32)

[0097] In the formula: The number of schemes, For the number of indicators, .

[0098] Calculate the entropy value of the j-th index: (33) Where: For the first The entropy value of each indicator; ,when At that time, it was stipulated The coefficient of difference is then... .

[0099] Calculate objective weights: (34) Obtain the objective weight vector: (35) Where: Let be the objective weight of the j-th indicator.

[0100] ③ Game theory weighting methods

[0101] Subjective weights and objective weights are considered as two game participants, and the combined weights are obtained by reaching equilibrium through game theory.

[0102] Define the optimization goal: (36) Constraints: (37) Where: Let be the combined weight vector to be determined.

[0103] Solve using the Lagrange multiplier method, and construct the Lagrange function: (38) respectively . and Taking the partial derivative and setting it to zero, we obtain the combined weights as follows: (39) Further generalization to a weighted average form: (40) Where: This is the coefficient for subjective weighting, usually taken as 0.5, but can be adjusted according to the actual situation. sj Let be the subjective weight of the j-th indicator.

[0104] 3.2.3 Improved Multi-Attribute Decision Model Introducing Grey Relational Analysis

[0105] (1) Improve the TOPSIS method

[0106] The TOPSIS method, based on geometric distance, finds the solution closest to the ideal solution and furthest from the negative ideal solution. A weighted normalized decision matrix is ​​then established. (41) Where: Let be the weighted normalized value of scheme i on the j-th index.

[0107] Considering the directionality of the indicator, determine the positive and negative ideal solutions.

[0108] (42) (43) Where: The vector is the positive ideal solution vector; It is the negative ideal solution vector; This represents the positive adjustment amount for the j-th indicator, reflecting technological development trends; This represents the negative adjustment amount for the j-th indicator, reflecting potential risks; if no adjustment is needed, it can be taken as... .

[0109] Traditional Euclidean distance is sensitive to the dimensions of the index; therefore, Mahalanobis distance is used instead. (44) (45) Where: For the plan The weighted normalized vector; is the inverse of the covariance matrix; the superscript T indicates transpose.

[0110] Calculate the covariance matrix between indicators : (46) Where: For the first The covariance between the k-th indicator and the k-th indicator; Let be the mean of the j-th indicator; Let v represent the average value of the k-th index across all options; ik Let be the weighted normalized value of scheme i on the k-th indicator; m is the total number of schemes participating in the evaluation.

[0111] The relative similarity of TOPSIS is then: (47) Where: For a solution based on improved TOPSIS Relative proximity, with a value range of [0,1].

[0112] (2) Introducing Grey Relational Analysis (GRA)

[0113] Grey relational analysis, based on curve similarity, analyzes the degree of correlation between the proposed solution and the ideal solution. For the proposed solution... In indicators The correlation coefficient below: (48) (49) Where: , These are the correlation coefficients for the positive and negative ideal solutions, respectively. The resolution coefficient is usually set to 0.5.

[0114] Then the grey relational degree of scheme i is: (50) (51) Where: , These represent the grey relational degrees between scheme i and the positive and negative ideal solutions, respectively.

[0115] Then, based on the relative closeness of grey relational degree: (52) Where: Let represent the relative proximity of scheme i based on grey relational analysis, with a value range of [0,1].

[0116] (3) Calculation of comprehensive benefit index

[0117] Based on the TOPSIS and GRA results, calculate the overall benefit index: (53) Where: Let i be the initial comprehensive benefit index of scheme i; This is the binding coefficient, which is usually taken as 0.5.

[0118] (4) Key constraint adjustment mechanism

[0119] After obtaining the initial CBI value, three key constraint indicators are set: Let i represent the actual value of scheme i under the c-th constraint, where: safety constraint Power supply constraints Policy constraints Each constraint has a minimum threshold. And the maximum value among all solutions. .

[0120] For scheme i, check if it satisfies: (54) If any constraint is not satisfied, then .

[0121] If the key constraints are not violated, calculate the relative performance of solution i on each key constraint. If its performance on the constraint indicators is close to the optimal value, it will be rewarded (product factor close to 1); if its performance is poor, it will be penalized (product factor much less than 1). Adjustment coefficient. The larger the value, the more the policy places emphasis on the constraint c, and the greater the impact of performance differences.

[0122] (55)

[0123] In the formula: Let represent the relative performance of scheme i on the c-th key constraint, with a value range of [0,1].

[0124] Calculation of the final CBI value: (56) Where: This is the reward adjustment factor for the c-th key constraint, typically ranging from 0.1 to 0.3.

[0125] With a high proportion of renewable energy being integrated into the power grid, this mechanism ensures that any recommended scheme must meet the physical limits of the power grid and basic safety principles, avoiding the sacrifice of power grid safety for excessive pursuit of economic benefits, and further balancing safety and power supply requirements.

[0126] (5) Scheme ranking

[0127] After adjusting for key constraints, a final set of comprehensive benefit indices (CBI) for m alternatives is obtained. The m alternatives are then sorted from largest to smallest based on their final CBI values.

[0128] (57)

[0129] To more intuitively illustrate the differences between the options, the relative advantage of each option is calculated: (58) Where: Let represent the relative advantage of solution i, indicating the degree of improvement of solution i compared to the worst solution, with a value range of [0%, 100%]. This is the CBI value corresponding to the scheme at the k-th position after sorting; The minimum CBI among all options; The maximum value of CBI across all options.

[0130] Calculate the mean and standard deviation of the CBI value: (59) (60) Where: The average CBI value across all schemes; The standard deviation is denoted as .

[0131] Based on the CBI value distribution, the schemes are divided into several levels. Based on the level results of each scheme, the renewable energy carrying capacity scheme with the best overall benefit can be obtained. The level classification results are shown in Table 1: Table 1 Level Classification of Renewable Energy Carrying Capacity Schemes with Comprehensive Benefits

[0132] 3.3 Sensitivity Analysis

[0133] Sensitivity analysis aims to examine the robustness of changes in model parameters on the final ranking results. Through parameter sensitivity analysis, the stability of the model and the reliability of the decision results are examined and evaluated.

[0134] (1) Weight sensitivity analysis

[0135] Assess the degree of influence of the relative importance of subjective and objective weights (i.e., the combined weight coefficient α) on the ranking of the schemes.

[0136] Let the portfolio weights be: (61) Let α vary in the interval [0,1] with a step size of 0.1, observe the CBI values ​​and ranking changes of each scheme, and calculate the ranking stability index: (62) Where: To determine the sorting stability of scheme i; Let be the standard deviation of the ranking of scheme i under different α; Let be the average rank of scheme i.

[0137] (2) Sensitivity analysis of key constraint adjustment coefficients

[0138] Analysis of adjustment coefficients Impact on the final result. A single-factor variation method was used, changing only one factor at a time. Observe the changes in the ranking of the schemes and calculate the sensitivity index: (63) Where: Let i be the elasticity sensitivity of scheme i to the c-th adjustment coefficient; This is the sign for the partial derivative.

[0139] 4. Comprehensive Benefit Evaluation Process for New Energy Carrying Capacity Scheme

[0140] The overall evaluation process for the comprehensive benefits of new energy carrying capacity schemes is shown in Figure 4. Ultimately, it outputs the detailed configuration of the optimal carrying capacity scheme, the expected increase in new energy carrying capacity, and the Comprehensive Benefit Index (CEI) value.

[0141] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0142] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, characterized in that, include: S1. Construct a time-series production simulation optimization model with the objective functions of minimizing overall system operating cost, maximizing renewable energy output, and minimizing carbon emissions. Embed safety and stability constraints and power supply guarantee constraints. Use the whale optimization algorithm to solve the time-series production simulation optimization model to obtain the regional power grid's renewable energy carrying capacity under different power supply capacity configuration schemes. S2. Construct a multi-dimensional renewable energy carrying capacity evaluation index system and quantify the specific indicators under each dimension to obtain the original values ​​of the indicators for each candidate scheme. S3. Use the analytic hierarchy process (AHP) to obtain subjective weights and the improved entropy weight method to obtain objective weights. Treat the subjective and objective weights as game participants. By constructing an optimization objective and solving it using the Lagrange multiplier method, obtain the equilibrium index combination weights. S4. Combine the indicators... A weighted normalized decision matrix is ​​constructed by combining weights. The relative proximity is calculated using the improved TOPSIS method and grey relational analysis, and then fused to obtain the initial comprehensive benefit index. Key constraints such as safety, power supply guarantee, and policy are set, and a key constraint adjustment mechanism is established to reward and adjust the schemes that meet the constraints, thus obtaining the final comprehensive benefit index. S5: The candidate schemes are ranked in descending order according to the final comprehensive benefit index, and the relative advantage of each scheme is calculated. Based on the mean and standard deviation of the comprehensive benefit index, the schemes are divided into different levels. S6: Sensitivity analysis of weights and sensitivity analysis of key constraint adjustment coefficients are carried out to test the robustness of the scheme ranking results to the parameter changes of the time-series production simulation optimization model. Finally, the new energy carrying capacity scheme with the best comprehensive benefit is output.

2. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... The objective function is specifically: ① Minimize system operating cost. In the formula: T is the total optimized running time; The duration of each time period; 、 、 、 These represent the unit power generation costs of thermal power, hydropower, wind power, and photovoltaic power units, respectively. 、 、 、 These represent the various types of generating units: thermal power, hydropower, wind power, and photovoltaic power. The amount of effort or contribution made during the specified period; 、 、 、 、 These represent the penalty coefficients for wind curtailment, solar curtailment, load shedding, hydropower curtailment, and unit startup, respectively. 、 、 These represent the unit loss costs for wind curtailment, solar curtailment, and load shedding, respectively. 、 、 These represent the curtailed wind power, curtailed solar power, and load shedding power during time period t, respectively. For thermal power units in A 0-1 variable indicating whether the activity was started within the specified time period; ① The equivalent power output of the hydropower station; ② The largest output from new energy sources. In the formula: 、 These represent the total wind power and solar power that the system can support at time t, respectively; ③ Minimize carbon emissions by calculating the proportion of new energy installed capacity and the carbon neutrality rate over a certain period in the region, which measures the degree of carbon emissions from the power system. The proportion of new energy installed capacity is measured using... The carbon neutrality rate is expressed as... The carbon emission function is as follows: In the formula: For system assembly; C NE For the installation of new energy power in the system; This refers to the system's carbon capture capacity; This represents the system's carbon emissions.

3. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... The safety and stability constraints include line power flow constraints, branch power constraints, and voltage amplitude constraints; the power supply constraints include power balance, upper and lower limits of new capacity constraints, and energy storage ratio constraints.

4. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... The whale optimization algorithm specifically includes: S11. Data initialization: Set the initial installed capacity of new energy sources, and based on the initial installed capacity, generate the time-series output curve of new energy sources using a probability distribution model; integrate the loads of each region to form a comprehensive load curve for the entire network; set the solver parameters; S12. Initialize the whale pod: Randomly generate a group of individual whales, each representing a power capacity configuration scheme, and initialize the optimal solution record; S13. Time-series production simulation: Receive the installation capacity configuration schemes of various power sources and energy storage from the upper-level model; simulate the time-series power generation operation mode throughout the year according to the objective function, and obtain the optimal operation scheme with the lowest system cost and the highest new energy output; S14. Safety and stability verification: Perform power flow calculation on the key scenarios obtained from the time-series production simulation; monitor the system power angle, voltage, and frequency stability, and evaluate whether the safety and stability standards are met; S1 5. Capacity Iteration Adjustment: If any indicator exceeds the limit, the preset output value of the renewable energy power station needs to be adjusted, and the simulation should be repeated in S13. Iteration continues until the maximum renewable energy installed capacity that meets all safety and stability constraints is found, which is the renewable energy carrying capacity of the system. S16. Update Optimal Solution: The calculated renewable energy carrying capacity index results are returned to the upper-level model for fitness evaluation. The capacity scheme and fitness of the individual historical best and the current global best are compared and updated according to the WOA mechanism. S17. Convergence Judgment: If the maximum number of iterations is reached or the optimal solution is continuously stable, the optimal capacity scheme is output; otherwise, return to S14. If the maximum number of iterations is reached, or the change of the global optimal solution is less than the threshold after multiple iterations, the iteration is terminated; otherwise, k=k+1, return to S14 to continue iterating, and finally output the renewable energy carrying capacity and related index data.

5. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... The multi-dimensional new energy carrying capacity evaluation index system has three layers: the target layer, the criterion layer, and the index layer. The target layer is the ultimate goal pursued by the comprehensive evaluation, namely, to optimize the comprehensive benefits of the new energy carrying capacity scheme. The criterion layer is the dimension for classifying and integrating evaluation indicators, including safety indicators, economic indicators, reliability indicators, flexibility indicators, and cleanliness indicators. Among them, safety indicators include static safety margin, voltage stability margin, and frequency stability margin; economic indicators include total life cycle cost, investment payback period, and line network loss rate; reliability indicators include power shortage probability, power shortage expected value, and fault recovery capability; flexibility indicators include the proportion of flexible adjustment resources, adjustment capacity adequacy, and reserve capacity adequacy; and cleanliness indicators include carbon emission intensity, pollutant emission intensity, and biological environmental benefits.

6. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... The process of determining objective weights using the improved entropy weight method in S3 is as follows: S31, standardize the original values ​​of the indicators to obtain a normalized decision matrix; S32, calculate the entropy value and difference coefficient of each indicator based on the normalized decision matrix; S33, calculate the objective weight of each indicator based on the difference coefficient to obtain the objective weight vector.

7. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... In S4, the improved TOPSIS method uses Mahalanobis distance instead of traditional Euclidean distance, and introduces positive and negative adjustment quantities of indicators to determine the dynamic positive and negative ideal solutions, and then calculates the relative closeness; in the key constraint adjustment mechanism, if the scheme violates any key constraint, its comprehensive benefit index is set to 0; if the constraint is met, the reward adjustment is made according to the relative performance of the scheme on the key constraints.

8. The method for assessing the renewable energy carrying capacity of a regional power grid that balances safety and supply constraints, as described in claim 1, is characterized in that... S5 classifies the schemes into four levels: Level A: Comprehensive benefit index ≥ μ + σ, relative advantage ≥ 80%; Level B: μ ≤ Comprehensive benefit index < μ + σ, 60% ≤ Relative advantage < 80%; Level C: μ - σ ≤ Comprehensive benefit index < μ, 40% ≤ Relative advantage < 60%; Level D: Comprehensive benefit index < μ - σ, relative advantage < 40%; where μ is the average of the comprehensive benefit index of all schemes, and σ is the standard deviation.

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