A demand regulation method and system for a power system under extreme heat wave

Abstract

The application relates to the technical field of power system safety evaluation, and discloses a demand regulation method and system for a power system under extreme heat waves, which comprises the following steps: obtaining multi-dimensional operation comprehensive data of a power system under an extreme heat wave scenario; constructing a robustness evaluation index system of the power system, so as to quantitatively obtain index values respectively representing load guarantee capability, new energy consumption capability, market stability capability and low-carbon operation capability of the power system based on the multi-dimensional operation comprehensive data; establishing an evaluation hierarchy according to an analytic hierarchy process, and determining the weights of the index values through the evaluation hierarchy; determining a robustness evaluation grade of the power system by using the index values and the corresponding weights based on a fuzzy comprehensive evaluation method, and performing demand response regulation on the power system according to the robustness evaluation grade; and scientific and comprehensive evaluation of the supply-demand robustness of the power system under the extreme heat wave scenario is realized.

Description

Technical Field

[0001] This invention relates to the field of power system safety assessment technology, and in particular to a method and system for demand regulation of power systems under extreme heat waves. Background Technology

[0002] With global warming and the increasing frequency of extreme heat waves, receiving-end power systems that heavily rely on imported electricity and renewable energy sources pose a serious threat. Such weather conditions lead to a surge in cooling loads on the one hand, and a decrease in renewable energy output due to high temperatures and low wind speeds on the other. This combination exacerbates the supply-demand imbalance, potentially triggering load shedding and drastic fluctuations in electricity prices. Simultaneously, the increase in load and the decrease in supply lead to an increase in thermal power units, resulting in increased carbon emissions, creating a complex situation with multiple intertwined risks to safety, the economy, and the environment.

[0003] Currently, research on risk assessment and response to extreme weather conditions for power systems is mostly focused on a single dimension, such as static safety margin, reserve adequacy, or market clearing price. This makes it difficult to accurately assess the robustness of power systems under extreme heat waves, and thus difficult to use this as a basis for whether to implement demand response regulation.

[0004] Therefore, accurately assessing the robustness of power systems under extreme heat waves and using this information for demand response regulation has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for demand regulation of power systems under extreme heat waves, addressing the problem of how to accurately assess the robustness of power systems under extreme heat waves and thereby conduct demand response regulation accordingly.

[0006] To address the aforementioned technical problems, the first aspect of this invention provides a method for demand regulation of a power system under extreme heat waves, comprising: Acquire comprehensive multi-dimensional operational data of the power system under extreme heat wave scenarios; A robustness evaluation index system for the power system is constructed, and the index values ​​representing the load guarantee capacity, renewable energy absorption capacity, market stability capacity and low-carbon operation capacity of the power system are obtained by quantifying based on the multi-dimensional operation comprehensive data. An evaluation hierarchy structure is established based on the analytic hierarchy process, and the weights of each indicator value are determined through the evaluation hierarchy structure. Based on the fuzzy comprehensive evaluation method, the robustness assessment level of the power system is determined by using the values ​​of the various indicators and their corresponding weights, and the power system is subjected to demand response regulation according to the robustness assessment level.

[0007] As one preferred option, the multi-dimensional operational comprehensive data includes load data, wind and solar new energy output data, electricity price time series data, carbon emission data, and related temperature, wind speed, and irradiance; the robust evaluation index system includes graded load shedding rate index, new energy curtailment rate index, electricity price fluctuation index, and comprehensive carbon emission index.

[0008] As one preferred embodiment, the quantitative analysis of the multi-dimensional operational data to obtain various indicator values ​​representing the power system's load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity includes: The load data is classified according to the importance of the loads in the power system to obtain multi-level loads; The graded load shedding rate index, which characterizes the load guarantee capability of the power system, is determined by the ratio of the actual power shedding of the multi-level loads under the extreme heat wave scenario to their rated demand power.

[0009] As one preferred embodiment, the step of quantifying the various indicator values ​​representing the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system based on the multi-dimensional operational comprehensive data further includes: Based on the meteorological characteristics corresponding to temperature and wind speed in the extreme heat wave scenario, a wind power output model is constructed according to the temperature and wind speed. The wind power curtailment rate of the power system is determined based on the wind and solar new energy output data and the wind power output model. A photovoltaic power output model is constructed using the temperature and irradiance, and the photovoltaic curtailment rate of the power system is determined based on the photovoltaic power output model and the wind and solar new energy power output data. The renewable energy curtailment rate index, which characterizes the renewable energy absorption capacity of the power system, is determined by the wind power curtailment rate and the photovoltaic curtailment rate.

[0010] As one preferred embodiment, the step of quantifying the various indicator values ​​representing the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system based on the multi-dimensional operational comprehensive data further includes: The total load shedding rate of the power system is determined based on the graded load shedding rate index, and an average electricity price model is constructed based on the total load shedding rate. The standard deviation model for electricity prices is determined using the time-series data of electricity prices, the total load shedding rate, and the renewable energy curtailment rate. Based on the electricity price mean model and the electricity price standard deviation model, an indicator for the degree of electricity price volatility is determined to characterize the market stability capability of the power system.

[0011] As one preferred embodiment, the step of quantifying the various indicator values ​​representing the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system based on the multi-dimensional operational comprehensive data further includes: Based on the carbon emission data, determine the carbon emissions on the generation side and the user side of the power system respectively; The carbon emissions from the power generation side and the carbon emissions from the user side are weighted and summed to obtain the comprehensive carbon emission index used to characterize the low-carbon operation capability of the power system.

[0012] As one preferred embodiment, the step of establishing an evaluation hierarchy based on the analytic hierarchy process (AHP) and determining the weights of each indicator value through the evaluation hierarchy includes: The supply and demand robustness level of the power system under the extreme heat wave scenario is taken as the target layer, and load robustness, renewable energy robustness, electricity price robustness and carbon emission robustness are taken as the criterion layer. The graded load shedding rate index, renewable energy curtailment rate index, electricity price volatility index and carbon emission comprehensive index are taken as the indicator layer. Based on the target layer, the criterion layer, and the index layer, a judgment matrix between each layer is constructed using the 1-9 scaling method, and the weight vector of each judgment matrix is ​​calculated using the sum-product method. A consistency check is performed on each of the weight vectors to obtain the criterion layer weight vector and the indicator layer weight vector, which are used as the weights of each indicator value.

[0013] As one preferred embodiment, the method of determining the robustness rating of the power system based on the fuzzy comprehensive evaluation method, using the values ​​of each indicator and their corresponding weights, includes: A robustness evaluation level set is set; the robustness evaluation level set includes, from strongest to weakest, excellent interference resistance, good interference resistance, baseline interference resistance, interference resistance to be optimized, and interference resistance to be improved. The membership degree of each index value to different robustness evaluation levels is calculated using a trapezoidal membership function; Based on the membership degrees, a fuzzy relation matrix is ​​constructed. Combined with the weight vector of the index layer, a weighted average operator is used to perform a first-level fuzzy comprehensive evaluation of the index values ​​to obtain the evaluation results of the criterion layer. Based on the evaluation results of the criterion layer and the weight vector of the criterion layer, a second-level fuzzy comprehensive evaluation is performed on the values ​​of each indicator to obtain the comprehensive evaluation results of the target layer. Based on the principle of maximum membership, the robustness assessment level of the power system is determined according to the comprehensive evaluation results of the target layer.

[0014] As one preferred embodiment, the step of demand response regulation of the power system based on the robustness assessment level includes: When the robustness assessment level is the disturbance rejection optimization level or the disturbance rejection enhancement level, it is determined that the power system needs to initiate demand response control, and a response plan of corresponding strength is matched according to the robustness assessment level to control the power system to execute.

[0015] A second aspect of the present invention provides a demand regulation system for a power system under extreme heat waves, comprising: The data acquisition module is used to acquire comprehensive multi-dimensional operational data of the power system under extreme heat wave scenarios; The index quantification module is used to construct a robustness evaluation index system for the power system, and to quantify the index values ​​of various indicators that respectively characterize the load guarantee capacity, renewable energy absorption capacity, market stability capacity and low-carbon operation capacity of the power system based on the multi-dimensional operation comprehensive data. The weight calculation module is used to establish an evaluation hierarchy structure based on the analytic hierarchy process and to determine the weight of each indicator value through the evaluation hierarchy structure. The demand regulation module is used to determine the robustness assessment level of the power system based on the fuzzy comprehensive evaluation method, using the values ​​of the various indicators and their corresponding weights, and to perform demand response regulation of the power system according to the robustness assessment level.

[0016] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: By constructing a four-in-one evaluation index system covering load guarantee, renewable energy consumption, market stability, and low-carbon operation, and integrating the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, a multi-dimensional, refined, and quantifiable comprehensive assessment of the robustness of the receiving-end power system under extreme heat waves is achieved. This assessment conclusion can be used to determine demand response requirements and transform them into clear demand response initiation decisions, supporting the grid to operate safely, stably, and with low carbon emissions as much as possible under extreme heat waves. The robustness evaluation index system adopted is highly targeted, and for the first time, it couples physical security, resource utilization, and market stability risks into a unified model, solving the problem of the single-dimensionality of existing methods. The evaluation process is rigorous and the results are... The analytic hierarchy process (AHP) ensures the rationality of weights, while the fuzzy comprehensive evaluation method effectively handles uncertainty and outputs intuitive levels, facilitating engineering decision-making. The evaluation achieves carbon-electricity synergy, transforming carbon emission pressure into a dynamic evaluation index that directly assesses the degree of system robustness deterioration. When the system is forced to rely on high-carbon operation due to extreme heat waves, the carbon emission index will increase significantly, thus effectively supporting safety assessment and demand response decisions under carbon-electricity synergy. The decision conversion mechanism based on the comprehensive robustness level integrates assessment and decision-making. The robustness level obtained through the synergistic assessment of carbon and electricity indicators supports demand response decisions, achieving a seamless conversion from complex state assessment to clear and executable control commands. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a demand regulation method for a power system under extreme heat waves, provided in a certain embodiment of the present invention; Figure 2 This is a flowchart of a demand regulation method for a power system under extreme heat waves, provided by another embodiment of the present invention; Figure 3 This is a structural diagram of a demand control system for a power system under extreme heat waves, provided in a certain embodiment of the present invention. Figure label: The module includes: 10. Data acquisition module; 20. Indicator quantification module; 30. Weight calculation module; and 40. Demand control module. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art will be able to understand the specific meaning of the above terms in this application according to the specific circumstances.

[0021] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is merely for describing specific embodiments and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] In one embodiment, such as Figure 1 As shown, the first aspect of the present invention provides a method for demand regulation of a power system under extreme heat waves, comprising: S1. Acquire comprehensive multi-dimensional operational data of the power system under extreme heat wave scenarios. This comprehensive multi-dimensional operational data includes load data, wind and solar renewable energy output data, electricity price time-series data, carbon emission data, and associated temperature, wind speed, and irradiance. Specifically, collect comprehensive multi-dimensional operational data of the target power system under specific extreme heat wave prediction or actual occurrence scenarios, including load data: rated demand power and actual power cut-off for tiered loads; wind and solar renewable energy output data: theoretical maximum output, actual received output, and installed capacity ratio of wind and solar power; electricity price time-series data: historical and real-time electricity price sequences; carbon emission data: total carbon emissions from the generation side (local thermal power and imported electricity) and the user side; and meteorological data covering the assessment area, especially time-series data of temperature (critical temperature for extreme heat waves, maximum system tolerance temperature), wind speed, and solar radiation intensity. Subsequently, these collected data are cleaned and outlier removed to improve data accuracy.

[0023] S2. Construct a robustness assessment index system for the power system, quantifying various index values ​​based on the multi-dimensional operational comprehensive data to characterize the power system's load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity. The robustness assessment index system includes a tiered load shedding rate index, a renewable energy curtailment rate index, an electricity price volatility index, and a comprehensive carbon emission index. This invention couples four core dimensions—load guarantee, renewable energy absorption, market stability, and low-carbon operation—into a unified assessment framework to construct a robustness assessment index system for the power system. Based on this system, it calculates four core indicators: the tiered load shedding rate, the renewable energy curtailment rate considering temperature decay effects, the dynamic electricity price volatility related to the system's operating status, and the comprehensive carbon emission index based on carbon-electricity synergy. This overcomes the shortcomings of existing methods that are single-dimensional and fragmented, and achieves a comprehensive "carbon-electricity" synergistic diagnosis of complex system risks under extreme heat waves. Meanwhile, the design of these indicators closely follows the impact mechanism of extreme heat waves; among them, the tiered load shedding rate distinguishes the priority of supply guarantee; the new energy curtailment rate of wind and solar power accurately depicts the differences in meteorological impact; the dynamic electricity price fluctuation model reveals the root cause of market fluctuations; and the comprehensive carbon emission index dynamically reflects the carbon cost of supply guarantee.

[0024] In one embodiment, step S2 includes: The load data is classified according to the importance of the loads in the power system to obtain multi-level loads; The graded load shedding rate index, which characterizes the load guarantee capability of the power system, is determined by the ratio of the actual power shedding of the multi-level loads under the extreme heat wave scenario to their rated demand power.

[0025] Specifically, this invention classifies the load data of the power system according to its importance into the following levels, resulting in multi-level loads: Level 1 loads refer to the core loads that must be prioritized for protection during extreme heat waves. These loads include electricity consumption for public service facilities such as hospitals, transportation hubs, and emergency command centers, as well as basic cooling electricity consumption for residential buildings.

[0026] Secondary loads refer to important production and operation loads with certain regulation capabilities. These loads include the electricity consumption of key industrial enterprises and large commercial complexes.

[0027] Level 3 loads refer to interruptible non-core loads, including high-energy-consuming industrial enterprises and non-essential commercial electricity use.

[0028] To quantify the degree of forced shedding of loads at different levels under extreme heat waves, this invention uses a tiered load shedding rate index to measure and reflect the system's ability to guarantee the loads of different importance. Specifically, the ratio of the actual power shedding of multiple load levels under extreme heat wave scenarios to their rated demand power is used as the index value characterizing the power system's load guarantee capability—the tiered load shedding rate index. This calculation process is expressed by the following formula: In the formula, In order to be at an ambient temperature of Time Load shedding rate index for Class 1 loads; In order to be at an ambient temperature of Time The actual power cut off by the load level; In order to be at an ambient temperature of Time The rated power requirement of the load.

[0029] In one embodiment, to achieve a quantitative correlation between the graded load shedding rate index and temperature, this invention establishes an explicit mathematical model of load shedding rate and temperature to calculate the graded load shedding rate index. This process is expressed by the following formula: In the formula, This refers to the critical temperature threshold for extreme heat wave scenarios. This is the maximum temperature that the power system can withstand; if the temperature is exceeded, the system cannot guarantee power supply to any load. The temperature sensitivity coefficient for the load shedding rate of the k-th load level, in °C. -2 This reflects the rate at which the load shear rate increases with temperature; a larger value indicates greater sensitivity, and its value satisfies the following conditions: This means that the first-level load is the least sensitive, and its temperature sensitivity coefficient is generally (0.2~0.4)℃. -2 Secondary loads are the next most sensitive, with a temperature sensitivity coefficient typically ranging from 0.4 to 0.6℃. -2 Level 3 load is the most sensitive, with a temperature sensitivity coefficient typically ranging from 0.8 to 1.0℃. -2 .

[0030] This invention not only calculates the total load shedding, but also calculates the load shedding rate according to the load importance classification, and establishes an explicit mathematical model of its relationship with temperature, reflecting the order (priority) of different levels of loads being sacrificed when power supply is tight and their different sensitivities to temperature rise; at the same time, it quantifies the degree of forced shedding of different importance levels when power supply is tight through the graded load shedding rate index, thereby accurately reflecting the power system's ability to guarantee critical loads.

[0031] In one embodiment, step S2 further includes: Based on the meteorological characteristics corresponding to temperature and wind speed in the extreme heat wave scenario, a wind power output model is constructed according to the temperature and wind speed. The wind power curtailment rate of the power system is determined based on the wind and solar new energy output data and the wind power output model. A photovoltaic power output model is constructed using the temperature and irradiance, and the photovoltaic curtailment rate of the power system is determined based on the photovoltaic power output model and the wind and solar new energy power output data. The renewable energy curtailment rate index, which characterizes the renewable energy absorption capacity of the power system, is determined by the wind power curtailment rate and the photovoltaic curtailment rate.

[0032] Specifically, generally speaking, wind power output is positively correlated with wind speed and negatively correlated with temperature. This invention combines the meteorological characteristics of "high temperature and low wind speed" under extreme heat waves and establishes a quantitative correlation model of the theoretical maximum wind power output—the wind power output model—based on collected meteorological data—temperature and wind speed, as shown in the following formula: In the formula, This represents the theoretical maximum output of the wind turbine. Wind speed; Set the cut-in wind speed for the fan; The rated wind speed of the fan; Cut off the wind speed for the fan; This is the rated output of the fan; The temperature attenuation coefficient of wind power output, in °C. -1 The reference range for extreme heat waves is (1.0~3.0)×10⁻¹⁰. -3 ℃ -1 ; This is a reference temperature.

[0033] Actual power output of the power system to wind power Limited by load demand and external power supply capacity, when the sum of the theoretical maximum wind power output and the external power supply capacity exceeds the load demand, the excess must be curtailed and absorbed. The value of this curtailment is: In the formula, The ambient temperature is Total system load at that time; The ambient temperature is Maximum external power supply capacity at any given time.

[0034] Therefore, by combining the actual wind power output, wind and solar renewable energy output data, and the wind power output model, the wind power curtailment rate of the power system can be calculated. This process is illustrated by the following formula: In the formula, The ambient temperature is Wind speed is Wind power curtailment rate at that time.

[0035] Photovoltaic theoretical maximum output With solar radiation intensity Positive correlation with temperature Negative correlation. Ignoring short-term fluctuations in solar radiation intensity, this invention constructs a photovoltaic power output model based on temperature and irradiance, as shown in the following formula: In the formula, Rated output for photovoltaic modules; Rated solar radiation intensity for photovoltaic modules; The temperature degradation coefficient of photovoltaic power output, in °C. -1 The reference range for extreme heat waves is (3.0~4.0)×10⁻¹⁰. -3 ℃ -1 .

[0036] Actual photovoltaic power generation Limited by the remaining absorption capacity of the power system, its value is: Subsequently, based on the photovoltaic output model and wind and solar renewable energy output data, the photovoltaic curtailment rate of the power system can be determined, as shown in the following formula: In the formula, The ambient temperature is Solar radiation intensity is The photovoltaic curtailment rate at that time; The ambient temperature is Solar radiation intensity is The theoretical maximum output of photovoltaic power at that time.

[0037] After calculating the wind power curtailment rate and the photovoltaic curtailment rate, the weighted average method is used to calculate the index value representing the renewable energy absorption capacity of the power system—the renewable energy curtailment rate index, which is shown in the following formula: In the formula, This is an indicator of the curtailment rate of renewable energy in the power system. and The weights are the installed capacity percentages of wind power and photovoltaic power, respectively.

[0038] This invention, when calculating the theoretical maximum output of new energy sources, not only considers wind speed and irradiance but also introduces a temperature attenuation factor to quantify the direct impact of extreme high temperatures on the output of new energy power generation equipment. Actual output is constrained by the system's real-time absorption capacity (total load minus external power, etc.). The invention uses a new energy curtailment rate index to quantify the degree of limited absorption of wind power, photovoltaic, and other new energy sources by the power system, reflecting the comprehensive impact of meteorological conditions such as "high temperature and low wind speed" under extreme heat waves on new energy output and system regulation capabilities.

[0039] In one embodiment, step S2 further includes: The total load shedding rate of the power system is determined based on the graded load shedding rate index, and an average electricity price model is constructed based on the total load shedding rate. The standard deviation model for electricity prices is determined using the time-series data of electricity prices, the total load shedding rate, and the renewable energy curtailment rate. Based on the electricity price mean model and the electricity price standard deviation model, an indicator for the degree of electricity price volatility is determined to characterize the market stability capability of the power system.

[0040] Specifically, electricity prices in a power system are related to the system's operating status, specifically to the total load shedding rate and renewable energy curtailment rate of the power system, determined based on electricity price time-series data and tiered load shedding rate indicators. Therefore, this invention constructs an average electricity price model for the power system, expressed by the following formula: In the formula, This represents the average electricity price. This is the benchmark electricity price under normal meteorological conditions; This is the coefficient representing the impact of the load shedding rate on the average electricity price. This is the coefficient representing the impact of renewable energy curtailment rate on the average electricity price. The temperature is The total load shedding rate of the power system at that time.

[0041] The standard deviation of electricity prices is related to fluctuations in system operating conditions and can be constructed based on time-series electricity price data, fluctuations in total load shedding rate, and fluctuations in renewable energy curtailment rate. It is expressed by the following formula: In the formula, This is the benchmark value for the standard deviation of electricity prices under normal meteorological conditions; The coefficient representing the influence of the load shedding rate temperature change rate on the standard deviation of electricity price is expressed in yuan (°C). The reference range under extreme heat waves is (0.02~0.08) yuan (°C). The coefficient representing the influence of the rate of change in the curtailment rate of renewable energy on the standard deviation of electricity prices is expressed in yuan (°C). The reference range under extreme heat waves is (0.01~0.05) yuan (°C). and These represent the absolute rate of change of temperature with respect to the total load shedding rate and the renewable energy curtailment rate, respectively.

[0042] The electricity price volatility index is used to quantify the stability of electricity prices in the receiving-end power system under extreme heat waves, reflecting the severity of fluctuations in the system's supply and demand balance. This invention uses the coefficient of variation (the ratio of standard deviation to mean) as an indicator of the power system's market stability capability—the electricity price volatility index, which is expressed by the following formula: In the formula, For temperature An indicator of the degree of fluctuation in electricity prices at that time.

[0043] Therefore, based on the calculated , By analyzing its rate of change with temperature, a quantitative assessment of the degree of electricity price fluctuations under extreme heat waves can be achieved.

[0044] This invention uses the coefficient of variation (standard deviation / mean) of the electricity price series as a measure of volatility. Its innovation lies in establishing a dynamic mathematical model of this coefficient of variation in relation to system operating conditions (total load shedding rate, total renewable energy curtailment rate) and their rate of change (derivative with respect to temperature). This transforms the electricity price volatility indicator from an isolated financial market indicator into a "mirror image" reflecting the degree of supply and demand tension in the physical system and the rate of change. By using this electricity price volatility indicator to quantify the abnormal fluctuations in electricity market prices, it reflects the impact of supply and demand imbalances on market stability.

[0045] In one embodiment, step S2 further includes: Based on the carbon emission data, determine the carbon emissions on the generation side and the user side of the power system respectively; The carbon emissions from the power generation side and the carbon emissions from the user side are weighted and summed to obtain the comprehensive carbon emission index used to characterize the low-carbon operation capability of the power system.

[0046] The comprehensive carbon emission index constructed in this invention aims to quantify the carbon emission pressure generated by the receiving-end power system to meet unit electricity demand under extreme heat waves from both the power generation and consumption sides. The higher the index value, the lower the carbon efficiency of the system operating under high voltage and the worse its robustness in carbon-electricity coordination. It is divided into carbon emissions on the power generation side and carbon emissions on the user side.

[0047] First, carbon emissions on the power generation side are calculated based on carbon emission data. This index physically represents the average carbon emissions per kilowatt-hour (kWh) of electricity provided. This indicator directly measures the carbon efficiency of the power generation process; a higher value indicates a higher carbon intensity of the power structure the system relies on to meet electricity demand, and thus greater carbon emission pressure. The calculation formula is as follows: In the formula, Carbon emissions from the power generation side at temperature T; The total carbon emissions on the power generation side of the power system at temperature T include both local thermal power carbon emissions and the implicit carbon emissions from imported electricity. The actual output of thermal power is positively correlated with the net load of the system (total load minus the effective output of new energy sources and imported electricity). Let T be the total power supply of the power system at temperature T. Its value is the product of the actual output of all power sources in the system (including local thermal power, new energy sources and external power) in the same dispatch cycle and time.

[0048] Secondly, user-side carbon emissions are calculated based on carbon emission data. The physical meaning of this index is the amount of carbon emissions generated for every kilowatt-hour (kWh) of electricity consumed by a power user. It reflects the driving and correlation relationship between electricity demand and generation-side carbon emissions. An increase in its value means an increase in the "carbon cost" per kilowatt-hour for the user. It serves as a bridge connecting user behavior and environmental impact. Its calculation formula is as follows: In the formula, User-side carbon emissions at ambient temperature T; This represents the total carbon emission responsibility borne by all electricity users at ambient temperature T. Physically, the carbon responsibility of users stems from the carbon emissions generated on the generation side to meet their electricity demand. Therefore, ideally, this value equals the total carbon emissions on the generation side. To achieve model accuracy, a network loss coefficient less than 1 can be introduced for correction to account for energy loss during power transmission and distribution; Load(T) is the total system load demand at temperature T.

[0049] After calculating the GCEI and UCEI indices, they are normalized and dimensionless, then weighted and synthesized to obtain an index value characterizing the low-carbon operation capability of the power system—the comprehensive carbon emission index, which is expressed by the following formula: In the formula, CEI(T) is the comprehensive carbon emission index at temperature T, which is a dimensionless negative index. These are the benchmark values ​​for carbon emission indices on the power generation side and the user side, respectively, and are usually selected from historical statistical values ​​of the system under non-extreme, normal operating conditions; These are the weighting coefficients for the two sub-indices, which must satisfy ω1+ω2=1. The weighting can be flexibly adjusted according to the policy orientation or management focus of the assessment to reflect the different weights in the assessment.

[0050] The higher the comprehensive carbon emission index value mentioned above, the greater the carbon emission pressure on the corresponding system under stress scenarios such as extreme heat waves, the weaker its ability to maintain low-carbon and efficient operation, and the worse the robustness of carbon-electricity synergy. It should also be noted that all indicators in the robustness assessment index system are negative (the smaller the value, the stronger the robustness).

[0051] This invention innovatively measures carbon emission pressure from both the generation and user sides. The generation-side carbon intensity of electricity supply measures the "cleanliness" of the supply, while the user-side carbon responsibility intensity of electricity consumption measures the "carbon cost" of consumption. By normalizing and weighting both, a comprehensive index reflecting the overall carbon cost incurred by the system to meet unit electricity demand under pressure is obtained. This comprehensive carbon emission index quantifies the carbon emission pressure generated by the system to meet unit electricity demand from both the generation and user sides, directly reflecting the system's low-carbon operation capability. A higher index value indicates a greater environmental cost to the system in ensuring supply under extreme pressure, and a poorer carbon-electricity synergy robustness.

[0052] S3. Establish an evaluation hierarchy structure based on the analytic hierarchy process, and determine the weights of each indicator value through the evaluation hierarchy structure. In one embodiment, step S3 includes: The supply and demand robustness level of the power system under the extreme heat wave scenario is taken as the target layer, and load robustness, renewable energy robustness, electricity price robustness and carbon emission robustness are taken as the criterion layer. The graded load shedding rate index, renewable energy curtailment rate index, electricity price volatility index and carbon emission comprehensive index are taken as the indicator layer. Based on the target layer, the criterion layer, and the index layer, a judgment matrix between each layer is constructed using the 1-9 scaling method, and the weight vector of each judgment matrix is ​​calculated using the sum-product method. A consistency check is performed on each of the weight vectors to obtain the criterion layer weight vector and the indicator layer weight vector, which are used as the weights of each indicator value.

[0053] Specifically, in order to reasonably determine the relative importance of each evaluation indicator and overcome the limitations of equal weighting, this invention uses the Analytic Hierarchy Process (AHP) to construct a hierarchical structure and calculate the weights corresponding to each indicator.

[0054] First, a hierarchical structure model for weight analysis is established. This model systematically decomposes the complex robustness evaluation problem into the following three levels: Target Layer (A): Defined as "the robustness level of the power system to supply and demand under extreme heat wave scenarios", representing the ultimate goal of the assessment.

[0055] The criteria layer (B) comprises four assessment dimensions: load robustness (B1), renewable energy robustness (B2), electricity price robustness (B3), and carbon emission robustness (B4). B1 characterizes the power system's ability to guarantee loads of different levels; B2 characterizes the power system's ability to absorb and utilize fluctuating renewable energy sources; B3 characterizes the stability of market supply and demand balance; and B4 characterizes the power system's ability to maintain low-carbon operation.

[0056] The indicator layer (C) contains six directly calculable quantitative indicators, namely: Level 1 load shedding rate indicator (C11), Level 2 load shedding rate indicator (C12), Level 3 load shedding rate indicator (C13), renewable energy curtailment rate indicator (C21), electricity price volatility indicator (C31), and comprehensive carbon emission indicator (C41). Among them, C11, C12, and C13 belong to B1, C21 belongs to B2, C31 belongs to B3, and C41 belongs to B4.

[0057] All of the above indicators are negative indicators; the smaller the value, the stronger the corresponding robustness.

[0058] Secondly, a judgment matrix is ​​constructed and local weights are calculated. Based on expert experience or historical data analysis, a 1-9 scale is used to compare the importance of elements within each level relative to a criterion in the previous level pairwise. For example, a judgment matrix AB is constructed for the criterion layer (B1, B2, B3, B4) relative to the target layer (A), and a judgment matrix B1C is constructed for the index layer elements (C11, C12, C13) relative to their respective criterion (B1). The criterion layer typically satisfies the importance ranking of B1>B2>B3>B4, and the load index satisfies the ranking of C11>C12>C13. For criteria belonging to only one index (such as B2, B3, B4), their judgment matrix is ​​an identity matrix.

[0059] The weight vectors of each judgment matrix are calculated using either the sum-product method or the eigenvalue method. For the matrix... Normalized according to the column, we get (in ),Will The sum vector is obtained by adding each row together. (in ), then Normalization yields the weight vector. (in ).

[0060] Finally, a consistency check is performed and a comprehensive weight is synthesized. A consistency index for each judgment matrix is ​​calculated. (λ) maxTo determine the largest eigenvalue of a matrix (where n is the matrix order) and the consistency ratio. , where RI is the average random consistency index. It is required that the RI of all matrices... This ensures the logical consistency of subjective judgments. The final result is the criterion layer weight vector. and indicator layer weight vector Calculate the overall weight of each indicator relative to the overall goal A. For example, the overall weight of indicator C11 = W B1 ×W C11 Among them, W B1 W represents the weight of criterion B1 with respect to A. C11 The weight of indicator C11 relative to criterion B1.

[0061] This invention employs the analytic hierarchy process (AHP) to construct a hierarchical model with the overall objective of robust carbon-electricity supply and demand coordination under extreme heat waves, the aforementioned four dimensions as intermediate criteria, and specific quantitative indicators as underlying indicators. This model determines the relative importance weights of each dimension and indicator in addressing the common goal of extreme heat waves, ensuring that the evaluation system focuses on the core contradictions and avoids subjective arbitrariness.

[0062] S4. Based on the fuzzy comprehensive evaluation method, the robustness assessment level of the power system is determined by using the values ​​of the various indicators and their corresponding weights, and the power system is subjected to demand response regulation according to the robustness assessment level. In one embodiment, determining the robustness rating of the power system based on the fuzzy comprehensive evaluation method using the values ​​of each indicator and their corresponding weights includes: A robustness evaluation level set is set; the robustness evaluation level set includes, from strongest to weakest, excellent interference resistance, good interference resistance, baseline interference resistance, interference resistance to be optimized, and interference resistance to be improved. The membership degree of each index value to different robustness evaluation levels is calculated using a trapezoidal membership function; Based on the membership degrees, a fuzzy relation matrix is ​​constructed. Combined with the weight vector of the index layer, a weighted average operator is used to perform a first-level fuzzy comprehensive evaluation of the index values ​​to obtain the evaluation results of the criterion layer. Based on the evaluation results of the criterion layer and the weight vector of the criterion layer, a second-level fuzzy comprehensive evaluation is performed on the values ​​of each indicator to obtain the comprehensive evaluation results of the target layer. Based on the principle of maximum membership, the robustness assessment level of the power system is determined according to the comprehensive evaluation results of the target layer.

[0063] Specifically, in order to address the fuzziness and uncertainty in the evaluation and to transform quantitative indicators into intuitive grade evaluations, this invention employs the fuzzy comprehensive evaluation method to comprehensively assess the robustness of the system.

[0064] First, define the evaluation set and calculate the single-factor membership degree. Set the robustness evaluation level set of the power system as V={V1,V2,V3,V4,V5}, representing five levels: "Excellent Disturbance Immunity," "Good Disturbance Immunity," "Baseline Disturbance Immunity," "Disturbance Immunity Needs Optimization," and "Disturbance Immunity Needs Improvement." It should be noted that other terms can also be used to represent the level, such as "Very Strong," "Relatively Strong," "Average," "Relatively Weak," and "Very Weak." For each evaluation indicator (C11, C12, C13, C21, C31, C41), calculate the indicator value x based on its value. i The membership degree r of a character to each level in the comment set V is calculated using a predefined trapezoidal membership function. ij Taking the first-level load shedding rate (C11) as an example, its relationship with level V... j The membership function can be defined as: In the formula, For level The threshold. Similarly, the membership degree of other indicators can be obtained.

[0065] Based on the membership degree, construct the fuzzy evaluation matrix under each criterion: , , , .

[0066] Secondly, a first-level fuzzy comprehensive evaluation is performed. The evaluation results of the criterion layer are calculated. Here, a weighted average calculation is used, for example... The component is .

[0067] Then, a second-level fuzzy comprehensive evaluation is performed. The evaluation results from the criterion layer are then combined into a matrix. Calculate the comprehensive evaluation results of the target layer. ,in .

[0068] Finally, based on the principle of maximum membership, the level corresponding to the maximum membership in A is selected as the final evaluation result. If ties occur, scores are assigned to the levels, and the robustness assessment level of the power system is determined by a weighted average score. This invention integrates the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation Method. The AHP ensures the rationality of weight allocation, while the Fuzzy Comprehensive Evaluation Method handles evaluation uncertainties, resulting in an intuitive level output that is easy for engineering applications.

[0069] In one embodiment, the step of demand response regulation of the power system based on the robustness assessment level includes: When the robustness assessment level is the disturbance rejection optimization level or the disturbance rejection enhancement level, it is determined that the power system needs to initiate demand response control, and a response plan of corresponding strength is matched according to the robustness assessment level to control the power system to execute.

[0070] Based on the system robustness level output by the aforementioned fuzzy comprehensive evaluation method, the final control decision is formed. When the comprehensive evaluation level is "disturbance resistance needs optimization" or "disturbance resistance needs improvement," it is determined that the system faces a high risk of supply-demand imbalance under extreme heat waves. This also indicates that the system faces comprehensive risks such as high load shedding rate, high renewable energy curtailment rate, large electricity price fluctuations, and severe carbon emissions. At this point, the system is approaching the critical point of safe and low-carbon operation. Demand response control for users needs to be initiated immediately. Based on the assessed risk level, response plans of different intensities, pre-constructed based on historical data or expert experience, should be matched to control the execution of the power system. Load adjustments should be guided through price signals or incentive mechanisms to alleviate supply-demand imbalances, smooth market fluctuations, and reduce carbon emission pressure, thereby improving system robustness.

[0071] This invention establishes a direct mapping rule between "robustness level" and "demand response decision". When the evaluation level shows that the system robustness is "disturbance immunity needs to be optimized" or "disturbance immunity needs to be improved", the demand response start command is automatically triggered, realizing a seamless conversion from complex state evaluation to clear and executable control commands, so that the evaluation conclusions can directly and in real time support the operation of the power grid.

[0072] Another method for demand regulation of power systems under extreme heat waves is as follows: Figure 2As shown in the embodiments of this application, based on the problem of how to accurately assess the robustness of the power system under extreme heat waves and thus conduct demand response regulation, a demand regulation method for the power system under extreme heat waves is designed. This method constructs a four-in-one evaluation index system covering load guarantee, renewable energy consumption, market stability, and low-carbon operation, and integrates the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. This achieves a multi-dimensional, refined, and quantifiable comprehensive assessment of the supply and demand robustness of the receiving-end power system under extreme heat waves. The assessment conclusions can then be used to determine demand response requirements and transform them into clear demand response initiation decisions, supporting the grid to operate as safely, stably, and with low carbon emissions as possible under extreme heat waves. The robustness evaluation index system adopted is highly targeted, and for the first time integrates physical security, resource utilization, and market... Stability risk is coupled to a unified model, solving the problem of single-dimensionality in existing methods. The assessment process is rigorous and the results are intuitive. The analytic hierarchy process ensures the rationality of weights, while the fuzzy comprehensive evaluation method effectively handles uncertainty and outputs intuitive levels, facilitating engineering decision-making. The evaluation can achieve carbon-electricity synergy, transforming carbon emission pressure into a dynamic evaluation index that directly assesses the degree of robustness deterioration of the system. When the system is forced to rely on high-carbon operation due to extreme heat waves, the carbon emission index will increase significantly, thus effectively supporting safety assessment and demand response decision-making under carbon-electricity synergy. The decision-making conversion mechanism based on the comprehensive robustness level integrates assessment and decision-making. The robustness level obtained through the synergistic assessment of carbon and electricity indicators supports demand response decision-making, achieving a seamless conversion from complex state assessment to clear and executable control instructions.

[0073] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0074] In another embodiment, such as Figure 3 As shown, a second aspect of the present invention provides a demand regulation system for a power system under extreme heat waves, comprising: Data acquisition module 10 is used to acquire multi-dimensional comprehensive operation data of the power system under extreme heat wave scenarios; The index quantification module 20 is used to construct the robustness evaluation index system of the power system, so as to quantify the various index values ​​that respectively characterize the load guarantee capacity, new energy absorption capacity, market stability capacity and low-carbon operation capacity of the power system based on the multi-dimensional operation comprehensive data. The weight calculation module 30 is used to establish an evaluation hierarchy structure based on the analytic hierarchy process and to determine the weight of each indicator value through the evaluation hierarchy structure. The demand regulation module 40 is used to determine the robustness assessment level of the power system based on the fuzzy comprehensive evaluation method, using the values ​​of the various indicators and their corresponding weights, and to perform demand response regulation of the power system according to the robustness assessment level.

[0075] It should be noted that the various modules in the demand control system of a power system under extreme heat waves described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module. For specific limitations regarding the demand control system of a power system under extreme heat waves, please refer to the limitations of the demand control method for a power system under extreme heat waves described above; both have the same function and role, and will not be repeated here.

[0076] In summary, this invention relates to the field of power system security assessment technology, and discloses a method and system for demand regulation of a power system under extreme heat waves. The method includes: acquiring multi-dimensional comprehensive operational data of the power system under extreme heat wave scenarios; constructing a robustness assessment index system for the power system, quantifying various index values ​​based on the multi-dimensional comprehensive operational data to characterize the power system's load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity; establishing an assessment hierarchical structure using the analytic hierarchy process (AHP), and determining the weights of each index value through the hierarchical structure; determining the robustness assessment level of the power system using the fuzzy comprehensive evaluation method and its corresponding weights, and performing demand response regulation of the power system based on the robustness assessment level; thus achieving a scientific and comprehensive assessment of the supply and demand robustness of the power system under extreme heat wave scenarios.

[0077] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0078] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for demand regulation of a power system under extreme heat waves, characterized in that, include: Acquire comprehensive multi-dimensional operational data of the power system under extreme heat wave scenarios; A robustness evaluation index system for the power system is constructed, and the index values ​​representing the load guarantee capacity, renewable energy absorption capacity, market stability capacity and low-carbon operation capacity of the power system are obtained by quantifying based on the multi-dimensional operation comprehensive data. An evaluation hierarchy structure is established based on the analytic hierarchy process, and the weights of each indicator value are determined through the evaluation hierarchy structure. Based on the fuzzy comprehensive evaluation method, the robustness assessment level of the power system is determined by using the values ​​of the various indicators and their corresponding weights, and the power system is subjected to demand response regulation according to the robustness assessment level.

2. The demand regulation method for a power system under extreme heat waves according to claim 1, characterized in that, The multidimensional operational comprehensive data includes load data, wind and solar power output data, electricity price time series data, carbon emission data, and related temperature, wind speed, and irradiance; the robust evaluation index system includes graded load shedding rate index, renewable energy curtailment rate index, electricity price fluctuation index, and comprehensive carbon emission index.

3. The demand regulation method for a power system under extreme heat waves according to claim 2, characterized in that, The various indicator values, which are quantified based on the multi-dimensional operational comprehensive data to represent the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system, include: The load data is classified according to the importance of the loads in the power system to obtain multi-level loads; The graded load shedding rate index, which characterizes the load guarantee capability of the power system, is determined by the ratio of the actual power shedding of the multi-level loads under the extreme heat wave scenario to their rated demand power.

4. The demand regulation method for a power system under extreme heat waves according to claim 2, characterized in that, The various indicator values, which are quantified based on the multi-dimensional operational comprehensive data to represent the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system, also include: Based on the meteorological characteristics corresponding to temperature and wind speed in the extreme heat wave scenario, a wind power output model is constructed according to the temperature and wind speed. The wind power curtailment rate of the power system is determined based on the wind and solar new energy output data and the wind power output model. A photovoltaic power output model is constructed using the temperature and irradiance, and the photovoltaic curtailment rate of the power system is determined based on the photovoltaic power output model and the wind and solar new energy power output data. The renewable energy curtailment rate index, which characterizes the renewable energy absorption capacity of the power system, is determined by the wind power curtailment rate and the photovoltaic curtailment rate.

5. A demand regulation method for a power system under extreme heat waves according to claim 2, characterized in that, The various indicator values, which are quantified based on the multi-dimensional operational comprehensive data to represent the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system, also include: The total load shedding rate of the power system is determined based on the graded load shedding rate index, and an average electricity price model is constructed based on the total load shedding rate. The standard deviation model for electricity prices is determined using the time-series data of electricity prices, the total load shedding rate, and the renewable energy curtailment rate. Based on the electricity price mean model and the electricity price standard deviation model, an indicator for the degree of electricity price volatility is determined to characterize the market stability capability of the power system.

6. A method for demand regulation of a power system under extreme heat waves according to claim 2, characterized in that, The various indicator values, which are quantified based on the multi-dimensional operational comprehensive data to represent the load guarantee capacity, renewable energy absorption capacity, market stability capacity, and low-carbon operation capacity of the power system, also include: Based on the carbon emission data, determine the carbon emissions on the generation side and the user side of the power system respectively; The carbon emissions from the power generation side and the carbon emissions from the user side are weighted and summed to obtain the comprehensive carbon emission index used to characterize the low-carbon operation capability of the power system.

7. A demand regulation method for a power system under extreme heat waves according to claim 2, characterized in that, The step of establishing an evaluation hierarchy based on the analytic hierarchy process (AHP) and determining the weights of each indicator value through the evaluation hierarchy includes: The supply and demand robustness level of the power system under the extreme heat wave scenario is taken as the target layer, and load robustness, renewable energy robustness, electricity price robustness and carbon emission robustness are taken as the criterion layer. The graded load shedding rate index, renewable energy curtailment rate index, electricity price volatility index and carbon emission comprehensive index are taken as the indicator layer. Based on the target layer, the criterion layer, and the index layer, a judgment matrix between each layer is constructed using the 1-9 scaling method, and the weight vector of each judgment matrix is ​​calculated using the sum-product method. A consistency check is performed on each of the weight vectors to obtain the criterion layer weight vector and the indicator layer weight vector, which are used as the weights of each indicator value.

8. A method for demand regulation of a power system under extreme heat waves according to claim 7, characterized in that, The method based on fuzzy comprehensive evaluation, which uses the values ​​of various indicators and their corresponding weights to determine the robustness assessment level of the power system, includes: A robustness evaluation level set is set; the robustness evaluation level set includes, from strongest to weakest, excellent interference resistance, good interference resistance, baseline interference resistance, interference resistance to be optimized, and interference resistance to be improved. The membership degree of each index value to different robustness evaluation levels is calculated using a trapezoidal membership function; Based on the membership degrees, a fuzzy relation matrix is ​​constructed. Combined with the weight vector of the index layer, a weighted average operator is used to perform a first-level fuzzy comprehensive evaluation of the index values ​​to obtain the evaluation results of the criterion layer. Based on the evaluation results of the criterion layer and the weight vector of the criterion layer, a second-level fuzzy comprehensive evaluation is performed on the values ​​of each indicator to obtain the comprehensive evaluation results of the target layer. Based on the principle of maximum membership, the robustness assessment level of the power system is determined according to the comprehensive evaluation results of the target layer.

9. A demand regulation method for a power system under extreme heat waves according to claim 8, characterized in that, The demand response regulation of the power system based on the robustness assessment level includes: When the robustness assessment level is the disturbance rejection optimization level or the disturbance rejection enhancement level, it is determined that the power system needs to initiate demand response control, and a response plan of corresponding strength is matched according to the robustness assessment level to control the power system to execute.

10. A demand control system for a power system under extreme heat waves, characterized in that, include: The data acquisition module is used to acquire comprehensive multi-dimensional operational data of the power system under extreme heat wave scenarios; The index quantification module is used to construct a robustness evaluation index system for the power system, and to quantify the index values ​​of various indicators that respectively characterize the load guarantee capacity, renewable energy absorption capacity, market stability capacity and low-carbon operation capacity of the power system based on the multi-dimensional operation comprehensive data. The weight calculation module is used to establish an evaluation hierarchy structure based on the analytic hierarchy process and to determine the weight of each indicator value through the evaluation hierarchy structure. The demand regulation module is used to determine the robustness assessment level of the power system based on the fuzzy comprehensive evaluation method, using the values ​​of the various indicators and their corresponding weights, and to perform demand response regulation of the power system according to the robustness assessment level.

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