A method for determining the flexibility requirements of distribution networks considering the impact of extreme weather

Abstract

This invention relates to a method for determining the flexibility requirements of a distribution network considering the impact of extreme weather, belonging to the field of power system dispatching and planning technology. The method includes: determining multiple typical scenarios of flexibility requirements under extreme weather conditions and their corresponding scenario probabilities based on an extreme weather element dataset; constructing an uncertain fuzzy set of flexibility requirements based on the scenario probabilities; obtaining the output of wind turbines, photovoltaic units, base load, and climate-sensitive loads under extreme weather conditions based on the typical flexibility requirement scenarios; constructing a bibliometric optimization model with the objective of minimizing the operating cost of the regional distribution network; and iteratively solving the model by decomposing the original objective into a main problem and sub-problems using the C&CG algorithm to obtain the operating schemes of each unit in the regional distribution network and the flexibility requirements for adapting to extreme weather conditions. This invention accurately quantifies the multi-scale flexibility requirements of the distribution network to adapt to extreme weather conditions.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and planning technology, and in particular to a method for determining the flexibility requirements of distribution networks that takes into account the impact of extreme weather. Background Technology

[0002] The flexibility requirements of distribution networks primarily stem from the uncertainty and volatility of load and renewable energy sources. The greater the fluctuation in net load (the power difference between load and renewable energy), the greater the flexibility requirement. Currently, there are two main methods for analyzing the flexibility requirements of distribution networks: the first is to quantitatively analyze the flexibility requirements of the system through quantitative and evaluation methods; the second is dynamic simulation evaluation, which involves setting up power system operation scenarios for optimized scheduling and production simulation to analyze the flexibility requirements of the power system. However, extreme weather events such as high temperatures and droughts, low temperatures and cold waves, prolonged periods without wind or sunlight, and strong winds and sunlight can have uncertain impacts on renewable energy generation and load demand, further affecting the volatility of the net load curve. Existing methods do not consider the nonlinear coupling effect of extreme weather on renewable energy output fluctuations and load abrupt changes, leading to a deviation between flexibility resource allocation and actual demand, and failing to meet the operational needs of the power system under extreme weather conditions. Summary of the Invention

[0003] Based on the above analysis, this invention aims to disclose a method for determining the flexibility requirements of a distribution network that takes into account the impact of extreme weather. By analyzing the impact of extreme weather on new energy power generation and load, an uncertain fuzzy set of flexibility requirements under extreme weather conditions is constructed, and the existing flexibility resource adjustment capability of the distribution network is evaluated, thereby realizing the quantification of multi-scale flexibility requirements of the distribution network.

[0004] This invention provides a method for determining the flexibility requirements of a distribution network considering the impact of extreme weather, which specifically includes the following steps:

[0005] Based on an extreme climate element dataset, several typical scenarios of flexibility requirements under extreme climate and corresponding scenario probabilities are identified, and an uncertain fuzzy set of flexibility requirements is constructed based on the scenario probabilities.

[0006] Based on the typical scenarios of the aforementioned flexibility requirements, we obtain the wind turbine output, photovoltaic unit output, base load, and climate-sensitive load under extreme weather conditions.

[0007] A sub-Bluerge optimization model is constructed with the goal of minimizing the operating cost of the regional distribution network. By decomposing the original objective into a main problem and sub-problems, the C&CG algorithm is used to iteratively solve the model based on the output of wind turbines, output of photovoltaic units, base load, climate-sensitive load, set deterministic constraints, and the uncertain fuzzy set, so as to obtain the operating scheme of each unit of the regional distribution network and the flexibility requirements for adapting to extreme climates.

[0008] Furthermore, the process of determining the scenario probabilities of multiple typical flexibility demand scenarios under extreme climate conditions based on extreme climate element datasets, and constructing an uncertain fuzzy set of flexibility demands based on the scenario probabilities, includes:

[0009] The K-means clustering algorithm was used to cluster multiple typical scenarios of flexibility requirements under extreme climate conditions and their corresponding scenario probabilities based on the extreme climate element dataset;

[0010] Based on the scenario probabilities, construct confidence sets based on the 1-norm and ∞-norm;

[0011] Constructing fuzzy sets with uncertain flexibility requirements based on confidence sets of 1-norm and ∞-norm.

[0012] Furthermore, the wind power, photovoltaic unit output, base load, and climate-sensitive load under extreme weather conditions obtained based on the typical scenarios of flexibility requirements include:

[0013] Based on climate data from typical scenarios requiring flexibility, the output of wind power and photovoltaic power units were calculated using wind turbine output models and photovoltaic power unit output models, respectively.

[0014] Based on climate data from typical scenarios requiring flexibility, the corresponding base load and climate-sensitive load are predicted using a trained hybrid CNN and LSTM model.

[0015] Furthermore, the objective function for minimizing the operating cost of the regional distribution network is expressed as:

[0016] ;

[0017] ;

[0018] in, for The cost of purchasing electricity from the upper-level power grid for the regional distribution network at any given time; , , These are the gas-fired, wind-powered, and photovoltaic units in the regional power distribution network. The cost of generating electricity at any given moment; For energy storage units in the regional distribution network The scheduling cost at any given moment; The cost of implementing price-based demand response for a regional distribution network; Compensation for incentive-based demand response; for The interaction power between the regional distribution network and the upstream power grid at any given time; and They are respectively Load demand after and before implementing price-based demand response at a certain moment; is Value of electricity price change at a certain moment; is Purchasing cost of the regional distribution network from the superior power grid at a certain moment; Superscript and represent the unit type; is a function between the unit operation cost and the output; is Unit output at a certain moment; , , , represent gas, energy storage, wind power and photovoltaic units respectively; , are the start-up and shut-down states of the unit; , are the start-up and shut-down costs of the unit; is the unit interruptible load compensation cost; is the interruptible load at Response power at a certain moment.

[0019] Furthermore, the flexible demand uncertain fuzzy set is expressed as:

[0020] ;

[0021] where is the flexible demand uncertain fuzzy set; is the typical scenario of flexible demand Set of positive real numbers of probability distribution; is the typical scenario of flexible demand True occurrence probability; Number of typical scenarios of flexible demand; is the occurrence probability of the typical scenario obtained by clustering; is the total number of samples in the extreme climate element dataset; , are the confidence levels satisfied by the scenario probability distribution based on the 1-norm and -norm respectively.

[0022] Furthermore, the training process of the trained CNN and LSTM hybrid model includes;

[0023] Determine the historical reference load and historical climate-sensitive load at the corresponding time based on the total load at the corresponding time of the historical climate element data;

[0024] The training set is composed of historical climate element data, historical baseline loads and historical climate-sensitive loads to train a hybrid CNN and LSTM model, resulting in a well-trained hybrid CNN and LSTM model.

[0025] Furthermore, the defined deterministic constraints include power balance constraints, which are expressed as follows:

[0026] ;

[0027] in, for The interaction power between the regional distribution network and the upstream power grid at any given time; , , They are respectively The power output of gas-fired, wind-powered, and photovoltaic units is constantly being generated; for Implement load demand in real time following price-based demand response; For interruptible loads Response power at any given time; , They are respectively Energy storage and charging / discharging power at all times.

[0028] Furthermore, using price-based demand response constraints, based on load demand before implementing price-based demand response. Determine the Load demand after implementing price-based demand response at all times The price demand response constraint is expressed as:

[0029] ;

[0030] in, , , These are the electricity prices for peak hours, off-peak hours, and normal hours, respectively. , These are the minimum and maximum electricity prices, respectively. Peak-valley electricity price ratio; , These are the minimum and maximum peak-valley electricity price ratios, respectively. This represents the number of scheduling periods.

[0031] Furthermore, the load demand before implementing price-based demand response is determined based on baseline load and climate-sensitive load. ,include:

[0032] ;

[0033] in, The baseline load is predicted based on historical baseline load data; This refers to the climate-sensitive load.

[0034] Furthermore, the historical baseline load is expressed as:

[0035] ;

[0036] in, Historical baseline load; The curve representing the five curves with the lowest historical average baseline load is the [number missing]. The curves represent the loads; This represents the fifth curve among the five curves indicating the lowest historical average weekend baseline load. The curves represent the load.

[0037] The present invention can achieve at least one of the following beneficial effects:

[0038] By characterizing the changing characteristics of renewable energy output and load demand under extreme weather conditions, a typical scenario set of flexibility demand is constructed, and the existing flexibility resource adjustment capability of the distribution network is evaluated. This enables a multi-scale quantitative assessment of the flexibility demand of the distribution network, solving the problem in traditional distribution network planning where the nonlinear coupling effect of extreme weather on renewable energy output fluctuations and load abrupt changes leads to a deviation between flexibility resource allocation and actual demand. This improves the renewable energy absorption rate and the reliability of power grid supply.

[0039] By constructing a training set based on historical climate element data and corresponding climate-sensitive loads, a hybrid CNN and LSTM model was developed, improving the accuracy of the model's prediction of climate-sensitive loads. Cluster analysis of extreme climate element datasets yielded a typical scenario set for flexibility demand, accurately characterizing the impact of extreme weather on renewable energy output and load demand. Demand-side response modeling accurately described the demand-side load response potential in real-world application scenarios. This improved the accuracy of determining distribution network flexibility demand under extreme weather conditions, providing a scientific basis for the allocation of power system flexibility resources.

[0040] Other features and advantages of the invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained from what is particularly pointed out in this application. Attached Figure Description

[0041] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0042] Figure 1This is a flowchart of the present invention;

[0043] Figure 2 A typical scenario diagram is generated to illustrate the flexibility requirements of embodiments of the present invention.

[0044] Figure 3 This is a price demand curve according to an embodiment of the present invention;

[0045] Figure 4 This is a user response curve from an embodiment of the present invention. Detailed Implementation

[0046] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0047] One embodiment of the present invention discloses a method for determining the flexibility requirements of a distribution network considering the impact of extreme weather, such as... Figure 1 As shown, the specific steps include S1-S3.

[0048] Specifically, the extreme weather events include sudden weather events such as high temperatures, torrential rain, cold waves, and typhoons.

[0049] Specifically, during the operation of a distribution network, the difference between electricity load and renewable energy power creates a demand for network flexibility. Extreme weather events can have uncertain impacts on both renewable energy generation and electricity load, leading to significant fluctuations in flexibility requirements. Therefore, analyzing typical scenarios of extreme weather events can reveal the typical scenarios for distribution network flexibility requirements.

[0050] Step S1: Based on historical extreme climate element datasets, determine multiple typical scenarios of flexibility requirements under extreme climate conditions and their corresponding scenario probabilities. Construct an uncertain fuzzy set of flexibility requirements based on these scenario probabilities. This includes:

[0051] S11. Using the K-means clustering algorithm, cluster the extreme climate element dataset to obtain multiple typical scenarios of flexibility requirements under extreme climate and the corresponding scenario probabilities.

[0052] Specifically, before clustering, based on historical climate element data and corresponding climate-sensitive loads, climate elements with a significant impact on these loads are screened. Pearson correlation is used to measure the correlation between climate-sensitive loads and climate element variables, identifying climate elements with high correlation as the basis for clustering. For example, climate elements with high correlation calculated include temperature, wind speed, light intensity, and humidity.

[0053] Specifically, the clustering process is as follows: Figure 2 As shown, it includes:

[0054] a. Set the number of clusters to... For each category, initial cluster centers for each climate element are randomly selected;

[0055] b. Calculate the samples of each climate element and The Euclidean distance between each cluster center is used to assign a sample to the class containing the cluster center with the smallest distance.

[0056] c. Update the cluster center of each category to the average of all samples in that category.

[0057] d. Repeat steps b and c until all cluster centers remain unchanged, or the maximum number of iterations is reached.

[0058] This yields several typical scenarios for flexibility requirements under extreme weather conditions; the scenario probability for each typical flexibility requirement scenario is as follows: ;in, For the first Number of samples per class; This represents the total number of samples.

[0059] S12. Construct a confidence set based on the 1-norm and ∞-norm based on the scenario probability.

[0060] Specifically, it is expressed as follows:

[0061] ;

[0062] ;

[0063] in, and The confidence sets for the 1-norm and ∞-norm are respectively. This represents the true distribution of the probability of the scenario occurring. The probability distribution of typical scenarios obtained from clustering; for The first in One element; A set of typical scenarios; This represents the probability of a typical scenario occurring. and These correspond to the 1-norm and - Permissible limits for probability deviation under norm constraints.

[0064] S13. Construct a fuzzy set for flexibility requirements based on the confidence set of 1-norm and ∞-norm.

[0065] Specifically, the uncertain fuzzy set of flexibility requirements is represented as:

[0066] ;

[0067] in, For the purpose of flexibility, uncertain fuzzy sets are required; Typical scenarios for flexibility requirements The set of positive real numbers in a probability distribution; Typical scenarios for flexibility requirements The actual probability of occurrence; Number of typical scenarios requiring flexibility; The probability of occurrence of typical scenarios obtained from clustering; This represents the total number of samples in the extreme climate element dataset. , The probability distributions of the scenarios are based on the 1-norm and - The confidence level satisfied by the norm.

[0068] Step S2: Based on the typical scenarios of flexibility requirements, obtain the output of wind turbines, photovoltaic units, and climate-sensitive loads under extreme climate conditions.

[0069] S21. Based on the climate element data of the typical scenario of the flexibility requirement, the wind power and photovoltaic power output are calculated using the wind turbine output model and the photovoltaic power output model, respectively.

[0070] Specifically, the power output model of the wind turbine is represented as follows:

[0071] ;

[0072] in, For wind turbine units Real-time output at all times; The air density in the environment where the wind turbine is located; The radius of the wind turbine blade; The efficiency coefficient of the wind turbine unit; The environment where the wind turbine is located The wind speed at any given moment.

[0073] Furthermore, to ensure the safety and stability of the wind turbine, the cut-in wind speed is... Cut-off wind speed With rated wind speed Limit real-time output:

[0074] .

[0075] Furthermore, air density It decreases as temperature increases; it is a variable value, and its calculation method is as follows:

[0076] ;

[0077] in, The molar mass of air is expressed in kg / mol. This represents the ambient pressure where the wind turbine is located, in Pa. is the ideal gas constant, with units of J / (mol·K); For the environment where the wind turbine is located Temperature at any given moment in Celsius, expressed in °C.

[0078] Specifically, the output model of the photovoltaic unit is expressed as follows:

[0079] ;

[0080] in, For photovoltaic units in Real-time power output at any given moment, measured in MW; for Light intensity at any given time, in units ; for The ambient temperature at any given time, in °C; This refers to the output of the photovoltaic unit under standard test conditions.

[0081] S22. Based on the climate data of typical scenarios requiring flexibility, the corresponding climate-sensitive loads are predicted using a trained CNN and LSTM hybrid model.

[0082] Specifically, the training process of the trained CNN and LSTM hybrid model includes:

[0083] S22-b: Determine the historical baseline load and historical climate-sensitive load for the corresponding time period based on the total load of historical climate element data for the corresponding time period; the historical climate element data includes wind speed, temperature, light intensity, etc.

[0084] S22-c: Based on historical climate element data and corresponding historical baseline loads and climate-sensitive loads, a training set is constructed to train a hybrid CNN and LSTM model, resulting in a well-trained hybrid CNN and LSTM model.

[0085] Furthermore, in S22-b, the climate-sensitive load for the corresponding time period in historical climate element data is the difference between the load curve and the historical baseline load curve:

[0086] ;

[0087] in, This represents the climate-sensitive loads at the corresponding time points based on historical climate element data, reflecting the impact of climate elements on the loads. This serves as a historical baseline load, reflecting the overall trend of load development over a relatively long period. For history Total load at any given time;

[0088] Furthermore, the historical baseline load is expressed as:

[0089] ;

[0090] in, Historical baseline load; The curve representing the five curves with the lowest historical average baseline load is the [number missing]. The curves represent the loads; This represents the fifth curve among the five curves indicating the lowest historical average weekend baseline load. The curves represent the loads. The following describes, for example, a method for determining the historical baseline load:

[0091] The historical baseline load is selected from the historical baseline load data, with weekday and weekend baseline load curves in spring (March-May) and autumn (September-November) when the daily average temperature is between 10℃ and 24℃, as candidate baseline load curves. Based on the candidate load curves, the five curves with the lowest average baseline load on weekdays and weekends are selected to calculate the historical baseline load.

[0092] Furthermore, when training the hybrid model in S22-c, the mean absolute percentage error is used. Root mean square error and prediction accuracy Three metrics were used to evaluate the predictive performance of the hybrid model.

[0093] Step S3: Construct a sub-Blu-ray optimization model with the objective of minimizing the operating cost of the regional distribution network; by decomposing the original objective into a main problem and sub-problems, the C&CG algorithm is used to iteratively solve the model based on the output of the wind turbine units, the output of the photovoltaic units, the base load, the climate-sensitive load, the set deterministic constraints, and the uncertain fuzzy set, to obtain the operating schemes of each unit of the regional distribution network and the flexibility requirements for adapting to extreme climates.

[0094] Specifically, in step S3, various flexible resources such as gas (gas turbine in this invention), energy storage, and demand side are first modeled to describe the operational constraints and flexibility adjustment capabilities of various flexible resources.

[0095] Specifically, in modeling a gas turbine, the flexibility that a gas turbine can provide for both upward and downward adjustments is expressed as follows:

[0096] ;

[0097] ;

[0098] in, and Gas turbines The flexibility to adjust upwards and downwards at any time; For gas turbines Efforts made at all times; , These represent the maximum and minimum output of the gas turbine. , This represents the upward and downward ramp rates of the gas turbine.

[0099] Specifically, the energy storage units are modeled, including the charging and discharging power model of the energy storage units and the upward and downward adjustment flexibility model of the energy storage units.

[0100] The charging and discharging power model of the energy storage unit is expressed as follows:

[0101] ;

[0102] in, Indicates that the energy storage unit is in The charging and discharging power at any given moment; and For energy storage units The charging and discharging power at any given time; , As an auxiliary binary variable, it represents the energy storage unit in The charging and discharging state at any given time; For energy storage units in The state of charge at any given moment; and These represent the charging and discharging efficiencies of the energy storage unit, respectively.

[0103] The flexibility model for adjusting energy storage unit rates upwards and downwards is expressed as follows:

[0104] ;

[0105] ;

[0106] in, and For energy storage units The flexibility to adjust upwards and downwards at any time; For energy storage units in The charging and discharging power at any given moment; , For energy storage units The charging and discharging power at any given time; , State variables for charging and discharging of energy storage units; For energy storage units in The state of charge at any given moment; and These are the charging and discharging efficiencies of energy storage, respectively. and These are the rated charging and discharging power of the energy storage, respectively. and The minimum charging and discharging power for energy storage; and These are the maximum and minimum energy storage capacities.

[0107] Specifically, the demand-side price-based demand response model is represented as follows:

[0108] ;

[0109] in, for Implement load demand in real time after price demand response; For the price elasticity matrix, , The number of scheduling periods is represented by the diagonal elements, which are the self-elasticity coefficients, describing the impact of the relative change in electricity price during this period on the electricity demand during this period. The remaining elements are the mutual elasticity coefficients, describing the impact of the relative change in electricity price during this period on the electricity demand during other periods. The self-elasticity coefficients should be negative, and the mutual elasticity coefficients should be positive. Price elasticity coefficient ( ), ,when When, it is called the self-elasticity coefficient, when When the price elasticity coefficient is 100%, it is called the reciprocal elasticity coefficient (the price elasticity coefficient describes the degree to which changes in electricity prices affect changes in electricity demand, reflecting the relationship between the rate of change in electricity demand and the rate of change in electricity prices, such as...). Figure 3 (as shown) , They are respectively Real-time load demand and changes in load demand; , They are respectively Initial electricity price and price change at any given time;

[0110] Specifically, modeling incentive-based demand response is performed on the demand side. Incentive-based demand response reaches agreements with users through economic compensation or reward contracts to regulate electricity load, with interruptible loads being the most common and widely used form. During peak load periods or emergencies, interruption requests are sent to participating users. Upon receiving dispatch instructions, users proactively reduce their electricity load, thereby mitigating system operational risks and receiving economic compensation. If no dispatch instructions are received, user loads remain in normal operation.

[0111] Furthermore, the calculation method for the compensation cost of incentive-based demand response modeling is as follows:

[0112] ;

[0113] in, Cost of interruptible load compensation per unit; For interruptible loads Response power at any given time.

[0114] Furthermore, after modeling the price-based demand response and incentive-based demand response on the demand side, a demand response model considering user responsiveness is constructed, including a price-based demand response model considering user responsiveness and an incentive-based demand response model considering user responsiveness.

[0115] It should be noted that demand-side price-based and incentive-based demand response models are built on the ideal condition that users can use electricity rationally. However, in reality, users' demand response behavior is also influenced by factors such as the intensity of demand response policy promotion and user group behavior. The relationship between the degree of participation in demand response and the intensity of information is as follows: Figure 4 As shown in the figure, the horizontal axis For information intensity, the vertical axis The user responsiveness curve has a dead zone, a linear zone, and a saturation zone. When it is in the dead zone, user demand is not significantly affected by changes in electricity price, and the demand response responsiveness is 0. This is because the economic benefits of user participation in demand response are not high. When it is in the linear zone, the user responsiveness increases with changes in electricity price. When it is in the saturation zone, the user responsiveness reaches its maximum value and remains unchanged as the intensity of electricity price information increases.

[0116] Therefore, the price demand response model considering user responsiveness is constructed as follows:

[0117] ;

[0118] Convert to: ;

[0119] in, To take into account the user responsiveness of price-based demand response under time-of-use pricing.

[0120] Furthermore, the incentive-based demand response model, taking into account user responsiveness, is constructed as follows:

[0121] ;

[0122] in, To consider user responsiveness to incentive-driven demand responses to interruptible loads; For interruptible loads Maximum response power at any given time.

[0123] Specifically, in step S3, the objective of minimizing the operating cost of the regional distribution network is represented by the objective function as follows:

[0124] ;

[0125] ;

[0126] where is the power purchase cost of the regional distribution network from the superior grid at time , , are respectively the power generation costs of the gas, wind power and photovoltaic units of the regional distribution network at time ; is the scheduling cost of the energy storage unit of the regional distribution network at time is the cost of implementing price-based demand response for the regional distribution network; is the interaction power between the regional distribution network and the superior grid at time and are respectively the load demands after and before implementing price-based demand response at time is the change value of the electricity price at time[[ID=(48]] is the power purchase cost of the regional distribution network from the superior grid at time; the superscripts and[[ID=(54]] represent the unit types; is the function between the unit operating cost and the output; is , , , represent the gas, energy storage, wind power and photovoltaic units respectively; , are , the start-up and shut-down states of the unit; is the unit interruptible load compensation cost; is

[0127] Specifically, in step S3, the set deterministic constraints include the gas turbine output constraint, the energy storage constraint, the power balance constraint, and the price-based demand response constraint.

[0128] Furthermore, the gas turbine output constraint is expressed as:

[0129] ;

[0130] in, For gas turbines in The running status at any given moment; , For gas turbines in The system's start-up and shutdown status at all times; , These are the minimum continuous start-up and shutdown times for the gas turbine, respectively. For gas turbines in Efforts made at all times; , These are the minimum and maximum outputs of the gas turbine, respectively. , These are the maximum permissible upward and downward ramp rates for the gas turbine, respectively. This represents the time interval between each moment.

[0131] Furthermore, the energy storage constraint is expressed as:

[0132] ;

[0133] in, For energy storage units in The charging and discharging power at any given moment; , For energy storage units The charging and discharging power at any given time; , State variables for charging and discharging of energy storage units; For energy storage units in The state of charge at any given moment; and These are the charging and discharging efficiencies of energy storage, respectively. and These are the rated charging and discharging power of the energy storage, respectively. and The minimum charging and discharging power for energy storage; and These are the maximum and minimum energy storage capacities.

[0134] Furthermore, the power balance constraint is expressed as:

[0135] ;

[0136] in, for The interaction power between the regional distribution network and the upstream power grid at any given time; , , , They are respectively It can continuously supply power from gas, energy storage, wind power, and photovoltaic units. for Implement load demand in real time following price-based demand response; For interruptible loads Response power at any given time; for Energy storage and charging power at all times.

[0137] Furthermore, using price-based demand response constraints, based on load demand before implementing price-based demand response. Determine the Load demand after implementing price-based demand response at all times .

[0138] Specifically, the price demand response constraint is expressed as:

[0139] ;

[0140] in, , , These are the electricity prices for peak hours, off-peak hours, and normal hours, respectively. , These are the minimum and maximum electricity prices, respectively. Peak-valley electricity price ratio; , These are the minimum and maximum peak-valley electricity price ratios, respectively. This represents the number of scheduling periods.

[0141] Furthermore, the load demand before implementing price-based demand response is determined based on baseline load and climate-sensitive load. ,include:

[0142] ;

[0143] in, The baseline load under extreme weather conditions is predicted based on historical baseline load data; This refers to the climate-sensitive load.

[0144] Furthermore, the historical reference load step has been described in S22-b.

[0145] Specifically, in order to find the operating schemes of each unit under the worst-case scenario probability distribution, a distribution network flexibility demand analysis model based on sub-Bruker is established based on models constructed from various flexibility resources, the objective function, the set deterministic constraints, and the uncertain fuzzy set. The C&CG algorithm is used to iteratively solve the sub-Bruker optimization model. The sub-Bruker optimization model is a two-stage, three-level optimization problem of min-max-min.

[0146] Its compact form is expressed as:

[0147] (1)

[0148] in, These are integer variables, including the start-up and shutdown schedule of the gas turbine and the charging / discharging status of the energy storage. For all uncertain terms Continuous decision variables (wind power, solar power, load) include gas turbine output, energy storage charging / discharging power, and demand response power; , It is a feasible set; , This is the coefficient vector of the objective function; It is a fuzzy set; , , where is the constraint coefficient matrix; , are constraint constant vectors.

[0149] Furthermore, the C&CG algorithm is used to iteratively solve the bibliometric optimization model, decomposing the original objective into a main problem and sub-problems.

[0150] Main problem: Given the probability distribution of scenarios requiring flexibility Solve for the optimal solution that satisfies the economic efficiency of the system, and provide a lower bound for equation (1).

[0151] (2)

[0152] in, To set a threshold; This represents the number of iterations.

[0153] Sub-problems:

[0154] Subproblems yield variables from the main problem In this case, we find the worst-case probability distribution for typical scenarios requiring flexibility, return it to the main problem, and provide an upper bound for equation (1):

[0155] (3)

[0156] Since the outer and inner layer problems have structural decoupling characteristics in the constraint space, equation (3) can be decomposed into two steps for solution, as shown in equations (4) and (5):

[0157] ; (4)

[0158] ; (5)

[0159] The iterative solution process is as follows:

[0160] ① Set the upper and lower bounds of the initial objective function as follows: and The number of iterations is Convergence threshold ;

[0161] ② Solve the main problem and obtain the optimal solution. Update the lower bound of the objective function ;

[0162] ③ Based on step ② Solve the subproblems to obtain the optimal solution. With the worst probability distribution Update the upper bound of the objective function ;

[0163] ④ Determine if convergence has occurred. If convergence is satisfied... If the iteration stops, output the result. , , Otherwise, let Proceed to step ② and continue solving until convergence.

[0164] The final solution yields the operating schemes for each generating unit in the regional power distribution network and the flexibility requirements for adapting to extreme weather conditions. These flexibility requirements include fluctuations in wind power output, photovoltaic power output, and load demand caused by extreme weather.

[0165] This embodiment discloses a method for determining the flexibility requirements of a distribution network that considers the impact of extreme weather. By characterizing the changing characteristics of renewable energy output and load demand under extreme weather conditions, a typical scenario set of flexibility requirements is constructed, and the existing flexibility resource adjustment capabilities of the distribution network are evaluated. This enables a multi-scale quantitative assessment of the flexibility requirements of the distribution network, solving the problem in traditional distribution network planning where the nonlinear coupling effect of extreme weather on renewable energy output fluctuations and load demand leads to a deviation between flexibility resource allocation and actual demand. This improves the renewable energy absorption rate and the reliability of power grid supply.

[0166] By constructing a training set based on historical climate element data and corresponding climate-sensitive loads, a hybrid CNN and LSTM model was used to improve the accuracy of climate-sensitive load prediction. Cluster analysis of extreme climate element datasets yielded a typical scenario set for flexibility demand, accurately characterizing the impact of extreme weather on renewable energy output and load demand. Demand-side response modeling accurately described the demand-side load response potential in real-world application scenarios. This improved the accuracy of determining distribution network flexibility demand under extreme weather conditions, providing a scientific basis for the allocation of power system flexibility resources.

[0167] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the flexibility requirements of a distribution network considering the impact of extreme weather, characterized in that, Includes the following steps: Based on an extreme climate element dataset, several typical scenarios of flexibility requirements under extreme climate conditions and the scenario probabilities of each typical scenario of flexibility requirements are determined. Based on the scenario probabilities, an uncertain fuzzy set of flexibility requirements is constructed. The climate elements in the extreme climate element dataset include temperature, wind speed, light intensity, and humidity. Based on the typical scenarios of the aforementioned flexibility requirements, we obtain the wind turbine output, photovoltaic unit output, base load, and climate-sensitive load under extreme weather conditions. A sub-Bruker optimization model is constructed with the goal of minimizing the operating cost of the regional distribution network. By decomposing the original objective into a main problem and sub-problems, the C&CG algorithm is used to iteratively solve the model based on the output of the wind turbine, the output of the photovoltaic unit, the base load, the climate-sensitive load, the set deterministic constraints, and the uncertain fuzzy set, so as to obtain the operating scheme of each unit of the regional distribution network and the flexibility requirements for adapting to extreme climates. The objective function for minimizing the operating cost of the regional distribution network is as follows: ； ； Wherein, is the power purchase cost of the regional distribution network from the superior power grid at time ; ; are respectively the power generation costs of the gas, wind power, and photovoltaic units of the regional distribution network at time; is the scheduling cost of the energy storage unit of the regional distribution network at time; is the cost of implementing price-based demand response for the regional distribution network; is ; are respectively the load demands after and before implementing price-based demand response at time; is the change value of the electricity price at time; The superscripts and represent the unit types; is a function between the unit operation cost and the output; is ; ; ; respectively represent the gas, energy storage, wind power, and photovoltaic units; ; are ; the start-up and shut-down states of the unit; is the compensation cost per unit of interruptible load; is the response power of the interruptible load at The process of determining multiple typical scenarios of flexibility requirements under extreme climate conditions and the scenario probabilities of each typical scenario based on an extreme climate element dataset, and constructing an uncertain fuzzy set of flexibility requirements based on the scenario probabilities, includes: time.

2. The method for determining the flexibility requirements of a distribution network according to claim 1, characterized in that, The process of determining multiple typical scenarios of flexibility requirements under extreme climate conditions and the scenario probabilities of each typical scenario based on an extreme climate element dataset, and constructing an uncertain fuzzy set of flexibility requirements based on the scenario probabilities, includes: The K-means clustering algorithm was used to cluster multiple typical scenarios of flexibility requirements under extreme climate and the scenario probability of each typical scenario of flexibility requirements based on the extreme climate element dataset. Based on the scenario probabilities, construct confidence sets based on the 1-norm and ∞-norm; Constructing fuzzy sets with uncertain flexibility requirements based on confidence sets of 1-norm and ∞-norm.

3. The method for determining the flexibility requirements of a distribution network according to claim 1 or 2, characterized in that, The wind power and photovoltaic unit output, base load, and climate-sensitive load under extreme weather conditions, derived from the typical scenarios of the aforementioned flexibility requirements, include: Based on climate data from typical scenarios requiring flexibility, the output of wind power and photovoltaic power units were calculated using wind turbine output models and photovoltaic power unit output models, respectively. Based on climate data from typical scenarios of flexibility requirements, the corresponding base load and climate-sensitive load are predicted using a trained hybrid CNN and LSTM model.

4. The method for determining the flexibility requirements of a distribution network according to claim 2, characterized in that, The uncertain fuzzy set of the flexibility requirement is represented as: ； in, For the purpose of flexibility, uncertain fuzzy sets are required; The set of positive real numbers representing the probability distribution of typical scenarios requiring flexibility; Typical scenarios for flexibility requirements The actual probability of occurrence; Number of typical scenarios for flexibility requirements; The probability of typical scenarios occurring to meet the flexibility requirements obtained from clustering; This represents the total number of samples in the extreme climate element dataset. , For typical scenarios requiring flexibility, the probability distributions are based on the 1-norm and - The confidence level satisfied by the norm.

5. The method for determining the flexibility requirements of a distribution network according to claim 3, characterized in that, The training process of the trained CNN and LSTM hybrid model includes: Based on historical climate element data, the historical baseline load and historical climate-sensitive load for the corresponding time are determined according to the total load for the corresponding time. The training set is composed of historical climate element data, historical baseline loads and historical climate-sensitive loads to train a hybrid CNN and LSTM model, resulting in a well-trained hybrid CNN and LSTM model.

6. The method for determining the flexibility requirements of a distribution network according to claim 1, characterized in that, The established deterministic constraints include power balance constraints, which are expressed as follows: ； in, for The interaction power between the regional distribution network and the upstream power grid at any given time; , , They are respectively The power output of gas-fired, wind-powered, and photovoltaic units is constantly being generated; for Implement load demand in real time following price-based demand response; For interruptible loads Response power at any given time; , They are respectively Energy storage and charging / discharging power at all times.

7. The method for determining the flexibility requirements of a distribution network according to claim 6, characterized in that, Using price demand response constraints, based on load demand before implementing price-based demand response. Determine the Load demand after implementing price-based demand response at all times The price demand response constraint is expressed as: ； in, , , These are the electricity prices for peak hours, off-peak hours, and normal hours, respectively. , These are the minimum and maximum electricity prices, respectively. Peak-valley electricity price ratio; , These are the minimum and maximum peak-valley electricity price ratios, respectively. This represents the number of scheduling periods.

8. The method for determining the flexibility requirements of a distribution network according to claim 7, characterized in that, The load demand before implementing price-based demand response is determined based on baseline load and climate-sensitive load. ,include: ； in, The baseline load is predicted based on historical baseline load data; This refers to the climate-sensitive load.

9. The method for determining the flexibility requirements of a distribution network according to claim 8, characterized in that, The historical baseline load is represented as follows: ； in, Historical baseline load; The curve representing the five curves with the lowest historical average baseline load is the [number missing]. The curves represent the loads; This represents the fifth curve among the five curves indicating the lowest historical average weekend baseline load. The curves represent the load.

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