A method for assessing power system adequacy considering the impact of non-disaster extreme weather

Abstract

The present invention relates to a method for assessing the adequacy of a power system taking into account the impact of non-disaster extreme weather, which belongs to the technical field of power dispatching and solves the problems of inaccurate existing adequacy assessment and low utilization rate of new energy. The method includes obtaining the wind and solar power output forecast value, load forecast value, probability of occurrence of extreme weather and resource supply rate of extreme weather at each moment in the period to be assessed; according to the probability of occurrence of extreme weather and resource supply rate of extreme weather, using Monte Carlo sampling method to obtain the wind and solar power generation status at each moment in the period to be assessed; according to the wind and solar power output forecast value, load forecast value, wind and solar power generation status and coal-fired unit operation status at each moment in the period to be assessed, constructing an adequacy assessment model with minimization of power system operation and penalty cost as the objective function; by solving the adequacy assessment model, the adequacy assessment result of the period to be assessed is obtained. Accurate long-term adequacy assessment is achieved, and the utilization rate of new energy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a method for evaluating the adequacy of an electric power system taking into account the impact of non-disaster extreme weather. Background Art

[0002] The power system provides necessary energy support for industrial production and is one of the basic infrastructures for the operation of large industrial systems. In recent years, new energy represented by wind power has developed rapidly, and the construction of new energy power systems has gradually accelerated. However, the output of fluctuating power sources such as wind power and photovoltaics is uncertain and easily affected by changes in meteorological conditions. Coal resources are also restricted by positioning transformation, economic price changes, etc., which poses a huge challenge to the adequacy of the goal of ensuring the safe and stable energy supply of the power system. Therefore, with the development of the power system, it is very important to conduct adequacy assessments of new energy power systems at different stages of development.

[0003] Existing adequacy assessments focus on the failure rate and repair rate of components under disaster weather conditions, ignoring the frequent and widespread occurrence of non-disaster extreme conditions, and failing to fully consider the impact of temporal changes and extreme weather on new energy. There is a lack of predictions on the occurrence rate of low wind or low light under future extreme weather conditions and the resource supply rate after wind and light are restricted, resulting in inaccurate adequacy assessments and reduced utilization of new energy.

[0004] Moreover, the current adequacy assessment focuses more on the short term, which brings more uncertainty to the real-time balance of the power system and cannot guarantee the long-term safe and stable operation of the system. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for evaluating the adequacy of an electric power system taking into account the impact of non-disaster extreme weather, so as to solve the problem that the existing method ignores the impact of non-disaster extreme weather on the electric power system, resulting in inaccurate adequacy evaluation and low utilization rate of new energy.

[0006] The embodiment of the present invention provides a method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather, comprising the following steps:

[0007] Based on historical meteorological data and historical operation data of the power system, obtain the wind and solar power output forecast value, load forecast value, probability of occurrence of extreme weather and resource supply rate of extreme weather at each moment in the evaluation period;

[0008] According to the probability of extreme weather and the resource supply rate of extreme weather, the Monte Carlo sampling method is used to obtain the wind and solar power generation status at each moment in the evaluation period;

[0009] According to the wind and solar power output forecast value, load forecast value, wind and solar power generation status and coal-fired unit operation status at each moment in the evaluation period, an adequacy evaluation model and its constraints are constructed with the objective function of minimizing the operation and penalty costs of the power system;

[0010] By solving the adequacy assessment model, the adequacy assessment result of the period to be assessed is obtained.

[0011] Based on further improvement of the above method, the probability of occurrence of extreme weather includes: the probability of occurrence of extreme weather with little wind or no wind, and the probability of occurrence of extreme weather with little light or no light; the resource supply rate of extreme weather includes: the resource supply rate after wind power is restricted and the resource supply rate after light is restricted.

[0012] Based on the further improvement of the above method, the wind and solar power generation status at each moment in the evaluation period includes: wind resource power generation status and photovoltaic resource power generation status, which are calculated by the following formula:

[0013]

[0014] Among them, W i,t and V j,t They represent the wind resource power generation state of the i-th wind turbine and the photovoltaic resource power generation state of the j-th photovoltaic unit at the t-th moment in the evaluation period respectively; w i,t and v j,t They represent the random numbers generated for the i-th wind turbine and the j-th photovoltaic unit at the t-th moment in the evaluation period; ε wind,t represents the probability of extreme weather with little or no wind at the tth moment in the evaluation period, η wind,t represents the resource supply rate after wind power is restricted at the tth moment in the evaluation period; ε PV,t represents the probability of occurrence of extreme weather with little or no light at the tth moment in the evaluation period; η PV,t It represents the resource supply rate after light restriction at the tth moment in the evaluation period.

[0015] Based on the further improvement of the above method, the objective function of the adequacy assessment model is expressed by the following formula:

[0016]

[0017] in, and They represent the penalty cost coefficients for wind curtailment, solar curtailment, and load shedding respectively; and They represent the wind curtailment, solar curtailment and load shedding at the tth moment, Ng represents the number of coal-fired units in the power system, represents the output of the kth coal-fired unit at the tth moment, the function F(·) represents the output cost function of the coal-fired unit, ΔT represents the time interval, and T represents the total number of moments in the period to be evaluated.

[0018] Based on the further improvement of the above method, the constraints of the adequacy assessment model include: power balance constraints, wind and solar power abandonment and load shedding constraints, coal-fired unit output constraints and coal-fired unit output ramping constraints.

[0019] Based on the further improvement of the above method, the power balance constraint is expressed by the following formula:

[0020]

[0021] in, represents the predicted wind power output value of the i-th wind turbine at the t-th time, represents the predicted photovoltaic output value of the j-th photovoltaic unit at the t-th time, L t represents the load forecast value at the tth moment; Nw and Nv represent the number of wind turbines and photovoltaic units respectively.

[0022] Based on the further improvement of the above method, the wind and solar power abandonment and load shedding constraints are expressed by the following formula:

[0023]

[0024] in, represents the predicted wind power output value of the i-th wind turbine at the t-th time, represents the predicted photovoltaic output value of the j-th photovoltaic unit at the t-th time, L t represents the load forecast value at the tth moment; Nw and Nv represent the number of wind turbines and photovoltaic units respectively.

[0025] Based on the further improvement of the above method, by solving the adequacy assessment model, the adequacy assessment results of the period to be assessed are obtained, including:

[0026] Identify whether the sum of the wind and solar curtailment at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the sufficient capacity of the coal-fired resource response at the corresponding moment is insufficient; by calculating the proportion of the number of moments when the sufficient capacity of the coal-fired resource response is insufficient to the total number of moments, the probability of wind and solar curtailment in the period to be evaluated is obtained; by summarizing the wind and solar curtailment at each moment, the wind and solar curtailment in the period to be evaluated is obtained;

[0027] Identify whether the load shedding amount at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the sufficient supply capacity of each resource in the power system at the corresponding moment is insufficient; by calculating the proportion of the number of moments when the sufficient supply capacity of each resource is insufficient to the total number of moments, the probability of insufficient energy supply in the period to be evaluated is obtained; by summarizing the load shedding amount at each moment, the insufficient energy supply in the period to be evaluated is obtained.

[0028] Based on the further improvement of the above method, the wind and solar power output forecast value and the load forecast value are predicted by respectively training the corresponding long short-term memory network models and passing in the weather forecast data at each moment in the evaluation period.

[0029] Based on the further improvement of the above method, the probability of occurrence of extreme weather with little or no wind, and the probability of occurrence of extreme weather with little or no light are predicted according to the wind speed and light intensity at each moment by fitting the corresponding seasonal autoregressive integrated moving average models respectively.

[0030] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0031] 1. For power systems containing large-scale new energy generating units and coal-fired generating units, taking into account possible extreme weather and tight energy supply, a method for assessing the adequacy of energy supply taking into account extreme weather and energy supply is given to ensure the long-term safe and stable operation of the system and improve the utilization rate of new energy.

[0032] 2. Based on the long short-term memory network model, the relationship between meteorological data and wind and solar power output and load is learned to accurately predict the wind and solar power output and load sequence of the planning period, providing accurate prediction based on historical data.

[0033] 3. Based on the seasonal autoregressive integrated moving average model, effectively process and analyze the trends and seasonal changes in historical time series data, accurately predict the occurrence of little or no wind, and little or no light under future extreme weather conditions, and further combine it with the calculation of the power generation status of wind resources and photovoltaic resources to establish the power balance constraints of the adequacy assessment model, significantly perceive the adequacy status affected by extreme weather in the future development planning process, reduce system load shedding caused by meteorology, etc., and improve the safety and reliability of the power system.

[0034] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0036] Figure 1 The present invention is a flowchart of a method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a 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 used to limit the scope of the present invention.

[0038] The present invention is aimed at the future development of power system scenarios that contain a large amount of renewable energy power generation and coal-fired power generation positioning transformation, with wind-solar-fire as the main power sources, including multiple wind turbines, photovoltaic units and coal-fired units. A specific embodiment of the present invention discloses a method for assessing the adequacy of a power system that takes into account the impact of non-disaster extreme weather, conducts an adequacy assessment of the power system for at least the next year, and predicts whether the unit combination planned for the future power system will result in the amount and probability of wind and solar power abandonment due to insufficient coal-fired response; and the amount of load shedding and the probability of insufficient energy supply due to insufficient supply capacity of various types of resources. Figure 1 As shown, the following steps are included:

[0039] S1. Based on historical meteorological data and historical operation data of the power system, obtain the wind and solar power output forecast value, load forecast value, probability of occurrence of extreme weather and resource supply rate of extreme weather at each moment in the evaluation period.

[0040] It should be noted that this embodiment is applicable to the adequacy assessment of the long-term development plan of the power system, and therefore, the period to be assessed is at least one year. Preferably, 1 hour is used as the unit time.

[0041] In this embodiment, the wind and solar power output prediction value and the load prediction value are predicted by respectively training the corresponding long short-term memory network models and inputting the weather forecast data at each moment in the evaluation period. The wind and solar power output prediction value includes the wind power output prediction value and the photovoltaic output prediction value.

[0042] Specifically, three long short-term memory network models are constructed to predict wind power output, photovoltaic output and load respectively; the meteorological data in the historical period are matched with the wind power output, photovoltaic output and load values ​​in the historical operation data of the power system at the same time point; the three long short-term memory network models take the meteorological data of the time series as input, and take the wind power output, photovoltaic output and load values ​​corresponding to the meteorological data as the actual values ​​respectively, and take the mean square error between the predicted value output by the long short-term memory network model and the corresponding actual value as the loss function, and continuously update the network parameters by minimizing the loss function until the training is completed. The three trained long short-term memory network models learn the relationship between meteorological data and wind power output, photovoltaic output and load values ​​respectively, so as to achieve accurate prediction.

[0043] During implementation, the weather forecast data at each moment in the evaluation period are input into three long-short term memory network models respectively, and the wind power output forecast value, photovoltaic output forecast value and load forecast value at the corresponding moment are output respectively.

[0044] Preferably, different meteorological data features are extracted from historical meteorological data as inputs to three long short-term memory network models. For example, wind speed and temperature are extracted as meteorological data features for predicting wind power output, light intensity and temperature are extracted as meteorological data features for predicting photovoltaic output, and temperature, humidity, wind speed and light intensity are extracted as meteorological data features for predicting load. Correspondingly, meteorological data features of the same dimension as those during training are input when making predictions.

[0045] Furthermore, the probability of occurrence of extreme weather includes: the probability of occurrence of extreme weather with little or no wind, and the probability of occurrence of extreme weather with little or no light; the resource supply rate of extreme weather includes: the resource supply rate after wind power is limited and the resource supply rate after light is limited.

[0046] It should be noted that extreme weather with little wind or no wind is determined based on wind speed, and extreme weather with little light or no light is determined based on light intensity. For example, when the wind speed is lower than 1 m / s, it is determined as extreme weather with little wind or no wind, and when the light intensity is lower than 200 lux, it is determined as extreme weather with little light or no light.

[0047] Furthermore, statistical analysis is performed on historical meteorological data to obtain the wind speed at each historical moment and its corresponding probability of occurrence of extreme weather with little wind or no wind as the first historical data, and the light intensity at each historical moment and its corresponding probability of occurrence of extreme weather with little light or no light as the second historical data; and each type of historical data obtained is processed for stability.

[0048] Specifically, extreme weather such as little or no wind and little or no light are all short-time scales. When calculating the probability of occurrence of extreme weather at each moment, the historical meteorological data of the week before each moment is taken as the range to be analyzed. By calculating the proportion of the number of moments when extreme weather occurs in each range to be analyzed to the total number of moments, the probability of occurrence of extreme weather at the corresponding moment is obtained.

[0049] For example, taking 1 hour as the unit time and taking the historical meteorological data of the week before time A, there are 168 hours in total; according to the statistics of the average wind speed per hour, there are 50 hours with little wind or no wind in these 168 hours, so the probability of extreme weather at time A is 50 / 168=0.298.

[0050] Two seasonal autoregressive integrated moving average (SARIMA) models were constructed respectively. By analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) of historical wind speed data and historical light data, the parameters of each SARIMA model were determined, including the number of non-seasonal autoregressive terms p, the number of differences d and the number of moving average terms q, as well as the number of seasonal autoregressive terms P, the number of seasonal differences D, the number of seasonal moving average terms Q and the length s of the seasonal cycle.

[0051] The determined parameters and the corresponding historical data are used to fit the SARIMA model respectively, so that the mean square error between the predicted value and the actual value of each SARIMA model is minimized. Preferably, the optimal parameter value of the model is determined by a grid search method.

[0052] During implementation, the probability of extreme weather with little wind or no wind and the probability of extreme weather with little light or no light at each moment in the period to be evaluated are predicted based on the wind speed and light intensity before the period to be evaluated.

[0053] It should be noted that the resource supply rate is the main technical parameter of wind turbines and photovoltaic units. The resource supply rate after wind limitation and the resource supply rate after light limitation are constructed at each moment during the evaluation period based on the technical characteristics of the wind turbines and photovoltaic units in the area.

[0054] S2. Based on the probability of occurrence of extreme weather and the resource supply rate of extreme weather, the Monte Carlo sampling method is used to obtain the wind and solar power generation status at each moment in the evaluation period.

[0055] It should be noted that the wind and solar power generation status at each moment in the period to be evaluated includes: wind resource power generation status and photovoltaic resource power generation status. Considering the impact of extreme weather, the wind and solar power generation status is not always normal. Therefore, according to the number of wind turbines Nw and the number of photovoltaic units Nv, a random number between [0,1] is generated for each wind turbine and each photovoltaic unit at each moment. Combined with the probability of occurrence of extreme weather and the resource supply rate of extreme weather calculated in step S1, the Monte Carlo sampling method is used to obtain the wind and solar power generation status at each moment in the period to be evaluated.

[0056] Specifically, the wind power generation status at each moment is calculated by the following formula:

[0057]

[0058] Among them, W i,t represents the wind power resource generation state of the i-th wind turbine at the t-th time in the evaluation period, i = 1, 2, ..., Nw; w i,t represents the random number generated for the i-th wind turbine at the t-th moment in the evaluation period; ε wind,t represents the probability of extreme weather with little or no wind at the tth moment in the evaluation period, η wind,t represents the resource supply rate after wind power is restricted at the tth moment in the evaluation period; w i,t >ε wind,t It means that the wind force of the i-th wind turbine at the t-th moment is in a normal state and has not dropped to the critical value of the extreme weather state, otherwise the wind force is in an extreme weather state.

[0059] The photovoltaic resource power generation status at each moment is calculated by the following formula:

[0060]

[0061] Among them, V j,t represents the photovoltaic resource power generation state of the j-th photovoltaic unit at the t-th time in the evaluation period, j = 1, 2, ..., Nv; v j,t represents the random number generated for the j-th PV unit at the t-th moment in the evaluation period; ε PV,t represents the probability of occurrence of extreme weather with little or no light at the tth moment in the evaluation period; η PV,t represents the resource supply rate after light restriction at the tth moment in the evaluation period; v j,t >ε PV,t It means that at the t-th moment, the photovoltaic power of the j-th photovoltaic unit is in a normal state and has not dropped to the critical value of the extreme weather state, otherwise the photovoltaic power is in an extreme weather state.

[0062] S3. Based on the predicted wind and solar power output, load forecast value, wind and solar power generation status and coal-fired unit operation status at each moment in the evaluation period, construct an adequacy assessment model and its constraints with the objective function of minimizing the operation and penalty costs of the power system.

[0063] It should be noted that, in this embodiment, the objective function of the adequacy assessment model is expressed by the following formula:

[0064]

[0065] in, and They represent the penalty cost coefficients for wind curtailment, solar curtailment, and load shedding, respectively, and are determined by the power system operation rules; and They represent the wind curtailment, solar curtailment and load shedding at the tth moment, respectively, and are variables to be solved; Ng represents the number of coal-fired units in the power system, represents the output of the kth coal-fired unit at the tth moment, k = 1, 2, ..., Ng, the function F(·) represents the output cost function of the coal-fired unit, ΔT represents the time interval, and T represents the total number of moments in the evaluation period. For example, when the evaluation period is one year and one moment is 1 hour, T is 8760.

[0066] Furthermore, the constraints of the adequacy assessment model include: power balance constraints, wind and solar power abandonment and load shedding constraints, coal-fired unit output constraints and coal-fired unit output ramping constraints.

[0067] Specifically, the power balance constraint is expressed by the following formula:

[0068]

[0069] in, represents the predicted wind power output value of the i-th wind turbine at the t-th time, represents the predicted photovoltaic output value of the j-th photovoltaic unit at the t-th time, L t Represents the load forecast value at the tth moment.

[0070] The following formula is used to express the constraints of wind and solar power abandonment and load shedding:

[0071]

[0072] The output constraint of coal-fired units is expressed by the following formula:

[0073]

[0074] Among them, K k,tIndicates the operating status of the kth coal-fired unit at the tth moment, which is obtained by the start-stop operation status of each coal-fired unit represented by 0-1 at each moment according to the start-stop plan of the coal-fired unit in the evaluation period; represents the lower limit of the output of the kth coal-fired unit, It represents the upper limit of the output of the kth coal-fired unit. The upper and lower limits of the output of the coal-fired unit are the basic operating parameters of the coal-fired unit and are determined by the structure of the coal-fired unit itself.

[0075] The output ramp constraint of coal-fired units is expressed by the following formula:

[0076]

[0077] Among them, R up and R down They represent the maximum upward and downward climbing rates of the unit respectively. They are the basic operating parameters of the coal-fired unit and are determined by the structure of the unit itself.

[0078] S4. By solving the adequacy assessment model, the adequacy assessment result of the period to be assessed is obtained.

[0079] It should be noted that the standard optimization model solver CPLEX or GUROBI is used to solve the adequacy assessment model to obtain the wind abandonment volume at each time. Amount of discarded light Load shedding By summarizing the amount of wind and solar power abandoned at each moment, the amount of wind and solar power abandoned due to insufficient coal response in the evaluation period is obtained. co , by summarizing the load shedding at each moment, the energy supply shortage of the period to be evaluated is obtained, and the formula is as follows:

[0080]

[0081] Identify whether the sum of the wind and solar curtailment at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the wind and solar curtailment are caused by insufficient response of coal resources at the corresponding moment, that is, the sufficient capacity of coal resources response at this moment is insufficient; by calculating the proportion of the number of moments with insufficient sufficient capacity of coal resources response to the total number of moments, the probability of wind and solar curtailment p in the period to be evaluated is obtained. co , the formula is as follows:

[0082]

[0083] Here, sgn(·) represents the sign function and returns 1 when the value is positive.

[0084] Identify whether the load shedding amount at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the adequacy of the supply of each resource in the power system at the corresponding moment is insufficient; by calculating the proportion of the number of moments with insufficient adequacy of the supply of each resource to the total number of moments, the probability of insufficient energy supply p in the period to be evaluated is obtained. re , the formula is as follows:

[0085]

[0086] The adequacy assessment result of the period to be assessed is obtained according to the above formula.

[0087] Compared with the prior art, the present embodiment provides a method for assessing the adequacy of a power system taking into account the impact of non-disaster extreme weather. For a power system containing large-scale new energy generators and coal-fired generators, taking into account the possible extreme weather and energy supply tension, a method for assessing the adequacy of a power system taking into account extreme weather and energy supply is provided to ensure the long-term safe and stable operation of the system and improve the utilization rate of new energy. The relationship between meteorological data and wind and solar output and load is learned based on the long-term and short-term memory network model, and the wind and solar output and load sequence of the planned period are accurately predicted, providing an accurate prediction based on historical data. Based on the seasonal autoregressive integrated moving average model, the trend and seasonal changes in the historical time series data are effectively processed and analyzed, and the occurrence rate of less wind or no wind, and less light or no light in future extreme weather is accurately predicted. It is further combined with the calculation of the power generation status of wind resources and photovoltaic resources to establish the power balance constraint of the adequacy assessment model, significantly perceive the adequacy status affected by extreme weather in the future development planning process, reduce the system load shedding caused by meteorology, etc., and improve the safety and reliability of the power system.

[0088] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0089] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for assessing the adequacy of a power system taking into account the impact of non-disaster extreme weather, characterized in that: The following steps are involved: According to the historical meteorological data and the historical operation data of the power system, the wind and solar power output forecast value, load forecast value, probability of occurrence of extreme weather and resource supply rate of extreme weather at each moment in the evaluation period are obtained; the probability of occurrence of extreme weather includes: probability of occurrence of extreme weather with little wind or no wind, and probability of occurrence of extreme weather with little light or no light; the resource supply rate of extreme weather includes: resource supply rate after wind power is restricted and resource supply rate after light is restricted; the probability of occurrence of extreme weather with little wind or no wind, and probability of occurrence of extreme weather with little light or no light are predicted according to wind speed and light intensity at each moment by fitting the corresponding seasonal autoregressive integrated moving average models respectively; According to the occurrence probability of the extreme weather and the resource supply rate of the extreme weather, the Monte Carlo sampling method is used to obtain the wind and solar power generation status at each moment in the period to be evaluated; According to the wind and solar power output forecast value, load forecast value, wind and solar power generation status and coal-fired unit operation status at each moment in the evaluation period, an adequacy evaluation model and its constraints with minimizing the operation and penalty costs of the power system as the objective function are constructed; By solving the adequacy assessment model, an adequacy assessment result of the period to be assessed is obtained.

2. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 1, characterized in that: The wind and solar power generation status at each moment in the period to be evaluated includes: wind resource power generation status and photovoltaic resource power generation status, which are calculated by the following formula: Among them, W i,t and V j,t They represent the wind resource power generation state of the i-th wind turbine and the photovoltaic resource power generation state of the j-th photovoltaic unit at the t-th moment in the evaluation period respectively; w i,t and v j,t They represent the random numbers generated for the i-th wind turbine and the j-th photovoltaic unit at the t-th moment in the evaluation period; ε wind,t represents the probability of extreme weather with little or no wind at the tth moment in the evaluation period, η wind,t represents the resource supply rate after wind power is restricted at the tth moment in the evaluation period; ε PV,t represents the probability of occurrence of extreme weather with little or no light at the tth moment in the evaluation period; η PV,t It represents the resource supply rate after light restriction at the tth moment in the evaluation period.

3. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 1, characterized in that: The objective function of the adequacy assessment model is expressed by the following formula: in, and They represent the penalty cost coefficients for wind curtailment, solar curtailment, and load shedding respectively; and They represent the wind curtailment, solar curtailment and load shedding at the tth moment, Ng represents the number of coal-fired units in the power system, represents the output of the kth coal-fired unit at the tth moment, the function F(·) represents the output cost function of the coal-fired unit, ΔT represents the time interval, and T represents the total number of moments in the period to be evaluated.

4. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 3 is characterized in that: The constraints of the adequacy assessment model include: power balance constraints, wind and solar power abandonment and load shedding constraints, coal-fired unit output constraints and coal-fired unit output ramping constraints.

5. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 4, characterized in that: The power balance constraint is expressed by the following formula: in, represents the predicted wind power output value of the i-th wind turbine at the t-th time, It represents the predicted photovoltaic output value of the j-th photovoltaic unit at the t-th moment, Lt represents the predicted load value at the t-th moment; Nw and Nv represent the number of wind turbines and photovoltaic units respectively.

6. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 4, characterized in that: The wind and solar power abandonment and load shedding constraints are expressed by the following formula: in, represents the predicted wind power output value of the i-th wind turbine at the t-th time, It represents the predicted photovoltaic output value of the j-th photovoltaic unit at the t-th moment, Lt represents the predicted load value at the t-th moment; Nw and Nv represent the number of wind turbines and photovoltaic units respectively.

7. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 3, characterized in that: The step of obtaining the adequacy assessment result of the period to be assessed by solving the adequacy assessment model includes: Identify whether the sum of the wind and solar curtailment at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the sufficient capacity of the coal-fired resource response at the corresponding moment is insufficient; by calculating the proportion of the number of moments when the sufficient capacity of the coal-fired resource response is insufficient to the total number of moments, the probability of wind and solar curtailment in the period to be evaluated is obtained; by summarizing the wind and solar curtailment at each moment, the wind and solar curtailment in the period to be evaluated is obtained; Identify whether the load shedding amount at each moment in the period to be evaluated is equal to 0. If it is not equal to 0, it means that the sufficient supply capacity of each resource in the power system at the corresponding moment is insufficient; by calculating the proportion of the number of moments when the sufficient supply capacity of each resource is insufficient to the total number of moments, the probability of insufficient energy supply in the period to be evaluated is obtained; by summarizing the load shedding amount at each moment, the insufficient energy supply in the period to be evaluated is obtained.

8. The method for evaluating the adequacy of a power system taking into account the impact of non-disaster extreme weather according to claim 1, characterized in that: The wind and solar power output forecast value and load forecast value are predicted by respectively training corresponding long short-term memory network models and inputting weather forecast data at each moment in the period to be evaluated.

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