Method for evaluating insufficient power supply capacity of power system by considering uncertainty of new energy

Abstract

The invention provides an electric power system power supply capacity insufficiency evaluation method considering new energy uncertainty, and the method comprises the steps: building the probability distribution of electric quantity prediction results at different time scales in a to-be-evaluated time period through Gaussian mixture distribution, and obtaining the probability distribution of the electric quantity prediction results in a total time period; constructing a joint probability density function according to the prediction error distribution of the generated power of the plurality of wind power plants and the prediction error distribution of the generated power of the plurality of photovoltaic power stations, and obtaining a space joint probability density function; establishing the probability density distribution of a wind-solar joint prediction error band in the to-be-evaluated time period according to the probability distribution of the electric quantity prediction result in the total time period and the spatial joint probability density function; calculating to obtain load shedding probability distribution through probability density distribution of a day-ahead operation domain and a wind-solar combined prediction error band; therefore, the risk of insufficient power supply is determined. And flexible assessment of the short-term scale power supply capacity insufficiency risk in the power system with short-term uncertainty in the new energy is realized.

Description

Technical Field

[0001] The present invention relates to the field of power supply risk assessment, and in particular to a method, device, equipment and storage medium for assessing power supply shortage in a power system taking into account the uncertainty of new energy sources. Background Art

[0002] One of the keys to risk assessment is modeling the output of renewable energy, which is often divided into deterministic and uncertain methods. Deterministic methods only use a single value to characterize the output of renewable energy, which does not include uncertainty. Therefore, in risk assessment, for some low-probability, high-impact events, deterministic modeling may have large assessment biases. Uncertainty modeling methods focus on providing the probability distribution characteristics of the prediction results and are often divided into parametric and non-parametric methods. In parametric methods, typical distribution functions such as the Cauchy distribution are often used for uncertainty modeling, but if the actual distribution does not match the assumption, it may lead to large errors. Non-parametric methods do not make prior assumptions about the uncertainty distribution and can handle complex and non-standard data characteristics. However, non-parametric methods generally have high data volume requirements and high computational complexity.

[0003] Extensive research has been conducted domestically and internationally on the power balance risks faced by power systems with a high proportion of renewable energy. This research primarily focuses on short- and medium-term power risk assessments, with limited consideration of power risk. Furthermore, the uncertainty management methods employed primarily rely on probabilistic forecasting, scenario-based approaches, and uncertainty aggregation. These power results cannot be directly aggregated to obtain power risk, as such a direct aggregation approach would fail to account for short- and medium-term uncertainties. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for assessing power supply shortages in power systems taking into account the uncertainty of renewable energy sources, so as to address the defects of the existing technology that mainly focus on medium- and short-term power risk assessment and give less consideration to power risk, and realize flexible assessment of medium- and short-term power supply shortage risks in power systems based on a mixed Gaussian model to characterize the medium- and short-term uncertainty of renewable energy sources.

[0005] The present invention provides a method for evaluating insufficient power supply in a power system taking into account the uncertainty of new energy sources, comprising the following steps: The probability distribution of power forecast results at different time scales within the time period to be evaluated is established through mixed Gaussian distribution, and the probability distribution of power forecast results for the total period is obtained; wherein the time period to be evaluated is the period between the observation point and the cutoff point; A joint probability density function is constructed based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations to obtain a spatial joint probability density function; The probability density distribution of the wind-solar joint forecast error band for the time period to be evaluated is established based on the probability distribution of the total period power forecast results and the spatial joint probability density function. The load shedding probability distribution is calculated based on the day-ahead operation domain and the probability density distribution of the wind-solar joint forecast error band. The risk of insufficient power supply is determined by load shedding probability distribution and continuous graded probability scoring distance.

[0006] According to a method for assessing power shortage in a power system considering the uncertainty of renewable energy, the present invention provides a method for establishing a probability distribution of power forecast results at different time scales within a time period to be assessed by using a mixed Gaussian distribution, and obtaining a probability distribution of power forecast results for the total time period, including: The prediction joint probability distribution of different time scales with the observation point as the starting moment is established through the mixed Gaussian distribution, and the sub-period prediction joint probability distribution is obtained; The linear combination invariance of mixed Gaussian distribution is used to predict the joint probability distribution of sub-periods and obtain the probability distribution of the total period power forecast result; The probability distribution of the total time period power forecast result is calculated using the following formula: in, t 1 indicates the observation point moment; t N Indicates the cutoff point; δ t1 ,…, δ tN Respectively t 1 ,…,t N Joint probability distribution of sub-period predictions at each time point.

[0007] According to a method for assessing power shortage in a power system considering the uncertainty of renewable energy, a joint probability density function is constructed based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations. The spatial joint probability density function is obtained, including: A random variable is constructed based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations through a mixed Gaussian distribution to obtain a spatial joint random variable; The joint probability density function is constructed for the spatial joint random variables through mixed Gaussian distribution to obtain the spatial joint probability density function.

[0008] According to a method for evaluating power supply shortage in a power system considering the uncertainty of renewable energy, the present invention provides a method for establishing a probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power forecast result and the spatial joint probability density function, including: Construct a correlation matrix based on the grid connection status and the forecast time scale to be evaluated to obtain an adjustable correlation matrix; According to the adjustable correlation matrix and the wind-solar joint random variables, a linear transformation relationship is constructed to obtain a spatial linear transformation; According to the probability distribution of the total period power forecast results, the spatial joint probability density function and the spatial linear transformation, the probability density distribution of the wind-solar joint forecast error band for the time period to be evaluated is established.

[0009] According to the present invention, a method for assessing power shortage in a power system considering the uncertainty of renewable energy is provided. The load shedding probability distribution is calculated by using the probability density distribution of the day-ahead operation domain and the wind-solar joint forecast error band, including: Determine the day-ahead operation domain based on output range constraints and power balance constraints; The output distribution function of uncertain variables is calculated based on the mixed Gaussian distribution model and the correlation matrix method; According to the output distribution function of uncertain variables and the range of the day-ahead operation domain, the load shedding probability distribution is calculated.

[0010] According to the present invention, a method for assessing power supply shortage in a power system considering the uncertainty of renewable energy is provided. The method determines the risk of power supply shortage by using load shedding probability distribution and continuous graded probability scoring distance, including: The load shedding probability and the load shedding expected value are calculated based on the load shedding probability distribution; The continuous graded probability score distance is obtained through the continuous graded probability score evaluation, where the continuous graded probability score distance refers to the distance between the load shedding probability distribution and the actual observation value; The risk of insufficient power supply is determined by load shedding probability distribution and continuous graded probability scoring distance.

[0011] The present invention also provides a device for assessing insufficient power supply in a power system taking into account the uncertainty of new energy sources, comprising the following modules: The total period probability distribution determination module is used to establish the probability distribution of power forecast results at different time scales within the time period to be evaluated through a mixed Gaussian distribution, thereby obtaining the probability distribution of power forecast results for the total period; wherein the time period to be evaluated is the period between the observation point and the cutoff point; a spatial probability density function determination module, configured to construct a joint probability density function based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations, thereby obtaining a spatial joint probability density function; The load shedding probability distribution determination module is used to establish the probability density distribution of the wind-solar joint forecast error band for the time period to be evaluated based on the probability distribution of the total period power forecast results and the spatial joint probability density function; the load shedding probability distribution is calculated based on the day-ahead operation domain and the probability density distribution of the wind-solar joint forecast error band; The risk assessment module is used to determine the risk of insufficient power supply through load shedding probability distribution and continuous graded probability scoring distance.

[0012] According to the present invention, a device for assessing power supply shortage in a power system considering the uncertainty of new energy sources is provided, wherein a total time period probability distribution determination module includes: The sub-period probability distribution determination module is used to establish the prediction joint probability distribution of different time scales with the observation point as the starting time through the mixed Gaussian distribution to obtain the sub-period prediction joint probability distribution; The total period probability distribution determination submodule is used to obtain the probability distribution of the total period power prediction result according to the joint probability distribution of the sub-period predictions through the linear combination invariance of the mixed Gaussian distribution; The probability distribution of the total time period power forecast result is calculated using the following formula: in, t 1 indicates the observation point moment; t N Indicates the cutoff point; δ t1 ,…, δ tN Respectively t 1 ,…,t N Joint probability distribution of sub-period predictions at each time point.

[0013] According to the present invention, a device for assessing power supply shortage in a power system considering the uncertainty of new energy sources is provided, and a spatial probability density function determination module includes: a spatial joint random variable determination submodule, configured to construct a random variable based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations through a mixed Gaussian distribution, thereby obtaining a spatial joint random variable; The spatial joint probability density function determination submodule is used to construct a joint probability density function for spatial joint random variables through a mixed Gaussian distribution to obtain a spatial joint probability density function.

[0014] According to the present invention, a device for assessing power shortage in a power system considering the uncertainty of new energy sources is provided, and a load shedding probability distribution includes: An adjustable correlation matrix determination submodule is used to construct a correlation matrix according to the grid connection status and the forecast time scale to be evaluated, thereby obtaining an adjustable correlation matrix; The spatial linear transformation determination submodule is used to construct a linear transformation relationship based on the adjustable correlation matrix and the wind-solar joint random variable to obtain the spatial linear transformation; The wind-solar joint prediction error band probability density distribution determination submodule is used to establish the wind-solar joint prediction error band probability density distribution of the time period to be evaluated based on the total time period power prediction result probability distribution, spatial joint probability density function and spatial linear transformation.

[0015] According to the present invention, a device for assessing power shortage in a power system considering the uncertainty of new energy sources is provided, and a load shedding probability distribution includes: The day-ahead operating domain determination submodule is used to determine the day-ahead operating domain based on the output range constraint and the power balance constraint; The uncertain variable output distribution function determination submodule is used to calculate the uncertain variable output distribution function based on the mixed Gaussian distribution model according to the correlation matrix method; The load shedding probability distribution determination submodule is used to calculate the load shedding probability distribution according to the output distribution function of the uncertain variable and the range of the day-ahead operation domain.

[0016] According to the present invention, a device for assessing insufficient power supply in a power system taking into account the uncertainty of new energy sources is provided, and the risk assessment module includes: The load shedding parameter value calculation submodule is used to calculate the load shedding probability and the load shedding expected value according to the load shedding probability distribution; A scoring distance determination submodule is used to obtain a continuous graded probability scoring distance through continuous graded probability scoring evaluation, wherein the continuous graded probability scoring distance refers to the distance between the load shedding probability distribution and the actual observation value; The risk assessment submodule is used to determine the risk of insufficient power supply through load shedding probability distribution and continuous graded probability scoring distance.

[0017] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described methods for assessing insufficient power supply in an electric power system taking into account the uncertainty of new energy.

[0018] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating insufficient power supply of an electric power system considering the uncertainty of new energy as described above is implemented.

[0019] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for assessing insufficient power supply in a power system considering the uncertainty of new energy.

[0020] The present invention provides a method, apparatus, device, and storage medium for assessing power system power shortages that consider the uncertainty of renewable energy. The method uses a mixed Gaussian distribution to establish probability distributions of power forecast results at different time scales within the time period to be assessed, obtaining a probability distribution of the total time period power forecast result. A joint probability density function is constructed based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations, obtaining a spatial joint probability density function. A probability density distribution of the wind-solar combined prediction error band for the time period to be assessed is established based on the total time period power forecast result probability distribution and the spatial joint probability density function. The load shedding probability distribution is calculated based on the day-ahead operation domain and the wind-solar combined prediction error band probability density distributions. The risk of power shortage is determined based on the load shedding probability distribution and the continuous graded probability scoring distance. Based on the additivity principle of the mixed Gaussian distribution, the proposed model can dynamically and flexibly characterize the joint probability distribution of the multi-moment output errors of renewable energy sources according to the demand period, resulting in a highly unified uncertainty modeling of the multi-moment output errors. This method enables flexible assessment of the medium- and short-term power shortage risk of power systems based on the mixed Gaussian model to characterize the medium- and short-term uncertainties of renewable energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 It is a flow chart of a method for evaluating insufficient power supply in an electric power system taking into account the uncertainty of new energy sources provided by the present invention.

[0023] Figure 2 It is a schematic diagram of the load shedding probability distribution solution process provided by the present invention.

[0024] Figure 3 This is a flow chart of risk assessment of insufficient power supply provided by the present invention.

[0025] Figure 4a This is the load shedding risk obtained based on the 7-day-ahead forecast data provided by the present invention.

[0026] Figure 4b This is the load shedding risk obtained based on the one-day-in-advance forecast data provided by the present invention.

[0027] Figure 5 This is the insufficient power risk assessment result provided by the present invention.

[0028] Figure 6 It is the correlation between the day-ahead risk result provided by the present invention and the actual electricity shortage.

[0029] Figure 7 It is the change of CRPS distance under the scenario method provided by the present invention and the power system power supply shortage assessment method considering the uncertainty of new energy provided by the present invention.

[0030] Figure 8 It is the load shedding probability result of the time period at the transformation starting point provided by the present invention.

[0031] Figure 9 This is a comparison diagram of the load shedding probability distribution in two periods provided by the present invention.

[0032] Figure 10 This is a high-risk period analysis chart provided by the present invention.

[0033] Figure 11 It is a structural schematic diagram of the power system power supply shortage assessment device considering the uncertainty of new energy provided by the present invention.

[0034] Figure 12 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0036] The following combination Figures 1-12 The present invention is described.

[0037] To facilitate understanding, we first introduce the joint probability distribution based on the Gaussian Mixture Model (GMM).

[0038] The Gaussian mixture model is used to describe the joint probability distribution of a random vector X. It is defined as a convex combination of multiple Gaussian distribution functions. By adjusting the number of Gaussian kernels, weight coefficients, expectations, and covariances, any random variable can be accurately characterized. The mathematical expressions of GMM are shown in formulas (1) to (3): (1) (2) (3) in, f X ( x )express X The joint probability density function of ω m is the weight coefficient; W in the denominator of equation (3) represents X Dimensions; N m (·) represents a multidimensional normal distribution, which is called the first m Gaussian components; det(·) represents the matrix determinant; M represents the total number of Gaussian components; μ m and σ m Respectively represent m The mean vector and covariance matrix of the Gaussian components.

[0039] Gaussian mixture models have linear combination invariance: if the random variables X is represented by the formula GMM, and the random variable Y yes X The linear transformation of Y The distribution of is also a GMM, each component of which has a mean of Aμ m +C, the covariance matrix is ​​AΣ m A T Gaussian distribution, the weight coefficient is ω m , the dimension is K. Specifically, Y The probability density function of is given by: (4) (5) The linear combination invariance of the Gaussian mixture model provides a basis for constructing the probability density of power supply within a time period. Data from different stations and different prediction scales are multiple random variables. The Gaussian mixture model can accurately characterize each random variable. By establishing a linear transformation relationship, using formulas (4) and (5), we can obtain a joint probability distribution that takes into account temporal and spatial correlations.

[0040] Figure 1This is one of the flow charts of the method for evaluating insufficient power supply of a power system considering the uncertainty of new energy provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Establish the probability distribution of power forecast results at different time scales within the time period to be evaluated by mixed Gaussian distribution to obtain the probability distribution of power forecast results for the total period; wherein the time period to be evaluated is the period between the observation point and the cutoff point.

[0041] In step 101 above, the observation point can be the current time point. If the current time point is used as the observation point, the power supply in the future period to be evaluated is the superposition of the power at different times in the period. If equal time intervals are considered, the power supply in the period to be evaluated can be expressed as: (6) in, Q Indicates the power supply during the time period to be evaluated. N Indicates that the time period to be evaluated is N The time points are divided into N -1 time period, ∆ t is the length of the divided time interval, W k For the k The power generated by the power supply at a point in time.

[0042] Different time points in the evaluation period have different time distances from the current time point, and the corresponding power W k It should be obtained through predictions at different time scales. According to this idea, the joint probability distribution of predictions at different time scales can be established through GMM, and its linear combination invariance can be used to obtain the probability distribution of the power supply prediction results for that period.

[0043] Optionally, step 101 includes steps A1 to A2: Step A1: Establish the prediction joint probability distribution of different time scales with the observation point as the starting time through the mixed Gaussian distribution to obtain the sub-period prediction joint probability distribution.

[0044] Step A2: Obtain the probability distribution of the total time period power forecast result based on the joint probability distribution of the sub-time period predictions through the linear combination invariance of the mixed Gaussian distribution.

[0045] The probability distribution of the total time period power forecast result is calculated using the following formula: in, t 1 indicates the observation point moment; t NIndicates the cutoff point; δ t1 ,…, δ tN Respectively t 1 ,…,t N The joint probability distribution of sub-period predictions at each time point, that is, t 1 ,…,t N Error distribution of renewable energy power generation prediction results at each time point corresponding to the time scale.

[0046] Step 102: construct a joint probability density function based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations to obtain a spatial joint probability density function.

[0047] In the above step 102, benefiting from the linear correlation of the GMM method, the prediction results of different types of new energy power sources and different sites can be further refined and divided, and the establishment of a joint probability distribution can also be considered.

[0048] Optionally, step 102 includes steps B1 and B2: Step B1: constructing a random variable based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations through a mixed Gaussian distribution to obtain a spatial joint random variable.

[0049] Step B2: construct a joint probability density function for the spatial joint random variables through a mixed Gaussian distribution to obtain the spatial joint probability density function.

[0050] In the above steps B1 to B2, it is assumed that there is N w wind farms, N p PV stations, then construct the random variable X m as follows.

[0051] (8) in, represents the prediction error distribution of each wind farm’s power generation, represents the distribution of prediction errors of photovoltaic power station power generation, and each random variable is in the form of the above formula (7). X m , according to formula (1), its joint probability density function can be constructed.

[0052] Step 103: Establish the probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power forecast result and the spatial joint probability density function; calculate the load shedding probability distribution through the day-ahead operation domain and the probability density distribution of the wind-solar joint prediction error band.

[0053] In the above step 103, by utilizing the linear combination invariance property of GMM, the spatiotemporal joint probability prediction result of the power supply forecast error in the evaluation period can be calculated.

[0054] Optionally, step 103 establishes a probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power prediction result and the spatial joint probability density function, including steps C1 to C3: Step C1: Construct a correlation matrix based on the grid connection status and the forecast time scale to be evaluated to obtain an adjustable correlation matrix.

[0055] Step C2: Construct a linear transformation relationship based on the adjustable correlation matrix and the wind-solar joint random variable to obtain a spatial linear transformation.

[0056] Step C3: Establish the probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power forecast results, the spatial joint probability density function, and the spatial linear transformation.

[0057] In the above steps C1 to C3, the correlation matrix and linear transformation relationship are constructed: (9) (10) in, A m Contains ( N w + N p )× N dimension, yes Linear transformation of . Incidence matrix A m The numerical setting of depends on the following two factors, which gives flexibility in two aspects of the calculation of power forecast probability distribution.

[0058] First, according to whether a power source in the power system is currently in the grid-connected state, the corresponding station can be set ( N w + N p ) associated values. When it is in the grid-connected state, the value is 1, and when it is off-grid, the value is 0.

[0059] Secondly, the prediction time scale to be evaluated is selected. The corresponding inertia matrix value at this time scale is set to 1, and the corresponding prediction time interval ∆t is adjusted.

[0060] From the above analysis, we can see that adjusting the correlation matrix can directly change the joint probability distribution of the prediction error according to the actual situation, avoiding the repeated reconstruction of the distribution when the power system operation status changes. A m Substituting into formulas (4) and (5), we can calculate the probability density distribution of the wind-solar joint prediction error band during the evaluation period: .

[0061] This paper constructs a joint probability model for wind and solar power output errors at multiple times based on a mixed Gaussian distribution and introduces an adjustable correlation matrix to control the size and position of the observation window. Furthermore, a multidimensional evaluation index for power shortage is proposed based on the feasible region of system operation.

[0062] Optionally, the load shedding probability distribution is calculated by using the probability density distribution of the day-ahead operation domain and the wind-solar joint prediction error band in step 103, including steps D1 to D3: Step D1: Determine the day-ahead operating domain based on the output range constraint and the power balance constraint; Step D2: Calculate the output distribution function of uncertain variables based on the mixed Gaussian distribution model according to the correlation matrix method; Step D3: Calculate the load shedding probability distribution based on the output distribution function of the uncertain variables and the range of the day-ahead operating domain.

[0063] In the above steps D1 to D3, with the large-scale access of renewable energy sources such as wind power and photovoltaics, the uncertainty faced by the power system operation process has increased significantly. , thermal power and other controllable power output is recorded as For simplicity, considering the linear constraint model, the power grid dispatch problem can be written as: (11) In the formula is a parameter matrix or vector, which is determined by the power system operation constraints. This paper explores the risk of insufficient power supply in the medium term. Therefore, only the day-ahead operation domain under the output range constraint and power balance constraint is considered, which is denoted as The present invention is concerned with Operational risks existing outside the domain.

[0064] In actual operational scenarios, there are often multiple uncertainties, resulting in a high-dimensional domain for the day-ahead operation, which is not conducive to rapid risk calculation. Using the joint probability distribution method mentioned above, this high-dimensional domain can be reduced to a single dimension, preserving as much error information as possible and facilitating risk calculation.

[0065] After the day-ahead dispatch plan of conventional units is known, the day-ahead operation domain can be calculated as follows. The upper and lower limits of the output range of conventional thermal power units are denoted as , , subscript is the observation window length.

[0066] (12) (13) Where, express Moment The power generation units are in startup state. Represent the maximum power and minimum power of each unit respectively. Therefore, the flexible operation range of thermal power is , and denote this interval as .

[0067] (14) Where, express Moment The node load will The total load during the period is recorded as .

[0068] (15) Where, Respectively represent Uncontrolled power supply The lower and upper limits of output at all times, is the total uncontrollable power output range, and this interval is recorded as , also known as the current operation domain .

[0069] According to the power balance constraint: (16) The day-ahead operating domain is obtained from the above formula: (17) According to the probability density distribution of the wind-solar joint prediction error band obtained above , and further obtain the uncertain variable output distribution function : (18) Where, Indicates the total predicted output value of uncontrollable power supply, Indicates the possible output value of the uncontrollable power source considering uncertainty.

[0070] Step 104: Determine the risk of insufficient power supply based on the load shedding probability distribution and the continuous graded probability scoring distance.

[0071] In the above step 104, the power supply shortage risk assessment is to calculate the probability distribution of possible load shedding based on the distribution of the forecast error band and the range of the day-ahead operation domain. The load shedding amount in period T is based on the range of the day-ahead operation domain. , then the load shedding probability distribution function is as follows: (19) (20) like Figure 2 As shown, Figure 2 The solution process from the probability distribution of wind-solar joint prediction error band to the load shedding probability distribution is demonstrated.

[0072] Optionally, step 104 includes steps E1 to E3: Step E1: Calculate the load shedding probability and the load shedding expected value according to the load shedding probability distribution; Step E2: obtaining a continuous graded probability score distance through continuous graded probability score evaluation, wherein the continuous graded probability score distance refers to the distance between the load shedding probability distribution and the actual observation value; Step E3: Determine the risk of insufficient power supply through the load shedding probability distribution and the continuous graded probability scoring distance.

[0073] In steps E1 through E3 above, the load shedding probability and expected value are calculated based on the load shedding probability distribution. However, for this type of distribution, describing it solely with the expected value is insufficient. Not all probability predictions are equally accurate; it is also necessary to measure the distance between the distribution and the actual power shortage. Therefore, the present invention uses three indicators: load shedding probability, load shedding expectation, and continuous graded probability scoring distance to measure the risk of power shortage.

[0074] 1) Indicator 1: Load shedding probability and expected value According to the load shedding probability distribution function, the load shedding probability can be calculated and load shedding expectation : (twenty one) (twenty two) Where, Indicates the maximum load power in one day.

[0075] Only the probability and expected value cannot fully reflect the risk of load shedding. If necessary, the load shedding distribution function can be directly referred to. .

[0076] 2) Indicator 2: CRPS distance The Continuous Ranked Probability Score (CRPS) takes the risk prediction distribution as a whole and evaluates the distance between the distribution and the actual observed value. Its mathematical calculation formula is as follows

[20] : (twenty three) Where, is the predicted load shedding cumulative distribution function, is the actual power shortage. and is the cumulative distribution function The first term in the above formula represents the mean absolute error between the predicted distribution and the actual observed value, and the second term represents the spread of the predicted distribution.

[0077] Figure 2 It also shows how to calculate the CRPS distance between the load shedding probability distribution and the actual power gap based on the load shedding probability distribution. The shaded area in the load shedding cumulative distribution function diagram represents the physical meaning of CRPS.

[0078] The complete process of power supply shortage risk assessment is as follows Figure 3 The input information includes the grid connection status of the wind and solar power stations, the time scale to be observed, the station's historical data, and the conventional unit scheduling plan. The output information is the high-risk period of power supply shortage.

[0079] In order to further explain the present invention, the following example analysis is provided.

[0080] The present invention adopts an improved IEEE39 node system to carry out a case study on the risk of power imbalance considering medium- and short-term uncertainties. In the system, nodes 37 and 38 are both wind farms with a capacity of 250MW, and node 39 is a photovoltaic station with a capacity of 160MW. The wind and solar historical data used in the present invention are the actual output of the wind and solar cluster in Gansu Province from January 1, 2021 to December 31, 2021 and the forecast data 1-7 days in advance, with a resolution of 15 minutes. The load data used is a typical load curve. The simulation example is carried out in the MATLAB R2020a environment, and the unit combination scheme is solved by calling GUROBI 10.0. The computer processor is Intel(R) Core(TM) i5-8265U CPU with 16GB of memory.

[0081] The example setting of the present invention is divided into three steps: first, the effectiveness of the proposed method is demonstrated through the statistical results of multi-day risk assessment indicators; then, the risk assessment results are compared with the traditional scenario method to demonstrate the accuracy of the proposed method; finally, the advantages of the proposed method in multi-time scale assessment are demonstrated by locating the risk period.

[0082] Step 1: Risk Assessment Results In order to analyze the risk of insufficient power in days, Figure 3 The process shown corresponds to the setting of the correlation matrix; the conventional unit scheduling plan used in the evaluation model is the robust unit combination result.

[0083] 1) Load shedding probability distribution A typical day was selected for risk analysis. Based on the seven-day forecast data, the probability of load shedding on that day was 26.92%. Based on the one-day forecast data, the probability of load shedding on that typical day was 6.2%. The load shedding probability distribution is shown in Figure 4. Figure 4a shows the load shedding risk based on the seven-day forecast data, and Figure 4b shows the load shedding risk based on the one-day forecast data.

[0084] Based on the given load shedding probability value and detailed probability distribution information, the dispatching department can make corresponding safeguard measures according to its own risk acceptance level.

[0085] 2) Risk outcomes under different unit combinations Risk calculations were performed using both deterministic predictive unit commitment and robust unit commitment methods as inputs to conventional unit scheduling. Based on forecast data from one to seven days in advance, power shortage risk assessment results were obtained at corresponding scales. To demonstrate their statistical characteristics, the following example averages the risk results for a total of 30 days.

[0086] according to Figure 5The risk assessment results for both unit combinations show an overall increasing trend with increasing forecast days. Physically speaking, the longer the forecast horizon, the greater the uncertainty range, and this increase in risk assessment results is expected. Day-ahead (one-day-ahead) forecasts are relatively accurate, resulting in relatively lower risk assessment results.

[0087] As the forecast scale decreases, the uncertainty of the forecast decreases, the accuracy increases, and the difference in risk values ​​between the two schemes also decreases. Especially at the day-ahead scale, since the results of the deterministic forecast have achieved a high degree of accuracy, the difference between its risk assessment results and those of the robust method becomes less significant.

[0088] This example verifies that the proposed method is applicable to various time scales in the medium and short term when assessing the risk of insufficient power, and can be used to compare different unit combination schemes.

[0089] 3) Risk assessment results inspection Compare the above risk assessment results 30 days ago with the actual power shortage results. Figure 6 , where the colors mark their daily load shedding probabilities.

[0090] according to Figure 6 The scattered distribution in the figure shows a certain degree of linear correlation, with a mean absolute error of 395.4 MWh, representing 2% of the daily load. Because the expected risk value cannot accurately characterize load shedding, its probability distribution needs to be considered simultaneously. Furthermore, some points are found to have low predicted risk and load shedding probabilities, but actual power shortages are large. This means that overall 24-hour observations may not necessarily reflect local power shortages. This issue will be further explained in the third part of the example.

[0091] Step 2: The present invention uses the CRPS indicator to explore the changes in the distance between the insufficient power risk assessment results and the actual power gap values ​​under different risk assessment methods.

[0092] To verify the accuracy of this risk assessment method, we compared it with the commonly used scenario-based method. This method randomly selects 1,000 output scenarios, calculates the power shortage for each scenario, and accumulates the 24-hour results to determine the daily power shortage risk. We calculated the CRPS (Counter-Proof-Surplus) risk assessment for both the traditional scenario-based method and our method from one to seven days in advance. We also calculated the reduction in CRPS distance (i.e., the improvement in accuracy) achieved by our method compared to the scenario-based method. To demonstrate its statistical properties, the following example averages the risk results for 30 days.

[0093] according to Figure 7, the CRPS distance of the scenario method does not change much with the increase of the number of days in advance prediction, which reflects the inherent random characteristics of the scenario method, indicating that the improvement of prediction accuracy has little effect on the improvement of the risk accuracy of the scenario method; in contrast, this method shows an overall upward trend in risk with the increase of the number of days in advance prediction, which is different from Figure 5 The conclusions are consistent. The scenario-based risk assessment CRPS distance is generally higher than that of this method. This can be attributed to the scenario-based method's simplistic treatment of electricity consumption and its failure to fully account for short- and medium-term uncertainties. This method, which accounts for short- and medium-term uncertainties, is more accurate than the traditional scenario-based method in risk assessments 1-7 days in advance, with a maximum improvement of 40.70% and an average improvement of 29.27%. However, as the forecast lead time increases, the accuracy advantage of this method over the scenario-based method gradually decreases. This phenomenon can be attributed to the fact that the uncertainty forecast results themselves decrease in accuracy over time.

[0094] Step 3: Risk period positioning and analysis By adjusting the correlation matrix Am, the joint probability distribution of the prediction error can be changed, the length and position of the observation window can be changed, and the high-risk concentrated period can be further identified.

[0095] A typical day is selected for illustration. The load shedding probability is 14.15% and the power shortage risk accounts for 0.44% of the daily load ratio. Considering the use of a 12-hour observation window to locate the high-risk interval, the observation results at different window starting points are as follows: Figure 8 shown.

[0096] according to Figure 8 As the time window starts from 1 hour to 12 hours, the load shedding probability in period 1 first increases and then decreases, while the load shedding probability in period 2 first decreases and then increases. The difference between the two and the envelope value both reach their maximum when the starting point is 7 hours. In other words, the best observation results are obtained with a time window of 12 hours and a starting point of 7 hours. At this time, the load shedding probability in period 1, from 7 hours to 18 hours, is 34.89%, with an expected value of 1678MW. The load shedding probability in period 2, from 1 hour to 6 hours and from 19 hours to 24 hours, is 5.15%, with an expected value of 120MW. Therefore, for this day, although the overall daily load shedding probability is 14%, there is actually a high-risk period from 7 hours to 18 hours, where the load shedding probability can reach over 30%. Figure 9 Detailed probability information of load shedding in two time periods is provided so that the system can respond to risks more accurately.

[0097] To verify the correctness of risk positioning, the actual load and wind and solar output on that day are plotted as follows. Figure 10 It can be found that in the high-risk period, there is a large load ramp from 7h to 9h, while from 12h to 16h there is a sudden drop in the output of new energy. Therefore, there is a large overall load shedding risk during this period, and the risk period is correctly located.

[0098] In summary, there are instances where the overall risk level remains stable, or where high risk is concentrated during certain periods. Using a time window analysis approach, we can further pinpoint high-risk periods based on a full-day risk assessment, thus providing system operators with a basis for a reasonable response strategy.

[0099] The effectiveness of the proposed method was verified by analyzing multi-day risk assessment results across different time scales and unit combinations over the short and medium term. A comparative analysis of the proposed method's improvement in assessment accuracy compared to traditional scenario-based methods, using the Continuous Probability Score (CRPS) distance, revealed a maximum improvement of 40.70% in risk assessment accuracy, with an average improvement of 29.27%. Furthermore, the proposed method can quickly identify high-risk periods by flexibly adjusting the correlation matrix, avoiding the repeated reconstruction of new error distributions.

[0100] The present invention provides a method for assessing power supply shortages in a power system that considers the uncertainty of renewable energy. The method uses a mixed Gaussian distribution to establish probability distributions of power forecast results at different time scales within the time period to be assessed, obtaining a probability distribution of total time period power forecast results. A joint probability density function is constructed based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations, obtaining a spatial joint probability density function. A probability density distribution of the wind-solar joint prediction error band for the time period to be assessed is established based on the total time period power forecast result probability distribution and the spatial joint probability density function. The load shedding probability distribution is calculated based on the day-ahead operation domain and the wind-solar joint prediction error band probability density distributions. The risk of power shortage is determined based on the load shedding probability distribution and the continuous graded probability scoring distance. Based on the additivity principle of the mixed Gaussian distribution, the proposed model can dynamically and flexibly characterize the joint probability distribution of renewable energy output errors at multiple times according to the demand period, making the uncertainty modeling of multi-time output errors highly unified. This method implements a flexible assessment of the medium- and short-term power supply shortage risk in a power system based on the mixed Gaussian model to characterize the medium- and short-term uncertainties of renewable energy.

[0101] The following describes the power system power supply shortage assessment device considering the uncertainty of new energy provided by the present invention. The power system power supply shortage assessment device considering the uncertainty of new energy described below and the power system power supply shortage assessment method considering the uncertainty of new energy described above can be referenced to each other.

[0102] Figure 11 This is one of the flow charts of the power system power supply shortage assessment device considering the uncertainty of new energy provided by the present invention, such as Figure 11 As shown, the device includes the following modules: The total period probability distribution determination module 1101 is used to establish the probability distribution of power forecast results at different time scales within the time period to be evaluated through a mixed Gaussian distribution, and obtain the probability distribution of power forecast results for the total period; wherein the time period to be evaluated is the period between the observation point time and the cutoff point time.

[0103] Optionally, the total time period probability distribution determination module 1101 includes: The sub-period probability distribution determination module is used to establish the prediction joint probability distribution of different time scales with the observation point as the starting time through the mixed Gaussian distribution to obtain the sub-period prediction joint probability distribution; The total period probability distribution determination submodule is used to obtain the probability distribution of the total period power prediction result according to the joint probability distribution of the sub-period predictions through the linear combination invariance of the mixed Gaussian distribution; The probability distribution of the total time period power forecast result is calculated using the following formula: in, t 1 indicates the observation point moment; t N Indicates the cutoff point; δ t1 ,…, δ tN Respectively t 1 ,…,t N The joint probability distribution of sub-period predictions at each time point, that is, t 1 ,…,t N Error distribution of renewable energy power generation prediction results at each time point corresponding to the time scale.

[0104] The spatial probability density function determination module 1102 is configured to construct a joint probability density function based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations to obtain a spatial joint probability density function.

[0105] Optionally, the spatial probability density function determination module 1102 includes: a spatial joint random variable determination submodule, configured to construct a random variable based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations through a mixed Gaussian distribution, thereby obtaining a spatial joint random variable; The spatial joint probability density function determination submodule is used to construct a joint probability density function for spatial joint random variables through a mixed Gaussian distribution to obtain a spatial joint probability density function.

[0106] According to the present invention, a device for assessing power shortage in a power system considering the uncertainty of new energy sources is provided, and a load shedding probability distribution includes: An adjustable correlation matrix determination submodule is used to construct a correlation matrix according to the grid connection status and the forecast time scale to be evaluated, thereby obtaining an adjustable correlation matrix; The spatial linear transformation determination submodule is used to construct a linear transformation relationship based on the adjustable correlation matrix and the wind-solar joint random variable to obtain the spatial linear transformation; The wind-solar joint prediction error band probability density distribution determination submodule is used to establish the wind-solar joint prediction error band probability density distribution of the time period to be evaluated based on the total time period power prediction result probability distribution, spatial joint probability density function and spatial linear transformation.

[0107] The load shedding probability distribution determination module 1103 is used to establish the probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power prediction results and the spatial joint probability density function; and calculate the load shedding probability distribution through the day-ahead operation domain and the wind-solar joint prediction error band probability density distribution.

[0108] Optionally, the load shedding probability distribution 1103 includes: The day-ahead operating domain determination submodule is used to determine the day-ahead operating domain based on the output range constraint and the power balance constraint; The uncertain variable output distribution function determination submodule is used to calculate the uncertain variable output distribution function based on the mixed Gaussian distribution model according to the correlation matrix method; The load shedding probability distribution determination submodule is used to calculate the load shedding probability distribution according to the output distribution function of the uncertain variable and the range of the day-ahead operation domain.

[0109] The wind assessment module 1104 is used to determine the risk of insufficient power supply through load shedding probability distribution and continuous graded probability scoring distance.

[0110] Optionally, the risk assessment module 1104 includes: The load shedding parameter value calculation submodule is used to calculate the load shedding probability and the load shedding expected value according to the load shedding probability distribution; A scoring distance determination submodule is used to obtain a continuous graded probability scoring distance through continuous graded probability scoring evaluation, wherein the continuous graded probability scoring distance refers to the distance between the load shedding probability distribution and the actual observation value; The risk assessment submodule is used to determine the risk of insufficient power supply through load shedding probability distribution and continuous graded probability scoring distance.

[0111] The present invention provides a device for assessing power shortages in a power system that considers the uncertainty of renewable energy sources. The device uses a mixed Gaussian distribution to establish probability distributions of power forecast results at different time scales within the time period to be assessed, obtaining a probability distribution of the total time period power forecast result. A joint probability density function is constructed based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations, obtaining a spatial joint probability density function. A probability density distribution of the wind-solar combined prediction error band for the time period to be assessed is established based on the total time period power forecast result probability distribution and the spatial joint probability density function. The load shedding probability distribution is calculated based on the day-ahead operation domain and the wind-solar combined prediction error band probability density distributions. The risk of power shortage is determined based on the load shedding probability distribution and the continuous graded probability scoring distance. Based on the additivity principle of the mixed Gaussian distribution, the proposed model can dynamically and flexibly characterize the joint probability distribution of the multi-moment output errors of renewable energy sources according to the demand period, resulting in a highly unified modeling of the uncertainty of the multi-moment output errors. This device implements a flexible assessment of the medium- and short-term power shortage risk in a power system based on the mixed Gaussian model to characterize the medium- and short-term uncertainties of renewable energy sources.

[0112] Figure 12 An example of a physical structure diagram of an electronic device is shown below. Figure 12 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logic instructions in the memory 830 to execute a method for assessing power supply shortage in a power system considering the uncertainty of new energy sources.

[0113] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0114] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the power system power supply shortage assessment method considering the uncertainty of new energy provided by the above methods.

[0115] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the power system power supply shortage assessment method considering the uncertainty of new energy provided by the above methods.

[0116] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0117] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for assessing power shortage in a power system considering the uncertainty of new energy sources, characterized in that: include: The probability distribution of power forecast results at different time scales within the time period to be evaluated is established by mixed Gaussian distribution to obtain the probability distribution of power forecast results for the total time period; wherein the time period to be evaluated is the period between the observation point and the cutoff point; A joint probability density function is constructed based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations to obtain a spatial joint probability density function; Establishing a probability density distribution of the wind-solar joint prediction error band for the time period to be evaluated based on the probability distribution of the total time period power prediction result and the spatial joint probability density function; The load shedding probability distribution is calculated by using the day-ahead operation domain and the probability density distribution of the wind-solar joint prediction error band; The risk of insufficient power supply is determined by the load shedding probability distribution and the continuous graded probability scoring distance.

2. The method for assessing power system power shortage considering the uncertainty of new energy sources according to claim 1 is characterized in that: The method of establishing the probability distribution of power forecast results at different time scales within the time period to be evaluated by using a mixed Gaussian distribution to obtain the probability distribution of power forecast results for the total time period includes: The prediction joint probability distribution of different time scales with the observation point as the starting moment is established through the mixed Gaussian distribution, and the sub-period prediction joint probability distribution is obtained; Obtain the probability distribution of the total time period power forecast result according to the joint probability distribution of the sub-time period predictions through the linear combination invariance of the mixed Gaussian distribution; The probability distribution of the total time period power forecast result is calculated using the following formula: in, t 1 indicates the observation point moment; t N Indicates the cutoff point moment; δ t1 ,…, δ tN Respectively t 1 ,…,t N Joint probability distribution of sub-period predictions at each time point.

3. The method for evaluating power system power shortage considering the uncertainty of new energy sources according to claim 1 is characterized in that: The joint probability density function is constructed based on the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations to obtain a spatial joint probability density function, including: Constructing a random variable according to the prediction error distribution of the power generation of multiple wind farms and the prediction error distribution of the power generation of multiple photovoltaic power stations through the mixed Gaussian distribution to obtain a spatial joint random variable; A joint probability density function is constructed for the spatial joint random variables through the mixed Gaussian distribution to obtain a spatial joint probability density function.

4. The method for assessing power system power shortage considering the uncertainty of new energy sources according to claim 3 is characterized in that: The establishing of the wind-solar joint prediction error band probability density distribution of the time period to be evaluated based on the total time period power prediction result probability distribution and the spatial joint probability density function includes: Construct a correlation matrix based on the grid connection status and the forecast time scale to be evaluated to obtain an adjustable correlation matrix; Constructing a linear transformation relationship according to the adjustable correlation matrix and the wind-solar joint random variable to obtain a spatial linear transformation; The probability density distribution of the wind-solar joint prediction error band of the time period to be evaluated is established according to the probability distribution of the total time period power prediction result, the spatial joint probability density function and the spatial linear transformation.

5. The method for evaluating insufficient power supply in a power system considering the uncertainty of new energy sources according to claim 1 is characterized in that: The load shedding probability distribution is calculated by using the day-ahead operation domain and the probability density distribution of the wind-solar joint prediction error band, including: Determine the day-ahead operation domain based on output range constraints and power balance constraints; The output distribution function of uncertain variables is calculated based on the mixed Gaussian distribution model and the correlation matrix method; The load shedding probability distribution is calculated based on the uncertain variable output distribution function and the range of the day-ahead operation domain.

6. The method for assessing power system power shortage considering the uncertainty of new energy sources according to claim 1 is characterized in that: The determining of the risk of insufficient power supply by using the load shedding probability distribution and the continuous graded probability scoring distance includes: Calculating the load shedding probability and the load shedding expected value according to the load shedding probability distribution; A continuous graded probability scoring distance is obtained through continuous graded probability scoring evaluation, wherein the continuous graded probability scoring distance refers to the distance between the load shedding probability distribution and the actual observation value; The risk of insufficient power supply is determined by the load shedding probability distribution and the continuous graded probability scoring distance.

7. A device for assessing insufficient power supply in a power system considering the uncertainty of new energy sources, characterized in that: include: A total period probability distribution determination module is used to establish the probability distribution of power forecast results at different time scales within the time period to be evaluated through a mixed Gaussian distribution, thereby obtaining the probability distribution of power forecast results for the total period; wherein the time period to be evaluated is the period between the observation point and the cutoff point; a spatial probability density function determination module, configured to construct a joint probability density function based on the prediction error distributions of the power generation of multiple wind farms and the prediction error distributions of the power generation of multiple photovoltaic power stations, thereby obtaining a spatial joint probability density function; a load shedding probability distribution determination module, configured to establish a wind-solar joint prediction error band probability density distribution for the time period to be evaluated based on the total time period power forecast result probability distribution and the spatial joint probability density function; and calculate the load shedding probability distribution using the day-ahead operation domain and the wind-solar joint prediction error band probability density distribution; The risk assessment module is used to determine the risk of insufficient power supply based on the load shedding probability distribution and the continuous graded probability scoring distance.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for evaluating insufficient power supply in a power system considering the uncertainty of new energy sources as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating insufficient power supply in a power system considering the uncertainty of new energy sources as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating insufficient power supply in a power system considering the uncertainty of new energy sources as described in any one of claims 1 to 6 is implemented.

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