A power grid new energy carrying capacity evaluation method based on ISODATA scene clustering

Abstract

This invention discloses a method for assessing the renewable energy carrying capacity of a power grid based on ISODATA scenario clustering. The method includes: obtaining a set of wind power and photovoltaic output curves from historical power system operation data and standardizing them; using the ISODATA clustering algorithm to cluster the per-unit values ​​of wind power and photovoltaic output to construct typical wind power and photovoltaic output scenarios; constructing a multi-objective optimization model for assessing the renewable energy carrying capacity of the power grid based on the typical output scenarios; calculating the Pareto front of the installed capacity of wind power and photovoltaic using a constraint method; and assessing the renewable energy carrying capacity based on the installed capacity of wind power and photovoltaic in the Pareto front. This invention fully considers the operating costs of the power system, renewable energy absorption rate requirements, and system operation constraints, obtaining a compromise solution between the scale of renewable energy installed capacity and the economic efficiency of system operation. It avoids the one-sided result of only pursuing the penetration rate of renewable energy installed capacity while ignoring the economic efficiency of system operation, and has stronger engineering applicability.

Description

Technical Field

[0001] This invention relates to the technical field of power system optimization planning, and in particular to a method for assessing the renewable energy carrying capacity of power grids based on ISODATA scenario clustering. Background Technology

[0002] Currently, the limits of grid access to new energy sources and their evolution are still unclear. Most existing studies on grid new energy carrying capacity assessment methods are based on the historical absorption of new energy sources in the system. They use the new energy absorption coefficient method to predict the future grid new energy absorption level based on factors such as the future scale of conventional power sources and system load level, and then determine the future grid new energy installed capacity. However, they ignore the various constraints in the actual operation of the power system.

[0003] New energy sources such as wind and solar power are characterized by strong randomness, volatility, and intermittency. A new power system dominated by new energy sources will exhibit a high proportion of uncertain new energy connections and peak-valley differences, making the issues of new energy consumption and peak regulation increasingly prominent. Therefore, fully considering the uncertainty of new energy power generation output and the complex constraints of power system operation can determine the grid's new energy carrying capacity and rationally plan the scale of new energy development. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing methods for assessing the carrying capacity of new energy sources in power grids do not adequately consider the uncertainty of new energy output and ignore various constraints in the actual operation of the power system.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution, including:

[0008] The set of wind power and photovoltaic output curves is obtained from historical power system operation data and then normalized.

[0009] The ISODATA clustering algorithm was used to cluster the per-unit values ​​of wind power and solar power output to construct typical scenarios for wind power and solar power output.

[0010] A multi-objective optimization model for assessing the renewable energy carrying capacity of the power grid is constructed based on the aforementioned typical power output scenarios.

[0011] The Pareto fronts of wind power and photovoltaic installed capacity were calculated using the constraint method on the multi-objective optimization model.

[0012] The Pareto Frontier assesses the capacity of new energy sources based on wind and solar power installed capacity.

[0013] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0014] The set of power generation curves for wind and solar power includes:

[0015] {p w,i i = 1, 2, ..., N w}{p p,i i = 1, 2, ..., N p}

[0016] p w,i ={p w,i,t ,t=1,2,...,T},p p,i ={p p,i,t ,t=1,2,...,T}

[0017] Where, N w N is the number of samples for wind power output curves. p The number of photovoltaic output curve samples is T, which is the study time period, and p is p. w,i This is the i-th wind power output curve, p w,i,t p is the wind power output value at time t in the i-th wind power output curve. p,i This is the i-th photovoltaic output curve, p p,i,t It is the photovoltaic output value at time t in the i-th photovoltaic output curve.

[0018] The power generation curves of wind power and photovoltaic power are normalized to per-unit values, and the set of per-unit curves is represented as follows:

[0019]

[0020]

[0021] in, It is the per-unit output value of the i-th output curve of the wind power at time t. It is the per-unit output value of the i-th output curve of the photovoltaic system at time t; This is the wind power output curve after per-unit scaling. This is the photovoltaic output curve after standardization.

[0022] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0023] Using the set of per-unit power output curves for wind and solar power as samples, ISODATA was used to cluster the samples, including:

[0024] Pre-select n w The cluster centers of the per-unit wind power output curves are represented as follows:

[0025]

[0026] Collection of per-unit power output curves N in w Each sample is assigned to the nearest cluster W. j W j by The minimum distance D between a sample and each cluster center is the cluster center. j Represented as:

[0027]

[0028] in, The curve corresponding to the j-th cluster center; This represents the specific value of the curve corresponding to cluster center j at time t;

[0029] The cluster centers are modified as follows:

[0030]

[0031] Where, n j For clustering W j The total number of samples in the sample.

[0032] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0033] According to the clustering W j Calculate the sample variance and the pairwise distances between each cluster center, including:

[0034] The sample variance is expressed as:

[0035]

[0036] in, Each cluster W j The sample variance in the data;

[0037] The pairwise distances between each cluster center are expressed as:

[0038]

[0039] Among them, D ij It is the distance between each pair of cluster centers.

[0040] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0041] Based on the sample variance Distance D from cluster center ij Determining whether the requirements are met includes:

[0042] like And D ij ≥D * If so, it means that the requirements are met and a typical scenario for output is constructed;

[0043] like Or D ij <D * If the condition is not met, then the cluster center splitting and aggregation operations should be performed.

[0044] in, and D * These are the pre-defined expected values ​​of the variance of the cluster samples and the expected values ​​of the distance between the cluster centers;

[0045] The fragmentation of cluster centers is represented as:

[0046]

[0047]

[0048] The aggregation of cluster centers is represented as:

[0049]

[0050] The total number of cluster centers is adjusted and expressed as:

[0051]

[0052] in, This is the corrected total number of cluster centers, n i n j They are clustering W i W j The number of samples in the sample.

[0053] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0054] Typical wind power and photovoltaic output scenarios are constructed using the cluster centers, and the scenario probability of each scenario is calculated, including:

[0055] Typical wind power output scenarios:

[0056]

[0057] Probability of typical power output scenarios:

[0058]

[0059] Where, n s It is clustering W s The number of samples in, ρ w,s It is the probability corresponding to wind power output scenario s;

[0060] Typical photovoltaic power output scenarios:

[0061]

[0062] Probability of typical power output scenarios:

[0063]

[0064] in, It is the cluster center of the per-unit value curve of photovoltaic power output. n is the number of photovoltaic power generation cluster centers. s It is clustering W s The number of samples in, ρ p,s It represents the probability corresponding to photovoltaic power output scenario s.

[0065] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0066] Based on the typical wind power and photovoltaic power output scenarios, new energy joint power output scenarios are constructed, and the scenario probability of each joint power output scenario is calculated, including:

[0067] A scenario of joint power generation from new energy sources is represented as follows:

[0068]

[0069] The scenario probabilities for each joint effort scenario are expressed as follows:

[0070] ρ wp,s =ρ w,s ·ρ p,s

[0071]

[0072] Where, ρ wp,sIt is the scenario probability corresponding to scenario s in the combined wind and solar power output scenario.

[0073] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0074] A multi-objective optimization model for assessing the grid's renewable energy carrying capacity in typical renewable energy scenarios includes:

[0075] The overall operating cost of a power system is expressed as:

[0076]

[0077] Among them, f fuel,s It is the coal consumption cost of thermal power units under a typical scenario s of combined new energy power generation; for a given typical scenario s of combined new energy power generation, P T,s,it Let i be the output of thermal power unit i at time t;

[0078] The system's renewable energy curtailment rate is expressed as:

[0079]

[0080] Among them, P curt,w,s,t This refers to the power curtailment (P) of wind power at time t under a typical scenario of combined renewable energy output (s). curt,p,s,t This refers to the power curtailment of photovoltaic power at time t under a typical scenario of combined renewable energy output (S). w It is the unsold installed capacity of wind power, S p This refers to the unsold installed capacity of photovoltaic power.

[0081] The optimization objective is expressed as:

[0082]

[0083] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0084] The Pareto fronts of wind power and photovoltaic installed capacity are calculated using the constraint method on the multi-objective optimization model, including:

[0085] Power balance constraints are expressed as:

[0086]

[0087] The output constraint of thermal power units is expressed as:

[0088] P T,min,i ≤P T,s,it ≤P T,max,i

[0089] Wind power output constraints are expressed as:

[0090]

[0091] Photovoltaic output constraints are expressed as:

[0092]

[0093] The constraint on the curtailment rate of renewable energy is expressed as:

[0094]

[0095] The Pareto front for wind power and solar power installed capacity is represented as:

[0096]

[0097] in, It is the installed capacity of wind power and photovoltaic power (S) w ,S p The Pareto frontier.

[0098] As a preferred embodiment of the power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering described in this invention, wherein:

[0099] The Pareto Frontier assesses the carrying capacity of new energy sources based on wind and solar power installed capacity, including:

[0100] The maximum sum of wind power and photovoltaic installed capacity is used as a quantitative representation of the grid's renewable energy carrying capacity, expressed as:

[0101]

[0102] The larger the installed capacity of new energy sources that the power grid can withstand, the greater the quantitative representation of the power grid's new energy carrying capacity, and the stronger the power grid's new energy carrying capacity; conversely, the smaller the capacity, the weaker the carrying capacity.

[0103] The beneficial effects of this invention are as follows: The power grid renewable energy carrying capacity assessment method provided by this invention obtains typical renewable energy output scenarios by using the ISODATA clustering method. Based on the typical renewable energy scenarios, a multi-objective optimization model for assessing the power grid renewable energy carrying capacity is constructed. This model fully considers the operating costs of the power system, the renewable energy absorption rate requirements, and the system operation constraints, and obtains a compromise solution between the system's renewable energy installed capacity and the system's operating economy. This avoids the one-sided result of only pursuing the renewable energy installed capacity penetration rate while ignoring the system's operating economy, and has stronger engineering applicability. Attached Figure Description

[0104] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0105] Figure 1 This is a flowchart illustrating a method for assessing the renewable energy carrying capacity of a power grid based on ISODATA scenario clustering, as described in the first embodiment of the present invention.

[0106] Figure 2 This is a flowchart illustrating the construction of a grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering in the first embodiment of the present invention, covering typical wind power and photovoltaic power output scenarios.

[0107] Figure 3 This is a wind power output clustering scenario diagram for a power grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering as described in the first embodiment of the present invention.

[0108] Figure 4 This is a photovoltaic output clustering scenario diagram for a grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering, as described in the first embodiment of the present invention. Detailed Implementation

[0109] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0110] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0111] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0112] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0113] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0114] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0115] Example 1

[0116] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for assessing the renewable energy carrying capacity of a power grid based on ISODATA scenario clustering, including:

[0117] S1: Obtain the set of wind power and photovoltaic output curves based on historical power system operation data and perform per-unit processing;

[0118] Furthermore, the set of power generation curves for wind power and solar power includes:

[0119] {p w,i i = 1, 2, ..., N w}{p p,i i = 1, 2, ..., N p}

[0120] p w,i ={p w,i,t ,t=1,2,...,T},p p,i ={p p,i,t ,t=1,2,...,T}

[0121] Where, N w N is the number of samples for wind power output curves. pThe number of photovoltaic output curve samples is T, which is the study time period, and p is p. w,i This is the i-th wind power output curve, p w,i,t p is the wind power output value at time t in the i-th wind power output curve. p,i This is the i-th photovoltaic output curve, p p,i,t It is the photovoltaic output value at time t in the i-th photovoltaic output curve.

[0122] The power generation curves of wind power and photovoltaic power are normalized to per-unit values, and the set of per-unit curves is represented as follows:

[0123]

[0124]

[0125] in, It is the per-unit output value of the i-th output curve of the wind power at time t. It is the per-unit output value of the i-th output curve of the photovoltaic system at time t; This is the wind power output curve after per-unit scaling. This is the photovoltaic output curve after standardization.

[0126] S2: The ISODATA clustering algorithm is used to cluster the per-unit values ​​of wind power and photovoltaic power output to construct typical scenarios of wind power and photovoltaic power output;

[0127] Furthermore, using the set of per-unit power output curves for wind and solar power as samples, ISODATA was used to cluster the samples, including:

[0128] Pre-select n w The cluster centers of the per-unit wind power output curves are represented as follows:

[0129]

[0130] Collection of per-unit power output curves N in w Each sample is assigned to the nearest cluster W. j W j by The minimum distance D between a sample and each cluster center is the cluster center. j Represented as:

[0131]

[0132] in, The curve corresponding to the j-th cluster center; This represents the specific value of the curve corresponding to cluster center j at time t;

[0133] It should be noted that, When the sample is at its minimum

[0134] The cluster centers are modified as follows:

[0135]

[0136] Where, n j For clustering W j The total number of samples in the sample.

[0137] Furthermore, based on clustering W j Calculate the sample variance and the pairwise distances between each cluster center, including:

[0138] The sample variance is expressed as:

[0139]

[0140] in, Each cluster W j The sample variance in the data;

[0141] The pairwise distances between each cluster center are expressed as:

[0142]

[0143] Among them, D ij It is the distance between each pair of cluster centers.

[0144] Furthermore, based on sample variance Distance D from cluster center ij Determining whether the requirements are met includes:

[0145] like And D ij ≥D * If so, it means that the requirements are met and a typical scenario for output is constructed;

[0146] like Or D ij <D * If the condition is not met, then the cluster center splitting and aggregation operations should be performed.

[0147] in, and D * These are the pre-defined expected values ​​of the variance of the cluster samples and the expected values ​​of the distance between the cluster centers;

[0148] It should be noted that the expected value is determined based on the actual variance and actual distance between each sample. α% (0 < α < 1) of the sample set variance is taken as the expected variance value, and β% (0 < β < 1) of the maximum distance is taken as the expected distance between cluster centers. The values ​​of α and β are determined by the technicians based on experience and the anticipated dispersion of the clusters. If the technicians want the curve clusters to be relatively dispersed, they can take α = 30% and β = 70%. If the technicians want the curve clusters to be relatively concentrated, they can take α = 70% and β = 30%. If the technicians have no obvious preference for the dispersion of the curve clusters, they can take α = β = 50%.

[0149] The fragmentation of cluster centers is represented as:

[0150]

[0151]

[0152] The aggregation of cluster centers is represented as:

[0153]

[0154] The total number of cluster centers is adjusted and expressed as:

[0155]

[0156] in, This is the corrected total number of cluster centers, n i n j They are clustering W i W j The number of samples in the sample.

[0157] It should be noted that for clustering W j If the sample variance Then its cluster centers Perform lysis processing; for cluster W j With clustering W i If the distance between the cluster centers of the two is D ij <D * If the cluster centers are clustered, then the clusters are aggregated; count the number n clusters that need to be split. c The number of clusters to be aggregated is n. a ISODATA clustering, through the splitting and aggregation of cluster centers, more accurately reflects the uncertainty of new energy processing curve samples.

[0158] Furthermore, cluster centers construct typical wind power and solar power output scenarios and calculate the scenario probability for each scenario, including:

[0159] Typical wind power output scenarios:

[0160]

[0161] Probability of typical power output scenarios:

[0162]

[0163] Where, n s It is clustering W s The number of samples in, ρ w,s It is the probability corresponding to wind power output scenario s;

[0164] Typical photovoltaic power output scenarios:

[0165]

[0166] Probability of typical power output scenarios:

[0167]

[0168] in, It is the cluster center of the per-unit value curve of photovoltaic power output. n is the number of photovoltaic power generation cluster centers. s It is clustering W s The number of samples in, ρ p,s It represents the probability corresponding to photovoltaic power output scenario s.

[0169] It should be noted that the ISODATA clustering analysis method was used to construct typical renewable energy output scenarios. Aggregation and splitting operations were performed on each cluster center, avoiding the blind selection of cluster centers inherent in traditional clustering methods. The typical renewable energy output scenarios obtained through ISODATA clustering enable a more accurate assessment of the grid's renewable energy carrying capacity.

[0170] Furthermore, based on typical wind power and solar power output scenarios, we construct new energy joint output scenarios and calculate the scenario probability of each joint output scenario, including:

[0171] A scenario of joint power generation from new energy sources is represented as follows:

[0172]

[0173] The scenario probabilities for each joint effort scenario are expressed as follows:

[0174] ρ wp,s =ρ w,s ·ρ p,s

[0175]

[0176] Where, ρ wp,sIt is the scenario probability corresponding to scenario s in the combined wind and solar power output scenario.

[0177] S3: Construct a multi-objective optimization model for assessing the renewable energy carrying capacity of the power grid based on typical power output scenarios;

[0178] Furthermore, a multi-objective optimization model for assessing the grid's renewable energy carrying capacity in typical renewable energy scenarios includes:

[0179] The overall operating cost of a power system is expressed as:

[0180]

[0181] Among them, f fuel,s It is the coal consumption cost of thermal power units under a typical scenario s of combined new energy power generation; for a given typical scenario s of combined new energy power generation, P T,s,it Let i be the output of thermal power unit i at time t;

[0182] The system's renewable energy curtailment rate is expressed as:

[0183]

[0184] Among them, P curt,w,s,t This refers to the power curtailment (P) of wind power at time t under a typical scenario of combined renewable energy output (s). curt,p,s,t This refers to the power curtailment of photovoltaic power at time t under a typical scenario of combined renewable energy output (S). w It is the unsold installed capacity of wind power, S p This refers to the unsold installed capacity of photovoltaic power.

[0185] The optimization objective is expressed as:

[0186]

[0187] It should be noted that the minimum overall operating cost of the power system is minf. op and the minimum curtailment rate of renewable energy in the system (minf) curt To optimize the objectives, a multi-objective optimization model for assessing the renewable energy carrying capacity of the power grid based on typical renewable energy scenarios was constructed. The model measures the renewable energy installed capacity that the power grid can accept from both the economic and technical constraints of system operation, and can adapt to the requirements of energy-saving and economical operation and low-carbon development of the future power system.

[0188] S4: The Pareto fronts of wind power and photovoltaic installed capacity are calculated using the constraint method on the multi-objective optimization model;

[0189] Furthermore, the Pareto fronts of wind power and photovoltaic installed capacity are calculated using a constraint method on the multi-objective optimization model, including:

[0190] Power balance constraints are expressed as:

[0191]

[0192] The output constraint of thermal power units is expressed as:

[0193] P T,min,i ≤P T,s,it ≤P T,max,i

[0194] Wind power output constraints are expressed as:

[0195]

[0196] Photovoltaic output constraints are expressed as:

[0197]

[0198] The constraint on the curtailment rate of renewable energy is expressed as:

[0199]

[0200] The Pareto front for wind power and solar power installed capacity is represented as:

[0201]

[0202] in, It is the installed capacity of wind power and photovoltaic power (S) w ,S p The Pareto frontier.

[0203] S5: Assess the capacity of new energy sources based on the installed capacity of wind and solar power in the Pareto frontier.

[0204] Furthermore, based on the Pareto Frontier's assessment of wind and solar power installed capacity, the capacity to support new energy sources is evaluated, including:

[0205] The maximum sum of wind power and photovoltaic installed capacity is used as a quantitative representation of the grid's renewable energy carrying capacity, expressed as:

[0206]

[0207] The larger the installed capacity of new energy sources that the power grid can withstand, the greater the quantitative representation of the power grid's new energy carrying capacity, and the stronger the power grid's new energy carrying capacity; conversely, the smaller the capacity, the weaker the carrying capacity.

[0208] Example 2

[0209] Reference Figures 3-4This is the second embodiment of the present invention, which provides a method for evaluating the carrying capacity of new energy in power grids based on ISODATA scenario clustering. In order to verify the beneficial effects, a comparative experiment on the example system is conducted for scientific demonstration.

[0210] The improved IEEE-RTS96 system was used for the case analysis. The system contains 10 thermal power units with a total installed capacity of 2827MW. The unit parameters are shown in Table 1. The maximum load of the system is 3156MW. The load per unit value curve is a typical load curve of a certain province in southern China. The historical annual wind power and photovoltaic power output curves of a certain province in southern China are used as the sample set of wind power and photovoltaic power output curves.

[0211] Table 1 Technical and economic parameters of thermal power units

[0212]

[0213] Option 1: The renewable energy carrying capacity of the improved IEEE-RTS96 system is evaluated using the grid renewable energy carrying capacity assessment method based on ISODATA scenario clustering.

[0214] Figure 3 shows typical per-unit output curves for wind power and solar power obtained using ISODATA scene clustering. Figure 4 As shown in Table 2, the probabilities of wind power and photovoltaic clustering scenarios are as follows:

[0215] Table 2. Probability of Wind Power and Solar Power Clustering Scenarios

[0216]

[0217] Based on the clustering scenarios of wind power and photovoltaic power, the method of this invention is used to set an upper limit for the curtailment rate of new energy power. The renewable energy carrying capacity of the improved IEEE-RTS96 system was evaluated at 5%, and the evaluation results and the overall system operating cost are shown in Table 3.

[0218] Table 3 Evaluation Results of Option 1

[0219]

[0220] Option 2: Use the new energy absorption coefficient method to evaluate the new energy carrying capacity of the improved IEEE-RTS96 system.

[0221] The potential for renewable energy consumption is determined based on the minimum technical output of thermal power units and typical load curves.

[0222]

[0223] Where Q represents the space for new energy consumption, and L represents the space for new energy consumption. tP represents the value of the system's typical load curve at time t. T,min,i To achieve the minimum technical output for the i-th thermal power unit, NT This represents the total number of thermal power units in the system.

[0224] Set an upper limit on the curtailment rate of renewable energy. The value is 5%, and the carrying capacity of new energy sources is calculated and expressed as:

[0225]

[0226] in, T represents the new energy carrying capacity, and T represents the research time period.

[0227] Based on the determined new energy carrying capacity, i.e. the maximum installed capacity of new energy, the power balance calculation of the power system is carried out, and the comprehensive operating cost of the system is calculated.

[0228] The results of the assessment of the new energy carrying capacity and the overall system operating cost are shown in Table 4.

[0229] Table 4 Evaluation Results of Option 2

[0230]

[0231] By comparing the assessment results of the new energy carrying capacity and the overall system operating cost of Scheme 1 and Scheme 2, the actual effect of the method of the present invention is verified, as shown in Table 5:

[0232] Table 5 Comparison of Evaluation Results for Schemes 1 and 2

[0233]

[0234] The results comparison shows that, compared to the results of Scheme 2 using the renewable energy absorption coefficient method for assessing renewable energy carrying capacity, the renewable energy carrying capacity assessed using the method of the present invention in Scheme 1 is slightly lower than that assessed by the absorption coefficient method. However, the overall system operating cost is also lower than that of the absorption coefficient method. Therefore, the carrying capacity assessment method proposed in this invention better balances the renewable energy penetration rate and the system's operational economy. The resulting renewable energy carrying capacity is a compromise between the system's renewable energy installation scale and its operational economy, avoiding the one-sided result of the renewable energy absorption coefficient method, which only pursues the renewable energy installation penetration rate and ignores the system's operational economy. This method has stronger engineering applicability.

[0235] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power grid new energy carrying capacity evaluation method based on ISODATA scene clustering, characterized in that, include: The set of wind power and photovoltaic output curves is obtained from historical power system operation data and then normalized. The ISODATA clustering algorithm is used to cluster the per-unit output values ​​of wind power and solar power to construct typical scenarios for wind power and solar power output; specifically including: Pre-select cluster centers, assign samples from the per-unit curve set to the nearest cluster centers, and refine the cluster centers; Calculate the sample variance of each cluster and the pairwise distances between each cluster center; Determine whether the sample variance is less than a preset expected value for cluster sample variance, and whether the distance between the cluster centers is greater than a preset expected value for cluster center distance; If the judgment result is negative, then perform splitting and aggregation operations on the cluster centers, correct the total number of cluster centers, and return to redistribute the samples until the judgment condition is met. If the judgment result is yes, then the typical wind power and photovoltaic output scenarios are constructed using the final cluster center, and the scenario probability of each scenario is calculated. A multi-objective optimization model for assessing the renewable energy carrying capacity of the power grid is constructed based on the aforementioned typical power output scenarios. The Pareto fronts of wind power and photovoltaic installed capacity were calculated using the constraint method on the multi-objective optimization model. The Pareto Frontier assesses the capacity of new energy sources based on wind and solar power installed capacity.

2. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 1, wherein, The set of power generation curves for wind and solar power includes: ； ， ； in, It is the number of samples for wind power output curves. It represents the number of samples for the photovoltaic power output curve. It is the research time period. This is the i-th wind power output curve. It is the wind power output value at time t in the i-th wind power output curve. This is the i-th photovoltaic power output curve. It is the photovoltaic output value at time t in the i-th photovoltaic output curve; The power generation curves of wind power and photovoltaic power are normalized to per-unit values, and the set of per-unit curves is represented as follows: ， ； ， ； in, It is the first wind power The output curve is in The per-unit value of output at any given moment. It is the first photovoltaic The output curve is in The per-unit value of output at any given moment; This is the wind power output curve after per-unit scaling. This is the photovoltaic output curve after standardization.

3. The method for assessing the renewable energy carrying capacity of a power grid based on ISODATA scenario clustering as described in claim 2, characterized in that, Using the set of per-unit power output curves for wind and solar power as samples, ISODATA was used to cluster the samples, including: Preliminary The cluster centers of the per-unit wind power output curves are represented as follows: ， ； Set of per-unit value curves for wind power output In Each sample is assigned to the nearest cluster. ,in by The minimum distance between a sample and each cluster center is the cluster center. Represented as: ； wherein, is the curve corresponding to the th cluster center; is the specific value of the curve corresponding to the cluster center at the time instant t. The cluster centers are modified as follows: ； wherein, is the total number of samples in the cluster is the total number of samples in the cluster 4. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 3, characterized in that, According to the clustering Computing sample variances and distances between each pair of cluster centers, including: The sample variance is expressed as: ； wherein, is the sample variance in each cluster of samples; The pairwise distances between each cluster center are expressed as: ； wherein, is the distance between each pair of cluster centers.

5. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 4, characterized in that, According to the sample variance Distance from cluster center Determining whether the requirement is met includes: like and If so, it means that the requirements are met and a typical scenario for output is constructed; If or then it is explained that the requirements are not met, and the clustering center splitting and merging operations are performed. wherein, and are respectively a preset cluster sample variance expectation value and a cluster center distance expectation value; The fragmentation of cluster centers is represented as: ； ； The aggregation of cluster centers is represented as: ； The total number of cluster centers is adjusted and expressed as: ； wherein, is the total number of corrected cluster centers, , is the number of samples in the cluster , , is the number of clusters for cleavage processing, is the number of clusters for aggregation processing.

6. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 5, wherein, Typical wind power and photovoltaic output scenarios are constructed using the cluster centers, and the scenario probability of each scenario is calculated, including: Typical wind power output scenarios: ； Probability of typical power output scenarios: ； wherein, is the number of samples in the cluster is the number of samples in the cluster is the wind power output scenario corresponding probability; Typical photovoltaic power output scenarios: ； Probability of typical power output scenarios: ； in, It is the cluster center of the per-unit value curve of photovoltaic output. It is the number of photovoltaic power generation cluster centers. It is clustering The number of samples in It is a photovoltaic power output scenario The corresponding probability.

7. The method for assessing the renewable energy carrying capacity of a power grid based on ISODATA scenario clustering as described in claim 6, characterized in that, Based on the typical wind power and photovoltaic power output scenarios, new energy joint power output scenarios are constructed, and the scenario probability of each joint power output scenario is calculated, including: A scenario of joint power generation from new energy sources is represented as follows: ； The scenario probabilities for each joint effort scenario are expressed as follows: ； ； wherein, is a wind and solar power output scenario corresponding scenario probability.

8. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 7, wherein, A multi-objective optimization model for assessing the grid's renewable energy carrying capacity in typical renewable energy scenarios includes: The overall operating cost of a power system is expressed as: ； in, This is a typical scenario of joint efforts in new energy. Coal consumption cost of thermal power units under given conditions; for a typical scenario of combined power generation from new energy sources. , For thermal power units exist Efforts made at all times; The system's renewable energy curtailment rate is expressed as: ； in, This is a typical scenario of joint efforts in new energy. Wind power in Power curtailment at all times This is a typical scenario of joint efforts in new energy. Photovoltaics Power curtailment at all times It is the unsold installed capacity of wind power. This refers to the unsold installed capacity of photovoltaic power. The optimization objective is expressed as: 。 9. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 8, wherein, The Pareto fronts of wind power and photovoltaic installed capacity are calculated using the constraint method on the multi-objective optimization model, including: Power balance constraints are expressed as: ； The output constraint of thermal power units is expressed as: ； Wind power output constraints are expressed as: ； Photovoltaic output constraints are expressed as: ； The constraint on the curtailment rate of renewable energy is expressed as: ； The Pareto front for wind power and solar power installed capacity is represented as: ； in, It refers to the installed capacity of wind power and photovoltaic power. The Pareto Frontier.

10. The power grid new energy carrying capacity evaluation method based on ISODATA scene clustering of claim 9, wherein, The Pareto Frontier assesses the carrying capacity of new energy sources based on wind and solar power installed capacity, including: The maximum sum of wind power and photovoltaic installed capacity is used as a quantitative representation of the grid's renewable energy carrying capacity, expressed as: ； The larger the installed capacity of new energy sources that the power grid can withstand, the greater the quantitative representation of the power grid's new energy carrying capacity, and the stronger the power grid's new energy carrying capacity; conversely, the smaller the capacity, the weaker the carrying capacity.

Images