Supply and demand balance evaluation method and system for layered distribution coupling power system

Abstract

The invention relates to the technical field of construction data optimization, in particular to a layered distribution coupling power system supply and demand balance evaluation method and system, and the method comprises the steps: constructing a full-system multi-dimensional coupling index system; historical operation data is mapped into a whole-system high-dimensional feature vector, collaborative clustering and joint screening are executed, and a probabilistic typical day scene set is generated; constructing a main and distribution cooperative distributed operation simulation model, and introducing a load shedding loss term and a new energy power abandoning penalty term into the optimization objective function of the main network and the distribution network to perform cooperative iterative calculation; and calculating main network supply-demand balance, distribution network supply-demand balance and interlayer power interaction risk indexes to obtain a supply-demand balance evaluation result of the layered distribution coupling power system. According to the invention, the problem that it is difficult to describe the coupling characteristics of the main network and the distribution network in a unified manner and consider main network supply guarantee and distribution network consumption risks in the prior art is effectively solved, and the accuracy and engineering applicability of supply and demand balance evaluation of the layered distribution coupling power system are improved.

Description

Technical Field

[0001] This invention relates to the field of power system assessment technology, and in particular to a hierarchical distributed coupled power system supply and demand balance assessment method and system. Background Technology

[0002] With the large-scale integration of new energy sources such as wind power and photovoltaics into the power system, the uncertainty of power output and load has increased significantly, and the power system supply and demand balance problem has become increasingly complex. At the same time, the power system structure is gradually showing the development characteristics of hierarchical distribution and mutual coupling between the main grid and the distribution network. The main grid side is centrally connected to large-scale new energy power sources, while the distribution network side is widely connected to distributed power sources and flexible loads. The main grid and the distribution network exchange power through interconnection lines, and changes in the source and load of the distribution network will affect the operation status of the main grid in the form of inter-layer net load.

[0003] Against this backdrop, existing power system supply and demand balance assessment methods mostly focus on single-level systems or analyze the main grid and distribution network independently. They lack assessment methods that can simultaneously characterize the source-load characteristics of the main grid, the source-load characteristics of the distribution network, and the inter-level net load characteristics between the main grid and the distribution network within a unified framework. This makes it difficult to reflect the uncertainties and interactions of multi-level source-loads in a hierarchically distributed coupled power system, resulting in assessment results that fail to accurately reflect the overall system operating characteristics. Furthermore, existing assessment methods often employ traditional scenario generation or simplified analysis techniques when processing historical operating data, failing to fully consider the varying importance of different levels of characteristics in supply and demand balance analysis. Moreover, during operational simulations, they struggle to simultaneously address the power supply security risks of the main grid and the risks of renewable energy absorption in the distribution network, thus limiting the applicability and guiding significance of supply and demand balance assessment results under extreme operating conditions. Summary of the Invention

[0004] This invention provides a method for assessing the supply and demand balance of a hierarchical distributed coupled power system, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for assessing the supply and demand balance of a hierarchical distributed coupled power system, the method comprising: Construct a multi-dimensional coupled index system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network; Based on the multi-dimensional coupling index system of the whole system, historical operation data is mapped into a high-dimensional feature vector of the whole system, and collaborative clustering and joint screening are performed by introducing a similarity measurement mechanism that takes into account hierarchical importance to generate a probabilistic typical daily scene set; For the aforementioned probabilistic typical daily scenario set, a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis is constructed, and load shedding loss term and renewable energy curtailment penalty term are introduced into the optimization objective function of the main grid and distribution network for collaborative iterative calculation; Based on the results of the collaborative iterative calculation, the supply and demand balance index of the main grid, the supply and demand balance index of the distribution network, and the risk index of inter-layer power interaction are calculated to obtain the supply and demand balance assessment results of the hierarchical distributed coupled power system.

[0006] Furthermore, a multi-dimensional coupled index system for the entire system is constructed, encompassing the source-load characteristics of the main grid, the source-load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network. This system includes: Based on the time-series matching relationship between the output of large-scale new energy sources in the main grid and the load of the main grid, a set of main grid source-load coupling characteristic indicators is constructed. Based on the matching relationship between the output of distributed power sources and the local loads within each distribution network, a set of distribution network source-load coupling characteristic indicators is constructed. Based on the main grid source-load coupling characteristic index set and the distribution network source-load coupling characteristic index set, an inter-layer net load characteristic index set between the main grid and each distribution network is constructed, and the multi-dimensional coupling index system of the whole system is obtained.

[0007] Furthermore, the similarity metric mechanism that takes into account hierarchical importance is introduced to perform collaborative clustering and joint screening, including: Normalize the indicators in the multidimensional coupling index system of the whole system, and represent the data corresponding to a single historical operating day as a high-dimensional feature vector. Different hierarchical importance weights are set for the main network layer indicators, the distribution network layer, and the inter-layer indicators, and a weighted similarity measurement function that takes into account hierarchical importance is constructed. Based on the weighted similarity metric function, the high-dimensional feature vectors are collaboratively clustered to divide the historical operating days into multiple scene clusters that are interconnected by the source-load characteristics of the main grid and distribution network and the power interaction characteristics between layers.

[0008] Furthermore, the scene clusters obtained from collaborative clustering are subjected to joint scene screening to generate a set of typical daily scenes with occurrence probabilities, including: For each of the aforementioned scenario clusters, based on the main grid supply and demand balance risk, the historical operating day with the largest main grid net load peak in that scenario cluster is selected as a typical scenario reflecting the main grid power shortage risk. For the same cluster of scenarios, based on the risk of local absorption of power in the distribution network, the historical operating days with the highest output of distributed power sources or the lowest net load of the distribution network in the cluster of scenarios are screened. For the same scenario cluster, based on the risk of inter-layer power interaction between the main network and the distribution network, the historical operating days with the largest extreme value of inter-layer net exchange power or the largest net load ramp rate in the scenario cluster are screened. The selected typical scenes are merged and deduplicated to form the typical daily scene set.

[0009] Furthermore, for the aforementioned probabilistic typical daily scenario set, a master-slave collaborative distributed operation simulation model based on risk-cost embedded target cascade analysis is constructed, including: The hierarchical distributed coupled power system is divided into a main grid layer and a distribution network layer, and the exchange power between the main grid and each distribution network is selected as the coupling variable shared between the layers. A main grid optimization sub-model and each distribution network optimization sub-model are constructed, and a deviation penalty term for characterizing the consistency of inter-layer switching power is introduced into the main grid optimization sub-model and the distribution network optimization sub-model to obtain the main grid-distribution coordinated distributed operation simulation model.

[0010] Furthermore, the main grid optimization sub-model aims to minimize the sum of the main grid operating cost and the main grid load shedding loss cost, and sets main grid power supply and demand balance constraints and equipment operation constraints; each distribution network optimization sub-model aims to minimize the sum of the distribution network local operating cost and the renewable energy curtailment penalty cost, and sets distribution network local power balance constraints and distributed power source operation constraints.

[0011] Furthermore, the mainnet supply and demand balance indicators include: ; ; ; Wherein: , and These represent the maximum value of insufficient main grid power, the probability of insufficient main grid power, and the expected value of insufficient main grid power, respectively. This is a collection of typical daily scenarios for the entire system; For the scene The probability of occurrence; For all time periods throughout the day; For the scene exist Main network load shedding at any given time; This is an indicator function that takes the value 1 when the condition is met, and 0 otherwise. For time step.

[0012] Furthermore, the power distribution network supply and demand balance indicators include: ; ; ; in, , and The first The maximum, probability, and expected value of power shortage in a distribution network; For the scene exist Time of the first The load shedding capacity of each distribution network.

[0013] Furthermore, the inter-layer power interaction risk indicator includes: ; ; ; in, The probability of blocking at the coupling point; The probability of power back transmission; This represents the maximum reverse power. For the scene exist Time of the first The actual switching power at each coupling point is defined as the flow from the main network to the distribution network in the positive direction. This represents the maximum transmission capacity of the line at this coupling point.

[0014] A hierarchical distributed coupled power system supply and demand balance assessment system, the system comprising: The indicator system construction module constructs a multi-dimensional coupled indicator system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network. The daily scene set generation module, based on the multi-dimensional coupling index system of the whole system, maps historical operation data into a high-dimensional feature vector of the whole system, and performs collaborative clustering and joint screening by introducing a similarity measurement mechanism that takes into account hierarchical importance, to generate a probabilistic typical daily scene set; The simulation model construction module constructs a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis for the probabilistic typical daily scenario set, and introduces load shedding loss term and new energy curtailment penalty term into the optimization objective function of the main grid and distribution network for collaborative iterative calculation. The evaluation result acquisition module calculates the main grid supply and demand balance index, distribution network supply and demand balance index, and inter-layer power interaction risk index based on the results of the collaborative iterative calculation, and obtains the supply and demand balance evaluation results of the hierarchical distributed coupled power system.

[0015] The technical solution of this invention can achieve the following technical effects: First, by constructing a multi-dimensional coupling index system encompassing the source-load characteristics of the main grid, the source-load characteristics of the distribution network, and the net load characteristics between the main grid and each distribution network, a comprehensive characterization of the source-load characteristics and their interactions at different levels in a hierarchically distributed coupled power system is achieved within a unified evaluation framework. This effectively overcomes the problems of separate analysis of the main grid and distribution network in existing evaluation methods, which makes it difficult to reflect the impact of inter-level coupling, thus improving the ability of supply and demand balance assessment to characterize the overall system operation characteristics. Second, based on the aforementioned multi-dimensional coupling index system, historical operating data is mapped into high-dimensional feature vectors. By introducing a similarity measurement mechanism that considers hierarchical importance to perform collaborative clustering and joint screening, the importance differences between the main grid and distribution network characteristics can be distinguished during scenario generation. While compressing the scale of historical scenarios, typical characteristics and extreme operating condition information of source-load changes at different levels are retained, thereby improving the supply and demand balance assessment. The representativeness and reliability of the estimation results under complex operating conditions are assessed. Furthermore, for the generated probabilistic typical daily scenario set, a main grid-distribution coordinated distributed operation simulation model based on risk-cost embedded objective cascade analysis is constructed. Load shedding loss terms and renewable energy curtailment penalty terms are introduced into the optimization objective functions of the main grid and distribution network, respectively, enabling the operation simulation process to simultaneously reflect the power supply security risks of the main grid and the renewable energy absorption risks of the distribution network. This enhances the adaptability of the supply and demand balance assessment to different levels of risk focus. Further, by calculating the main grid supply and demand balance index, distribution network supply and demand balance index, and inter-layer power interaction risk index based on the collaborative iterative calculation results, a hierarchical and systematic assessment of the supply and demand balance state of the hierarchically distributed coupled power system is achieved. This provides a more accurate and engineering-significant assessment basis for identifying potential supply and demand imbalance risks and supporting power grid planning and operation decisions.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

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

[0018] Figure 1 This is a flowchart illustrating a hierarchical distributed coupled power system supply and demand balance assessment method. Figure 2 A flowchart illustrating the process of constructing a multi-dimensional coupled index system for the entire system; Figure 3 A flowchart illustrating the process of implementing collaborative clustering and joint screening by introducing a similarity measurement mechanism that takes into account hierarchical importance; Figure 4 A flowchart illustrating the process of jointly filtering scene clusters obtained from collaborative clustering and generating a typical daily scene set; Figure 5 A flowchart illustrating the process of constructing a master-slave collaborative distributed operation simulation model. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: like Figure 1 As shown, this application provides a method for assessing the supply and demand balance of a hierarchical distributed coupled power system, the method comprising: S1: Construct a multi-dimensional coupled index system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution grid, and the inter-layer net load characteristics between the main grid and each distribution grid; Specifically, this implementation method is based on the physical structure and operating characteristics of a hierarchical distributed coupled power system. It describes the system's supply-demand balance characteristics in an index-based manner from three levels: the main grid layer, the distribution network layer, and the inter-layer power interaction between the main grid and the distribution network. Specifically, for the main grid layer, source-load characteristic indicators reflecting the power output and load variation characteristics of the main grid are selected to characterize the supply-demand matching status of the main grid under different operating conditions. For the distribution network layer, source-load characteristic indicators reflecting the output characteristics of distributed power sources and the local load absorption characteristics are constructed for each distribution network to characterize the differences in supply-demand balance and absorption capacity at the distribution network level. Simultaneously, considering the operating characteristics of power exchange between the main grid and each distribution network through interconnection lines, the power interaction behavior of the distribution network as a whole with respect to the main grid is abstractly described, and an indicator reflecting the inter-layer net load variation characteristics between the main grid and each distribution network is constructed to characterize the impact of distribution network source-load fluctuations on the main grid's operating status. By unifying and integrating the above-mentioned main grid source load characteristics, distribution network source load characteristics, and inter-layer net load characteristics, a multi-dimensional coupled index system covering the main grid, distribution network, and their interaction relationships is formed. This index system can simultaneously reflect the source load characteristics and their interaction relationships at different levels under the same evaluation framework, providing basic data support for subsequent scenario analysis and supply-demand balance assessment based on historical operating data.

[0022] S2: Based on the multi-dimensional coupling index system of the whole system, historical operation data is mapped into a high-dimensional feature vector of the whole system, and collaborative clustering and joint screening are performed by introducing a similarity measurement mechanism that takes into account the importance of hierarchy, so as to generate a probabilistic typical daily scene set; Specifically, the operational data collected from the hierarchical distributed coupled power system over multiple historical operating days is first mapped daily according to the aforementioned multi-dimensional coupling index system for the entire system. This results in a high-dimensional feature vector for each historical operating day that simultaneously characterizes the source-load characteristics of the main grid, the source-load characteristics of each distribution network, and the net load characteristics between layers. This transforms the original time-series operational data into a feature representation suitable for scenario analysis. Based on this, to avoid treating features at different levels equally during scenario analysis and weakening the importance differences between the main grid and distribution networks, a similarity measurement mechanism that considers hierarchical importance is introduced. Different importance weights are assigned to features related to the main grid layer, distribution network layer, and inter-layer features, making the scenario similarity assessment process more consistent with the actual operation of the hierarchical distributed coupled power system. Subsequently, based on the aforementioned similarity measurement mechanism, collaborative clustering is performed on the high-dimensional feature vector of the entire system, dividing historical operating days with similar main grid source-load characteristics, distribution network source-load characteristics, and inter-layer power interaction characteristics into multiple scenario clusters. After clustering is completed, in order to retain representative operational characteristics while controlling the number of scenarios, joint screening is further implemented from each scenario cluster to extract typical operating days that can reflect the supply and demand balance characteristics and their interaction behaviors at different levels. Based on the scale relationship of the scenario clusters to which each typical operating day belongs, the typical operating day is assigned a corresponding probability of occurrence, and finally a probabilistic typical day scenario set is formed for subsequent supply and demand balance assessment.

[0023] S3: For a set of probabilistic typical daily scenarios, a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis is constructed, and load shedding loss term and new energy curtailment penalty term are introduced into the optimization objective function of the main grid and distribution network for collaborative iterative calculation. Specifically, considering the differences in operational objectives and risk focus between the main grid and multiple distribution networks in a hierarchically distributed coupled power system, a modeling approach based on risk-cost embedded objective cascade analysis is introduced. The overall system operation problem is structurally divided into two collaborative layers: the main grid layer and the distribution network layer. The main grid layer serves as the upper-level system, while each distribution network acts as a parallel lower-level system. By selecting the power exchange behavior between the main grid and each distribution network as the inter-layer coordination object, the main grid model and the distribution network model can be collaboratively correlated while maintaining their independence. During model construction, considering the operational characteristics of the main grid layer, a load shedding loss term to characterize power supply security risks is introduced into the main grid's optimization objectives, enabling the main grid's operational decisions to reflect its focus on supply-demand imbalance risks. Simultaneously, considering the operational characteristics of the distribution network layer, a curtailment penalty term to characterize the risks of renewable energy absorption is introduced into the optimization objectives of each distribution network, enabling the distribution network's operational decisions to reflect the impact of limited distributed power generation absorption. Based on this, by establishing a collaborative iteration mechanism between the main grid and distribution network models, the main grid and each distribution network alternately perform operation calculations under typical daily scenarios, and continuously adjust their respective operation decisions according to the power interaction status between the layers until they reach a coordinated consistency. This forms a main grid-distribution collaborative distributed operation simulation model that can simultaneously reflect the power supply security risks of the main grid and the renewable energy consumption risks of the distribution network, providing a unified operational analysis basis for subsequent supply and demand balance assessment.

[0024] S4: Based on the results of collaborative iterative calculation, calculate the main grid supply and demand balance index, the distribution network supply and demand balance index, and the inter-layer power interaction risk index to obtain the supply and demand balance assessment results of the hierarchical distributed coupled power system.

[0025] Specifically, by utilizing the operational simulation results of the main grid and distribution networks under typical daily scenarios, key operational information reflecting the supply-demand balance is extracted from the main grid layer, distribution network layer, and the power interaction layer between the main grid and distribution networks. Corresponding supply-demand balance and risk assessment indicators are then calculated accordingly. For the main grid layer, supply-demand balance indicators characterizing the main grid's power supply security level are calculated based on supply-demand imbalances occurring during the simulation. For the distribution network layer, evaluation indicators characterizing the distribution network's supply-demand balance and absorption capacity are calculated based on local supply-demand imbalances or limited renewable energy absorption occurring during the simulation. Simultaneously, risk indicators reflecting the safety of inter-layer power interaction are calculated by combining the power exchange state between the main grid and distribution networks during the simulation. Through comprehensive analysis of the aforementioned main grid supply-demand balance indicators, distribution network supply-demand balance indicators, and inter-layer power interaction risk indicators, an overall assessment of the supply-demand balance state of the hierarchical distributed coupled power system is formed, providing a basis for system operation analysis and planning decisions.

[0026] This invention effectively solves the problem that existing methods are unable to uniformly characterize the coupling characteristics of the main grid and distribution network and take into account the risks of main grid supply guarantee and distribution network absorption, thereby improving the accuracy and engineering applicability of supply and demand balance assessment of hierarchical distributed coupled power systems.

[0027] As a preferred embodiment of the above, such as Figure 2 As shown, step S1 involves constructing a multi-dimensional coupling index system for the entire system, encompassing the source-load characteristics of the main grid, the source-load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network. This system includes: S11: Based on the time-series matching relationship between the output of large-scale new energy sources and the load of the main grid, construct a set of main grid source-load coupling characteristic indicators; S12: Based on the matching relationship between the output of distributed power sources and the local load within each distribution network, construct a set of distribution network source-load coupling characteristic indicators; S13: Based on the source-load coupling characteristic index set of the main grid and the source-load coupling characteristic index set of the distribution network, construct the inter-layer net load characteristic index set between the main grid and each distribution network, and obtain the multi-dimensional coupling index system of the whole system.

[0028] Specifically, based on the time-series matching characteristics of large-scale renewable energy sources and grid loads, a set of grid source-load coupling characteristic indicators is constructed. Including the main grid wind power penetration rate Main grid wind power daily peak-valley difference Minimum support force for wind power during peak load periods Maximum counter-peak capacity of wind power during off-peak hours ; In the formula, For all time periods throughout the day; and The mainnet is located at Wind power output and load at any given time; and These are the peak and off-peak periods of the main grid's daily load, respectively.

[0029] Based on the The matching characteristics of distributed generation and local load absorption within a distribution network are used to construct a set of distribution network source-load coupling characteristic indicators. Including distribution network photovoltaic penetration rate Peak daily output of photovoltaic power and the average daily output of photovoltaic power ; In the formula, and The first A distribution network in Real-time photovoltaic output and local load; This represents the total number of time periods throughout the day.

[0030] Consider the first The power interaction characteristics of each distribution network as a whole presented to the main network at the coupling point are used to construct a set of inter-layer net load characteristic indicators. Including the maximum net daily exchange power at the coupling point Minimum daily net exchange power and maximum ramp rate of net load ; Among them, the net exchange power at the coupling point Defined as the difference between the local load of the distribution network and the total output of distributed generation: In the formula, For the first A distribution network in The total output of various distributed power sources at any given time; when When this occurs, it indicates that the distribution network is sending power back to the main network. Through the above steps, a multi-dimensional coupled index system covering the main network layer, the distribution network layer, and their interactions is formed, providing a unified and complete feature description basis for subsequent scenario analysis and supply-demand balance assessment based on historical operating data.

[0031] As a preferred embodiment of the above, such as Figure 3 As shown, a similarity metric mechanism that takes into account hierarchical importance is introduced to perform collaborative clustering and joint screening, including: A10: Normalize the indicators in the multi-dimensional coupled indicator system of the whole system, and represent the data corresponding to a single historical operating day as a high-dimensional feature vector. A20: Set different hierarchical importance weights for the main grid layer indicators, distribution network layer indicators, and inter-layer indicators, and construct a weighted similarity measurement function that takes into account hierarchical importance; A30: Based on a weighted similarity metric function, high-dimensional feature vectors are collaboratively clustered to divide historical operating days into multiple scene clusters that are interconnected by the source-load characteristics of the main grid and distribution grid and the power interaction characteristics between layers.

[0032] Specifically, firstly, the indicators in the multi-dimensional coupled indicator system of the entire system are subjected to unified scaling. By normalizing indicators with different dimensions and value ranges, the data corresponding to a single historical operating day can be represented as a high-dimensional feature vector with a unified dimension basis, thereby eliminating the impact of differences in numerical scales of different indicators on subsequent similarity analysis. Based on this, considering the functional positioning and importance differences between the main grid layer and the distribution network layer in the hierarchical distributed coupled power system, different hierarchical importance weights are assigned to the relevant indicators of the main grid layer, as well as the relevant indicators of the distribution network layer and inter-layer indicators. Differential weights are then assigned to the features at different levels. A similarity metric that considers hierarchical importance is constructed to enable the similarity assessment process between historical operating days to better highlight the dominant role of the main grid's operating characteristics, while also taking into account the influence of distribution network and inter-layer power interaction characteristics. Subsequently, based on the weighted similarity metric, collaborative clustering is performed on the high-dimensional feature vectors formed by historical operating data. Historical operating days with similar operating characteristics in terms of main grid source-load characteristics, distribution network source-load characteristics, and inter-layer power interaction characteristics are divided into multiple scenario clusters, thereby forming scenario classification results that can reflect the typical operating states of a hierarchically distributed coupled power system, providing a foundation for subsequent typical scenario screening and supply-demand balance assessment.

[0033] The following explanation uses formulas to illustrate this: Considering the spatiotemporal correlation of wide-area weather, a multi-dimensional coupling characteristic index set for the entire system is constructed. This index set includes the main grid source-load coupling characteristic index set. k distribution network source-load coupling characteristic index sets and the set of net load characteristics between k coupling points It can depict the coordinated change patterns of a stratified distribution system under the same meteorological background.

[0034] The entire system The actual data from each historical operating day is mapped daily to samples under this indicator set, forming the sample space of the entire system. And normalize the indicators of each dimension.

[0035] We define a weighted dissimilarity index that takes into account hierarchical importance, introduce a hierarchical importance weighting factor, and construct a weighted generalized Euclidean distance to measure the similarity between samples. Strengthen the leading role of main network layer indicators in the clustering process: In the formula: and These are the main network layer indicator weights and the distribution network / coupling layer indicator weights, respectively, and they satisfy the following conditions: ; , , These are the corresponding mainnet and the first in the sample, respectively. The distribution network and the first A normalized vector of a set of coupling point indices.

[0036] Perform collaborative clustering based on weighted dissimilarity optimization, and use the K-means algorithm to select initial cluster centers based on the principle of maximizing dissimilarity. Perform clustering. First, calculate the clustering for each sample. Weighted average dissimilarity relative to the sample space : Select The sample with the largest distance from the selected center that is greater than the standard difference threshold is selected as... Initially, cluster centers are selected, followed by iterations based on weighted distance. The calculation continues until the clustering converges, dividing the sample space into... A cluster of scenes with strong spatiotemporal correlation.

[0037] As a preferred embodiment of the above, such as Figure 4 As shown, the scene clusters obtained from collaborative clustering are subjected to joint scene filtering to generate a set of typical daily scenes with occurrence probabilities, including: B10: For each scenario cluster, based on the main grid supply and demand balance risk, the historical operating day with the largest main grid net load peak in the scenario cluster is selected as a typical scenario reflecting the main grid power shortage risk. B20: For the same scenario cluster, based on the local absorption risk of the distribution network, the historical operating days with the highest output ratio of distributed power sources or the lowest net load of the distribution network in the scenario cluster are screened. B30: For the same scenario cluster, based on the risk of inter-layer power interaction between the main network and the distribution network, the historical operating days with the largest extreme value of inter-layer net exchange power or the largest net load ramp-up rate in the scenario cluster are screened. B40: Merge and deduplicate the selected typical scenarios to form a set of typical daily scenarios.

[0038] Specifically, for each clustered scenario cluster, a three-dimensional screening strategy covering the main grid, distribution network, and inter-layer interactions is adopted to extract a subset of typical scenarios from the original scenarios contained in the cluster. Dimension 1 considers the risk of main grid supply guarantee, selecting the scenario with the largest peak net load of the main grid to assess the risk of main grid power shortage; Dimension 2 considers the risk of distribution network absorption, selecting the scenario with the highest proportion of daily photovoltaic power generation in the distribution network or the lowest net load at noon to assess the risk of local curtailment and voltage exceedance in the distribution network; Dimension 3 considers the risk of inter-layer interaction, selecting the scenario with the largest backfeed power at the coupling point or the largest net load ramp-up rate to assess the risk of tie line blockage and main grid ramp-up adjustment; combining the scenarios obtained from the above three dimensions and removing duplicates yields... A typical daily scenario for the entire system; calculate the probability of the scenario occurring, the first... Probability of occurrence of a typical daily scenario across the entire system : In the formula, For the first The number of original historical scenes contained in the scene cluster to which a typical daily scene belongs. This represents the number of typical scenes ultimately selected from this scene cluster.

[0039] As a preferred embodiment of the above, such as Figure 5 As shown, for a set of probabilistic typical daily scenarios, a master-slave collaborative distributed operation simulation model based on risk-cost embedded target cascade analysis is constructed, including: C10: Divide the hierarchical distributed coupled power system into a main grid layer and a distribution network layer, and select the exchange power between the main grid and each distribution network as the coupling variable shared between the layers; C20: Construct a main grid optimization sub-model and each distribution network optimization sub-model, and obtain a main grid-distribution coordinated distributed operation simulation model by introducing a deviation penalty term to characterize the consistency of inter-layer switching power in the main grid optimization sub-model and the distribution network optimization sub-model.

[0040] As a preferred embodiment of the above, the main grid optimization sub-model aims to minimize the sum of the main grid operating cost and the main grid load shedding loss cost, and sets main grid power supply and demand balance constraints and equipment operation constraints; each distribution network optimization sub-model aims to minimize the sum of the distribution network local operating cost and the renewable energy curtailment penalty cost, and sets distribution network local power balance constraints and distributed power generation operation constraints.

[0041] Specifically, based on the objective cascading analysis theory, the global optimization problem of minimizing the overall system operating cost is decomposed into a sub-problem of optimizing the upper-level main network and... A parallel sub-problem of lower-level distribution network optimization is proposed. The power exchanged at the coupling point is selected as the coupling variable shared between layers. By introducing an enhanced Lagrange penalty function to relax the consistency constraint of power balance between layers, the physical and information decoupling of the main network model and the distribution network model is realized.

[0042] Taking the main network as the upper-layer system, and using the minimization of its own operating cost and the minimization of the deviation from the response value feedback from the distribution network as optimization objectives, the expected target value of the coupling point exchange power is calculated. It has its objective function Defined as: In the formula: Mainnet operating cost function; Mainnet in The vector of control variables at any given time; The first calculation for mainnet optimization Target exchange power at each coupling point; For the first iteration The actual switching power (response value) of the distribution network feedback; This is a penalty term for L2 norm deviation; It is a Lagrange multiplier.

[0043] The first As a lower-level subsystem, the distribution network aims to minimize local operating costs and the tracking deviation from the target values ​​issued by the main network. It calculates the actual coupling point switching power response value, and has its own objective function. Defined as: In the formula: For the first The local operating cost function of a distribution network; For distribution network in The vector of control variables at any given time; The actual coupling point switching power (response value) calculated for distribution network optimization. This represents the expected switching power (target value) issued by the mainnet in the previous iteration.

[0044] The system uses an iterative convergence algorithm to dynamically correct the inter-layer exchange power. First, the main network calculates the target value based on the initial conditions. The data is then distributed to each distribution network; subsequently, each distribution network solves its local model in parallel to obtain the response value. The response is fed back to the main network; the main network then updates the deviation penalty term and Lagrange multipliers based on the response value, and recalculates the target value; the above process is repeated until the deviation between the target value of the main network and the response value of the distribution network at the coupling point is less than the preset convergence threshold, thereby obtaining the optimal operating state of the system.

[0045] The simulation optimization model for the coordinated distributed operation of the main grid and distribution networks in the above steps also needs to meet the following constraints during the solution process, including the power supply and demand balance constraints of the main grid, the power supply and demand balance constraints of the k distribution networks, the line transmission capacity constraints of the k coupling points, and the system reserve and equipment operation constraints.

[0046] The power supply and demand balance constraint of the main grid is used to ensure the real-time balance of power generation and consumption at the main grid level, and can be expressed as: In the formula: Mainnet Taiwan conventional thermal power units Efforts made at all times; The main grid thermal power units collection; Mainnet in The actual output of wind power at any given moment; Mainnet in The amount of load shedding at any given moment; Mainnet in The workload of the moment; The first calculated by the mainnet The switching power at each coupling point is defined as positive when flowing towards the distribution network; the power supply and demand balance constraints of the distribution network are used to ensure local source-load balance at the distribution network level. The power balance constraints of a distribution network can be expressed as: In the formula: For the first A distribution network in The actual output of photovoltaic power at any given moment; For the output of the distribution network energy storage system, discharging is defined as positive and charging as negative; For distribution network in The amount of load shedding at any given moment; For local load of the distribution network; The actual power received from the main network by the distribution network; the line transmission capacity constraint at the coupling point is used to characterize the transmission limit of the inter-layer physical tie line, the first The line constraint at each coupling point can be expressed as: In the formula: To connect the main network and the second The maximum transmission capacity of the tie line in a distribution network, which limits the physical range of power interaction between the main and distribution networks, is a key condition for identifying congestion risks.

[0047] In addition, the model must also meet the upper and lower limits of the output of conventional units in the main grid and the ramp rate constraint, the state of charge (SOC) constraint and the charging and discharging power constraint of the distribution network energy storage system, and the positive and negative spinning reserve constraints of the entire system.

[0048] As a preferred embodiment of the above, the mainnet supply and demand balance index includes: ; ; ; Wherein: , and These represent the maximum value of insufficient main grid power, the probability of insufficient main grid power, and the expected value of insufficient main grid power, respectively. This is a collection of typical daily scenarios for the entire system; For the scene The probability of occurrence; For all time periods throughout the day; For the scene exist Main network load shedding at any given time; This is an indicator function that takes the value 1 when the condition is met, and 0 otherwise. For time step.

[0049] Specifically, in order to quantitatively assess the power shortage risk at the main grid level, a supply and demand balance evaluation index at the main grid level is constructed, which includes three dimensions: maximum value, probability, and expectation, to calculate the main grid power shortage index.

[0050] As a preferred embodiment of the above, the power distribution network supply and demand balance index includes: ; ; ; in, , and The first The maximum, probability, and expected value of power shortage in a distribution network; For the scene exist Time of the first The load shedding capacity of each distribution network.

[0051] Specifically, in order to quantitatively assess the structural shortage risk at the distribution network level, a supply and demand balance evaluation index at the distribution network level is constructed, and the local power shortage index is calculated from three dimensions: maximum value, probability, and expectation.

[0052] As a preferred embodiment of the above, the inter-layer power interaction risk indicator includes: ; ; ; in, The probability of blocking at the coupling point; The probability of power back transmission; This represents the maximum reverse power. For the scene exist Time of the first The actual switching power at each coupling point is defined as the flow from the main network to the distribution network in the positive direction. This represents the maximum transmission capacity of the line at this coupling point.

[0053] Specifically, to quantitatively assess the over-limit risk of inter-layer power interaction and the impact risk of distributed generation on the main grid, an interaction risk assessment index is constructed at the coupling point level, targeting the first... For each coupling point, calculate its tie line blockage probability, power backflow probability, and maximum backflow power.

[0054] Example 2: Based on the same inventive concept as the hierarchical distributed coupled power system supply and demand balance assessment method in the foregoing embodiments, the present invention also provides a hierarchical distributed coupled power system supply and demand balance assessment system, comprising: The indicator system construction module constructs a multi-dimensional coupled indicator system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network. The daily scene set generation module, based on the multi-dimensional coupling index system of the whole system, maps historical operation data into a high-dimensional feature vector of the whole system, and performs collaborative clustering and joint screening by introducing a similarity measurement mechanism that takes into account hierarchical importance, to generate a probabilistic typical daily scene set; The simulation model construction module constructs a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis for a set of probabilistic typical daily scenarios. It also introduces load shedding loss terms and renewable energy curtailment penalty terms into the optimization objective functions of the main grid and distribution network for collaborative iterative calculation. The assessment result acquisition module calculates the main grid supply and demand balance index, distribution network supply and demand balance index, and inter-layer power interaction risk index based on the results of collaborative iterative calculation, and obtains the supply and demand balance assessment results of the hierarchical distributed coupled power system.

[0055] The evaluation system described above in this invention can effectively realize the hierarchical distributed coupled power system supply and demand balance evaluation method, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0056] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for assessing the supply and demand balance of a hierarchical distributed coupled power system, characterized in that, The method includes: Construct a multi-dimensional coupled index system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network; Based on the multi-dimensional coupling index system of the whole system, historical operation data is mapped into a high-dimensional feature vector of the whole system, and collaborative clustering and joint screening are performed by introducing a similarity measurement mechanism that takes into account hierarchical importance to generate a probabilistic typical daily scene set; For the aforementioned probabilistic typical daily scenario set, a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis is constructed, and load shedding loss term and renewable energy curtailment penalty term are introduced into the optimization objective function of the main grid and distribution network for collaborative iterative calculation; Based on the results of the collaborative iterative calculation, the supply and demand balance index of the main grid, the supply and demand balance index of the distribution network, and the risk index of inter-layer power interaction are calculated to obtain the supply and demand balance assessment results of the hierarchical distributed coupled power system.

2. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, Construct a multi-dimensional coupled index system for the entire system, encompassing the source-load characteristics of the main grid, the source-load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network, including: Based on the time-series matching relationship between the output of large-scale new energy sources in the main grid and the load of the main grid, a set of main grid source-load coupling characteristic indicators is constructed. Based on the matching relationship between the output of distributed power sources and the local loads within each distribution network, a set of distribution network source-load coupling characteristic indicators is constructed. Based on the main grid source-load coupling characteristic index set and the distribution network source-load coupling characteristic index set, an inter-layer net load characteristic index set between the main grid and each distribution network is constructed, and the multi-dimensional coupling index system of the whole system is obtained.

3. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, The similarity metric mechanism that takes into account hierarchical importance performs collaborative clustering and joint screening, including: Normalize the indicators in the multidimensional coupling index system of the whole system, and represent the data corresponding to a single historical operating day as a high-dimensional feature vector. Different hierarchical importance weights are set for the main network layer indicators, the distribution network layer, and the inter-layer indicators, and a weighted similarity measurement function that takes into account hierarchical importance is constructed. Based on the weighted similarity metric function, the high-dimensional feature vectors are collaboratively clustered to divide the historical operating days into multiple scene clusters that are interconnected by the source-load characteristics of the main grid and distribution network and the power interaction characteristics between layers.

4. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 3, characterized in that, The scene clusters obtained from collaborative clustering are jointly filtered to generate a set of typical daily scenes with occurrence probabilities, including: For each of the aforementioned scenario clusters, based on the main grid supply and demand balance risk, the historical operating day with the largest main grid net load peak in that scenario cluster is selected as a typical scenario reflecting the main grid power shortage risk. For the same cluster of scenarios, based on the risk of local absorption of power in the distribution network, the historical operating days with the highest output of distributed power sources or the lowest net load of the distribution network in the cluster of scenarios are screened. For the same scenario cluster, based on the risk of inter-layer power interaction between the main network and the distribution network, the historical operating days with the largest extreme value of inter-layer net exchange power or the largest net load ramp rate in the scenario cluster are screened. The selected typical scenes are merged and deduplicated to form the typical daily scene set.

5. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, For the aforementioned probabilistic typical daily scenario set, a master-slave collaborative distributed operation simulation model based on risk-cost embedded target cascade analysis is constructed, including: The hierarchical distributed coupled power system is divided into a main grid layer and a distribution network layer, and the exchange power between the main grid and each distribution network is selected as the coupling variable shared between the layers. A main grid optimization sub-model and each distribution network optimization sub-model are constructed, and a deviation penalty term for characterizing the consistency of inter-layer switching power is introduced into the main grid optimization sub-model and the distribution network optimization sub-model to obtain the main grid-distribution coordinated distributed operation simulation model.

6. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 5, characterized in that, The main grid optimization sub-model aims to minimize the sum of the main grid operating cost and the main grid load shedding loss cost, and sets main grid power supply and demand balance constraints and equipment operation constraints; the distribution network optimization sub-model aims to minimize the sum of the distribution network local operating cost and the renewable energy curtailment penalty cost, and sets distribution network local power balance constraints and distributed power generation operation constraints.

7. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, The mainnet supply and demand balance indicators include: ； ； ； Wherein: , and These represent the maximum value of insufficient main grid power, the probability of insufficient main grid power, and the expected value of insufficient main grid power, respectively. This is a collection of typical daily scenarios for the entire system; For the scene The probability of occurrence; For all time periods throughout the day; For the scene exist Main network load shedding at any given time; This is an indicator function that takes the value 1 when the condition is met, and 0 otherwise. For time step.

8. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, The power distribution network supply and demand balance indicators include: ； ； ； in, , and The first The maximum, probability, and expected value of power shortage in a distribution network; For the scene exist Time of the first The load shedding capacity of each distribution network.

9. The method for assessing the supply and demand balance of a hierarchical distributed coupled power system according to claim 1, characterized in that, The interlayer power interaction risk indicators include: ； ； ； in, The probability of blocking at the coupling point; The probability of power back transmission; This represents the maximum reverse power. For the scene exist Time of the first The actual switching power at each coupling point is defined as the flow from the main network to the distribution network in the positive direction. This represents the maximum transmission capacity of the line at this coupling point.

10. A hierarchical distributed coupled power system supply and demand balance assessment system, characterized in that, The system includes: The indicator system construction module constructs a multi-dimensional coupled indicator system for the entire system, including the source load characteristics of the main grid, the source load characteristics of the distribution network, and the inter-layer net load characteristics between the main grid and each distribution network. The daily scene set generation module, based on the multi-dimensional coupling index system of the whole system, maps historical operation data into a high-dimensional feature vector of the whole system, and performs collaborative clustering and joint screening by introducing a similarity measurement mechanism that takes into account hierarchical importance, to generate a probabilistic typical daily scene set; The simulation model construction module constructs a main grid and distribution network collaborative distributed operation simulation model based on risk cost embedded target cascade analysis for the probabilistic typical daily scenario set, and introduces load shedding loss term and new energy curtailment penalty term into the optimization objective function of the main grid and distribution network for collaborative iterative calculation. The evaluation result acquisition module calculates the main grid supply and demand balance index, distribution network supply and demand balance index, and inter-layer power interaction risk index based on the results of the collaborative iterative calculation, and obtains the supply and demand balance evaluation results of the hierarchical distributed coupled power system.

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