Comprehensive elasticity evaluation method of power distribution network and electronic equipment

Abstract

The invention relates to a comprehensive elasticity evaluation method for a power distribution network and electronic equipment, and the method comprises the steps: building a comprehensive elasticity index system of the power distribution network by comprehensively considering the indexes of four dimensions, namely elasticity, economical efficiency, low-carbon property and safety; generating a typical severe scene; respectively calculating a subjective weight and an objective weight of each index in the comprehensive elastic index system in a typical severe scene by using an FAHP method and a risk entropy weight method, and carrying out combined weighting on the subjective weight and the objective weight to obtain a final weight; and based on the final weight, adopting a grey correlation analysis method to improve TOPSIS, and carrying out comprehensive scoring on the comprehensive elastic index system of the power distribution network. Compared with the prior art, the method has the advantages that the multi-dimensional elastic evaluation of the power distribution network is realized, the index weight is more appropriate, the evaluation is more accurate and the like.

Description

Technical Field

[0001] The present invention relates to the field of power distribution system operation evaluation, and in particular to a comprehensive elasticity evaluation method and electronic equipment for a power distribution network. Background Art

[0002] In recent years, with the complexity of the new power system structure and the intensification of global climate change, the stable operation of the distribution system has been greatly affected. By building a highly resilient distribution network, we can better cope with the occurrence of climate change, load fluctuations, equipment failures and other problems, and thus improve the overall efficiency of the distribution network. This is one of the current research hotspots in the distribution network. At present, domestic and foreign scholars have done a lot of research on the evaluation of distribution network resilience indicators. Tian Tian et al. Three-dimensional resilience index evaluation system for distribution network under flood disasters [J]. New Technology of Electrical Engineering and Energy, 2022, 41(07): 80-88. By constructing a three-dimensional resilience index system, a comprehensive resilience evaluation of the safety and stability of the distribution network is carried out, and then the weak links of the distribution network planning and dispatching are accurately identified. Gu Jingda et al. Comprehensive resilience evaluation method for distribution network under high temperature weather [J / OL]. Electric Power Construction: 1-13 [2024-06-27]. Based on the resilience characteristics of the distribution network such as strain, resistance, synergy and recovery, a comprehensive resilience evaluation index system for the distribution network is established to accurately reflect the impact of severe weather on the distribution network. Li Zhenkun et al. Resilience evaluation of smart distribution network under extreme weather conditions [J]. Automation of Electric Power Systems, 2020, 44(09): 60-68. By considering that the distribution network is in two stages of disaster prevention and disaster reduction under severe weather conditions, a distribution network resilience evaluation index system is constructed, and a more comprehensive resilience evaluation of the distribution network is carried out through a specific evaluation method. Liu J et al. Research on the construction and application ofevaluation index system of resilient distribution network [C]. 2021IEEE 4thInternational Conference on Renewable Energy and Power Engineering (REPE). IEEE, 2021: 186-191. A distribution network resilience evaluation index system is constructed through the multi-objective segmentation principle, and static and dynamic indicators are refined. The resilience level of the distribution network is comprehensively evaluated through a combined weighting method. Du Yaxin et al., Evaluation of the elasticity of distribution network under extreme weather conditions taking into account the impact of electricity-gas interconnection [J]. Power Supply and Use, 2019, 36(05): 8-13, by supplying power to the load in the isolated island, the missing area of ​​the distribution network load curve is used as the elasticity index to reflect the load shortage in the power grid, and then the elasticity of the distribution network is quantitatively evaluated. Most of the above studies only focus on the establishment of indicators for improving the elasticity and safe operation of the distribution network, but do not involve systematic evaluation in areas such as low-carbon economy.

[0003] In order to take other areas such as economy and low carbon into account in the resilience of the power system, a small number of scholars have carried out multi-faceted integration, making the entire system more comprehensive. Lou et al. Research on optimal configuration of energy storage considering the comprehensive resilience and economy of distribution network [J]. Journal of Wuhan University (Engineering Edition), 2022, 55(09): 901-909. Based on the establishment of indicators such as resilience and economy, a comprehensive quantitative analysis of distribution network indicators was conducted, and a configuration model with the optimal resilience and economy as the goal was established, so as to achieve a high level of resilience while maintaining high economy in the distribution network. Bian Xiaoyan et al. Multi-energy complementary planning of regional integrated energy system considering resilience and economy [J / OL]. Modern Electric Power, 1-15[2024-06-27]. Taking the optimal resilience index and economic index as the planning target, a dual-objective three-layer robust planning model of regional integrated energy system was established, so as to achieve the coordination of resilience and economy. Zhang Lu et al. proposed an optimal configuration strategy for mobile energy storage in distribution network considering the balance between resilience and economy [J]. Automation of Electric Power Systems, 2020, 44(21): 23-31. They took the mobile energy storage capacity as the decision variable and used the Nash negotiation method to construct a distribution network economy and resilience equilibrium model, thus achieving comprehensive consideration of resilience and economy of the power system.

[0004] In terms of distribution network resilience assessment methods, Li K et al. proposed a knowledge-informed data-driven resilience assessment method based on knowledge-based data to address the challenges of severe weather in distribution networks. Through a powerful evaluation of distribution network historical data, it provides a scientific basis and decision-making support for power systems to cope with severe weather. Liao C et al. introduced the changes in the state of external forces in mechanics into the resilience of power systems and proposed a novel evaluation method based on power supply and network topology to accurately analyze the impact of topology and power distribution changes on system resilience. Shen Jianliang et al. studied the comprehensive evaluation method of power grid resilience based on process decomposition [J]. Journal of Electric Power Science and Technology, 2023, 38(05): 226-235. They proposed a resilience evaluation method that comprehensively considers the power grid operation process, decomposes the resilience curve into power outage and restoration processes that can occur simultaneously, and reasonably evaluates the power supply restoration time of the distribution network. The above research mainly focuses on the evaluation methods of distribution network resilience, and does not analyze the specific evaluation indicators and evaluation methods in detail. The study of multi-dimensional evaluation indicators can comprehensively consider the rational use of distribution network resources from multiple aspects, and help guide the effective configuration and operation of the distribution network. It has important practical significance and far-reaching impact on improving the risk resistance of the distribution network and improving the overall operation efficiency and system reliability of the distribution network.

[0005] After searching, the Chinese invention patent publication number CN117495199A discloses a distribution network operation status evaluation method, system and storage medium, including constructing a new distribution network evaluation index system; using the hierarchical analysis method and the entropy weight method to calculate the subjective and objective weights of the distribution network respectively, and using the linear weighting method to obtain the combined weight of the index; calculating the Bayesian feedback corrected cloud model digital features of each index corresponding to each evaluation level; calculating the digital features of each primary and secondary index cloud model, and calculating the comprehensive evaluation cloud digital features of the new distribution network layer by layer; comparing the digital features of the comprehensive evaluation cloud model of the new distribution network with the evaluation level cloud model, so as to determine the final evaluation result of the distribution network. The existing patent has the problem of not considering the low-carbon dimension and elasticity index, so that the evaluation is incomplete and inaccurate.

[0006] How to achieve a comprehensive resilience assessment of distribution networks that takes into account multiple dimensions has become a technical problem that needs to be solved. Summary of the invention

[0007] The purpose of the present invention is to provide a comprehensive elasticity evaluation method and electronic equipment for a distribution network in order to overcome the defects of the above-mentioned prior art.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to one aspect of the present invention, a method for evaluating the comprehensive elasticity of a distribution network is provided, the method comprising:

[0010] Comprehensively consider the indicators of four dimensions: flexibility, economy, low carbon and safety, and build a comprehensive resilience indicator system for distribution networks;

[0011] Generate typical harsh scenarios;

[0012] The FAHP method and the risk entropy weight method are used to calculate the subjective weight and objective weight of each indicator in the comprehensive resilience index system under typical adverse scenarios, and the final weight is obtained by combining the subjective weight and the objective weight.

[0013] And based on the final weight, the grey correlation analysis method is used to improve TOPSIS, and the comprehensive elasticity index system of the distribution network is comprehensively scored.

[0014] Preferably, the elasticity indicators include load loss rate, network loss and voltage deviation; the economic indicators include load economic loss and carbon trading income; the economic indicators include carbon emission reduction; and the safety indicators include voltage qualification rate and average voltage fluctuation rate.

[0015] More preferably, the load loss rate represents the impact caused by the inability of the distribution network to fully absorb uncertainty in typical severe scenarios, specifically:

[0016]

[0017] In the formula, represents the active load of node i at time t, P load,i,s represents the active load of node i at time t under the typical severe scenario s.

[0018] More preferably, the network loss represents the partial loss of electric energy caused by the increase of line resistance and reduction of equipment efficiency during the transmission of electric energy in the distribution network under typical harsh scenarios, which is specifically:

[0019]

[0020] In the formula, Ω line represents the set of distribution network lines, r ij,s is the equivalent impedance of branch ij in a typical severe scenario s, I ij,s,t It represents the current flowing into line ij at time t under typical severe scenario s.

[0021] More preferably, the voltage deviation characterizes the situation that the voltage level in the power distribution system in a typical harsh scenario deviates from its rated value due to the influence of the harsh environment, specifically:

[0022]

[0023] Where U i,s,t represents the voltage amplitude of node i at time t under typical severe scenario s; Represents the rated voltage amplitude of node i at time t under typical severe scenario s.

[0024] More preferably, the carbon trading income is based on a tiered carbon trading mechanism, specifically:

[0025]

[0026] R C =κf C

[0027] In the formula, f C represents the step carbon price; λ is the base price; d is the length of the step carbon emission interval; σ is the growth coefficient, and the carbon trading price increases by λσ for each step increase; E C is the actual carbon emissions; E L is the free carbon emission allowance, R C is the carbon trading income; κ represents the carbon emissions that the distribution system can sell.

[0028] Preferably, the process of generating a typical severe scenario includes:

[0029] Step S2-1, based on historical wind-solar joint output data, using the Monte Carlo simulation method to generate a set of uncertain scenarios that define wind-solar joint output;

[0030] Step S2-2, introducing additional auxiliary variables and auxiliary constraints in each representative scenario, and screening a subset of potential severe scenarios according to the wind and solar prediction threshold;

[0031] Step S2-3, based on the minimum value of the total power output when the wind and solar power are combined, the severe scene subset of the potential severe scene is identified to obtain a typical severe scene set.

[0032] Preferably, the final weight obtained by combining subjective weight and objective weight is specifically as follows: the relative importance coefficients of subjective index weight and objective index weight are set to be τ and υ respectively, and the relative importance coefficient of subjective and objective weight of the jth index is calculated to be τ j and j ,

[0033]

[0034]

[0035] Among them, ω j is the comprehensive weight value of the jth indicator, and n is the number of comprehensive elasticity indicators.

[0036] Preferably, the process of comprehensively scoring the comprehensive elasticity index system of the distribution network includes:

[0037] Step S4-1: Assume that the distribution network has m targets to be evaluated and n comprehensive elasticity indicators, and construct the original matrix Q∈R mn , and standardize it by the range method;

[0038] Step S4-2, calculating a weighted normalization matrix based on the normalization processing matrix and the combination weight vector;

[0039] Step S4-3, determining the positive and negative ideal solutions of the weighted normalization matrix;

[0040] Step S4-4, respectively calculate the grey correlation coefficients of the i-th solution and the positive and negative ideal solutions in the j-th indicator, which are and

[0041] Step S4-5, calculate the distances from the ith solution to the positive and negative ideal solutions, respectively, defined as and

[0042] Step S4-6, calculate the relative closeness of each solution to the positive ideal solution,

[0043]

[0044]

[0045]

[0046] In the formula, x represents the decision maker’s preference for location and shape characteristics under different schemes, V i The larger the value, the closer the solution is to the ideal value and the better the overall score; conversely, the worse the overall score.

[0047] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1) Based on typical severe scenarios and with distribution network resilience indicators as the center, the present invention comprehensively considers indicators of four dimensions, namely, resilience, economy, low carbon and safety, to construct a comprehensive resilience indicator system for distribution networks. The FAHP method and risk entropy weight method are used to calculate the subjective and objective weights of each indicator respectively, and the final weight is obtained by weighting the combination of subjective and objective factors. The improved TOPSIS is used to comprehensively score the solution, considering the comprehensive dimensions. The results obtained have both subjective and objective attributes, and the obtained indicator weights are more appropriate. Therefore, the constructed comprehensive resilience indicator evaluation system and evaluation method are of great significance for guiding the planning and construction of distribution network resilience and the stable operation of low-carbon economy.

[0050] 2) The present invention defines multiple resilience indicators of distribution networks in typical harsh scenarios, and takes the resilience indicators of distribution networks as the center, adopts harsh scenario identification method to generate typical harsh scenarios, and applies them to the established comprehensive resilience evaluation system to determine the weak links and potential risks of distribution networks in different harsh scenarios, help evaluate the system's ability to respond to these risks, and thereby obtain more accurate comprehensive evaluation data.

[0051] 3) The simulation comparison results of the present invention show that the combined weighting method combining FAHP and risk entropy weight method takes into account the influence of subjective factors and objective factors, and the obtained combined indicator weights are more appropriate and have the best relative closeness, which verifies the priority and feasibility of this method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the comprehensive elasticity index system of the distribution network in the present invention;

[0053] Figure 2It is a schematic diagram of the comprehensive elasticity index evaluation process of the distribution network in the present invention;

[0054] Figure 3 This is a schematic diagram of the topology of the distribution network structure in the present invention;

[0055] Figure 4 This is a schematic diagram of a wind turbine output scenario in the present invention;

[0056] Figure 5 This is a schematic diagram of the photovoltaic output scenario in the present invention;

[0057] Figure 6 This is a schematic diagram for identifying typical severe scenarios of wind turbine output in the wind-solar combined output of the present invention;

[0058] Figure 7 This is a schematic diagram for identifying typical severe scenarios of photovoltaic processing for wind-solar combined output in the present invention;

[0059] Figure 8 It is a schematic diagram of the load loss in the present invention;

[0060] Fig. 9 It is a schematic diagram of network loss in the present invention;

[0061] Fig.10 It is a schematic diagram of voltage deviation in the present invention;

[0062] Fig.11 This is a schematic diagram of the comparison of weight values ​​between the method of the present invention and other methods;

[0063] Fig.12 This is a schematic diagram of the comparison results of the relative closeness between the method of the present invention and other methods. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0065] This embodiment relates to a distribution network comprehensive elasticity evaluation method, which is based on the FAHP-risk entropy weight method. In the process of elastic distribution network research, a multi-dimensional comprehensive elasticity index evaluation system and an evaluation method are constructed, which can effectively guide the elastic planning and construction of distribution network and the stable operation of low-carbon economy. The FAHP algorithm (Fuzzy Analytic Hierarchy Process) is a fuzzy hierarchical analysis method.

[0066] like Figure 2, the method comprises the following steps:

[0067] Step S1, constructing a comprehensive elasticity evaluation index system for distribution networks. Comprehensively considering the indicators of four dimensions, namely, elasticity, economy, low carbon and safety, a comprehensive elasticity index system for distribution networks is constructed, and each indicator is quantitatively analyzed.

[0068] Step S2, generate typical severe scenarios for the uncertainty of renewable energy output. Wind and solar power systems are extremely susceptible to various uncertainties. The microgrid random adaptive robust model based on the severe scenario identification method [J]. China Southern Power Grid Technology, 2021, 15(04): 26-35 is used to generate typical severe scenarios and apply them to the established comprehensive resilience assessment system to determine the weak links and potential risks of the distribution network under different severe scenarios, help evaluate the system's ability to cope with these risks, and thus obtain more accurate comprehensive assessment data.

[0069] Step S3, based on the establishment of the weighting method of each indicator combination. The subjective weight of the indicator is calculated by the FAHP algorithm based on subjective will judgment, and the objective weight of the indicator is calculated by the improved risk entropy weight method. The optimal weight combination model is established, combining subjective and objective factors, and the comprehensive weight is derived.

[0070] Step S4, comprehensively scoring each indicator. Based on the respective advantages of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution, also known as the superior and inferior solution distance method) and grey correlation analysis, TOPSIS is improved by introducing grey correlation, so that the matching degree between the target to be evaluated and the standard level can be more accurately reflected, thereby rationally scoring the indicator system comprehensively.

[0071] Step S5, the feasibility of the method of the present invention is verified by simulation analysis in an improved IEEE33 node power distribution system.

[0072] Note: There is no strict order requirement for step S2 and step S1.

[0073] In step S1, Figure 1 , the elasticity indicators of the comprehensive elasticity evaluation index system include:

[0074] Load loss rate: The load loss rate in typical severe scenarios refers to the inability of the distribution network to fully absorb the impact of uncertainty, resulting in partial load loss of power supply. The load loss rate reflects the degree of power loss of the distribution network in typical severe scenarios. The smaller the value, the stronger the elasticity of the distribution network.

[0075]

[0076] Where: P represents the active load of node i at time t. load,i,s represents the active load of node i at time t under the typical severe scenario s.

[0077] Network loss: Network loss in typical harsh scenarios refers to the partial loss of electricity due to the influence of harsh environment during the transmission of electricity in the distribution network, which increases line resistance and reduces equipment efficiency. Network loss reflects the degree of energy loss of the distribution network in harsh environment. The lower the network loss, the stronger the distribution network's ability to resist.

[0078]

[0079] Where: Ω line represents the distribution network line set; r ij,s is the equivalent impedance of branch ij in typical severe scenario s; I ij,s,t It represents the current flowing into line ij at time t under typical severe scenario s.

[0080] Voltage deviation: In typical harsh scenarios, voltage deviation refers to the voltage level in the power distribution system that deviates from its rated value due to the influence of harsh environment, increased line loss, load fluctuations, etc. Voltage deviation reflects the degree to which the voltage deviates from the normal operating level. The greater the deviation, the more serious the impact on the distribution network voltage.

[0081]

[0082] Where: U i,s,t represents the voltage amplitude of node i at time t under typical severe scenario s; Represents the rated voltage amplitude of node i at time t under typical severe scenario s.

[0083] The economic indicators of the comprehensive elasticity evaluation index system include:

[0084] Load economic loss: the cost of partial load shutdown of the distribution network due to harsh environment in severe scenarios and the cost of repairing or replacing aging distribution network equipment.

[0085]

[0086] Where: N s represents the number of typical severe scenarios; T is the entire time period; Ω represents the set of all nodes in the distribution network; a is the economic loss cost per unit load, and its size depends on the weight coefficient of each load; is the running status of node i, which is 1 when normal and 0 otherwise; π i is the load weight at node i; is the load loss of node i under typical severe scenario s at time t; b is the cost of equipment maintenance or replacement; N t,s is the number of equipment losses at time t under a typical severe scenario s.

[0087] Carbon trading benefits: As a market mechanism, the ladder carbon trading mechanism sets a total emission limit based on the principle of "cap-and-trade" and allocates it to various enterprises or entities. These enterprises can freely buy and sell emission quotas in the market, thereby encouraging enterprises to gradually reduce emissions, enhancing their flexibility and fairness, and effectively achieving environmental protection and low-carbon transformation goals. It can be expressed as:

[0088]

[0089] Where: f C represents the step carbon price; λ is the base price; d is the length of the step carbon emission interval; σ is the growth coefficient, and the carbon trading price increases by λσ for each step increase; E C is the actual carbon emissions; E L Free carbon emission allowance.

[0090] Therefore, the carbon emission rights trading income R C for:

[0091] R C =κf C

[0092] Where: κ represents the carbon emissions that the distribution system can sell.

[0093] The low-carbon indicators of the comprehensive elasticity evaluation index system include: carbon emission reduction. In the distribution network, carbon emission reduction is an important indicator reflecting its low carbon degree. Carbon emission reduction usually refers to the reduction in CO2 emissions caused by replacing fossil energy generation with clean energy generation. This article refers to the 2019 Emission Reduction Project Report of China's Regional Grid Baseline Emission Factor and the National Standardization Administration. GB 55015-2021 General Specification for Building Energy Conservation and Renewable Energy Utilization [S]. Beijing: China Architecture & Building Press, 2021, and uses the baseline method for measurement.

[0094] The calculation formula for baseline carbon emissions is:

[0095] E x =E f,x ×E g,CM,x

[0096] Where: E x represents the baseline carbon emissions in year x; E f,x E represents the net on-grid electricity of clean energy in year x;g,CM,x It represents the marginal carbon dioxide emission factor of the distribution system in year x, which can be expressed as

[0097] E g,CM,x =F g,OM,x ×ω OM +F g,BM,x ×ω BM

[0098] Where: F g,OM,x F represents the marginal emission factor of the power distribution system in year x; g,BM,x represents the capacity marginal emission factor of the distribution system in year x; ω OM and ω BM They are respectively expressed as the weights of the marginal emission factor of electricity and the marginal emission factor of capacity.

[0099] The carbon emission reduction is:

[0100] R=EP

[0101] R x =E x -P x

[0102] Where: R x represents the carbon emission reduction of the distribution system in year x; P x is the carbon emissions of the power distribution system. Since the greenhouse gas emissions generated by the production and operation of the clean energy storage system are relatively small, they can be ignored.

[0103] The safety indicators of the comprehensive elasticity evaluation index system include voltage qualification rate and average voltage fluctuation rate.

[0104] The voltage qualification rate is an indicator used to evaluate the power supply voltage level of the distribution network during normal operation. It measures whether the distribution network line voltage is within the specified range within an operating cycle. Its expression is as follows:

[0105]

[0106] Where: L is the number of lines participating in the assessment; T represents the statistical time; t ij,s is the voltage limit-crossing time of line ij under severe scenario s, N s The number of severe scenes.

[0107] The average voltage fluctuation rate is one of the important indicators for evaluating the safe and stable operation of the distribution network. It is used to judge the operating status of the distribution network by describing the degree of voltage fluctuation in the power grid. The lower the average voltage fluctuation rate, the smaller the voltage fluctuation and the better the voltage safety and stability of the power grid; on the contrary, if the average voltage fluctuation rate is high, the voltage may fluctuate frequently, which may affect the safe and stable operation of the distribution network.

[0108]

[0109] Where: U i,s,t represents the voltage value measured at node i at time t in a typical severe scenario s; is the mean voltage of the distribution network system, and Ω is the node set.

[0110] In step S2, the process of generating a typical severe scenario includes:

[0111] Step S2-1, using the Monte Carlo simulation method based on historical data to generate a set of uncertain scenarios that define the wind-solar joint output Y = {pv1·wt1, pv2·wt2, …, pv s ·wt s}, which consists of a series of representative scenarios, where s is the number of representative scenarios of PV output.

[0112] Step S2-2, introduce additional auxiliary variables and auxiliary constraints in each representative scenario, and select a subset of potential severe scenarios based on the current wind and solar forecast threshold. a ={y0}, where auxiliary variables include climate factors (temperature, wind speed, etc.), and auxiliary constraints include power generation upper and lower limits, equipment operating status, etc. Specifically: if the wind and solar power forecast value is lower than the wind and solar power forecast threshold, it may lead to a scenario with low power generation efficiency, forming a potential severe scenario subset.

[0113] Step S2-3, based on the wind-solar combined output P pv,wt The minimum value is used as the standard to identify the harsh scene. pv,wt It is the total power output generated when wind and photovoltaic power generation equipment are running simultaneously in a specific scenario. It is a key indicator used to measure the power generation capacity of wind and photovoltaic combination under different environmental and meteorological conditions.

[0114] When analyzing and screening typical adverse scenarios, special attention should be paid to those scenarios that lead to a significant reduction in combined output. These scenarios may represent adverse weather conditions or other unfavorable factors, which affect the overall power generation capacity. They are then screened out as typical adverse scenarios, which are more representative.

[0115] Step S2-4, repeat step S2-3, perform iterative screening of the calculated scene sets for the worst scenes, and add them to the typical worst scene set Ω b ={y1,y m}, m refers to the number of typical severe scenarios obtained.

[0116] The Monte Carlo simulation method is used to obtain the wind and solar power output scenarios under the distribution network. Figure 4 and Figure 5As known, based on the harsh scene identification method, Figure 6 and Figure 7 The five typical severe scenarios are shown and the comprehensive resilience indicators are calculated based on them.

[0117] In step S3, a combined weighting method is established, and the subjective weight and objective weight of each indicator are calculated using the FAHP method based on subjective will judgment and the improved risk entropy weight method, and the comprehensive weight is derived by establishing an optimal weight combination model and combining subjective and objective factors. The details are as follows:

[0118] 1) FAHP method (Fuzzy Analytic Hierarchy Process) to calculate subjective weights

[0119] First, the three-scale method is used to compare the two-layer index elements to determine the relative importance. The distribution network measures the importance of each index through its own subjective factors, and then determines c according to the three-scale method. ij The value of is used to construct the fuzzy priority matrix C, which is composed of m objects to be evaluated and n evaluation indicators:

[0120]

[0121] In order to characterize the relative importance, the 0.1-0.9 scale method is adopted, as shown in Table 1. The larger the value, the more important it is.

[0122] Secondly, the fuzzy priority matrix C is converted into the fuzzy consistency matrix O, which can be expressed as:

[0123]

[0124]

[0125] Finally, calculate the weight value of each indicator, weight The calculation formula is:

[0126]

[0127] In the formula, μ represents the importance differentiation factor, and its value range is μ≥(n-1) / 2.

[0128] 2) Risk entropy weight method to calculate objective weights

[0129] First, according to the randomness of the event, through the probability P i The random events that occur construct event B in the sample space i =(i=1,2,…,n), and event B i Impact level in power distribution system A i Determined as event B i For the risk factor of the distribution system, under the same indicator system, Ai It only represents relative meaning. The risk coefficient of each event and the normalized value of its corresponding probability distribution are expressed as event B i The utility coefficient is:

[0130]

[0131]

[0132] Therefore, this paper expresses the average value of the risk function of each event in the sample space in the utility coefficient space as the utility risk entropy of the system, that is:

[0133]

[0134] Secondly, assuming that the distribution network has m targets to be evaluated and n comprehensive elasticity indicators, then there are m schemes in the corresponding evaluation scheme set, and each scheme has n attributes. The jth indicator value of the i-th evaluation target is recorded as r ij ′, forming the indicator matrix R′.

[0135]

[0136] Because different evaluation indicators in the same indicator system lead to different corresponding dimensions, it is difficult to make direct comparisons and comprehensive evaluations. Therefore, the indicator matrix should be standardized. Evaluation indicators are mainly composed of two types: benefit-based and cost-based. The larger the benefit-based indicator value, the better the result, while the smaller the cost-based indicator value, the better the result. The indicator matrix is ​​standardized as follows:

[0137]

[0138] In the formula, (r ij ′) max =maxr ij ′,(r ij ′) min =minr ij ′, the standardized indicator matrix is ​​recorded as R.

[0139]

[0140] According to the utility risk entropy solved by the above formula, the utility risk entropy of the jth indicator is

[0141]

[0142] Where ξ=1 / lnm, A i is the influence degree of the i-th evaluation target in the distribution system, that is, the risk coefficient; 0<H j <1, and when Iij =0, it is specified that lnI ij = 0. Correspondingly, the utility risk entropy weight of the jth indicator is expressed as

[0143]

[0144] Where, 0≤ω j 1 ≤1, and

[0145] 3) Combination weighting of FAHP and risk entropy weight method

[0146] In this paper, subjective and objective weights are equally involved in the determination process. In order to reflect the importance of subjective and objective weights, the relative importance coefficients of subjective index weights and objective index weights are set as τ and υ. The relative importance coefficient of subjective and objective weights of the jth index is calculated by the following formula: j and j . And through the following calculation, the comprehensive weight value of each indicator is obtained.

[0147]

[0148]

[0149] In step S4, a comprehensive evaluation method is established: based on the respective advantages of TOPSIS and grey correlation analysis, TOPSIS is improved by introducing grey correlation, so that the matching degree between the target to be evaluated and the standard level can be reflected more accurately, thereby reasonably comparing the advantages and disadvantages of each indicator. The specific steps are as follows:

[0150] Step S4-1: Assume that the distribution network has m targets to be evaluated and n comprehensive elasticity indicators, and construct the original matrix Q∈R mn , and standardized by the range method.

[0151]

[0152] Step S4-2, calculating a weighted normalization matrix.

[0153]

[0154] Where: W = [ω1, ω2, …ω n ] is the combined weight vector of indicators.

[0155] Step S4-3, determine the positive and negative ideal solutions.

[0156]

[0157]

[0158] Where: and Respectively represent the maximum and minimum values ​​of the jth column of the standard matrix; J + It is a set of indicators with larger values, the better; J - It is a set of indicators where the smaller the value, the better.

[0159] Step S4-4, calculating the grey correlation coefficient between the i-th solution and the positive and negative ideal solutions at the j-th indicator.

[0160] The correlation of the ideal solution is:

[0161]

[0162] In the formula, φ∈[0,1] is the resolution coefficient, usually φ=0.5.

[0163] The grey correlation coefficient matrix of each scheme and the positive ideal scheme is:

[0164]

[0165] The grey correlation degree between the ith solution and the positive ideal solution is:

[0166]

[0167] Negative correlation of the ideal solution:

[0168]

[0169] The grey correlation coefficient matrix of each scheme and the negative ideal scheme is:

[0170]

[0171] The grey correlation degree between the i-th solution and the negative ideal solution is:

[0172]

[0173] Step S4-5, the distances from the ith solution to the positive and negative ideal solutions are defined as and Its expression is as follows:

[0174]

[0175]

[0176] Step S4-6, based on the above analysis, this paper respectively After dimensionless normalization, the expression is as follows:

[0177]

[0178] Among them, M i Respectively represent

[0179] Step S4-7, because and The larger the value, the closer the evaluation scheme is to the ideal solution; and The larger the value, the more the evaluation scheme deviates from the ideal solution, so it can be expressed by the following formula:

[0180]

[0181]

[0182] Where x represents the decision maker’s preference for location and shape characteristics under different schemes. The larger the value, the closer the solution is to the ideal solution. The larger the value, the more the solution deviates from the ideal solution.

[0183] Step S4-8, calculate the relative closeness of each solution to the positive ideal solution

[0184]

[0185] Where V i The larger the value, the closer the solution is to the ideal value and the better the overall score; conversely, the worse the score.

[0186] This embodiment also involves the verification of a comprehensive elasticity evaluation method for a distribution network. Taking the improved IEEE 33-node distribution system as an example, its structural topology diagram is as follows: Figure 3 As shown in the figure, a typical severe scenario is applied to the improved IEEE33-node distribution network, and the following indicators are simulated and calculated based on MATLAB simulation. The results are as follows Figure 8 , 9 As shown in Figure 10, each load area of ​​the distribution network has different load loss, network loss and voltage deviation under different severe climates. When the climate is more severe, their values ​​will increase relatively, and then they can be used as elasticity indicators to guide decision makers to implement specific plans and measures.

[0187] In order to highlight the priority and feasibility of the proposed method, this embodiment compares multiple methods for solving index weights with the method of the present invention and makes a final score.

[0188] Method 1: Only use the FAHP method to calculate the weight of the comprehensive elasticity index of the distribution network.

[0189] Method 2: Only the risk entropy weight method is used to calculate the weights of the comprehensive elasticity indicators of the distribution network.

[0190] Method 3: Use the entropy weight method to calculate the weights of the comprehensive elasticity indicators of the distribution network.

[0191] Method 4: Algorithm used by the present invention.

[0192] like Fig.11 , the FAHP of method 1 is highly subjective, more dependent on the evaluator himself, and lacks objective reality; the risk entropy weight law of method 2 is more inclined to theoretical data, subjective factors and experience; the entropy weight law of method 3 is more dependent on the size of the information entropy value. The combined weighting method of the FAHP-risk entropy weight method of the present invention considers the influence of both subjective and objective factors at the same time, and the result has both subjective and objective attributes, and the obtained indicator weight is more appropriate.

[0193] Depend on Fig.12 It can be seen that when the improved TOPSIS method is used to comprehensively evaluate each indicator, when only the FAHP method, risk entropy weight method and entropy weight method are considered, the degree of approximation to the ideal solution is less than the combined weighting method of method 4 combining FAHP and risk entropy weight method, which illustrates the priority and feasibility of the method proposed in this paper.

[0194] The comprehensive elasticity evaluation method of distribution network designed by the present invention, which is based on the combination of FAHP and risk entropy weight method, is based on typical severe scenarios and takes distribution network elasticity index as the center, comprehensively considers the indicators of four dimensions, namely elasticity, economy, low carbon and safety, and then constructs the comprehensive elasticity index system of distribution network, and uses FAHP method and risk entropy weight method to calculate the subjective and objective weights of each indicator respectively, and obtains the final weight by subjective and objective combined weighting. At the same time, the improved TOPSIS is used to comprehensively score the solution. The simulation comparison results show that the combined weighting method combining FAHP and risk entropy weight method considers the influence of subjective factors and objective factors at the same time, and the result has both subjective and objective attributes, and the obtained indicator weight is more appropriate. Therefore, the constructed comprehensive elasticity index evaluation system and evaluation method are of great significance for guiding the planning and construction of distribution network elasticity and the stable operation of low-carbon economy.

[0195] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0196] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0197] The processing unit performs the various methods and processes described above, such as methods S1 to S5. For example, in some embodiments, methods S1 to S5 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S5 in any other appropriate manner (e.g., by means of firmware).

[0198] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0199] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0200] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0201] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A comprehensive elasticity assessment method for a distribution network, characterized in that: The method includes: Comprehensively consider the indicators of four dimensions: flexibility, economy, low carbon and safety, and build a comprehensive resilience indicator system for distribution networks; Generate typical harsh scenarios; The FAHP method and the risk entropy weight method are used to calculate the subjective weight and objective weight of each indicator in the comprehensive resilience index system under typical adverse scenarios, and the final weight is obtained by combining the subjective weight and the objective weight. And based on the final weight, the grey correlation analysis method is used to improve TOPSIS, and the comprehensive elasticity index system of the distribution network is comprehensively scored.

2. A comprehensive elasticity evaluation method for a distribution network according to claim 1, characterized in that: The elasticity indicators include load loss rate, network loss and voltage deviation; the economic indicators include load economic loss and carbon trading income; the economic indicators include carbon emission reduction; the safety indicators include voltage qualification rate and average voltage fluctuation rate.

3. A comprehensive elasticity evaluation method for a distribution network according to claim 2, characterized in that: The load loss rate represents the impact caused by the inability of the distribution network to fully absorb uncertainty in typical severe scenarios, specifically: In the formula, represents the active load of node i at time t, P load,i,s represents the active load of node i at time t under the typical severe scenario s.

4. A comprehensive elasticity evaluation method for a distribution network according to claim 2, characterized in that: The network loss represents the partial loss of electric energy during the transmission of the distribution network in typical harsh scenarios, which is caused by the harsh environment, resulting in increased line resistance and reduced equipment efficiency, specifically: In the formula, Ω line represents the set of distribution network lines, r ij,s is the equivalent impedance of branch ij in a typical severe scenario s, I ij,s,t It represents the current flowing into line ij at time t under typical severe scenario s.

5. A comprehensive elasticity evaluation method for a distribution network according to claim 2, characterized in that: The voltage deviation represents the voltage level in the power distribution system under typical harsh scenarios. Due to the influence of harsh environment, the voltage is unstable and deviates from the rated value. Specifically: Where U i,s,t represents the voltage amplitude of node i at time t under typical severe scenario s; Represents the rated voltage amplitude of node i at time t under typical severe scenario s.

6. A comprehensive elasticity evaluation method for a distribution network according to claim 2, characterized in that: The carbon trading income is based on the tiered carbon trading mechanism, specifically: R C =κf C In the formula, f C represents the step carbon price; λ is the base price; d is the length of the step carbon emission interval; σ is the growth coefficient, and the carbon trading price increases by λσ for each step increase; E C is the actual carbon emissions; E L is the free carbon emission allowance, R C is the carbon trading income; κ represents the carbon emissions that the distribution system can sell.

7. A comprehensive elasticity evaluation method for a distribution network according to claim 1, characterized in that: The process of generating a typical severe scenario includes: Step S2-1, based on historical wind-solar joint output data, using the Monte Carlo simulation method to generate a set of uncertain scenarios that define wind-solar joint output; Step S2-2, introducing additional auxiliary variables and auxiliary constraints in each representative scenario, and screening a subset of potential severe scenarios according to the wind and solar prediction threshold; Step S2-3, based on the minimum value of the total power output when the wind and solar power are combined, the severe scene subset of the potential severe scene is identified to obtain a typical severe scene set.

8. A comprehensive elasticity assessment method for a distribution network according to claim 1, characterized in that: The final weight obtained by combining subjective weight and objective weight is specifically as follows: the relative importance coefficients of subjective index weight and objective index weight are set to τ and υ respectively, and the relative importance coefficient of subjective and objective weight of the jth index is calculated to be τ j and j , Among them, ω j is the comprehensive weight value of the jth indicator, and n is the number of comprehensive elasticity indicators.

9. A comprehensive elasticity evaluation method for a distribution network according to claim 1, characterized in that: The process of comprehensively scoring the comprehensive elasticity index system of the distribution network includes: Step S4-1: Assume that the distribution network has m targets to be evaluated and n comprehensive elasticity indicators, and construct the original matrix Q∈R mn , and standardize it by the range method; Step S4-2, calculating a weighted normalization matrix based on the normalization processing matrix and the combination weight vector; Step S4-3, determining the positive and negative ideal solutions of the weighted normalization matrix; Step S4-4, respectively calculate the grey correlation coefficients of the i-th solution and the positive and negative ideal solutions in the j-th indicator, which are and Step S4-5, calculate the distances from the ith solution to the positive and negative ideal solutions, respectively, defined as and Step S4-6, calculate the relative closeness of each solution to the positive ideal solution, In the formula, x represents the decision maker’s preference for location and shape characteristics under different schemes, V i The larger the value, the closer the solution is to the ideal value and the better the overall score; conversely, the worse the overall score.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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