A full-factor evaluation method for power distribution network station area virtual capacity expansion

Abstract

The present application relates to a kind of full-element evaluation methods for power distribution network area virtual capacity expansion, comprising: calculating the full-element index of each scheme according to the data of each scheme, after obtaining the full-element index of each scheme, the subjective weight of each secondary index is calculated by AHP method, the objective weight of each secondary index is calculated by entropy weight method, the combination weight of each secondary index is obtained by combining subjective weight and objective weight through slack factor, the benefit value of each scheme is calculated by each secondary index of scheme and its corresponding combination weight, select the scheme with the maximum benefit value as the implemented virtual capacity expansion scheme.The present application is developed around the improvement of power distribution network capacity reliability and flexibility, studies the full-element benefit evaluation method, realizes the quantitative evaluation of the benefit brought by large-scale access and regulation of distributed resources to the remaining capacity of power distribution network, as the evaluation basis of power distribution network optimization operation target, guide the formulation of power distribution network area virtual capacity expansion scheme.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network reconstruction, and particularly relates to a full-element evaluation method for virtual capacity expansion of a power distribution network station area. BACKGROUND

[0002] The supply form and load structure of Shanghai and other receiving end power grids are facing major changes. Including: 1) Electric energy substitution is rapidly advancing, and unordered access of loads such as electric vehicles, electric scooters, and all-electric kitchens is leading to an increasingly tight supply of capacity resources in the distribution network station area, and the remaining capacity needs to be improved; 2) Distributed new energy is connected to the grid, and the intermittency and uncertainty of new energy also lead to changes in the characteristics of net loads, changes in power flow distribution in the distribution network, and insufficient remaining capacity for connecting new energy; 3) The load characteristics have changed in the post-pandemic era, and the online and home-based nature of life and work has led to changes in the time sequence characteristics of the peak load of the station area. However, the traditional solution of relying only on transformer expansion increases the investment in the distribution network and further reduces the operating efficiency of the station transformer, which is not conducive to solving the problem of short-term heavy loading or overloading. Therefore, it is necessary to dynamically adjust the flexible load of the distribution station area through operation scheduling, and to control the load peaks of each line and station transformer of the distribution network in real time, so as to achieve the same dynamic virtual capacity expansion effect as expansion.

[0003] In the aspect of related regulations and pilot projects of flexible resource participation in distribution network station area capacity optimization configuration, the state has continuously put forward a series of standards and management methods, mainly including: “Electricity Demand Side Management Method”, “Orderly Electricity Utilization Management Method”, “General Technical Specification for Electricity Demand Response”, “Technical Guidelines for Smart Grid User Interface” and so on. At the same time, a number of demonstration projects have been established in China to promote the development of flexible resource regulation projects. In terms of project development, in 2015, China's first urban pilot project was launched in Shanghai. A total of 64 users participated in the pilot project, including 31 industrial users and 33 building users. The pilot project has achieved certain results and has helped the power system to save energy and reduce emissions. In April 2015, the National Development and Reform Commission issued a document to determine that Beijing, Tangshan, Suzhou and Foshan will organize pilot “demand response”. The purpose of this pilot is to improve the power emergency mechanism. In August 2015, the National 863 Project “Key Technology Research and Demonstration of Smart Electricity and Dynamic Demand Response of Power Users” was launched in Nanjing. In February 2016, according to the needs of smart grid construction and promotion, State Grid Corporation established the first automatic demand response project in the system - Zhongxin Ecological City. The project is based on the construction concept of “innovation, interaction, model and leadership”, and carries out innovative technologies such as smart home, smart city comprehensive energy information service and multi-level energy coordination control for demand response demonstration. In 2020, the work of promoting the construction of smart communities in Wuhan Zhanpu area was carried out, realizing the transformation of typical applications to ubiquitous applications for residential communities, and exploring more application scenarios. On the basis of mining the value of residential customer energy data, focusing on the adjustable function of residential load, further expanding to new regulation resources such as photovoltaic, energy storage and electric vehicle charging piles in residential communities, and carrying out the construction of residential side micro virtual power plant projects. However, how to comprehensively quantify the benefits of flexible resource participation in distribution network station area capacity optimization configuration to guide the formulation of implementation plans still needs to be researched and solved.

[0004] Qi Huanling's Cost-benefit Analysis and Evaluation of Power Technology and Economy, published in Electrical Technology and Economy, explores the cost-benefit analysis and evaluation methods of power technology and economy by collecting and analyzing relevant data, and applies them to practical cases. The results show that cost-benefit analysis is an important tool for measuring the feasibility and economic benefits of power technology, providing scientific basis for decision-makers and promoting the development and application of power technology. Zhang Kai, Yuan Jiahai, and Ding Baodi's New Type of Power System Flexibility Resource Benefit Evaluation, published in Climate Change Research Progress, mentions a multi-time scale time series operation simulation model that refines the cost structure of system flexibility improvement, quantifies the promotion effect of single flexibility resource on new energy consumption and the difference in system operation cost under different stages of new type of power system construction. Mu Juntong, Li Zhisuo, and Gu Chuanjie's Economic Benefit Evaluation of Power Transmission and Distribution System, published in Modern Industrial Economy and Informationization, aims to evaluate the carbon neutral economic benefits of power transmission and distribution system and provide reasonable policy recommendations. The carbon emissions of power transmission and distribution system are analyzed in detail, including emission sources, trends and influencing factors. Based on the carbon neutral target, carbon reduction strategies are developed, and the impact of different carbon reduction measures on economic benefits is simulated. Bian Xiang's Comprehensive Evaluation Model for Economic Benefits of Flexible Substation AC / DC Distribution Technology, published in Modern Industrial Economy and Informationization, uses economic methods to comprehensively evaluate the economic benefits of flexible substation AC / DC distribution technology and constructs an evaluation model, laying a good foundation for the application and future development of flexible substation AC / DC distribution technology, and helping enterprises maximize economic benefits in investment decision-making and technological innovation. Luo Tianlu, Li Xiaowei, and Sun Hujie's Construction and Research of Distribution Network Precise Investment Strategy Index System Based on AHP, published in Rural Electrification, takes the precise investment strategy of a power enterprise for distribution network as the research object, constructs the evaluation index system before and after investment, and uses AHP to evaluate the investment project, providing a basis for the power enterprise to realize precise investment and helping the power enterprise make investment decisions quickly. Zhang Kun, Zhao Qianyu, and Wang Shousheng's Construction and Calculation Method of Evaluation Indexes Reflecting the Distributed Photovoltaic Acceptance Capacity of Distribution Network, published in Power Supply and Use, considers the impact of large-scale distributed photovoltaic access on distribution network, constructs a primary evaluation index of distribution network distributed photovoltaic acceptance capacity from three aspects of power quality, grid management level, and economy, and then constructs nine secondary indexes such as voltage qualification rate, distributed photovoltaic source load matching degree, light rejection rate, power balance, and power balance. The matrix-AHP method is proposed to evaluate the distributed photovoltaic acceptance capacity of distribution network by combining matrix-AHP method with the distance method.Liu Dun-nan, Li Rui-qing, Chen Xue-qing published in power system automation on the power market supervision index and market evaluation system systematically introduces the definition, meaning and application method of five kinds of market supervision indexes such as market supply and demand, market structure, bidding strategy, supplier status and transaction result, and on this basis, the power market evaluation system is put forward, and the evaluation theme, evaluation object, evaluation index, index benchmark value and scoring method are preliminarily given. The above research puts forward a lot of effective and feasible power system benefit evaluation methods, but the full factor evaluation model for virtual capacity expansion of distribution network station area still needs to be established and improved. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a full factor evaluation method for virtual capacity expansion of distribution network station area.

[0006] The purpose of the present application can be realized by the following technical solutions:

[0007] The present application provides a full factor evaluation method for virtual capacity expansion of distribution network station area, comprising the following steps:

[0008] Obtain a plurality of virtual capacity expansion schemes for distribution network station area;

[0009] Calculate the full factor indexes of each scheme according to the data of each scheme;

[0010] The full factor indexes include the following primary indexes: economic index, environmental and low-carbon index, social index;

[0011] The economic index includes the following secondary indexes: station area energy storage configuration cost, flexible resource scheduling cost, network loss cost, system flexibility benefit and delay of distribution transformer investment and reconstruction benefit, the environmental and low-carbon index includes the following secondary indexes: new energy consumption index, carbon emission situation, the energy consumption index includes curtailment rate, reduction rate and penetration rate, the carbon emission situation includes carbon emission reduction benefit and carbon dioxide emission reduction amount, the social index includes the following secondary indexes: system reliability index and social welfare index, the system reliability index includes system outage frequency, system outage duration and energy supply reliability, the social welfare index includes the benefits of increasing the number of employees and promoting the development of other industries;

[0012] After obtaining the full factor indexes of each scheme, the subjective weight of each secondary index is calculated by AHP method, the objective weight of each secondary index is calculated by entropy weight method, and the combination weight of each secondary index is obtained by combining the subjective weight and the objective weight through the relaxation factor;

[0013] The benefit value of each scheme is calculated through each secondary index of the scheme and the corresponding combination weight;

[0014] The scheme with the maximum benefit value is selected as the implemented virtual capacity expansion scheme.

[0015] Further, the substation energy storage configuration cost is calculated according to the configuration capacity and power of the energy storage system, and the calculation formula is:

[0016]

[0017] wherein, C ESS is the substation energy storage configuration cost, p is the discount rate, which is the opportunity cost or interest rate of capital, represents the value growth rate of funds over time, r is the service life of the configured energy storage device, E int is the substation energy storage configuration capacity, P int is the substation energy storage configuration power, c ess is the corresponding cost coefficient of the substation energy storage configuration;

[0018] The flexible resource scheduling cost is calculated according to the power after the flexible resource scheduling of the virtual capacity expansion scheme, and the calculation formula is:

[0019]

[0020] wherein, C DR represents the flexible resource scheduling cost, is the scheduled electric vehicle load of the substation i, is the electric vehicle load of the substation i before scheduling, is the total electric vehicle power consumption of the substation i, is the scheduled air conditioner load of the substation i, is the air conditioner load of the substation i before scheduling, is the total air conditioner power consumption of the substation i, and a and b are the corresponding cost coefficients;

[0021] The network loss cost is calculated according to the corresponding distribution network loss of the virtual capacity expansion scheme, and the calculation formula is:

[0022] C φ =(φ d -φ0)×C loss

[0023] φ=W s -W r

[0024] wherein, C φ is the network loss cost, φ d is the distribution network loss after implementing the virtual capacity expansion, φ0 is the distribution network loss before implementing the virtual capacity expansion, C loss is the unit power loss cost, W r is the total power consumption of the regional distribution network actually used, and W sTotal power purchase from the superior main grid for the regional distribution network;

[0025] The system flexibility benefit is calculated according to the system operation flexibility corresponding to the virtual capacity expansion scheme, and the calculation formula is:

[0026] E K = (K d -K0) × E flex

[0027]

[0028] Wherein, E K is the flexibility benefit brought by the implementation of virtual capacity expansion for the system, K d is the system flexibility after the implementation of virtual capacity expansion, K0 is the system flexibility before the implementation of virtual capacity expansion, K is the system flexibility, E flex is the benefit brought by the unit power adjustable load, S p is the flexible regulation power capacity, S s is the energy storage capacity, S W is the maximum adjustable capacity of the tie line, P L,max is the maximum load of the whole network.

[0029] The delay distribution transformer investment reconstruction benefit is calculated according to the virtual capacity corresponding to the virtual capacity expansion scheme, and the calculation formula is:

[0030]

[0031] Wherein, C CAP is the delay distribution transformer investment reconstruction benefit, ΔS i is the virtual capacity of the substation i, c cap is the corresponding benefit coefficient.

[0032] Further, the energy consumption situation index includes the curtailment rate, the reduction rate and the penetration rate, and the calculation formula is:

[0033]

[0034] Wherein, α is the curtailment rate, ζ is the reduction rate, δ is the penetration rate, T is the evaluation period, P tot (t) is the new energy power generated by the regional distribution network at time t; P use (t) is the new energy power used by each user in the distribution network substation; W RF , W R are the ideal and actual power generation of the regional distribution network new energy respectively; S RES is the installed capacity of the regional distribution network new energy, P L,m is the total load peak power of the regional distribution network.

[0035] Further, the carbon emission situation is evaluated by the carbon emission reduction benefit of the distribution network side and the carbon dioxide emission reduction amount of the power generation side, and the calculation formula is:

[0036] ENI=P b ×C2×ρ

[0037] F CO2 =F×E sc

[0038] Wherein, ENI is the carbon emission reduction benefit of the distribution network side, P b is the power purchase amount of the regional distribution network from the upper main network after virtual capacity expansion, C2 is the emission coefficient, ρ is the carbon trading price, F CO2 is the carbon dioxide emission reduction amount of the power generation side, E sc is the carbon emission amount generated per ton of standard coal, and F is the annual fossil energy saving amount converted into standard coal.

[0039] Further, the system reliability index includes system outage frequency, system outage duration and energy supply reliability, and the calculation formula is:

[0040]

[0041] Wherein, f SIF is the system outage frequency, S SID is the system outage duration, A SSA is the energy supply reliability, N i is the number of users of the i-th load point in the transformer area, and λ is the load point failure rate; M is the annual outage time of the load point; Y is the length of one year, Y = 8766h;

[0042] The social welfare index includes the benefit of increasing the number of employed persons and promoting the development of other industries, and the calculation formula is:

[0043]

[0044] ECO=I y ×a sum ×b value

[0045] Wherein, q represents the number of employed persons, I aggregate is the total investment amount, r salary is the proportion of labor cost to the total investment, p salary is the average wage level of promoting employment, ECO represents the benefit of promoting the development of other industries, I y is the corresponding investment of the economic sector in the virtual capacity expansion project, a sum is the added value of other industries corresponding to unit investment, b value is the input-output added value coefficient.

[0046] Further, the subjective weight of each index calculated by the AHP method comprises the following steps:

[0047] According to the prior knowledge experience reference, the importance of each level two indicators is compared and judged to obtain the first level index judgment matrix and the second level index judgment matrix of each scheme, and the weight vector of each judgment matrix is calculated by using the eigenvalue method, and the calculation formula is:

[0048]

[0049] The judgment matrix is:

[0050]

[0051] n is the number of indexes of each level, is the weight vector value of index i,

[0052] According to the first level index judgment matrix, consistency check is performed;

[0053] If the test passes, the subjective weight of the corresponding index is calculated, the weights of each level corresponding to the index are multiplied to obtain the subjective weight of the index, and the formula is:

[0054]

[0055] Among them, w kj is the weight corresponding to the second level index j in the kth layer, and w j is the subjective weight of the second level index j.

[0056] Further, the consistency check according to the first level index judgment matrix comprises the following steps:

[0057] The maximum eigenvalue λ nax of the first level index judgment matrix is calculated, and the calculation formula is:

[0058]

[0059] The judgment value CR is calculated by the maximum eigenvalue λ max , and the calculation formula is:

[0060]

[0061] Among them, CI is the consistency index, and RI is the random consistency index.

[0062] If CR<0.1, the consistency check passes, the weight calculation is effective, and if CR≥0.1, the consistency check does not pass, and the index judgment matrix is re-established.

[0063] Further, the objective weight of each index calculated by the entropy weight method comprises the following steps:

[0064] The original data matrix X=(x ij ) m×n , m is the number of virtual capacity expansion schemes, and n is the number of secondary indexes of the schemes;

[0065] The original data matrix is normalized by using the range normalization method to obtain a standardized matrix Y=(y ij ) m×n For a positive index, the formula is as follows:

[0066]

[0067] For a negative index, the formula is as follows:

[0068]

[0069] Wherein, the positive index indicates that the greater the index value, the higher the scheme benefit value, and the negative index indicates that the smaller the index value, the higher the scheme benefit value;

[0070] The characteristic proportion p ij of the index j is calculated by the standardized matrix, and the formula is as follows:

[0071]

[0072] The entropy value E ij of each index is calculated by the characteristic proportion p j , and the formula is as follows:

[0073]

[0074] Wherein, when p ij =0, p ij ln(p ij )=0,

[0075] The objective weight h j is calculated according to the entropy value of each index,

[0076]

[0077] Further, the combined weight of each index is obtained by combining the subjective weight and the objective weight through a relaxation factor, and the combined weight comprises the following steps:

[0078] The subjective weight w j determined by the AHP method is multiplied by the relaxation factor η, the objective weight h j determined by the entropy weight method is corrected, and the combined weight is obtained:

[0079]

[0080] wherein 0≤η≤1, wherein W j is the combined weight of secondary index j.

[0081] Further, the benefit value of each scheme is calculated, and the calculation formula is:

[0082]

[0083] wherein R i is the benefit value of scheme i, p ij is the feature proportion of secondary index j of scheme i.

[0084] Compared with the prior art, the present application has the following advantages:

[0085] (1) The present application is developed around the improvement of power distribution network capacity reliability and flexibility, studies the full-factor evaluation method for power distribution network area virtual capacity expansion, realizes the quantitative evaluation of the benefits brought by large-scale access and regulation of distributed resources to the remaining capacity of the power distribution network, and serves as the evaluation basis of the optimization operation target of the power distribution network, guiding the formulation of the power distribution network area virtual capacity expansion scheme.

[0086] (2) The present application proposes a full-factor cost and benefit analysis model in order to evaluate the effect of area virtual capacity expansion, comprehensively considers the economic, social and environmental and low-carbon factors, and evaluates the regulation benefit of flexible resources. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 is the full-factor index system diagram of the virtual capacity expansion benefit of the present application. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0089] Embodiment 1:

[0090] Step 1: Construction of full-factor index system:

[0091] In the decision-making process of virtual capacity expansion in distribution area, the decision problem can be divided into three levels: target layer, criterion layer and scheme layer. This hierarchical analysis method helps to comprehensively evaluate the effect of virtual capacity expansion and ensures the scientificity and comprehensiveness of the decision-making. First, the target layer mainly evaluates the effect of capacity expansion according to the capacity expansion demand and load curve of a specific area. The core of this layer is to determine the overall goal of the capacity expansion project, that is, to meet the current and future load demand of the area through capacity expansion and optimize the stability and reliability of power supply. The criterion layer involves multiple evaluation indexes, which are divided into three first-level indexes: economy, environment and low carbon, and society. In terms of economy, it is further divided into five second-level indexes: distribution and storage cost, resource scheduling cost, network loss cost, system flexibility benefit and benefit of delaying investment in distribution transformer reconstruction; in terms of environment and low carbon, the second-level indexes include new energy consumption and carbon emission, which respectively evaluate the curtailment rate, reduction rate, penetration rate, carbon emission value and annual carbon dioxide reduction amount; in terms of society, the second-level indexes include system reliability index and social welfare index, which are respectively evaluated by system outage frequency, outage duration and power supply reliability, as well as the number of increased employment and the development of related industries. The virtual capacity expansion scheme includes adjusting the flexible resources (electric vehicles, variable frequency air conditioners, etc.) under the area and configuring the energy storage, and the specific index system is shown in Fig. 2. Figure 1

[0092] Step 2: Calculation of each level index

[0093] Step 2.1: Calculation of economy index

[0094] Step 2.1.1: Calculation of area energy storage configuration cost

[0095] Area energy storage is an important means of virtual capacity expansion in the current distribution area. The configuration cost of the energy storage system is mainly determined by the configuration capacity and power of the energy storage system. According to the configuration capacity and power of the virtual capacity expansion scheme, the energy storage configuration cost is obtained. The formula for calculating the daily equivalent energy storage cost is as follows:

[0096]

[0097] Where C ESS is the energy storage configuration cost of the area, ρ is the discount rate, which is the opportunity cost or interest rate of capital, r is the service life of the configured energy storage equipment, E int is the energy storage configuration capacity of the area, P int is the energy storage configuration power of the area, and c ess is the cost coefficient corresponding to the energy storage configuration of the area.

[0098] Step 2.1.2: Calculation of flexible resource scheduling cost

[0099] ​The artificial cost and the corresponding loss of electricity utility generated by the virtual capacity expansion based on flexible resources are represented by the resource scheduling cost. Taking two typical flexible resources, electric vehicles and variable frequency air conditioners, as examples, the flexible resource scheduling cost is calculated according to the power of the flexible resources after scheduling according to the virtual capacity expansion scheme.

[0100]

[0101] wherein C DR represents the flexible resource scheduling cost, is the electric vehicle load of the transformer area i after scheduling, is the electric vehicle load of the transformer area i before scheduling, is the total electricity consumption of the electric vehicle of the transformer area i, is the air conditioner load of the transformer area i after scheduling, is the air conditioner load of the transformer area i before scheduling, is the total electricity consumption of the air conditioner of the transformer area i, and a and b are the corresponding cost coefficients.

[0102] Step 2.1.3: Calculate the network loss cost:

[0103] The virtual capacity expansion will change the power flow distribution of the entire distribution network, which may cause an increase in network loss and changes in network loss cost.

[0104] Step 2.1.3: Calculate the network loss cost:

[0105] The virtual capacity expansion will change the power flow distribution of the entire distribution network, which may cause an increase in network loss and changes in network loss cost. The network loss cost is calculated according to the network loss of the distribution network corresponding to the virtual capacity expansion scheme.

[0106] The calculation formula is:

[0107] C φ =(φ d -φ0)×C loss

[0108] φ=W s -W r

[0109] wherein C φ is the network loss cost, φ d is the network loss of the distribution network after implementing the virtual capacity expansion, φ0 is the network loss of the distribution network before implementing the virtual capacity expansion, C loss is the unit electricity consumption cost, W r is the total electricity consumption of the regional distribution network actually used, and W s is the total electricity purchase of the regional distribution network from the superior main network.

[0110] Step 2.1.4: Calculate the system flexibility benefit:

[0111] Compared with traditional capacity expansion, virtual capacity expansion brings certain flexibility benefits to the system by configuring energy storage and dispatching flexible resources. The system operation flexibility of the system containing energy storage is defined as the ratio of the maximum adjustable capacity of power supply capacity, energy storage capacity and tie-line to the maximum load of the whole network. The flexibility benefits are calculated according to the system operation flexibility corresponding to the virtual capacity expansion scheme. The calculation formula is:

[0112] E K = (K d -K0) × E flex

[0113]

[0114] wherein E K is the flexibility benefits brought by implementing virtual capacity expansion to the system, K d is the system flexibility after implementing virtual capacity expansion, K0 is the system flexibility before implementing virtual capacity expansion, K is the system flexibility, E flex is the benefits brought by unit electric quantity adjustable load, S p is the flexible regulation power supply capacity, S S is the energy storage capacity, S W is the maximum adjustable capacity of tie-line, and P L,max is the maximum load of the whole network.

[0115] Step 2.1.5 calculates the benefits of delaying distribution transformer investment reconstruction:

[0116] Compared with traditional transformer capacity expansion, virtual capacity expansion does not need to expand and reform the existing stock distribution transformer, which can greatly delay the investment and reconstruction of distribution equipment and reduce the investment and operation and maintenance costs. The benefits of delaying distribution transformer investment reconstruction are calculated according to the virtual capacity expansion corresponding to the virtual capacity expansion scheme.

[0117] The calculation formula of the benefits of delaying distribution transformer investment reconstruction is:

[0118]

[0119] wherein C CAP is the benefits of delaying distribution transformer investment reconstruction, ΔS i is the virtual capacity expansion of the substation i, and c cap is the corresponding benefit coefficient.

[0120] Step 2.2 calculates the environmental and low-carbon indicators:

[0121] Step 2.2.1 evaluates new energy consumption:

[0122] The load time sequence of the distribution network station area can be changed by matching and scheduling flexible resources, which can promote new energy consumption while virtually increasing capacity. The present application selects three indexes of curtailment rate, reduction rate and penetration rate to evaluate the new energy consumption situation, and the energy consumption situation indexes include curtailment rate, reduction rate and penetration rate, and the new energy consumption situation indexes are calculated according to the new energy generation and consumption power corresponding to the virtual capacity increasing scheme. The calculation formula is:

[0123]

[0124] Wherein, α is the curtailment rate, ζ is the reduction rate, δ is the penetration rate, T is the evaluation period, P tot (t) is the regional distribution network new energy t period power generation; P use (t) is the new energy power used by each user in the distribution network station area; W RF , W R are the ideal and actual power generation of the regional distribution network new energy respectively; S RES is the installed capacity of the regional distribution network new energy, and P L,m is the total load peak power of the regional distribution network.

[0125] Step 2.2.2 evaluates the carbon emission situation:

[0126] After virtual capacity increasing, the power purchase amount of the regional distribution network from the upper main network will be reduced to a certain extent, and then carbon emission reduction is realized. The carbon emission reduction benefit is calibrated according to the corresponding carbon trading price of carbon emission, and the carbon emission reduction benefit is calculated according to the power purchase reduction amount corresponding to the virtual capacity increasing scheme. The calculation formula is as follows:

[0127] ENI=P b ×C2×ρ

[0128] In the formula, ENI represents the carbon emission reduction benefit of the distribution network side, P b is the power purchase amount of the regional distribution network from the upper main network after virtual capacity increasing, C2 is the emission coefficient, and ρ is the carbon trading price.

[0129] At the same time, the annual carbon dioxide emission reduction amount is used to represent the influence on the environment. The carbon dioxide emission reduction amount is calculated according to the fossil energy saving amount on the power generation side corresponding to the virtual capacity increasing scheme.

[0130] F CO2 =F×E sc

[0131] In the formula, Fco2 represents the carbon dioxide emission reduction amount on the power generation side, E sc is the carbon emission amount generated by each ton of standard coal power generation; and F is the annual fossil energy saving amount converted into standard coal.

[0132] Step 2.3 calculates the social index:

[0133] Step 2.3.1 Evaluate the reliability of the system after virtual capacity expansion:

[0134] The system reliability is from the system outage frequency f SIF , system outage duration S SID , power supply reliability A SSA These three dimensions are considered, and the system reliability index is calculated according to the load point failure rate and the load point annual outage time corresponding to the virtual capacity expansion scheme. Respectively:

[0135]

[0136] In the formula, N i is the number of users of the i-th load point in the transformer area, λ is the load point failure rate; M is the load point annual outage time; Y is the length of 1 year, Y = 8766h.

[0137] Step 2.3.2 Evaluate the social welfare brought by virtual capacity expansion:

[0138] The virtual capacity expansion project of the distribution network transformer area has certain contribution to promoting employment in the construction stage and operation and maintenance stage. Here we use the increase in the number of employed persons as an evaluation index of social welfare benefits, and calculate the increase in the number of employed persons according to the total investment amount and the proportion of labor cost corresponding to the virtual capacity expansion scheme.

[0139]

[0140] In the formula, q represents the increase in the number of employed persons, I aggregate is the total investment amount, r salary is the proportion of labor cost to the total investment, and p salary is the average wage level of promoting employment.

[0141] Traditional distribution network transformer expansion and reconstruction projects are limited to power grid construction related industries, while virtual capacity expansion also involves flexible resource regulation, energy storage construction and operation, etc., which can effectively drive the development of electrical equipment and device manufacturing industry, information network, energy saving and environmental protection, etc. High-tech industries and emerging industries. This part calculates the benefits of promoting industrial development of distribution network virtual capacity expansion combined with economic input-output. According to the investment in economy of virtual capacity expansion scheme, the benefits of promoting the development of other industries are calculated.

[0142] ECO = I y × a sum × b value

[0143] In the formula, ECO represents the benefits of promoting the development of other industries, I ya is the corresponding investment of the economic sector in the virtual capacity expansion project sum b is the added value of other industries corresponding to the unit investment value C is the input-output added value coefficient.

[0144] Step 3 determines the weight of the total factor index system:

[0145] After obtaining the values of each index in the index system corresponding to a certain scheme, the weights of each index need to be calculated to calculate the overall benefit change brought by the scheme. This patent combines subjective weighting and objective weighting methods, and uses a combination of subjective and objective weighting methods to calculate the benefit value corresponding to a virtual capacity expansion scheme.

[0146] Step 3.1 calculates the subjective weight:

[0147] The AHP method is to compare the different attention levels of each index of the unit sample according to the personal knowledge structure and experience preference of the expert, and then determine the weight. This method embodies the basic characteristics of decision-making thinking, such as decomposition, judgment and synthesis, etc. The specific steps are as follows:

[0148] Step 3.1.1 obtains the AHP scale matrix of each level:

[0149] The expert compares the importance of each level of two indicators according to the knowledge and experience reference table 1, and obtains the judgment matrix A=(a ij ) n×n , n represents the number of element indexes, as shown in the following formula:

[0150]

[0151] Table 1 AHP scale significance

[0152]

[0153] Step 3.1.2 calculates the weight of each level according to the scale matrix:

[0154] Calculate the nth power of the product of each row of the scale matrix to obtain an n-dimensional vector

[0155]

[0156] Standardize the vector to obtain the weight

[0157]

[0158] Step 3.1.3 consistency check:

[0159] First, calculate the maximum eigenvalue λ max ,

[0160]

[0161] Further calculate the consistency index CI:

[0162]

[0163] Look up the random consistency index RI, and calculate the judgment value:

[0164]

[0165] Table 2 Random consistency test table

[0166] 1 2 3 4 5 6 7 8 9 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0167] If CR < 0.1, the consistency test passes, and the weight calculation is effective. If the consistency test does not pass, it means that the AHP scale matrix is not reasonable, and needs to be re-established by experts.

[0168] Step 3.1.4 Calculate the subjective weight corresponding to the index:

[0169] Multiply the weights of each level corresponding to the index to get the subjective weight of the index

[0170]

[0171] Where w kj is the subjective weight of the secondary index j corresponding to the kth layer.

[0172] Step 3.2 Calculate the objective weight:

[0173] Entropy weight method (EWM) is used to determine the objective weight of each index by calculating the size of the index information entropy. Information entropy reflects the degree of uncertainty of the index. The greater the uncertainty, the greater the entropy value, indicating that the index has less information and lower weight. On the contrary, the smaller the uncertainty, the smaller the entropy value, indicating that the index provides more information and higher weight.

[0174] Step 3.2.1 Standardization of original decision matrix:

[0175] Suppose there are m evaluation objects m virtual capacity expansion schemes and n evaluation indexes, and the original decision matrix is X=(x ij ) m×n , the original decision matrix is normalized to get the standardized matrix Y=(y ij ) n×n .

[0176] For positive indicators, the formula is as follows:

[0177]

[0178] For negative indicators, the formula is as follows:

[0179]

[0180] Among them, positive indicators indicate that the larger the indicator value, the higher the benefit value of the plan; negative indicators indicate that the smaller the indicator value, the higher the benefit value of the plan.

[0181] Step 3.2.2 Calculate the characteristic weight of secondary indicator j:

[0182] The characteristic weight p of the secondary indicator j is calculated using a standardized matrix. ij The formula is:

[0183]

[0184] Step 3.2.3 Calculate the entropy values ​​of each indicator:

[0185] Through characteristic proportion p ij Calculate the entropy value E of each indicator. j The formula is:

[0186]

[0187] Where, when p ij When p = 0, ij ln(p ij ) = 0,

[0188] Step 3.2.4 Calculate the objective weights based on the entropy values ​​of each indicator:

[0189] Calculate the objective weight h based on the entropy values ​​of each indicator. j ,

[0190]

[0191] Step 3.3 Calculate the combined subjective and objective weights:

[0192] Since the entropy method determines weights entirely based on objective data without considering expert opinions, it may result in weights that are drastically different from the actual importance of the indicators. To compensate for its shortcomings in comprehensive evaluation, the entropy weights are linearly adjusted by multiplying the AHP weights by a relaxation factor α less than 1. This adapts to the needs of evaluation models that consider the degree of adjustment of objective weights by different subjective weights. The resulting combined weights take into account both objective data and expert experience preferences, and are therefore scientific and reasonable.

[0193] Subjective weights w determined by the AHP method j Multiply by the relaxation factor η, and apply the objective weight h determined by the entropy weight method. jThe combination weight is obtained by correction:

[0194]

[0195] wherein 0≤η≤1, wherein W j is the combination weight of the secondary index j.

[0196] Step 4: Calculate and compare the benefit values of different virtual capacity expansion schemes:

[0197] According to the normalized value and the weight value of each index, the benefit value of each virtual capacity expansion scheme is calculated, and the benefit value of each scheme is calculated, and the calculation formula is:

[0198]

[0199] wherein R i is the benefit value of scheme i, p ij is the characteristic proportion of the secondary index j of scheme i

[0200] Example 2:

[0201] The part not mentioned in this embodiment is the same as that in Example 1.

[0202] After assuming the score matrix and the unit value index set, the example analysis is performed:

[0203] Primary index:

[0204] Economicity (C1), environmental and low-carbonity (C2), and sociality (C3)

[0205] Secondary index:

[0206] Economicity (C1) includes: distribution and storage cost (C11), resource scheduling cost (C12), network loss cost (C13), system flexibility benefit (C14), and benefit of delaying distribution transformer investment reconstruction (C15)

[0207] Environmental and low-carbonity (C2) includes: new energy consumption (C21), and carbon emission (C22)

[0208] Sociality (C3) includes: system reliability (C31), and social welfare index (C32)

[0209] Comparison scheme:

[0210] Scheme A: flexible adjustable load ratio is 0%

[0211] Scheme B: flexible adjustable load ratio is 20%

[0212] Scheme C: flexible adjustable load ratio is 40%

[0213] Primary index judgment matrix

[0214]

[0215] Secondary index judgment matrix:

[0216] Economic (C1) judgment matrix

[0217]

[0218]

[0219] Environment and low carbon (C2) judgment matrix

[0220] Carbon emission reduction (C21) Energy utilization efficiency (C22) New energy consumption (C21) 1 4 Carbon emission (C22) 1 / 4 1

[0221] Social (C3) judgment matrix

[0222] System reliability (C31) Social welfare index (C32) System reliability (C31) 1 1 / 2 Social welfare index (C32) 2 1

[0223] The eigenvalue method is used to calculate the weight vector of each judgment matrix.

[0224] Primary index judgment matrix weight vector

[0225] Index Weight Economic efficiency (C1) 0.641 Environment and low carbon (C2) 0.261 Sociality (C3) 0.098

[0226] Secondary index judgment matrix weight vector:

[0227] Economic (C1) judgment matrix weight vector

[0228]

[0229] Environment and low carbon (C2) judgment matrix weight vector

[0230] Index Weight New energy consumption (C21) 0.800 Carbon emission (C22) 0.200

[0231] Social (C3) judgment matrix weight vector

[0232] Index Weight System energy supply reliability (C31) 0.667 Social welfare index (C32) 0.333

[0233] Consistency check of primary index judgment matrix

[0234] Calculate the maximum eigenvalue λ max :

[0235] λ max ≈3.027 Consistency index CI:

[0236]

[0237] Random consistency index RI (0.58 when n=3):

[0238]

[0239] Consistency check passed because CR < 0.1.

[0240] Calculate the comprehensive subjective weight vector:

[0241] The comprehensive weight vector is the weighted product of the weights of each layer:

[0242] Economic weight:

[0243]

[0244] Environmental and low-carbon weight:

[0245]

[0246] Social weight:

[0247]

[0248] The final weight vector is obtained by merging:

[0249]

[0250] Calculate the objective weight

[0251] Original data matrix

[0252] Index Scheme A Scheme B Scheme C C11 0.8 0.9 0.6 C12 0 0.4 0.8 C13 0.6 0.8 0.4 C14 0.9 0.9 0.7 C15 0.4 0.6 0.6 C21 0.6 0.8 0.9 C22 0.9 0.8 0.6 C31 0.9 0.7 0.7 C32 0.6 0.8 0.8

[0253] Data normalization

[0254] Use the range normalization method to normalize the data. For positive indicators, the formula is as follows:

[0255]

[0256] For negative indicators, the formula is as follows:

[0257]

[0258] Data matrix:

[0259] Index Scheme A Scheme B Scheme C C11 0.333333 0 1 C12 1 0.5 0 C13 0.5 0 1 C14 1 1 0 C15 0 1 1 C21 0 0.666667 1 C22 0 0.333333 1 C31 1 0 0 C32 0 1 1

[0260] Calculate the entropy value to calculate the characteristic weight p of the normalized matrix ij :

[0261]

[0262] Calculate the entropy value E of each indicator j , the formula is:

[0263]

[0264] Index Scheme A Scheme B Scheme C Entropy value C11 0.25 0 0.75 0.256 C12 0.666667 0.333333 0 0.290 C13 0.333333 0 0.666667 0.290 C14 0.5 0.5 0 0.315 C15 0 0.5 0.5 0.315 C21 0 0.4 0.6 0.306 C22 0 0.25 0.75 0.256 C31 1 0 0 0 C32 0 0.5 0.5 0.315

[0265] Objective weight is calculated as follows:

[0266] Weight formula:

[0267]

[0268] The weight is calculated as follows:

[0269] Index Entropy value Weight C11 0.256 0.111762 C12 0.290 0.106655 C13 0.290 0.106655 C14 0.315 0.102899 C15 0.315 0.102899 C21 0.306 0.104251 C22 0.256 0.111762 C31 0 0.150218 C32 0.315 0.102899

[0270] Combined weight

[0271] Assuming the relaxation factor is 0.4, the combined weight is obtained:

[0272]

[0273] The comprehensive score of the scheme is calculated by using the combined weight:

[0274]

[0275]

[0276] The scheme with the highest comprehensive score is scheme B, that is, the flexible adjustable load accounts for 20%.

[0277] Therefore, the benefit evaluation method proposed by the application can better evaluate the effect of different capacity increasing schemes in different transformer areas.

[0278] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application or the parts of the prior art that essentially contribute or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the various embodiments of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0279] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A full-factor evaluation method for power distribution network area virtual capacity expansion, characterized in that, The method comprises the following steps: Obtaining a plurality of virtual capacity expansion schemes for power distribution network areas; Calculating the total factor indexes of each scheme according to the data of each scheme; The total factor indexes comprise the following primary indexes: economic indexes, environmental and low-carbon indexes, and social indexes; The economic indexes comprise the following secondary indexes: transformer area energy storage configuration cost, flexible resource scheduling cost, network loss cost, system flexibility benefit, and benefit of delaying power distribution transformer investment and reconstruction, the environmental and low-carbon indexes comprise the following secondary indexes: new energy consumption index, carbon emission situation, the new energy consumption index comprises power curtailment rate, reduction rate and penetration rate, the carbon emission situation comprises carbon emission reduction benefit and carbon dioxide emission reduction amount, and the social indexes comprise the following secondary indexes: system reliability index and social welfare index, the system reliability index comprises system outage frequency, system outage duration and energy supply reliability, and the social welfare index comprises the benefit of increasing the number of employed persons and promoting the development of other industries; After obtaining the total factor indexes of each scheme, the subjective weight of each secondary index is calculated by AHP method, the objective weight of each secondary index is calculated by entropy weight method, and the subjective weight and the objective weight are combined by a relaxation factor to obtain the combined weight of each secondary index; The benefit value of each scheme is calculated according to each secondary index of the scheme and the corresponding combined weight; The scheme with the maximum benefit value is selected as the implemented virtual capacity expansion scheme. 2.The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The flexible resource scheduling cost is calculated according to the power after flexible resource scheduling of the virtual capacity expansion scheme, and the calculation formula is: wherein, denotes the flexible resource scheduling cost, is the substation the scheduled electric vehicle load, is the substation the unscheduled electric vehicle load, is the substation the total electric vehicle load, is the substation the scheduled air conditioner load, is the substation the unscheduled air conditioner load, is the substation the total air conditioner load, , are the corresponding cost coefficients, respectively, denotes the time instant; The network loss cost is calculated according to the network loss of the power distribution network corresponding to the virtual capacity expansion scheme, and the calculation formula is: wherein, is the network loss cost, is the network loss of the distribution network after implementing virtual capacity expansion, is the network loss of the distribution network before implementing virtual capacity expansion, is the unit power loss cost, is the total power consumption of the regional distribution network, is the total power purchase of the regional distribution network from the superior main network. The system flexibility benefit is calculated according to the system operation flexibility corresponding to the virtual capacity expansion scheme, and the calculation formula is: wherein, the flexibility benefit brought by implementing virtual capacity expansion for the system, the flexibility of the system after implementing virtual capacity expansion, the flexibility of the system before implementing virtual capacity expansion, the flexibility of the system, the benefit brought by the unit electricity adjustable load, the flexibly adjustable capacity of the power source, the energy storage capacity, the maximum adjustable capacity of the tie line, the maximum load of the whole network; The benefit of delaying power distribution transformer investment and reconstruction is calculated according to the virtual capacity corresponding to the virtual capacity expansion scheme, and the calculation formula is: wherein, To delay the investment benefit of power distribution transformer reconstruction, To the transformer area The virtual capacity, The corresponding benefit coefficient, The discount rate is the opportunity cost of capital or interest rate, which represents the growth rate of the value of funds over time, The configured energy storage device service life. 3.The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The new energy consumption index comprises power curtailment rate, reduction rate and penetration rate, and the calculation formula is: wherein, is the reduction rate, is the permeation rate, , are the ideal and actual power generation of the regional distribution network new energy, respectively; is the installed capacity of the regional distribution network new energy, is the total peak power of the regional distribution network load.

4. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, characterized in that, The carbon emission situation is evaluated by the carbon emission reduction benefit of the power distribution network side and the carbon dioxide emission reduction amount of the power generation side, and the calculation formula is: ENI is the carbon emission reduction benefit of the distribution network side, is the electricity purchase amount of the regional distribution network after virtual capacity expansion reduced from the upper master network, is the emission coefficient, is the carbon trading price, is the carbon dioxide emission reduction amount of the power generation side, is the carbon emission amount generated per ton of standard coal, is the annual fossil energy saving amount converted into standard coal.

5. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The system reliability index comprises system outage frequency and system outage duration, and the calculation formula is: wherein, is the frequency of system outages, is the duration of system outages, is the number of users in the th load point in the zone, is the failure rate of the load point; is the annual outage time of the load point; The social welfare index comprises the benefit of increasing the number of employed persons and promoting the development of other industries, and the calculation formula is: wherein, represents the number of jobs created, is the total amount of investment, is the proportion of labor costs to the total amount of investment, is the average wage level for promoting employment, represents the benefit of promoting the development of other industries, is the corresponding investment amount of the economic sector in the virtual capacity expansion project, is the added value of other industries corresponding to the unit investment amount, is the input-output added value coefficient.

6. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The subjective weight of each secondary index is calculated by AHP method, comprising the following steps: According to the prior knowledge and experience reference, the importance of each pair of indexes in each level is compared and judged to obtain the primary index judgment matrix and the secondary index judgment matrix of each scheme, and the weight vector of each judgment matrix is calculated by using the eigenvalue method, and the calculation formula is: Wherein, the judgment matrix is: a number of indicators for each level indicator, an indicator a weight vector value, Consistency test is performed according to the primary index judgment matrix; If the test passes, the subjective weight of the corresponding index is calculated, the weights of each level corresponding to the index are multiplied to obtain the subjective weight of the index, and the formula is: wherein, is the weight corresponding to the secondary index j at the kth layer, is the subjective weight of the secondary index j.

7. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 6, characterized in that, The consistency test is performed according to the primary index judgment matrix, comprising the following steps: The maximum eigenvalue of the first-level index judgment matrix is calculated The calculation formula is: By the largest eigenvalue A determination value CR is calculated by the following equation. wherein is a consistency index, is a random consistency index; If CR < 0.1, the consistency test is passed, the weight calculation is valid, if If CR < 0.1, the consistency test is passed, the weight calculation is valid, if 8.The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The objective weight of each secondary index is calculated by entropy weight method, comprising the following steps: The original data matrix is obtained through the full-factor index of each scheme , m is the number of virtual capacity expansion schemes, and n is the number of secondary indexes of the schemes The original data matrix is normalized by using the range normalization method to obtain a standardized matrix For the positive indicators, the formula is as follows: For negative indexes, the formula is as follows: Among them, the positive index indicates that the larger the index value, the higher the scheme benefit value, and the negative index indicates that the smaller the index value, the higher the scheme benefit value; The characteristic proportion of the index j is calculated by a standardized matrix The formula is: By characteristic specific gravity The entropy value of each index is calculated The formula is: wherein when , , ; According to the index entropy value, the objective weight is calculated , 。 9. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The combining of the subjective weight and the objective weight by the relaxation factor to obtain the combined weight of each secondary index comprises the following steps: Subjective weight determined by AHP method Multiply by relaxation factor Objective weight determined by entropy weight method Amend to get combined weight: wherein wherein is the combined weight of the secondary indicator j.

10. The full-factorial evaluation method for power distribution network area virtual capacity expansion according to claim 1, wherein, The benefit value of each scheme is calculated, and the calculation formula is: wherein, is the benefit value of the scheme is the secondary indicator of the scheme is the characteristic proportion of the secondary indicator j is the combined weight of the secondary indicators j is the combined weight of the secondary indicators j​

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