Total factor evaluation method for virtual capacity increase of power distribution network station area

Abstract

The invention relates to a total factor evaluation method for virtual capacity increase of a distribution network area, and the method comprises the steps: calculating the total factor index of each scheme according to the data of each scheme, calculating the subjective weight of each second-level index through an AHP method after obtaining the total factor index of each scheme, calculating the objective weight of each second-level index through an entropy weight method, and obtaining the total factor index of each scheme; and combining the subjective weight with the objective weight through a relaxation factor to obtain a combined weight of each secondary index, calculating a benefit value of each scheme through each secondary index of the scheme and the corresponding combined weight, and selecting the scheme with the maximum benefit value as an implemented virtual capacity increasing scheme. According to the method, the total-factor benefit evaluation method is researched by improving the reliability and flexibility of the capacity of the power distribution network, and quantitative evaluation of the benefit brought by improvement of the residual capacity of the power distribution network through large-scale access and regulation of distributed resources is realized and serves as an evaluation basis of an optimized operation target of the power distribution network; and formulating a virtual capacity increasing scheme of the distribution network area is guided.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network transformation, and in particular relates to a full-factor evaluation method for virtual capacity increase of distribution network substations. Background Art

[0002] The supply form and load structure of receiving power grids in Shanghai and other places are facing major changes. These include: 1) The rapid advancement of electric energy substitution, the disorderly access of loads such as electric vehicles, battery vehicles, and all-electric kitchens, has led to an increasing shortage of capacity resources in the distribution network substation area, and the remaining capacity needs to be improved; 2) Distributed new energy is connected to the grid, and the intermittent and uncertain nature of new energy also leads to changes in net load characteristics, changes in distribution network flow distribution, and insufficient remaining capacity for access to new energy; 3) Changes in load characteristics in the post-epidemic era, online and home-based life and work, have led to changes in the timing characteristics of substation load peaks. However, the traditional solution of relying solely on transformer capacity expansion has not only increased distribution network investment, but also further reduced the operating efficiency of substation transformers, which is not conducive to solving short-term heavy load or overload problems. Therefore, it is necessary to dynamically adjust the flexible load of the distribution substation area through operation and scheduling, and control the load peaks of each line and substation of the distribution network in real time to achieve the same dynamic virtual capacity expansion effect as capacity expansion.

[0003] In terms of the formulation of relevant regulations and the implementation of pilot projects for the participation of flexible resources in the optimization of distribution network capacity, the state has successively put forward a series of standards and management methods, including: "Power Demand Side Management Methods", "Orderly Power Management Methods", "Power Demand Response General Technical Specifications", "Smart Grid User Interface Technical Guidelines", etc. 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 implementation, in 2015, China's first urban pilot project was carried out 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 is conducive to energy conservation and emission reduction in the power system. 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 "Research and Demonstration of Key Technologies for Smart Power Consumption and Dynamic Demand Response for Power Users" was launched in Nanjing. In February 2016, State Grid Corporation of China established the first pilot automatic demand response project in the system, Sino-Singapore Eco-City, based on the needs of smart grid construction and promotion. The project takes "innovation, interactivity, exemplary and leading" as its construction concept, and carries out demand response demonstrations of innovative technologies such as smart home, smart city integrated energy information services, and multi-level energy coordination control. In 2020, the construction of smart communities will be promoted in the Zhuankou area of ​​Wuhan, and typical applications will be transformed into universal applications for residential communities. More application scenarios will be explored. On the basis of mining the value of residential customer energy consumption data, the adjustable function of residential loads will be focused on, and further expanded to new control resources such as photovoltaic, energy storage, and electric vehicle charging piles in residential communities, and the construction of micro virtual power plant projects on the resident side will be carried out. However, how to comprehensively and quantitatively evaluate the benefits of flexible resources participating in the optimal configuration of distribution network capacity to guide the formulation of implementation plans remains to be studied and resolved.

[0004] In the study on cost-benefit analysis and evaluation of power technology and economics published in Electrical Technology and Economics, Qi Huanling discussed the cost-benefit analysis and evaluation methods of power technology and economics by collecting and analyzing relevant data, and applied them to actual cases. The research results show that cost-benefit analysis is an important tool for measuring the feasibility and economic benefits of power technology, which can provide a scientific basis for decision makers and promote the development and application of power technology. In the benefit evaluation of the improvement of flexibility resources in new power systems published in Progress in Climate Change Research, Zhang Kai, Yuan Jiahai, and Ding Baodi mentioned that based on a multi-time scale timing operation simulation model, the cost structure of the improvement of system flexibility was refined, and the promotion effect of a single flexibility resource on the consumption of new energy and the difference in system operation costs at different stages of the construction of a new power system were quantitatively evaluated. The study on economic benefit evaluation of power generation, transmission and consumption system published by Mu Juntong, Li Zhishuo and Gu Chuanjie in Modern Industrial Economy and Informatization aims to evaluate the carbon neutral economic benefits of power generation, transmission and consumption system and provide reasonable policy recommendations. It conducts a detailed analysis of the carbon emissions of power generation, transmission and consumption system, including emission sources, trends and influencing factors, and formulates a carbon reduction strategy based on the carbon neutrality goal, and simulates the impact of different carbon reduction measures on economic benefits. In the construction of a comprehensive evaluation model for the economic benefits of AC and DC distribution technology of flexible substations published in Modern Industrial Economy and Informatization, Bian Xiang uses economic means to conduct a comprehensive evaluation of the economic benefits of AC and DC distribution technology of flexible substations, and constructs an evaluation model, which lays a good foundation for the application and future development of AC and DC distribution technology of flexible substations, and helps enterprises maximize economic benefits at the two levels of investment decision-making and technological innovation. Luo Tianlu, Li Xiaowei, and Sun Huijie published the article "Construction and Research of Index System of Distribution Network Precision Investment Strategy Based on AHP" in Rural Electrification Literature. Taking the precise investment strategy of a certain power company for distribution network as the research object, the article constructed the evaluation index system before and after investment, and adopted the AHP to evaluate the investment project, providing a basis for power companies to achieve precise investment, so as to help power companies make investment decisions quickly. Zhang Kun, Zhao Qianyu, and Wang Shouxiang published the article "Construction and Calculation Method of Evaluation Index Comprehensively Reflecting the Acceptance Capacity of Distributed Photovoltaic Network in Power Supply and Use". Considering the impact of large-scale distributed photovoltaic access on the distribution network, the first-level evaluation index of the acceptance capacity of distributed photovoltaic network in the distribution network was constructed from three aspects: power quality, grid management level, and economy. Then, nine second-level indicators such as voltage qualification rate, distributed photovoltaic source-load matching, abandoned light rate, power balance, and electricity balance were constructed, and the matrix-hierarchy analysis method was proposed. The distributed photovoltaic acceptance capacity of the distribution network was evaluated by combining the matrix-hierarchy analysis method with the superior and inferior solution distance method.Liu Dunnan, Li Ruiqing, and Chen Xueqing published a paper on power market regulation indicators and market evaluation system in Automation of Electric Power Systems, which systematically introduced the definitions, meanings, and application methods of five types of market regulation indicators, including market supply and demand, market structure, bidding strategy, supplier status, and transaction results. On this basis, they proposed a power market evaluation system, and preliminarily gave the evaluation theme, evaluation object, evaluation indicators, indicator benchmark values, and scoring methods. The above studies have proposed many effective and feasible methods for evaluating the benefits of power systems, but the full-factor evaluation model for virtual capacity expansion of distribution network areas still needs to be established and improved. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a full-factor evaluation method for virtual capacity expansion of distribution network substations.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a full-factor evaluation method for virtual capacity increase of distribution network substations, comprising the following steps:

[0008] Obtain multiple virtual capacity expansion solutions for distribution network substations;

[0009] Calculate the full factor index of each scheme based on the data of each scheme;

[0010] The total factor indicators include the following primary indicators: economic indicators, environmental and low-carbon indicators, and social indicators;

[0011] The economic indicators include the following secondary indicators: energy storage configuration cost of the substation, flexible resource scheduling cost, network loss cost, system flexibility benefit and benefit of delaying investment and transformation of distribution transformers; the environmental and low-carbon indicators include the following secondary indicators: new energy consumption situation indicators and carbon emissions; the energy consumption situation indicators include power abandonment rate, reduction rate and penetration rate; the carbon emissions include carbon emission reduction benefits and carbon dioxide emission reduction; the social indicators include the following secondary indicators: system reliability indicators and social welfare indicators; the system reliability indicators include system outage frequency, system outage duration and energy supply reliability; the social welfare indicators include the benefits of increasing employment and promoting the development of other industries;

[0012] After obtaining the full factor indicators of each scheme, the subjective weight of each secondary indicator is calculated by the AHP method, the objective weight of each secondary indicator is calculated by the entropy weight method, and the subjective weight is combined with the objective weight through the relaxation factor to obtain the combined weight of each secondary indicator;

[0013] Calculate the benefit value of each plan through each secondary indicator of the plan and its corresponding combination weight;

[0014] The solution with the largest benefit value is selected as the virtual capacity expansion solution to be implemented.

[0015] Furthermore, the energy storage configuration cost of the substation is calculated based on the configuration capacity and power of the energy storage system, and the calculation formula is:

[0016]

[0017] Among them, C ESS is the energy storage configuration cost of the substation, ρ is the discount rate, is the opportunity cost or interest rate of capital, and represents the value growth rate of funds over time, r is the service life of the configured energy storage equipment, and E int is the energy storage configuration capacity of the substation, P int The power of the energy storage configuration in the substation, c ess The corresponding cost coefficient for the energy storage configuration in the substation;

[0018] The flexible resource scheduling cost is calculated based on the power after the flexible resource scheduling of the virtual capacity increase solution, and the calculation formula is:

[0019]

[0020] Among them, C DR represents the flexible resource scheduling cost, is the electric vehicle load after dispatching in area i, is the electric vehicle load before dispatching in area i, is the total electricity consumption of electric vehicles in area i, is the air conditioning load after dispatching in area i, is the air conditioning load of area i before dispatch, is the total power consumption of air conditioners in area i, a and b are the corresponding cost coefficients respectively;

[0021] The network loss cost is calculated based on the distribution network loss corresponding to the virtual capacity increase solution, and the calculation formula is:

[0022] C φ =(φ d -φ0)×C loss

[0023] φ=W s -W r

[0024] Among them, C φ is the network loss cost, φ d is the distribution network loss after the implementation of virtual capacity increase, φ0 is the distribution network loss before the implementation of virtual capacity increase, C loss is the unit power loss cost, W r is the total power actually used by the regional distribution network, W sThe total amount of electricity purchased by the regional distribution network from the upper main grid;

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

[0026] E K =(K d -K0)×E flex

[0027]

[0028] Among them, E K To implement the flexibility benefits that virtual capacity expansion brings 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, and E flex The benefit brought by adjustable load per unit power, S p In order to flexibly adjust the power capacity, S s is the energy storage capacity, S W is the maximum adjustable capacity of the tie line, P L,max The maximum load of the entire network;

[0029] The benefit of delaying the investment and transformation of distribution transformers is calculated based on the virtual capacity increase corresponding to the virtual capacity increase scheme, and the calculation formula is:

[0030]

[0031] Among them, C CAP In order to delay the benefits of distribution transformer investment transformation, ΔS i is the virtual capacity increase of area i, c cap is the corresponding benefit coefficient.

[0032] Furthermore, the energy consumption indicators include power abandonment rate, reduction rate and penetration rate, and the calculation formula is:

[0033]

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

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

[0036] ENI=P b ×C2×ρ

[0037] F CO2 =F×E sc

[0038] Among them, ENI is the carbon emission reduction benefit on the distribution network side, P b is the amount of electricity purchased by the regional distribution network from the upper main grid after the virtual capacity increase, C2 is the emission coefficient, ρ is the carbon trading price, and F CO2 is the reduction of carbon dioxide emissions on the power generation side, E sc is the carbon emission generated by power generation per ton of standard coal, and F is the annual fossil energy savings converted into standard coal.

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

[0040]

[0041] Among them, f SIF is the system outage frequency, S SID is the duration of system outage, A SSA For energy supply reliability, N i is the number of users at the i-th load point in the substation, λ is the load point failure rate; M is the annual outage time of the load point; Y is the duration of 1 year, Y = 8766h;

[0042] The social welfare index includes the benefits of increasing employment and promoting the development of other industries, and the calculation formula is:

[0043]

[0044] ECO=I y ×a sum ×b value

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

[0046] Furthermore, the calculation of the subjective weight of each indicator by the AHP method includes the following steps:

[0047] Based on prior knowledge and experience, the importance of each level of indicators is compared and judged, and the first-level indicator judgment matrix and the second-level indicator judgment matrix of each scheme are obtained. The weight vector of each judgment matrix is ​​calculated using the eigenvalue method. The calculation formula is:

[0048]

[0049] Among them, the judgment matrix is:

[0050]

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

[0052] Conduct consistency check based on the first-level indicator judgment matrix;

[0053] If the test is passed, the subjective weight of the corresponding indicator is calculated, and the weights of each level corresponding to the indicator are multiplied to obtain the subjective weight of a certain indicator. The formula is:

[0054]

[0055] Among them, w kj is the weight corresponding to the secondary index j at the kth level, w j is the subjective weight of the secondary indicator j.

[0056] Furthermore, the consistency check is performed according to the primary indicator judgment matrix, including the following steps:

[0057] Calculate the maximum eigenvalue λ of the first-level indicator judgment matrix nax , the calculation formula is:

[0058]

[0059] By the maximum eigenvalue λ max Calculate the judgment value CR, 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 test passes and the weight calculation is valid. If CR≥0.1, the consistency test fails and the indicator judgment matrix is ​​reformulated.

[0063] Furthermore, the objective weight of each indicator is calculated by the entropy weight method, including the following steps:

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

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

[0066]

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

[0068]

[0069] Among them, the positive indicator means that the larger the indicator value, the higher the scheme benefit value, and the negative indicator means that the smaller the indicator value, the higher the scheme benefit value;

[0070] Calculate the characteristic weight p of index j by normalizing the matrix ij , the formula is:

[0071]

[0072] By characteristic weight p ij Calculate the entropy value E of each indicator j , the formula is:

[0073]

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

[0075] Calculate the objective weight h according to the entropy value of each indicator j ,

[0076]

[0077] Furthermore, the method of combining the subjective weight with the objective weight through the relaxation factor to obtain the combined weight of each indicator includes the following steps:

[0078] The subjective weight w determined by AHP method j Multiply by the relaxation factor η, the objective weight h determined by the entropy weight method j Make corrections and get the combined weights:

[0079]

[0080] Among them, 0≤η≤1, among which W j is the combined weight of the secondary index j.

[0081] Furthermore, the benefit value of each scheme is calculated using the following formula:

[0082]

[0083] Among them, R i is the benefit value of plan i, p ij is the characteristic proportion of secondary indicator j of scheme i.

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

[0085] (1) The present invention focuses on improving the reliability and flexibility of distribution network capacity, studies a full-factor evaluation method for virtual capacity expansion of distribution network substations, and realizes a quantitative evaluation of the benefits of large-scale access and regulation of distributed resources on the improvement of the remaining capacity of the distribution network. This method serves as the evaluation basis for the optimization operation objectives of the distribution network and guides the formulation of virtual capacity expansion plans for distribution network substations.

[0086] (2) In order to evaluate the effect of virtual capacity expansion in the substation area, the present invention proposes a total factor cost and benefit analysis model, which comprehensively considers the three factors of economy, society, environment and low carbon, and evaluates the regulatory benefits of flexible resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 This is a full-factor indicator system diagram of the virtual capacity increase benefit of the present invention. DETAILED DESCRIPTION

[0088] 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.

[0089] Embodiment 1:

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

[0091] In the decision-making process of virtual capacity expansion in distribution network substations, decision-making problems 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 ensure the scientificity and comprehensiveness of decision-making. First, the target layer mainly evaluates the capacity expansion effect based on the capacity expansion demand and load curve of a specific substation. 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 substation through capacity expansion and optimize the stability and reliability of power supply. The criterion layer involves multiple evaluation indicators, which are specifically divided into three primary indicators: economic, environmental and low-carbon, and social. In terms of economy, it is further refined into five secondary indicators: distribution storage cost, resource scheduling cost, network loss cost, system flexibility benefit, and delaying the investment and transformation benefit of distribution transformers; in terms of environment and low-carbon, the secondary indicators include new energy consumption and carbon emissions, which respectively evaluate the power abandonment rate, reduction rate, penetration rate, as well as carbon emission value and annual carbon dioxide emission reduction; in terms of society, the secondary indicators include system reliability indicators and social welfare indicators, which are evaluated by system outage frequency, outage duration and energy supply reliability, as well as the increase in employment and the development of related industries. The virtual capacity expansion plan includes adjusting the flexible resources (electric vehicles, variable frequency air conditioners, etc.) under the substation and configuring energy storage. The specific indicator system is as follows: Figure 1 shown.

[0092] Step 2: Calculation of indicators at each level:

[0093] Step 2.1 Calculate economic indicators:

[0094] Step 2.1.1 Calculate the energy storage configuration cost of the substation:

[0095] The energy storage allocation in the substation is an important means of virtual capacity expansion in the distribution network substation. The configuration cost of the energy storage system is mainly determined by the configuration capacity and power of the energy storage system. The energy storage configuration cost is obtained according to the allocation capacity and power of the virtual capacity expansion plan. The calculation formula for the daily equivalent allocation cost is as follows:

[0096]

[0097] Among them, C ESS is the energy storage configuration cost of the substation, ρ is the discount rate, is the opportunity cost or interest rate of capital, and represents the value growth rate of funds over time, r is the service life of the configured energy storage equipment, and E int is the energy storage configuration capacity of the substation, P int The power of the energy storage configuration in the substation, c ess The cost coefficient corresponding to the energy storage configuration in the substation.

[0098] Step 2.1.2 Calculate the flexible resource scheduling cost:

[0099] The labor cost required for virtual capacity expansion based on flexible resources and the corresponding electricity utility loss are represented by the resource scheduling cost. Here, taking electric vehicles and variable-frequency air conditioners as two typical flexible resources, the flexible resource scheduling cost is calculated according to the power after the flexible resources are scheduled in the virtual capacity expansion plan.

[0100]

[0101] Among them, C DR represents the flexible resource scheduling cost, is the electric vehicle load after dispatching in area i, is the electric vehicle load before dispatching in area i, is the total electricity consumption of electric vehicles in area i, is the air conditioning load after dispatching in area i, is the air conditioning load of area i before dispatch, is the total power consumption of air conditioners in area i, a and b are the corresponding cost coefficients.

[0102] Step 2.1.3 Calculate network loss cost:

[0103] Virtual capacity expansion will change the power flow distribution of the entire distribution network, which may cause an increase in network losses and bring about changes in network loss costs.

[0104] Step 2.1.3 Calculate network loss cost:

[0105] Virtual capacity expansion will change the power flow distribution of the entire distribution network, which may increase network losses and bring about changes in network loss costs. The network loss cost is calculated based on the distribution network loss corresponding to the virtual capacity expansion plan.

[0106] The calculation formula is:

[0107] C φ =(φ d -φ0)×C loss

[0108] φ=W s -W r

[0109] Among them, C φ is the network loss cost, φ d is the distribution network loss after the implementation of virtual capacity increase, φ0 is the distribution network loss before the implementation of virtual capacity increase, C loss is the unit power loss cost, W r is the total power actually used by the regional distribution network, W s It is the total amount of electricity purchased by the regional distribution network from the upper-level main grid.

[0110] Step 2.1.4 Calculate system flexibility benefits:

[0111] Compared with traditional capacity expansion and transformation, virtual capacity expansion brings certain flexibility benefits to the system by configuring energy storage and dispatching flexible resources. The system operation flexibility including energy storage is defined as the ratio of power supply capacity, energy storage capacity and maximum adjustable capacity of the interconnection line to the maximum load of the entire network. The flexibility benefit is calculated based on the system operation flexibility corresponding to the virtual capacity expansion plan. The calculation formula is:

[0112] E K =(K d -K0)×E flex

[0113]

[0114] Among them, E K To implement the flexibility benefits that virtual capacity expansion brings 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, and E flex The benefit brought by adjustable load per unit power, S p In order to flexibly adjust the power capacity, S S is the energy storage capacity, S W is the maximum adjustable capacity of the tie line, P L,max It is the maximum load of the entire network.

[0115] Step 2.1.5 Calculate the benefits of delaying investment in distribution transformer transformation:

[0116] Compared with traditional transformer capacity increase, virtual capacity increase does not require the expansion and reconstruction of existing distribution transformers, which can greatly delay the investment and reconstruction of distribution equipment and reduce investment and operation and maintenance costs. The benefits of delaying the investment and reconstruction of distribution transformers are calculated based on the virtual capacity increase corresponding to the virtual capacity increase scheme.

[0117] The calculation formula for the benefit of delaying investment in distribution transformer transformation is:

[0118]

[0119] Among them, C CAP In order to delay the benefits of distribution transformer investment transformation, ΔS i is the virtual capacity increase of area i, c cap is the corresponding benefit coefficient.

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

[0121] Step 2.2.1 Evaluate the consumption of new energy:

[0122] By allocating storage and dispatching flexible resources, the load sequence of the distribution network can be changed, which can promote the consumption of new energy while increasing capacity virtually. The present invention selects three indicators, namely, power abandonment rate, reduction rate, and penetration rate, to evaluate the consumption of new energy. The energy consumption indicators include power abandonment rate, reduction rate, and penetration rate. The new energy consumption indicators are calculated based on the new energy generation and consumption power corresponding to the virtual capacity increase scheme. The calculation formula is:

[0123]

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

[0125] Step 2.2.2 Evaluate carbon emissions:

[0126] After the virtual capacity increase, the amount of electricity purchased by the regional distribution network from the upper main grid will be reduced to a certain extent, thereby achieving carbon emission reduction. This study selects the carbon trading price corresponding to carbon emissions to calibrate the carbon emission reduction benefits, and calculates the carbon emission reduction benefits based on the reduction in electricity purchases corresponding to the virtual capacity increase scheme. The calculation formula is as follows:

[0127] ENI=P b ×C2×ρ

[0128] In the formula, ENI represents the carbon emission reduction benefit on the distribution network side, P b is the amount of electricity purchased by the regional distribution network from the upper main grid after the virtual capacity increase, C2 is the emission coefficient, and ρ is the carbon trading price.

[0129] At the same time, the impact on the environment is represented by the annual reduction in carbon dioxide emissions. The reduction in carbon dioxide emissions is calculated based on the fossil energy savings on the power generation side corresponding to the virtual capacity expansion plan.

[0130] F CO2 =F×E sc

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

[0132] Step 2.3 Calculate social indicators:

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

[0134] System reliability is determined by the system outage frequency f SIF 、System outage duration S SID 、Energy supply reliabilityA SSA Considering these three dimensions, the system reliability index is calculated according to the load point failure rate and load point annual outage time corresponding to the virtual capacity expansion plan. They are:

[0135]

[0136] Where N i is the number of users at the i-th load point in the substation, λ is the load point failure rate; M is the annual outage time of the load point; Y is the duration of 1 year, Y=8766h.

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

[0138] The virtual capacity expansion project of the distribution network substation has made certain contributions to promoting employment during the construction phase and the operation and maintenance phase. Here we use the increase in the number of jobs as an evaluation indicator of social welfare benefits, and calculate the increase in the number of jobs based on the total investment amount corresponding to the virtual capacity expansion plan and the proportion of labor costs.

[0139]

[0140] In the formula, q represents the increase in employment, I aggregate is the total investment amount, r salary is the proportion of labor costs to total investment, p salary To promote the average wage level of employment.

[0141] Traditional distribution network transformer expansion and transformation projects are limited to industries related to power grid construction, while virtual capacity expansion also involves flexible resource regulation, energy storage construction and operation and maintenance, etc., which can effectively drive the development of high-tech industries and emerging industries such as electrical equipment and device manufacturing, information networks, energy conservation and environmental protection. This section calculates the benefits of virtual capacity expansion of distribution networks in promoting industrial development based on economic input-output conditions. Calculate the benefits of promoting the development of other industries based on the economic investment amount of the virtual capacity expansion plan. Calculate the benefits of promoting the development of other industries based on the economic investment amount of the virtual capacity expansion plan.

[0142] ECO=I y ×a sum ×b value

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

[0144] Step 3: Determine the weight of the full factor indicator system:

[0145] After obtaining the values ​​of each indicator of the indicator system corresponding to a certain solution, it is necessary to calculate the weights corresponding to each indicator to calculate the overall benefit changes brought by the solution. This patent combines subjective weighting and objective weighting methods, and uses a subjective and objective combined weighting method to calculate the benefit value corresponding to a virtual capacity expansion solution.

[0146] Step 3.1 Calculate the subjective weight:

[0147] The AHP method is based on the experts' personal knowledge structure and experience preferences, and they compare the different levels of importance of each indicator of the unit sample, and then determine their weights. This method embodies the basic characteristics of decision-making thinking, namely decomposition, judgment and synthesis. The specific steps are as follows:

[0148] Step 3.1.1 Obtain the AHP scaling matrix at each level:

[0149] Experts refer to Table 1 based on their knowledge and experience, and compare and judge the importance of each level of indicators, and obtain the judgment matrix A = (a ij ) n×n , n represents the number of indicators of each factor, as shown in the following formula:

[0150]

[0151] Table 1 Meaning of AHP scale

[0152]

[0153] Step 3.1.2 Calculate the weights of each level based on the scale matrix:

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

[0155]

[0156] Normalize the vector to get the weight

[0157]

[0158] Step 3.1.3 Consistency check:

[0159] First, calculate the maximum eigenvalue λ of the first-level indicator judgment matrix max ,

[0160]

[0161] Then calculate the consistency index CI:

[0162]

[0163] Look up the table to get 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 valid. If the consistency test fails, it means that the AHP scaling matrix is ​​not formulated reasonably and needs to be reformulated by experts.

[0168] Step 3.1.4 Calculate the subjective weight of the corresponding indicator:

[0169] Multiply the weights of each level corresponding to the indicator to obtain the subjective weight of a certain indicator.

[0170]

[0171] where w kj is the subjective weight corresponding to the secondary indicator j at the kth level.

[0172] Step 3.2 Calculate the objective weight:

[0173] The Entropy Weight Method (EWM) is used to determine the objective weight of each indicator by calculating the size of the indicator information entropy. Information entropy reflects the uncertainty of the indicator. The greater the uncertainty, the greater the entropy value, indicating that the information of the indicator is less and the weight is lower. Conversely, the smaller the uncertainty, the smaller the entropy value, indicating that the amount of information provided by the indicator is greater and the weight is higher.

[0174] Step 3.2.1 Standardization of the original decision matrix:

[0175] Assume that there are m evaluation objects, m virtual capacity expansion schemes, and n evaluation indicators. The original decision matrix is ​​X = (x ij ) m×n , normalize the original decision matrix to obtain the standardized matrix Y = (y ij ) n×n .

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

[0177]

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

[0179]

[0180] Among them, the positive indicator means that the larger the indicator value, the higher the scheme benefit value, and the negative indicator means that the smaller the indicator value, the higher the scheme benefit value;

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

[0182] Calculate the characteristic weight p of the secondary index j by standardizing the matrix ij , the formula is:

[0183]

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

[0185] By characteristic weight p ij Calculate the entropy value E of each indicator j , the formula is:

[0186]

[0187] Among them, when p ij = 0, p ij ln(p ij )=0,

[0188] Step 3.2.4 Calculate the objective weight according to the entropy value of each indicator:

[0189] Calculate the objective weight h according to the entropy value of each indicator j ,

[0190]

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

[0192] Since the entropy method determines weights entirely from objective data without considering expert opinions, the weights of the indicators may be completely different from the actual importance. In order to make up for its shortcomings in comprehensive evaluation, the AHP weights are multiplied by a relaxation factor α that is less than 1, and the entropy weights are linearly adjusted to meet the needs of evaluation models with different subjective weights for the degree of adjustment of objective weights. The resulting combined weights take into account both objective data and expert experience preferences, and are scientific and reasonable.

[0193] The subjective weight w determined by AHP method j Multiply by the relaxation factor η, the objective weight h determined by the entropy weight method jMake corrections and get the combined weights:

[0194]

[0195] Among them, 0≤η≤1, among which W j is the combined weight of the secondary index j.

[0196] Step 4: Calculate and compare the benefits of different virtual capacity expansion solutions:

[0197] The benefit value of each virtual capacity expansion solution is calculated based on the normalized value and weight value of each indicator. The benefit value of each solution is calculated using the following formula:

[0198]

[0199] Among them, R i is the benefit value of plan i, p ij is the characteristic weight of secondary indicator j of scheme i

[0200] Embodiment 2:

[0201] The parts not mentioned in this embodiment are the same as those in Embodiment 1.

[0202] Assuming the scoring matrix and per-unit indicator set, an example analysis is performed:

[0203] First level indicators:

[0204] Economic (C1) Environmental and low-carbon (C2) Social (C3)

[0205] Secondary indicators:

[0206] Economic efficiency (C1) includes: distribution storage cost (C11), resource scheduling cost (C12), network loss cost (C13), system flexibility benefit (C14), and delayed distribution transformer investment and transformation benefit (C15)

[0207] Environment and low carbon (C2) includes: new energy consumption (C21), carbon emissions (C22)

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

[0209] Comparison plan:

[0210] Option A: Flexible adjustable load accounts for 0%

[0211] Option B: Flexible adjustable load accounts for 20%

[0212] Solution C: Flexible adjustable load accounts for 40%

[0213] First-level indicator judgment matrix

[0214]

[0215] Secondary indicator judgment matrix:

[0216] Economic (C1) judgment matrix

[0217]

[0218]

[0219] Environment and Low Carbon (C2) Judgment Matrix

[0220] Reduce carbon emissions (C21) Energy efficiency (C22) New energy consumption (C21) 1 4 Carbon emissions (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 weight vector of each judgment matrix is ​​calculated using the eigenvalue method.

[0224] First-level indicator judgment matrix weight vector

[0225] index Weight Economical (C1) 0.641 Environment and low carbon (C2) 0.261 Social (C3) 0.098

[0226] Secondary indicator 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 emissions (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 test of the first-level indicator judgment matrix

[0234] Calculate the maximum eigenvalue λ max :

[0235] λ max ≈3.027Consistency index CI:

[0236]

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

[0238]

[0239] Because CR<0.1, the consistency test passes.

[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] Merge to get the final weight vector:

[0249]

[0250] Calculating objective weights

[0251] Original data matrix

[0252] index Plan A Plan B Plan 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 Plan A Plan B Plan 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 and calculate the feature weight p of the normalized matrix ij :

[0261]

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

[0263]

[0264] index Plan A Plan B Plan C Entropy 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] Calculate objective weights:

[0266] Weight formula:

[0267]

[0268] The weights are calculated as follows:

[0269] index Entropy 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] Portfolio empowerment

[0271] Assuming the relaxation factor is 0.4, the combined weights are:

[0272]

[0273] Calculate the comprehensive score of the available solutions using the combined weights:

[0274]

[0275]

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

[0277] It can be seen that the benefit evaluation method proposed in the present invention can better evaluate the effects of different capacity expansion plans in different areas.

[0278] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0279] 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 full-factor evaluation method for virtual capacity expansion of distribution network areas, characterized in that: The following steps are involved: Obtain multiple virtual capacity expansion solutions for distribution network substations; Calculate the full factor index of each scheme based on the data of each scheme; The total factor indicators include the following primary indicators: economic indicators, environmental and low-carbon indicators, and social indicators; The economic indicators include the following secondary indicators: energy storage configuration cost of the substation, flexible resource scheduling cost, network loss cost, system flexibility benefit and benefit of delaying investment and transformation of distribution transformers; the environmental and low-carbon indicators include the following secondary indicators: new energy consumption situation indicators and carbon emissions; the energy consumption situation indicators include power abandonment rate, reduction rate and penetration rate; the carbon emissions include carbon emission reduction benefits and carbon dioxide emission reduction; the social indicators include the following secondary indicators: system reliability indicators and social welfare indicators; the system reliability indicators include system outage frequency, system outage duration and energy supply reliability; the social welfare indicators include the benefits of increasing employment and promoting the development of other industries; After obtaining the full factor indicators of each scheme, the subjective weight of each secondary indicator is calculated by the AHP method, the objective weight of each secondary indicator is calculated by the entropy weight method, and the subjective weight is combined with the objective weight through the relaxation factor to obtain the combined weight of each secondary indicator; Calculate the benefit value of each plan through each secondary indicator of the plan and its corresponding combination weight; The solution with the largest benefit value is selected as the virtual capacity expansion solution to be implemented.

2. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The energy storage configuration cost of the substation is calculated based on the configuration capacity and power of the energy storage system, and the calculation formula is: Among them, C ESS is the energy storage configuration cost of the substation, ρ is the discount rate, is the opportunity cost or interest rate of capital, and represents the value growth rate of funds over time, r is the service life of the configured energy storage equipment, and E int is the energy storage configuration capacity of the substation, P int The power of the energy storage configuration in the substation, c ess The corresponding cost coefficient for the energy storage configuration in the substation; The flexible resource scheduling cost is calculated based on the power after the flexible resource scheduling of the virtual capacity increase solution, and the calculation formula is: Among them, C DR represents the flexible resource scheduling cost, is the electric vehicle load after dispatching in area i, is the electric vehicle load before dispatching in area i, is the total electricity consumption of electric vehicles in area i, is the air conditioning load after dispatching in area i, is the air conditioning load of area i before dispatch, is the total power consumption of air conditioners in area I, A and b are the corresponding cost coefficients respectively; The network loss cost is calculated based on the distribution network loss corresponding to the virtual capacity increase solution, and the calculation formula is: C φ =(φ d -φ0)×C loss φ=W s -W r Among them, C φ is the network loss cost, φ d is the distribution network loss after the implementation of virtual capacity increase, φ0 is the distribution network loss before the implementation of virtual capacity increase, C loss is the unit power loss cost, W r is the total power actually used by the regional distribution network, W s The total amount of electricity purchased by the regional distribution network from the upper main grid; The system flexibility benefit is calculated based on the system operation flexibility corresponding to the virtual capacity expansion solution, and the calculation formula is: E K =(K d -K0)×E flex Among them, E K To implement the flexibility benefits that virtual capacity expansion brings 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, and E flex The benefit brought by adjustable load per unit power, S p To flexibly adjust the power capacity, S S is the energy storage capacity, S W is the maximum adjustable capacity of the tie line, P L,max The maximum load of the entire network; The benefit of delaying the investment and transformation of distribution transformers is calculated based on the virtual capacity increase corresponding to the virtual capacity increase scheme, and the calculation formula is: Among them, C CAP To delay the benefits of investment transformation of distribution transformers, ΔS i is the virtual capacity increase of area i, c cap is the corresponding benefit coefficient.

3. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The energy consumption indicators include power abandonment rate, reduction rate and penetration rate, and the calculation formula is: Among them, α is the power abandonment rate, ζ is the reduction rate, δ is the penetration rate, T is the evaluation period, P tot (t) is the power generation of renewable energy in the regional distribution network during period t; P use (t) is the new energy power used by each user in the distribution network area; W RF , W R are the ideal and actual power generation of renewable energy in the regional distribution network; S RES is the installed capacity of renewable energy in the regional distribution network, P L,m is the total load peak power of the regional distribution network.

4. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The carbon emission situation is evaluated by the carbon emission reduction benefits on the distribution network side and the reduction in carbon dioxide emissions on the power generation side. The calculation formula is: ENI=P b ×C2×ρ F CO2 =F×E sc Among them, ENI is the carbon emission reduction benefit on the distribution network side, P b is the amount of electricity purchased by the regional distribution network from the upper main grid after the virtual capacity increase, C2 is the emission coefficient, ρ is the carbon trading price, and F CO2 is the reduction of carbon dioxide emissions on the power generation side, E sc is the carbon emission generated by power generation per ton of standard coal, and F is the annual fossil energy savings converted into standard coal.

5. The method for evaluating the virtual capacity increase of distribution network area according to claim 1 is characterized in that: The system reliability index includes system outage frequency, system outage duration and energy supply reliability, and the calculation formula is: Among them, f SIF is the system outage frequency, S SID is the duration of system outage, A SSA For energy supply reliability, N i is the number of users at the i-th load point in the substation, λ is the load point failure rate; M is the annual outage time of the load point; Y is the duration of 1 year, Y = 8766h; The social welfare index includes the benefits of increasing employment and promoting the development of other industries, and the calculation formula is: ECO=I y ×a sum ×b value Among them, q represents the increase in employment, I aggregate is the total investment amount, r salary is the proportion of labor costs to total investment, p salary To promote the average wage level of employment, ECO represents the benefit of promoting the development of other industries, I y is the corresponding investment amount of the economic sector in the virtual capacity expansion project, a sum is the added value of other industries corresponding to the unit investment amount, b value is the input-output value-added coefficient.

6. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The calculation of the subjective weight of each indicator by the AHP method includes the following steps: Based on prior knowledge and experience, the importance of each level of indicators is compared and judged, and the first-level indicator judgment matrix and the second-level indicator judgment matrix of each scheme are obtained. The weight vector of each judgment matrix is ​​calculated using the eigenvalue method. The calculation formula is: Among them, the judgment matrix is: n is the number of indicators at each level, is the weight vector value of index i, Conduct consistency check based on the first-level indicator judgment matrix; If the test is passed, the subjective weight of the corresponding indicator is calculated, and the weights of each level corresponding to the indicator are multiplied to obtain the subjective weight of a certain indicator. The formula is: Among them, w kj is the weight corresponding to the secondary index j at the kth level, w j is the subjective weight of the secondary indicator j.

7. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 6, characterized in that: The consistency check according to the primary indicator judgment matrix includes the following steps: Calculate the maximum eigenvalue λ of the first-level indicator judgment matrix max , the calculation formula is: By the maximum eigenvalue λ max Calculate the judgment value CR, the calculation formula is: Among them, CI is the consistency index and RI is the random consistency index; If CR<0.1, the consistency test passes and the weight calculation is valid. If CR≥0.1, the consistency test fails and the indicator judgment matrix is ​​reformulated.

8. The method for evaluating the virtual capacity increase of distribution network area according to claim 1 is characterized in that: The objective weight of each indicator is calculated by the entropy weight method, including the following steps: The original data matrix X = (x ij ) m×n , m is the number of virtual capacity expansion schemes, n is the number of secondary indicators of the scheme; The original data matrix is ​​normalized using the range normalization method to obtain the standardized matrix Y = (y ij ) m×n , for the positive indicator, the formula is as follows: For negative indicators, the formula is as follows: Among them, the positive indicator means that the larger the indicator value, the higher the scheme benefit value, and the negative indicator means that the smaller the indicator value, the higher the scheme benefit value; Calculate the characteristic weight p of index j by normalizing the matrix ij , the formula is: By characteristic weight p ij Calculate the entropy value E of each indicator j , the formula is: Among them, when p ij = 0, p ij ln(p ij )=0, Calculate the objective weight h according to the entropy value of each indicator j , 9. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The method of combining the subjective weight with the objective weight through the relaxation factor to obtain the combined weight of each indicator includes the following steps: The subjective weight w determined by AHP method j Multiply by the relaxation factor η, the objective weight h determined by the entropy weight method j Make corrections and get the combined weights: Where 0≤η≤1, where W j is the combined weight of the secondary index j.

10. A full-factor evaluation method for virtual capacity expansion of distribution network areas according to claim 1, characterized in that: The calculation formula for calculating the benefit value of each scheme is: Among them, R i is the benefit value of plan i, p ij is the characteristic proportion of secondary indicator j of scheme i.

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