Power distribution network reliability cost-benefit analysis method based on life cycle cost

Abstract

A power distribution network reliability cost-benefit analysis method based on life cycle cost is characterized in that a reliability cost improvement model is constructed with optimal configuration of distributed resources of a power distribution network as a target, and the reliability cost improvement model is a distributed resource reliability cost-benefit oriented double-layer optimization configuration model. The reliability of the distributed resources is accurately predicted and evaluated, so that the cost and income of investment in each stage of planning, construction, operation, decommissioning and the like are systematically considered, and unnecessary cost waste is reduced. The method can achieve the balance of two time scales through the iterative coupling of the planning layer and the operation layer, obtains the final global optimal solution, effectively improves the reliability cost-benefit analysis effect, can obtain the reliability investment comprehensive benefit through employing a proper method according to different power distribution network scales, and improves the reliability cost-benefit analysis efficiency. And the application range of the design is effectively expanded.

Description

Technical Field

[0001] The present invention relates to a distribution network reliability cost-benefit analysis method based on full life cycle cost, and in particular to a distribution network reliability cost-benefit analysis method based on full life cycle cost. Background Art

[0002] With the widespread application of distributed systems, in order to meet the needs of power users for high-quality electricity, power supply departments need to continuously upgrade and transform distribution networks to improve the reliability of power supply. The improvement of power supply reliability seems to be endless, but while reliability is improved, huge grid investment reduces the economic efficiency of power supply companies; therefore, the reliability of distribution network power supply and the economic efficiency of operation are two mutually restrictive aspects. In the business decisions of power companies, they focus on the economic benefits of distribution network operations and strive to find a balance between reducing investment expenditures and ensuring reasonable reliability standards. How to improve the reliability of distributed resources and effectively control costs has become an important topic of current research.

[0003] Traditional distribution network planning meets the predetermined reliability of a certain load level with the least system investment. The distribution network planning with full life cycle cost comprehensively considers the cost of each stage of the power grid construction project, and coordinates the reliability and economy of the power grid.

[0004] Although this reliability cost analysis method can coordinate the reliability and economy of the power grid, it still has the following defects:

[0005] 1. The costs of each stage of the power grid construction project were not considered comprehensively from the perspective of the equipment life cycle, resulting in poor reliability cost-benefit analysis.

[0006] 2. The implementation of resource reliability assurance measures in distributed systems is often accompanied by resource consumption and cost increases. When large-scale distributed resources are connected, traditional reliability optimization models are difficult to adapt to large-scale new power systems and have a small scope of application.

[0007] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in the field. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art that the reliability cost-benefit analysis effect is poor and the scope of application is small, and to provide a distribution network reliability cost-benefit analysis method based on the full life cycle cost, which has a good reliability cost-benefit analysis effect and a wide scope of application.

[0009] To achieve the above objectives, the technical solution of the present invention is:

[0010] The present invention proposes a distribution network reliability cost-benefit analysis method based on full life cycle cost, the analysis method comprising:

[0011] S1. Constructing a comprehensive cost-benefit analysis model for distribution network reliability investment based on the full life cycle cost, wherein the comprehensive cost-benefit analysis model for distribution network reliability investment aims at the economic optimal configuration of distribution network reliability investment and takes into account installed capacity constraints and resource output constraints;

[0012] S2. Solve the comprehensive cost-benefit analysis model of distribution network reliability investment and obtain a comprehensive cost-benefit analysis plan for distribution network reliability investment based on the full life cycle cost.

[0013] The economic optimal configuration of the distribution network reliability investment includes the comprehensive cost of reliability investment and the optimization of reliability benefits over the entire life cycle;

[0014] The distribution network reliability investment comprehensive cost-benefit analysis model is a two-layer optimization model for the comprehensive cost-benefit of reliability investment. The two-layer optimization model includes an upper optimization model F based on the comprehensive cost optimization of reliability investment. up And the lower optimization model F based on reliability benefit optimization low ;

[0015] The upper optimization model F up include:

[0016]

[0017] In the above formula, C inv is the investment cost; C ope is the operating cost; C mai is the maintenance cost; C int is the power outage cost; C env For environmental benefits; C dis is the abandonment cost; r is the discount rate; y is the project period of the distribution network reliability improvement measures; N k is the number of distributed resource types; Ω k represents the set of k-th type of distributed resource access nodes; S i,k Represents Ω k The access capacity of the i-th node in; c inv,k represents the investment cost of the kth distributed resource, c mai,k represents the maintenance cost of the kth distributed resource, c dis,k represents the abandonment cost of the kth distributed resource;

[0018] The lower optimization model Flow include:

[0019]

[0020] In the above formula, N sce is the total number of scenarios, T is the research time scale; ρ w is the probability C of the w-th scene ope,w,t , C int,w,t and C env,w,t are the operating cost, power outage cost and environmental cost of scenario w at time t; c out,k is the unit output cost of the kth distributed resource, P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w; N load is the total number of load nodes, C LCR,w,n,t is the power outage loss of load node n at time t under scenario w; c emi is the environmental pollutant emission cost per unit thermal power output, P grid,w,t is the input power of the upstream power node of the distribution network at time t in scenario w;

[0021] The analysis method can use the power outage loss evaluation rate method or the improved power generation ratio method to calculate the reliability benefit, and the reliability benefit is the power outage loss. When the distribution network scale is less than 800 nodes, the power outage loss evaluation rate method is used. When the distribution network scale is greater than or equal to 800 nodes, a macroscopic statistical method, namely the improved power generation ratio method, is used, including:

[0022]

[0023] In the above formula, C IE The power outage loss for the user, C IE,0 is the initial user power outage loss, C env,0 is the initial environmental cost, and the data of load node n at time t under scenario w is substituted into the above formula to calculate the corresponding power outage loss C LCR,w,n,t ;

[0024] The power outage losses of users include:

[0025] C IE =IEAR×EENS;

[0026] In the above formula, C IE is the power outage loss for the user, IEAR is the power outage loss assessment rate for the system, and EENS is the expected value of power shortage for the system;

[0027] The benefits of distribution network reliability investment considering regional differences in power supply include:

[0028] R″ rel (t) = α″ele ×△E loss (t)+β″×△E loss (t);

[0029] In the above formula, R″ rel That is, the benefit of distribution network reliability investment considering the differences in power supply areas, ΔE loss (t) represents the difference in power loss between year t and year t-1, α″ ele represents the electricity price coefficient of this type of power supply area, and β″ represents the power generation ratio coefficient of this type of power supply area;

[0030] The electricity price coefficient α" of this type of power supply area ele The power generation ratio coefficient β″ of this type of power supply area includes:

[0031]

[0032] In the above formula, α′ ele,i represents the electricity price coefficient of the ith city, β′ i represents the power generation ratio coefficient of the i-th city; δ i represents the proportion of electricity consumption of this type of electricity consumption area in the i-th city; n is the number of cities, and the α′ ele,i and β′ i include:

[0033]

[0034] In the above formula, α 1,i , α 2,i , α 3,i , α 4,i Respectively represent the primary industry, secondary industry, tertiary industry and residential electricity prices; β 1,i , β 2,i , β 3,i Respectively represent the electricity generation ratio coefficients of the primary industry, secondary industry, and tertiary industry; ω 1,i ,ω 2,i ,ω 3,i ,ω 4,i Respectively represent the proportion of primary industry, secondary industry, tertiary industry and residential electricity consumption;

[0035] The power outage loss evaluation rate IEAR of the node i i include:

[0036]

[0037] In the above formula, N int,i is the total number of power outages at node i, P load,i is the average load value at node i, λ i,kis the failure rate of node i in the kth power outage, τi,k is the outage time in the kth power outage, CCDF(t i,k ) is the comprehensive user loss function;

[0038] The comprehensive user loss function CCDF of the node i i include:

[0039]

[0040] In the above formula, N c,i is the number of user classifications on a node i, SCDF j is the power outage time function of the j-th user, P j is the average load value of the j-th type of user;

[0041] The power outage loss assessment rate IEAR of the entire system is obtained through the power outage loss assessment rate of each node.

[0042] The upper optimization model F up The constraints are the installed capacity constraints of various distributed resources, including:

[0043] S i,k,min ≤S i,k ≤S i,k,max i∈Ω k ;

[0044] In the above formula, S i,k,min and S i,k,max They represent the minimum and maximum capacity of the i-th node access respectively;

[0045] The lower optimization model F low The constraints include resource output constraints, energy storage battery regulation constraints and grid-connected node power constraints. The resource output constraints include:

[0046] P w,i,k,t,min ≤P w,i,k,t ≤P w,i,k,t,max ;

[0047] P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w, P w,i,k,t,min , P w,i,k,t,max They are the upper and lower limits of the output of the kth distributed resource at node i at time t in scenario w.

[0048] The regulation constraints of the energy storage battery include:

[0049]

[0050] SOC BES,min ≤SOC BES,t ≤SOCBES,max ;

[0051] The grid-connected node power constraints include:

[0052] P grid,w,t,min ≤P grid,w,t ≤P grid,w,t,,max ;

[0053] In the above formula, and A 0-1 variable representing the charging or discharging state of the energy storage battery at time t, and A 0-1 variable representing the charging or discharging state of the upper power node in the distribution network at time t, SOC BES,t , SOC BES,max , SOC BES,min are the state of charge of the energy storage battery and its upper and lower limits, η ch and η dis are the charging and discharging efficiencies, S BES is the capacity of the energy storage battery; P grid,w,t is the input power of the upper power node of the distribution network at time t in scenario w, P grid,w,t,,max , P grid,w,t,min They are the upper and lower limits of the transmission power of the grid-connected nodes respectively.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. In a distribution network reliability cost-benefit analysis method based on the full life cycle cost of the present invention, a reliability cost improvement model is constructed with the optimal configuration of the distributed resources of the distribution network as the goal. The reliability cost improvement model is a two-layer optimization configuration model for the reliability cost-benefit of distributed resources, which accurately predicts and evaluates the reliability of distributed resources, thereby systematically considering the costs and benefits generated by the investment in various stages such as planning, construction, operation and decommissioning, and reducing unnecessary cost waste. Therefore, this design can accurately predict and evaluate the reliability of distributed resources through a two-layer optimization configuration model, effectively reducing cost waste.

[0056] 2. In the reliability cost-benefit analysis method of the distribution network based on the full life cycle cost of the present invention, the comprehensive investment cost of reliability in the full life cycle is used as the objective function of the upper optimization model, and the upper optimization model is the planning layer. At the same time, the minimum comprehensive operating cost is used as the objective function of the lower optimization model, and the lower optimization model is the operation layer. The planning layer uses the comprehensive cost of reliability investment based on the full life cycle cost as the objective function to preliminarily determine the installation capacity of various types of new energy power sources and energy storage; based on the results of the planning layer, the operation layer optimizes the system according to the specific operation scenario, and returns the optimized minimum operating cost to the upper layer, repeats the iteration until convergence, so that the obvious time scale difference between the planning layer and the operation layer under the long time scale of the full life cycle is balanced. Therefore, this design can achieve a balance on the two time scales through the iterative coupling of the planning layer and the operation layer, obtain the final global optimal solution, and effectively improve the reliability cost-benefit analysis effect.

[0057] 3. In the reliability cost-benefit analysis method of the distribution network based on the full life cycle cost of the present invention, since the comprehensive benefit of the reliability investment in the full life cycle is closely related to the scale of the distribution network, this design proposes a power outage loss evaluation rate method and an improved power generation ratio method for different distribution network scales. When the distribution network scale is small, it is suitable to adopt a refined method for power user statistics, namely the power outage loss evaluation rate method; and when the distribution network scale is large, it is more difficult to count the power outage indicators of power users, and it is suitable to adopt a macroscopic statistical method, namely the improved power generation ratio method. Therefore, this design can obtain the comprehensive benefit of reliability investment by adopting appropriate methods according to different distribution network scales, effectively expanding the scope of application of this design. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the method of the present invention.

[0059] Figure 2 It is a planning idea diagram of the method described in the present invention.

[0060] Figure 3 It is a structural diagram of the system described in the present invention.

[0061] Figure 4 It is a structural diagram of the distribution network in Example 3.

[0062] Figure 5 It is a schematic diagram of the expected cost-CVaR curve of Scheme 1 in Example 3.

[0063] Figure 6 It is a schematic diagram of the expected cost-CVaR curve of Option 2 in Example 3.

[0064] Figure 7 It is a schematic diagram of the expected cost-CVaR curve of Scheme 3 in Example 3.

[0065] Figure 8 It is a structural diagram of the device described in Example 4. DETAILED DESCRIPTION

[0066] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0067] Embodiment 1:

[0068] See also Figure 1 to Figure 2 , a distribution network reliability cost-benefit analysis method based on full life cycle cost, the analysis method comprising:

[0069] S1. Constructing a comprehensive cost-benefit analysis model for distribution network reliability investment based on the full life cycle cost, wherein the comprehensive cost-benefit analysis model for distribution network reliability investment aims at the economic optimal configuration of distribution network reliability investment and takes into account installed capacity constraints and resource output constraints;

[0070] S2. Solve the comprehensive cost-benefit analysis model of distribution network reliability investment and obtain a comprehensive cost-benefit analysis plan for distribution network reliability investment based on the full life cycle cost.

[0071] The economic optimal configuration of the distribution network reliability investment includes the comprehensive cost of reliability investment and the optimization of reliability benefits over the entire life cycle;

[0072] The distribution network reliability investment comprehensive cost-benefit analysis model is a two-layer optimization model for the comprehensive cost-benefit of reliability investment. The two-layer optimization model includes an upper optimization model F based on the comprehensive cost optimization of reliability investment. up And the lower optimization model F based on reliability benefit optimization low ;

[0073] The upper optimization model F up include:

[0074]

[0075] In the above formula, C inv is the investment cost; C ope is the operating cost; C mai is the maintenance cost; C int is the power outage cost; C env For environmental benefits; C dis is the abandonment cost; r is the discount rate; y is the project period of the distribution network reliability improvement measures; N k is the number of distributed resource types; Ω k represents the set of k-th type of distributed resource access nodes; S i,k Represents Ω kThe access capacity of the i-th node in; c inv,k represents the investment cost of the kth distributed resource, c mai,k represents the maintenance cost of the kth distributed resource, c dis,k represents the abandonment cost of the kth distributed resource;

[0076] The lower optimization model F low include:

[0077]

[0078] In the above formula, N sce is the total number of scenarios, T is the research time scale; ρ w is the probability C of the w-th scene ope,w,t , C int,w,t and C env,w,t are the operating cost, power outage cost and environmental cost of scenario w at time t; c out,k is the unit output cost of the kth distributed resource, P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w; N load is the total number of load nodes, C LCR,w,n,t is the power outage loss of load node n at time t under scenario w; c emi is the environmental pollutant emission cost per unit thermal power output, P grid,w,t is the input power of the upstream power node of the distribution network at time t in scenario w;

[0079] The analysis method can use the power outage loss evaluation rate method or the improved power generation ratio method to calculate the reliability benefit, and the reliability benefit is the power outage loss. When the distribution network scale is less than 800 nodes, the power outage loss evaluation rate method is used. When the distribution network scale is greater than or equal to 800 nodes, a macroscopic statistical method, namely the improved power generation ratio method, is used, including:

[0080]

[0081] In the above formula, C IE The power outage loss for the user, C IE,0 is the initial user power outage loss, C env,0 is the initial environmental cost, and the data of load node n at time t under scenario w is substituted into the above formula to calculate the corresponding power outage loss C LCR,w,n,t ;

[0082] The power outage losses of users include:

[0083] C IE =IEAR×EENS;

[0084] In the above formula, C IEis the power outage loss for the user, IEAR is the power outage loss assessment rate for the system, and EENS is the expected value of power shortage for the system;

[0085] The benefits of distribution network reliability investment considering regional differences in power supply include:

[0086] R″ rel (t) = α″ ele ×△E loss (t)+β″×△E loss (t);

[0087] In the above formula, R″ rel That is, the benefit of distribution network reliability investment considering the differences in power supply areas, ΔE loss (t) represents the difference in power loss between year t and year t-1, α″ ele represents the electricity price coefficient of this type of power supply area, and β″ represents the power generation ratio coefficient of this type of power supply area;

[0088] The electricity price coefficient α" of this type of power supply area ele The power generation ratio coefficient β″ of this type of power supply area includes:

[0089]

[0090] In the above formula, α′ ele,i represents the electricity price coefficient of the ith city, β′ i represents the power generation ratio coefficient of the i-th city; δ i represents the proportion of electricity consumption of this type of electricity consumption area in the i-th city; n is the number of cities, and the α′ ele,i and β′ i include:

[0091]

[0092] In the above formula, α 1,i , α 2,i , α 3,i , α 4,i Respectively represent the primary industry, secondary industry, tertiary industry and residential electricity prices; β 1,i , β 2,i , β 3,i Respectively represent the electricity generation ratio coefficients of the primary industry, secondary industry, and tertiary industry; ω 1,i ,ω 2,i ,ω 3,i ,ω 4,i Respectively represent the proportion of primary industry, secondary industry, tertiary industry and residential electricity consumption;

[0093] The investment cost C inv include:

[0094]

[0095] In the above formula, C des is the total cost of the design and development phase, C con is the total cost of the construction phase, C con,i is the purchase cost and construction and installation cost of the i-th type of equipment, C oi is other costs, n i is the purchase quantity of the i-th type of equipment, and N is the total number of equipment types;

[0096] The operating cost C ope include:

[0097] C ope =C line +C tran +C oo ;

[0098] In the above formula, C line is the line loss cost, C oo Other costs such as operating personnel training fees, C tran is the transformer loss cost;

[0099] The line loss cost C line include:

[0100] C line =ε p ×△E line =ε p ×△P max ×t max ;

[0101] In the above formula, ε p is the electricity price, ΔE line is the line loss, ΔP max is the maximum power loss in the distribution network flow, τ max Maximum load loss time;

[0102] The transformer loss costs include:

[0103] C tran =(γ a ×P 0 +β 2 ×P k )×t op ×ε p ;

[0104] In the above formula, β is the transformer load factor, P k is the transformer load loss, P 0 is the transformer no-load loss, γ and ais the transformer availability factor, τ op is the annual operating time;

[0105] The maintenance cost C mai include:

[0106]

[0107] In the above formula, λ i is the average annual maintenance frequency of the i-th equipment, C con,i is the average maintenance cost per time for the i-th equipment, n i is the purchase quantity of the i-th type of equipment;

[0108] The computing environment costs include:

[0109]

[0110] In the above formula, C env is the environmental cost, N pol is the number of pollutant types, V pol,m is the environmental value of the mth pollutant, U emi,m is the emission of the mth pollutant;

[0111] The abandonment cost C dis include:

[0112]

[0113] In the above formula, C dis,i is the decommissioning and cleaning cost of the i-th equipment, C res,i is the residual value of the i-th equipment, and N is the total number of equipment types.

[0114] The power outage loss evaluation rate IEAR of the node i i include:

[0115]

[0116] In the above formula, N int,i is the total number of power outages at node i, P load,i is the average load value at node i, λ i,k is the failure rate of node i in the kth power outage, τ i,k is the outage time in the kth outage, CCDF(t i,k ) is the comprehensive user loss function;

[0117] The comprehensive user loss function CCDF of the node i i include:

[0118]

[0119] In the above formula, N c,i is the number of user classifications on a node i, SCDF j is the power outage time function of the j-th user, P j is the average load value of the j-th type of user;

[0120] The power outage loss assessment rate IEAR of the entire system is obtained through the power outage loss assessment rate of each node.

[0121] The upper optimization model F up The constraints are the installed capacity constraints of various distributed resources, including:

[0122] S i,k,min ≤S i,k ≤S i,k,max i∈Ω k ;

[0123] In the above formula, S i,k,min and S i,k,max They represent the minimum and maximum capacity of the i-th node access respectively;

[0124] The lower optimization model F low The constraints include resource output constraints, energy storage battery regulation constraints and grid-connected node power constraints. The resource output constraints include:

[0125] P w,i,k,t,min ≤P w,i,k,t ≤P w,i,k,t,max ;

[0126] P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w, P w,i,k,t,min , P w,i,k,t,max They are the upper and lower limits of the output of the kth distributed resource at node i at time t in scenario w.

[0127] The regulation constraints of the energy storage battery include:

[0128]

[0129] SOC BES,min ≤SOC BES,t ≤SOC BES,max ;

[0130] The grid-connected node power constraints include:

[0131] P grid,w,t,min ≤P grid,w,t ≤P grid,w,t,,max ;

[0132] In the above formula, and A 0-1 variable representing the charging or discharging state of the energy storage battery at time t, and A 0-1 variable representing the charging or discharging state of the upper power node in the distribution network at time t, SOC BES,t , SOC BES,max , SOC BES,min are the state of charge of the energy storage battery and its upper and lower limits, η ch and η dis are the charging and discharging efficiencies, S BES is the capacity of the energy storage battery; P grid,w,t is the input power of the upper power node of the distribution network at time t in scenario w, P grid,w,t,,max , P grid,w,t,min They are the upper and lower limits of the transmission power of the grid-connected nodes respectively.

[0133] Embodiment 2:

[0134] See also Figure 3 , a distribution network reliability cost-benefit analysis system based on full life cycle cost, the system comprising a model building module and a model solving module;

[0135] The model building module is used to build a comprehensive cost-benefit analysis model for distribution network reliability investment based on the full life cycle cost, wherein the comprehensive cost-benefit analysis model for distribution network reliability investment takes the economic optimal configuration of distribution network reliability investment as the goal, and takes into account installed capacity constraints and resource output constraints;

[0136] The model solving module is used to solve the comprehensive cost-benefit analysis model of distribution network reliability investment, and obtain a comprehensive cost-benefit analysis plan of distribution network reliability investment based on the full life cycle cost.

[0137] The economic optimal configuration of the distribution network reliability investment includes the comprehensive cost of reliability investment and the optimization of reliability benefits over the entire life cycle;

[0138] The distribution network reliability investment comprehensive cost-benefit analysis model is a two-layer optimization model for the comprehensive cost-benefit of reliability investment. The two-layer optimization model includes an upper optimization model F based on the comprehensive cost optimization of reliability investment. up And the lower optimization model F based on reliability benefit optimization low ;

[0139] The upper optimization model F up include:

[0140]

[0141] In the above formula, C inv is the investment cost; Cope is the operating cost; C mai is the maintenance cost; C int is the power outage cost; C env For environmental benefits; C dis is the abandonment cost; r is the discount rate; y is the project period of the distribution network reliability improvement measures; N k is the number of distributed resource types; Ω k represents the set of k-th type of distributed resource access nodes; S i,k Represents Ω k The access capacity of the i-th node in; c inv,k represents the investment cost of the kth distributed resource, c mai,k represents the maintenance cost of the kth distributed resource, c dis,k represents the abandonment cost of the kth distributed resource;

[0142] The lower optimization model F low include:

[0143]

[0144] In the above formula, N sce is the total number of scenarios, T is the research time scale; ρ w is the probability C of the w-th scene ope,w,t , C int,w,t and C env,w,t are the operating cost, power outage cost and environmental cost of scenario w at time t; c out,k is the unit output cost of the kth distributed resource, P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w; N load is the total number of load nodes, C LCR,w,n,t is the power outage loss of load node n at time t under scenario w; c emi is the environmental pollutant emission cost per unit thermal power output, P grid,w,t is the input power of the upstream power node of the distribution network at time t in scenario w;

[0145] The analysis method can use the power outage loss evaluation rate method or the improved power generation ratio method to calculate the reliability benefit, and the reliability benefit is the power outage loss. When the distribution network scale is less than 800 nodes, the power outage loss evaluation rate method is used. When the distribution network scale is greater than or equal to 800 nodes, a macroscopic statistical method, namely the improved power generation ratio method, is used, including:

[0146]

[0147] In the above formula, C IE The power outage loss for the user, C IE,0is the initial user power outage loss, C env,0 is the initial environmental cost, and the data of load node n at time t under scenario w is substituted into the above formula to calculate the corresponding power outage loss C LCR,w,n,t ;

[0148] The power outage losses of users include:

[0149] C IE =IEAR×EENS;

[0150] In the above formula, C IE is the power outage loss for the user, IEAR is the power outage loss assessment rate for the system, and EENS is the expected value of power shortage for the system;

[0151] The benefits of distribution network reliability investment considering regional differences in power supply include:

[0152] R″ rel (t) = α″ ele ×△E loss (t)+β″×△E loss (t);

[0153] In the above formula, R″ rel That is, the benefit of distribution network reliability investment considering the differences in power supply areas, ΔE loss (t) represents the difference in power loss between year t and year t-1, α″ ele It indicates the electricity price coefficient of this type of power supply area, and β″ indicates the power generation ratio coefficient of this type of power supply area, including:

[0154]

[0155] In the above formula, α′ ele,i represents the electricity price coefficient of the ith city, β′ i represents the power generation ratio coefficient of the i-th city; δ i represents the proportion of electricity consumption of this type of electricity consumption area in the i-th city; n is the number of cities, and the α′ ele,i and β′ i include:

[0156]

[0157] In the above formula, α 1,i , α 2,i , α 3,i , α 4,i Respectively represent the primary industry, secondary industry, tertiary industry and residential electricity prices; β 1,i , β 2,i , β 3,i Respectively represent the electricity generation ratio coefficients of the primary industry, secondary industry, and tertiary industry; ω 1,i ,ω2,i ,ω 3,i ,ω 4,i They represent the proportion of primary industry, secondary industry, tertiary industry and residents' electricity consumption respectively.

[0158] The power outage loss evaluation rate IEAR of the node i i include:

[0159]

[0160] In the above formula, N int,i is the total number of power outages at node i, P load,i is the average load value at node i, λ i,k is the failure rate of node i in the kth power outage, τ i,k is the outage time in the kth outage, CCDF(t i,k ) is the comprehensive user loss function;

[0161] The comprehensive user loss function CCDF of the node i i include:

[0162]

[0163] In the above formula, N c,i is the number of user classifications on a node i, SCDF j is the power outage time function of the j-th user, P j is the average load value of the j-th type of user;

[0164] The power outage loss assessment rate IEAR of the entire system is obtained through the power outage loss assessment rate of each node.

[0165] The upper optimization model F up The constraints are the installed capacity constraints of various distributed resources, including:

[0166] S i,k,min ≤S i,k ≤S i,k,max i∈Ω k ;

[0167] In the above formula, S i,k,min and S i,k,max They represent the minimum and maximum capacity of the i-th node access respectively;

[0168] The lower optimization model F low The constraints include resource output constraints, energy storage battery regulation constraints and grid-connected node power constraints. The resource output constraints include:

[0169] P w,i,k,t,min ≤P w,i,k,t ≤P w,i,k,t,max;

[0170] P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w, P w,i,k,t,min , P w,i,k,t,max They are the upper and lower limits of the output of the kth distributed resource at node i at time t in scenario w.

[0171] The regulation constraints of the energy storage battery include:

[0172]

[0173] SOC BES,min ≤SOC BES,t ≤SOC BES,max ;

[0174] The grid-connected node power constraints include:

[0175] P grid,w,t,min ≤P grid,w,t ≤P grid,w,t,,max ;

[0176] In the above formula, and A 0-1 variable representing the charging or discharging state of the energy storage battery at time t, and A 0-1 variable representing the charging or discharging state of the upper power node in the distribution network at time t, SOC BES,t , SOC BES,max , SOC BES,min are the state of charge of the energy storage battery and its upper and lower limits, η ch and η dis are the charging and discharging efficiencies, S BES is the capacity of the energy storage battery; P grid,w,t is the input power of the upper power node of the distribution network at time t in scenario w, P grid,w,t,,max , P grid,w,t,min They are the upper and lower limits of the transmission power of the grid-connected nodes respectively.

[0177] Embodiment 3:

[0178] This embodiment provides a specific solution for analyzing the above content by example, including:

[0179] Through simulation analysis, quantitative evaluation is conducted on the improvement effect of various measures on distribution network reliability and their cost investment; Figure 4The distribution network system shown is used as a simulation example. The system includes 36 transmission lines, 22 power supply nodes, 22 fuse devices (not clearly marked in the figure) set at the starting end of each load branch, 20 user transformers, 18 sets of isolation switch devices and 4 circuit breakers; the load data is shown in Table 1:

[0180] Table 1 Example System Load parameters

[0181] Table 1 Load parameters of case system

[0182] Load Node Load Type Average load / MW Maximum load / MW Number of users 1-3、10、11 resident 0.535 0.8668 210 12、17-19 resident 0.450 0.7291 200 8 Small users 1.00 1.6279 1 9 Small users 1.15 1.8721 1 4、5、13、14、20、21 Government departments 0.566 0.9167 1 6、7、15、16、22 Business 0.454 0.7500 10 total 12.291 20.00 1908

[0184] The reliability data is shown in Table 2:

[0185] Table 2 System reliability parameters of the example

[0186] Table 2 Reliability parameters of case system

[0187]

[0188] In order to study the impact of distributed resources on the reliability improvement of distribution networks, the following three configuration schemes are set up in the example. Based on each scheme, the capacity and output of photovoltaic, wind power and energy storage are optimized, and the resulting reliability costs and benefits are analyzed:

[0189] Solution 1: Configure photovoltaic power at nodes 5, 8, 13, and 20, and configure wind power at nodes 7, 15, and 17;

[0190] Solution 2: Configure energy storage batteries at nodes 2, 6, 9, 11, 14, 19, and 22;

[0191] Solution 3: Combining Solution 1 and Solution 2, deploying distributed power generation and energy storage at the above nodes at the same time;

[0192] The optimization configuration results of different schemes are shown in Table 3:

[0193] Table 2 System reliability parameters of the example

[0194] Table 2 Reliability parameters of case system

[0195]

[0196] As can be seen from the table, among the three options, Option 1 has the highest total cost, Option 2 has a slightly lower total cost than Option 1, and Option 3 has a significantly lower total cost than the first two options. This is because, although photovoltaic and wind power have low operation and maintenance costs and good environmental costs, their strong randomness and volatility have caused a certain degree of decline in the reliability of the distribution network system and increased power outage costs. Although only configuring energy storage can make up for a certain amount of power shortage when the system load fluctuates and the power supply is insufficient, the investment cost of energy storage batteries is high, and environmental pollution and other costs will be generated when they are abandoned, so there is no significant improvement in the reduction of total costs. In Option 3, the planned capacity of distributed power sources has increased compared with Options 1 and 2, and the planned capacity of energy storage has increased more significantly. This is because the volatility of new energy power generation can be smoothed by regulating the charging and discharging of energy storage, and the weakening of photovoltaic and wind power on system reliability can be suppressed. At the same time, the increase in the installed capacity of distributed power sources reduces the demand for power supply to the upper power grid, which greatly reduces the investment cost and operation and maintenance cost, thereby significantly improving the total cost.

[0197] The cost and benefit details of different solutions are shown in Table 4:

[0198] Table 4 Cost and benefit details of different solutions

[0199] Table 4 Cost-benefit details of different schemes

[0200]

[0201] As can be seen from the table, the cost structures of different schemes vary greatly. In terms of investment cost, Scheme 3 is equipped with both distributed power sources and energy storage batteries, and the capacity of both is increased compared with the previous two schemes, so the initial equipment cost is much greater than that of Scheme 1 and Scheme 2. In terms of operating cost, the capacity of distributed power sources in Scheme 3 is large, which can make a large part of users self-sufficient, and at the same time, there is energy storage for peak load shifting, which further reduces the operating cost. The wind power and photovoltaic power in Scheme 1 can also reduce the demand of the distribution network system for the upper power supply to a certain extent, so the operating cost is moderate. Almost all of the power grid electricity comes from the upper grid, which consumes the most energy and has the highest operating cost. The overall trend of maintenance cost is basically the same as that of operating cost. In terms of disposal cost, there is almost no disposal cost for new energy power generation, while the recycling cost of energy storage batteries is relatively high. Therefore, the disposal cost of Scheme 2 is much higher than that of the other two schemes. The environmental cost mainly comes from the pollution emissions of the type of electricity consumption. In Schemes 1 and 3, most of the electricity consumption of the distribution network system comes from clean energy, while the power supply of Scheme 2 depends entirely on the upper power source, which is mainly thermal power, and the pollution is relatively serious. Therefore, the environmental costs of Schemes 1 and 3 are also significantly lower than those of Scheme 2.

[0202] In order to improve the economic benefits of distribution network reliability investment, it is necessary to evaluate the economic effects of different reliability investment transformation plans based on cost-benefit analysis; make decision analysis on different transformation plans through investment-benefit ratio method, and give priority to transformation plans with large investment-benefit ratio; the higher the investment-benefit ratio, the greater the benefit generated by the plan, indicating that the plan has greatly improved the power supply reliability level at a lower cost, bringing good social and economic benefits;

[0203] The reliability investment benefit ratio ρ of the distribution network is used to quantify the impact of investment transformation of weak links on the improvement of distribution network reliability. According to the above analysis, the main research here is the impact of unit investment on the improvement of the expected value of the total power shortage of the system. The expression is as follows:

[0204]

[0205] The calculation results of the distribution network reliability index and its investment benefit ratio in the example are shown in Table 5:

[0206] Table 5 Reliability indicators and investment benefit ratio of different schemes

[0207] Table 5 Reliability index and cost-benefit ratio of different schemes

[0208]

[0209] The reliability benefits in the table are calculated using the outage costs and environmental costs. A positive value indicates that the reliability benefits are obtained, while a negative value indicates that the reliability benefits are not obtained. It can be seen from the table that the comprehensive reliability costs of the three schemes are all less than the original operation mode. The reliability cost of Scheme 1 is the lowest, followed by Scheme 3, and Scheme 2 is the highest. However, due to the volatility of renewable energy power generation, although Scheme 1 reduces the total cost, the reliability benefit is less than 0, which means that Scheme 1 reduces the reliability of the distribution network system. The reliability benefits of Schemes 2 and 3 are similar, and since the reliability cost of Scheme 3 is lower, its investment benefit ratio is slightly higher than that of Scheme 3. The change of the system reliability index EENS is also consistent with the investment benefit ratio. In general, Scheme 3 has the best investment benefit ratio and is most worthy of adoption.

[0210] As mentioned above, the risk preference coefficient represents the decision maker's aversion to risk. The larger its value, the more inclined the planner is to make a conservative allocation strategy, such as reducing the installed capacity of renewable energy power generation to prevent high power outage costs caused by insufficient actual response power due to the compensation of fluctuating power when the wind and solar power output fluctuates greatly;

[0211] See also Figures 5 to 7 ,Through the Pareto optimal frontier curves of expected return-CVaR under the three schemes, ,it can be seen that as the risk preference coefficient increases, the ,expected total cost of the distribution network increases and the risk-return value ,increases, which means that the operator sacrifices part of the overall ,return in exchange for a lower risk cost;

[0212] Among the three schemes, Scheme 3 is the most sensitive to the risk preference coefficient. In the frontier curves of Schemes 1 and 2, adjacent points are very close or even overlapped. This is because the renewable energy generation capacity in Scheme 3 is the largest and the volatility is relatively large, while the renewable energy generation capacity in Scheme 1 is small and there is no distributed power supply in Scheme 2. Therefore, the differences in the random scenarios of the various schemes are not obvious. This can also be seen from the ratio of risk return to expected return. The difference between CVaR and expected return in Scheme 2 is the largest. Furthermore, the smoothness of the expected return-CVaR curve is related to the uncertainty. The greater the uncertainty, the smoother the curve. In terms of the absolute value of the change in the return value in the curve, the expected return and risk return in Scheme 2 change the least with the risk preference coefficient. This is because there are fewer random variables in Scheme 2 and the overall volatility of the distribution network is small.

[0213] In addition, it is not difficult to find that the expected return-CVaR Pareto optimal frontier curves in the three scenarios all show concave curve characteristics, that is, as the risk preference coefficient increases, the risk cost first increases more obviously while the expected cost increases less, and then the risk cost growth rate slows down while the expected return begins to rise sharply; it is also more reasonable to choose the L value before the inflection point of the curve, which can reduce the risk to a greater extent when the overall return is not much different; therefore, in the examples in this chapter, the optimal configuration strategy when the risk preference coefficient L = 0.5 is selected as the result of the example.

[0214] Embodiment 4:

[0215] See also Figure 8 , a distribution network reliability cost-benefit analysis device based on full life cycle cost, the device comprising a processor and a memory;

[0216] The memory is used to store computer program code and transmit the computer program code to the processor;

[0217] The processor is used to execute the distribution network reliability cost-benefit analysis method based on full life cycle cost described in Example 1 according to the instructions in the computer program code.

[0218] A computer medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the distribution network reliability cost-benefit analysis method based on full life cycle cost as described in Example 1.

[0219] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A distribution network reliability cost-benefit analysis method based on the full life cycle cost, characterized by: The analysis method comprises: S1. Constructing a comprehensive cost-benefit analysis model for distribution network reliability investment based on the full life cycle cost, wherein the comprehensive cost-benefit analysis model for distribution network reliability investment aims at the economic optimal configuration of distribution network reliability investment and takes into account installed capacity constraints and resource output constraints; S2. Solve the comprehensive cost-benefit analysis model of distribution network reliability investment and obtain a comprehensive cost-benefit analysis plan for distribution network reliability investment based on the full life cycle cost.

2. The method for cost-benefit analysis of distribution network reliability based on life cycle cost according to claim 1, characterized in that: The economic optimal configuration of the distribution network reliability investment includes the comprehensive cost of reliability investment and the optimization of reliability benefits over the entire life cycle; The distribution network reliability investment comprehensive cost-benefit analysis model is a two-layer optimization model for the comprehensive cost-benefit of reliability investment. The two-layer optimization model includes an upper optimization model F based on the comprehensive cost optimization of reliability investment. up And the lower optimization model F based on reliability benefit optimization low ; The upper optimization model F up include: In the above formula, C inv is the investment cost; C ope is the operating cost; C mai is the maintenance cost; C int is the power outage cost; C env For environmental benefits; C dis is the abandonment cost; r is the discount rate; y is the project period of the distribution network reliability improvement measures; N k is the number of distributed resource types; Ω k represents the set of k-th type of distributed resource access nodes; S i,k Represents Ω k The access capacity of the i-th node in; c inv,k represents the investment cost of the kth distributed resource, c mai,k represents the maintenance cost of the kth distributed resource, c dis,k represents the abandonment cost of the kth distributed resource; The lower optimization model F low include: In the above formula, N sce is the total number of scenarios, T is the research time scale; ρ w is the probability C of the w-th scene ope,w,t , C int,w,t and C env,w,t are the operating cost, power outage cost and environmental cost of scenario w at time t; c out,k is the unit output cost of the kth distributed resource, P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w; N load is the total number of load nodes, C LCR,w,n,t is the power outage loss of load node n at time t under scenario w; c emi is the environmental pollutant emission cost per unit thermal power output, P grid,w,t is the input power of the upstream power node of the distribution network at time t in scenario w; The analysis method can use the power outage loss evaluation rate method or the improved power generation ratio method to calculate the reliability benefit, and the reliability benefit is the power outage loss. When the distribution network scale is less than 800 nodes, the power outage loss evaluation rate method is used. When the distribution network scale is greater than or equal to 800 nodes, a macroscopic statistical method, namely the improved power generation ratio method, is used, including: In the above formula, C IE Power outage loss for users, C IE,0 is the initial user power outage loss, C env,0 is the initial environmental cost, and the data of load node n at time t under scenario w is substituted into the above formula to calculate the corresponding power outage loss C LCR,w,n,t .

3. The method for cost-benefit analysis of distribution network reliability based on life cycle cost according to claim 2 is characterized in that: The power outage losses of users include: C IE =IEAR×EENS; In the above formula, C IE is the power outage loss for the user, IEAR is the power outage loss assessment rate for the system, and EENS is the expected value of power shortage for the system; The power outage loss evaluation rate IEAR of the node i i include: In the above formula, N int,i is the total number of power outages at node i, P load,i is the average load value at node i, λ i,k is the failure rate of node i in the kth power outage, τ i,k is the outage time in the kth outage, CCDF(t i,k ) is the comprehensive user loss function; The comprehensive user loss function CCDF of the node i i include: In the above formula, N c,i is the number of user classifications on a node i, SCDF j is the power outage time function of the j-th user, P j is the average load value of the j-th type of user; The power outage loss assessment rate IEAR of the entire system is obtained through the power outage loss assessment rate of each node.

4. The method for cost-benefit analysis of distribution network reliability based on life cycle cost according to claim 3 is characterized in that: The benefits of distribution network reliability investment considering regional differences in power supply include: R″ rel (t)=a″ ele ×△E loss (t)+β″×△E loss (t); In the above formula, R″ rel That is, the benefit of distribution network reliability investment considering the differences in power supply areas, ΔE loss (t) represents the difference in power loss between year t and year t-1, α″ ele represents the electricity price coefficient of this type of power supply area, and β″ represents the power generation ratio coefficient of this type of power supply area; The electricity price coefficient α" of this type of power supply area ele The power generation ratio coefficient β″ of this type of power supply area includes: In the above formula, α′ ele,i represents the electricity price coefficient of the i-th city, β′ i represents the power generation ratio coefficient of the i-th city; δ i represents the proportion of electricity consumption of this type of electricity consumption area in the i-th city; n is the number of cities, and the α′ ele,i and β′ i include: In the above formula, α 1,i , α 2,i , α 3,i , α 4,i Respectively represent the primary industry, secondary industry, tertiary industry and residential electricity prices; β 1,i , β 2,i , β 3,i Respectively represent the electricity generation ratio coefficients of the primary industry, secondary industry, and tertiary industry; ω 1,i ,ω 2,i ,ω 3,i ,ω 4,i They represent the proportion of primary industry, secondary industry, tertiary industry and residents' electricity consumption respectively.

5. The method for cost-benefit analysis of distribution network reliability based on life cycle cost according to claim 4 is characterized in that: The upper optimization model F up The constraints are the installed capacity constraints of various distributed resources, including: S i,k,min ≤S i,k ≤S i,k,max i∈Ω k ; In the above formula, S i,k,min and S i,k,max They represent the minimum and maximum capacity of the i-th node access respectively; The lower optimization model F low The constraints include resource output constraints, energy storage battery regulation constraints and grid-connected node power constraints. The resource output constraints include: P w,i,k,t,min ≤P w,i,k,t ≤P w,i,k,t,max ; P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w, P w,i,k,t,min , P w,i,k,t,max are the upper and lower limits of the output of the kth distributed resource at node i at time t in scenario w; The regulation constraints of the energy storage battery include: SOC BES,min ≤SOC BES,t ≤SOC BES,max ; The grid-connected node power constraints include: P grid,w,t,min ≤P grid,w,t ≤P grid,w,t,,max ; In the above formula, and A 0-1 variable representing the charging or discharging state of the energy storage battery at time t, and A 0-1 variable representing the charging or discharging state of the upper power node in the distribution network at time t, SOC BES,t , SOC BES,max , SOC BES,min are the state of charge of the energy storage battery and its upper and lower limits, η ch and η dis are the charging and discharging efficiencies, S BES is the capacity of the energy storage battery; P grid,w,t is the input power of the upper power node of the distribution network at time t in scenario w, P grid,w,t,,max , P grid,w,t,min They are the upper and lower limits of the transmission power of the grid-connected nodes respectively.

6. A distribution network reliability cost-benefit analysis system based on full life cycle cost, characterized by: The system includes a model building module and a model solving module; The model building module is used to build a comprehensive cost-benefit analysis model for distribution network reliability investment based on the full life cycle cost, wherein the comprehensive cost-benefit analysis model for distribution network reliability investment takes the economic optimal configuration of distribution network reliability investment as the goal and takes into account installed capacity constraints and resource output constraints; The model solving module is used to solve the comprehensive cost-benefit analysis model of distribution network reliability investment, and obtain a comprehensive cost-benefit analysis plan of distribution network reliability investment based on the full life cycle cost.

7. A distribution network reliability cost-benefit analysis system based on full life cycle cost according to claim 6, characterized in that: The economic optimal configuration of the distribution network reliability investment includes the comprehensive cost of reliability investment and the optimization of reliability benefits over the entire life cycle; The distribution network reliability investment comprehensive cost-benefit analysis model is a two-layer optimization model for the comprehensive cost-benefit of reliability investment. The two-layer optimization model includes an upper optimization model F based on the comprehensive cost optimization of reliability investment. up And the lower optimization model F based on reliability benefit optimization low ; The upper optimization model F up include: In the above formula, C inv is the investment cost; C ope is the operating cost; C mai is the maintenance cost; C int is the power outage cost; C env For environmental benefits; C dis is the abandonment cost; r is the discount rate; y is the project period of the distribution network reliability improvement measures; N k is the number of distributed resource types; Ω k represents the set of k-th type of distributed resource access nodes; S i,k Represents Ω k The access capacity of the i-th node in; c inv,k represents the investment cost of the kth distributed resource, c mai,k represents the maintenance cost of the kth distributed resource, c dis,k represents the abandonment cost of the kth distributed resource; The lower optimization model F low include: In the above formula, N sce is the total number of scenarios, T is the research time scale; ρ w is the probability C of the w-th scenario ope,w,t , C int,w,t and C env,w,t are the operating cost, power outage cost and environmental cost of scenario w at time t; c out,k is the unit output cost of the kth distributed resource, P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w; N load is the total number of load nodes, C LCR,w,n,t is the power outage loss of load node n at time t under scenario w; c emi is the environmental pollutant emission cost per unit thermal power output, P grid,w,t is the input power of the upstream power node of the distribution network at time t in scenario w; The analysis method can use the power outage loss evaluation rate method or the improved power generation ratio method to calculate the reliability benefit, and the reliability benefit is the power outage loss. When the distribution network scale is less than 800 nodes, the power outage loss evaluation rate method is used. When the distribution network scale is greater than or equal to 800 nodes, a macroscopic statistical method, namely the improved power generation ratio method, is used, including: In the above formula, C IE The power outage loss for the user, C IE,0 is the initial user power outage loss, C env,0 is the initial environmental cost, and the data of load node n at time t under scenario w is substituted into the above formula to calculate the corresponding power outage loss C LCR,w,n,t .

8. A distribution network reliability cost-benefit analysis system based on full life cycle cost according to claim 7, characterized in that: The power outage losses of users include: C IE =IEAR×EENS; In the above formula, C IE is the power outage loss for the user, IEAR is the power outage loss assessment rate for the system, and EENS is the expected value of power shortage for the system; The power outage loss evaluation rate IEAR of the node i i include: In the above formula, N int,i is the total number of power outages at node i, P load,i is the average load value at node i, λ i,k is the failure rate of node i in the kth power outage, τ i,k is the outage time in the kth outage, CCDF(t i,k ) is the comprehensive user loss function; The comprehensive user loss function CCDF of the node i i include: In the above formula, N c,i is the number of user classifications on a node i, SCDF j is the power outage time function of the j-th user, P j is the average load value of the j-th type of user; The power outage loss assessment rate IEAR of the entire system is obtained through the power outage loss assessment rate of each node.

9. A distribution network reliability cost-benefit analysis system based on full life cycle cost according to claim 8, characterized in that: The benefits of distribution network reliability investment considering regional differences in power supply include: R r ″ el (t)=a e ″ le ×△E loss (t)+β″×△E loss (t); In the above formula, R r ″ el That is, the benefit of distribution network reliability investment considering the differences in power supply areas, ΔE loss (t) represents the difference in power loss between year t and year t-1, α e ″ le It indicates the electricity price coefficient of this type of power supply area, and β″ indicates the power generation ratio coefficient of this type of power supply area, including: In the above formula, α′ ele,i represents the electricity price coefficient of the i-th city, β′ i represents the power generation ratio coefficient of the i-th city; δ i represents the proportion of electricity consumption of this type of electricity consumption area in the i-th city; n is the number of cities, and the α′ ele,i and β′ i include: In the above formula, α 1,i , α 2,i , α 3,i , α 4,i Respectively represent the primary industry, secondary industry, tertiary industry and residential electricity prices; β 1,i , β 2,i , β 3,i Respectively represent the electricity generation ratio coefficients of the primary industry, secondary industry, and tertiary industry; ω 1,i ,ω 2,i ,ω 3,i ,ω 4,i They represent the proportion of primary industry, secondary industry, tertiary industry and residents' electricity consumption respectively.

10. A distribution network reliability cost-benefit analysis system based on full life cycle cost according to claim 9, characterized in that: The upper optimization model F up The constraints are the installed capacity constraints of various distributed resources, including: S i,k,min ≤S i,k ≤S i,k,max i∈Ω k ; In the above formula, S i,k,min and S i,k,max They represent the minimum and maximum capacity of the i-th node access respectively; The lower optimization model F low The constraints include resource output constraints, energy storage battery regulation constraints and grid-connected node power constraints. The resource output constraints include: P w,i,k,t,min ≤P w,i,k,t ≤P w,i,k,t,max ; P w,i,k,t is the output of the kth distributed resource at node i at time t in scenario w, P w,i,k,t,min , P w,i,k,t,max They are the upper and lower limits of the output of the kth distributed resource at node i at time t in scenario w. When resource i is a storage battery, its output at time t is P BES,t ,include: SOC BES,min ≤SOC BES,t ≤SOC BES,max ; The grid-connected node power constraints include: P grid,w,t,min ≤P grid,w,t ≤P grid,w,t,,max ; In the above formula, and A 0-1 variable representing the charging or discharging state of the energy storage battery at time t, and A 0-1 variable representing the charging or discharging state of the upper power node in the distribution network at time t, SOC BES,t , SOC BES,max , SOC BES,min are the state of charge of the energy storage battery and its upper and lower limits, η ch and η dis are the charging and discharging efficiencies, S BES is the capacity of the energy storage battery; P grid,w,t is the input power of the upper power node of the distribution network at time t in scenario w, P grid,w,t,,max , P grid,w,t,min They are the upper and lower limits of the transmission power of the grid-connected nodes respectively.

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