Fast charging station planning method considering healthy life and safety performance cost of equipment

Abstract

The invention discloses a fast charging station planning method considering equipment health life and safety performance cost, and belongs to the technical field of fast charging station planning, and the method comprises the following steps: 1, providing a safety performance cost planning model considering the equipment health state; 2, considering the equipment difference of the quick charging station, and proposing a health state evaluation model of the quick charging station; and step 3, in combination with the equipment health state, providing a dynamic planning period determination method considering the equipment health state, solving a Pareto frontier solution set through a vector ordinal optimization algorithm, and obtaining an optimal planning method according to a normalization target. According to the method, the fund time values of different stations in the healthy full life cycle are accurately calculated in the planning process, so that some charging stations are decommissioned and replaced in advance when the healthy life expires too early, some charging stations are delayed to be re-commissioned when the healthy life expires too late, on one hand, the utilization rate of equipment is increased, and the cost is reduced. And the economical efficiency of the planning scheme is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fast charging station planning, and specifically relates to a fast charging station planning method that takes into account the healthy life of equipment and the safety and efficiency cost. Background Art

[0002] In recent years, the scale of the electric vehicle industry and the number of electric vehicles in society have been gradually increasing, and the planning and construction of charging station infrastructure have begun to receive widespread attention from the academic community. In order to provide users with charging services in a short period of time and meet the user's demand for fast charging during travel, fast charging technology is gradually replacing the traditional slow charging mode. However, due to its large fluctuation and strong impact of charging power, it leads to faster wear of charging equipment, increases maintenance costs, and affects the life of the equipment.

[0003] At present, the planning and research of electric vehicle charging stations have been widely discussed. Especially in the theory of life cycle cost, the life cycle cost of each charging station has been fully calculated and analyzed. However, there is a significant problem in the current life cycle cost planning research, that is, there is a tendency to be too idealistic and theoretical when determining the commissioning time and retirement time of electrical equipment. In actual engineering applications, once the power equipment is put into operation, a large amount of state measurement is still required to determine the appropriate time for the equipment to retire or continue to serve. Therefore, there is a difference between the actual end of life of the equipment and the ideal commissioning and retirement time of the whole life cycle. Especially for the fast charging pile, with the continuous growth of the number of electric vehicle users and the continuous increase in penetration rate, the disorder and volatility of the fast charging pile will have a more significant adverse effect on the charging pile equipment compared with other traditional electrical equipment. Therefore, as a new type of power equipment, the health status of the charging pile is not as determined as other conventional power equipment, which will lead to differences in the health life cycle of each charging station. Therefore, it is necessary to take reasonable measures to consider the situation of early retirement and delayed service, and further improve the efficiency and safety of the equipment itself.

[0004] At the same time, the existing safety efficiency cost theory planning methods all use the maximum load scenario and calculate it through the maximum utilization hours. This will cause the planning scheme to remain unchanged for one scenario throughout the entire planning cycle, and the loss of the charging station and the equipment utilization rate will also remain constant. However, this single scenario design is too one-sided in the planning research of charging stations and cannot meet the accuracy requirements of planning. Therefore, there is an urgent need for a fast charging station planning method that takes into account the health life of equipment and safety efficiency costs. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a fast charging station planning method that takes into account the health life of the equipment and the safety and efficiency cost, thereby solving the problems in the above-mentioned background technology.

[0006] The object of the present invention is achieved as follows: A fast charging station planning method considering equipment health life and safety efficiency cost comprises the following steps:

[0007] Step 1: Propose a safety effectiveness cost planning model considering the health status of equipment;

[0008] Step 2: Considering the differences in fast charging station equipment, a health status assessment model for fast charging stations is proposed;

[0009] Step 3: Combined with the equipment health status, a dynamic planning cycle determination method considering the equipment health status is proposed, and the Pareto front solution set is solved by the vector order optimization algorithm, and the optimal planning method is obtained according to the normalized objective.

[0010] Step 1 includes the following steps:

[0011] Step 1-1: Construct the objective function

[0012] F(P EV-fast ,T health )=min[S(P EV-fast ,T health ),-E(P EV-fast ,T health ),C(P EV-fast ,T health )](1)

[0013] Where: P EV-fast is the total capacity vector of charging piles in the newly built fast charging station, where N is the total number of fast charging station addresses; T health is the healthy dynamic planning cycle of the fast charging station; S, E, and C are safety, efficiency, and cost indicators, respectively, where the negative sign indicates the opposite optimization direction.

[0014] Step 1-2: Build security metrics

[0015] The total annual voltage deviation of the charging station S

[0016] The voltage fluctuation of fast charging stations will affect the safety and reliability of electric vehicles and charging pile equipment, affect their charging efficiency, and have a negative impact on the health of charging pile equipment. Therefore, this paper constructs a safety index S based on the sum of voltage deviations of each station:

[0017]

[0018] Where: It is the maximum voltage deviation of each fast charging station within the planning period after the planning scheme is implemented; U is the node voltage value of each fast charging station in the initial planning year t0; i,t is the node voltage value of each fast charging station in year t; S indicates the sum of the maximum voltage deviations of each fast charging station. The smaller S is, the smaller the voltage deviation is, and the more stable the power supply performance and health status of the fast charging station are.

[0019] Step 1-3: Constructing the performance indicator E

[0020] The sum of the reliability benefit and charging profitability benefit of the fast charging station is used as the performance indicator.

[0021] E=E1+E2 (4)

[0022] Where: E1 is the reliability benefit; E2 is the power supply profit benefit.

[0023] (1) Reliability benefit E1 taking into account energy supply efficiency

[0024] Improving power supply reliability through reasonable fast charging station planning decisions can bring significant benefits

[21] Therefore, this paper calculates the reliability benefits of each planning scheme based on the expected value of insufficient power at the charging station, the power outage loss per unit of power, the total number of years of the planning period and the discount rate as follows:

[0025]

[0026]

[0027] Where: r is the discount rate; γ health An equal installment capital recovery factor to take into account the healthy life cycle; is the terminal value of reliability benefit in the tth year after the planning scheme δ is put into implementation; are the expected power shortage values ​​in the tth year before and after the implementation of the planning scheme; T health The planning period for the whole life health planning cycle; T t load is the annual charging hours in the operation scenario in year t; P t load is the daily charging load demand under different operation scenarios in year t; P t hour is the hourly power supply of each station under different operation scenarios in the tth year; χ is the electricity price.

[0028] The specific calculation formula for the expected value of insufficient power considering the aging state of the charging pile equipment is as follows:

[0029]

[0030] Where: To consider the power transmission efficiency of the charging pile, the optimal load shedding amount of node j under the fault state ζ in the tth year; is the power factor of the charging pile equipment; v is the starting efficiency of the charging pile as a whole; g is the performance attenuation rate of the charging pile as a whole.

[0031] (2) Power supply profit benefit E2

[0032] For operators, the reasonable commissioning of fast charging stations can effectively improve their profitability, which is specifically reflected in the increase in electricity sales after the commissioning of fast charging stations. This article defines charging profitability as the annual value of annual profit taking into account the time value of money. The specific expression is as follows:

[0033]

[0034] Steps 1-4: Build cost metrics

[0035] Considering the time value of money, the full life cycle cost is used as the cost indicator, and the calculation formula is:

[0036] C=C I +C O +C M +C F +C D (11)

[0037] Where: C is the annual value of the full life cycle cost within the cycle planning; C I , C O , C M , C F , C D It is the annual value of initial investment cost, operating cost, repair and maintenance cost, failure cost, and decommissioning cost within the planning period.

[0038] (1) Initial investment cost C I

[0039] The initial investment cost mainly includes the equipment cost, land acquisition cost and other infrastructure costs required for the construction of new fast charging stations. At the same time, this article also needs to consider the investment cost of some equipment reaching the healthy retirement cycle too early or too late. The calculation formula is as follows:

[0040]

[0041] Where: C ev-fast are the unit capacity costs of fast charging pile equipment within the planning period; P t ev-fastis the total capacity of charging piles put into operation in the corresponding year of the tth year; ω is the infrastructure cost of new charging stations during the planning period, including cables, transformers, safety monitoring equipment, etc.; μ is the discount factor.

[0042] (2) Operating cost C O

[0043] The operating cost is mainly the cost of line losses caused by the investment in fast charging stations from the initial year, and the calculation formula is as follows:

[0044]

[0045] Where: P t loss is the network loss value in the tth year; β is the electricity purchase price.

[0046] (3) Maintenance cost C M

[0047] The inspection and maintenance cost of fast charging stations is closely related to the total cost of charging pile equipment operation, which can be calculated based on the total initial investment in the equipment. The calculation formula is as follows:

[0048]

[0049] Where: α is the maintenance cost conversion coefficient.

[0050] (4) Failure cost C F

[0051] The power shortage cost of the charging station under fault condition is considered as the fault cost, which is obtained by the expected value of power shortage when the accident occurs under the operation scenario. The calculation formula is as follows:

[0052]

[0053] (5) Decommissioning cost C D

[0054] After the charging station equipment expires, it still has residual value. The decommissioning disposal cost mainly includes the scrap asset residual value recovery income and scrap disposal management expenses, among which the equivalent annual value C of the decommissioning disposal cost is D The calculation formula is as follows:

[0055]

[0056] In the formula: q is the residual value; b is the asset management fee ratio coefficient.

[0057] Steps 1-5: Constraints

[0058] (1) Restrictions on the number of charging piles at fast charging stations.

[0059] On the one hand, considering the needs of users, on the other hand, due to the limited available area and investment cost of each fast charging station, there are upper and lower limits on the number of charging piles in a fast charging station:

[0060]

[0061] Where: are the minimum and maximum number of charging piles in charging station i; n i is the number of charging piles in charging station i.

[0062] (2) Distribution network flow equation constraints.

[0063]

[0064] The voltage constraints for each node are:

[0065] U j,min ≤U j ≤U j,max (20)

[0066] Where: U j,δ ,U k,δ are the voltages of nodes j and k under the planning scheme δ; P j,δ ,Q j,δ are the active power and reactive power injected into node j respectively; G jk and B jk is the conductance and susceptance of the system; θ jk is the voltage phase difference between nodes j and k; U j,max and U j,min are the upper and lower limits of the node voltage amplitude respectively.

[0067] The beneficial effects of the present invention are as follows: the residual value of the equipment itself is taken into account, the health status of the equipment is used to determine its health dynamic planning cycle, the impact of early retirement and replacement of some equipment and delayed re-commissioning of some equipment within the entire health planning cycle is considered to accurately calculate its time value of money, and the safety efficiency cost is used as the optimization goal.

[0068] By accurately calculating the time value of money of different sites over their full healthy life cycle during the planning process, some charging stations can be retired and replaced in advance when their healthy life expires prematurely. This not only improves the utilization rate of the equipment, but also effectively alleviates the charging voltage fluctuations when a large number of electric vehicle users access the charging station. Some charging stations can be decommissioned later when their healthy life expires late, which not only improves the utilization rate of the equipment, but also improves the economy of the planning scheme.

[0069] The calculation of various indicators of its safety efficiency cost is no longer simulated based on the maximum load scenario as a single operating scenario. On the one hand, it fully considers the randomness of charging by electric vehicle users. On the other hand, it makes the residual value and usage of charging stations at different sites different, effectively improving the effectiveness of the calculation of various indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Health status diagram of fast charging station under different usage conditions;

[0071] Figure 2 This is a diagram of the retirement of fast charging stations in different health states;

[0072] Figure 3 This is the IEEE33 node simulation example diagram;

[0073] Figure 4 The OPC curve and Pareto non-dominated solution set diagram of method three;

[0074] Figure 5 The OPC curve and Pareto non-dominated solution set diagram of methods 1 and 2. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below in conjunction with the accompanying drawings. It should be pointed out that it is only for the purpose of more clearly describing and explaining the present invention.

[0076] like Figure 1-5 As shown, this embodiment discloses a fast charging station planning method considering equipment health life and safety efficiency cost, which specifically includes the following steps:

[0077] Step 1: Propose a safety effectiveness cost planning model considering the health status of equipment, including the following steps:

[0078] Step 1-1: Construct the objective function

[0079] F(P EV-fast ,T health )=min[S(P EV-fast ,T health ),-E(P EV-fast ,T health ),C(P EV-fast ,T health )] (1)

[0080] Where: P EV-fast is the total capacity vector of charging piles in the newly built fast charging station, where N is the total number of fast charging station addresses; T healthis the healthy dynamic planning cycle of the fast charging station; S, E, and C are safety, efficiency, and cost indicators, respectively, where the negative sign indicates the opposite optimization direction.

[0081] Step 1-2: Build security metrics

[0082] The total annual voltage deviation of the charging station S

[0083] The voltage fluctuation of fast charging stations will affect the safety and reliability of electric vehicles and charging pile equipment, affect their charging efficiency, and have a negative impact on the health of charging pile equipment. Therefore, this paper constructs a safety index S based on the sum of voltage deviations of each station:

[0084]

[0085] Where: is the maximum voltage deviation of each fast charging station within the planning period after the planning scheme is implemented; U i,t0 U is the node voltage value of each fast charging station in the initial planning year t0; i,t is the node voltage value of each fast charging station in year t; S indicates the sum of the maximum voltage deviations of each fast charging station. The smaller S is, the smaller the voltage deviation is, and the more stable the power supply performance and health status of the fast charging station are.

[0086] Step 1-3: Constructing the performance indicator E

[0087] The sum of the reliability benefit and charging profitability benefit of the fast charging station is used as the performance indicator.

[0088] E=E1+E2 (4)

[0089] Where: E1 is the reliability benefit; E2 is the power supply profit benefit.

[0090] (1) Reliability benefit E1 taking into account energy supply efficiency

[0091] Improving power supply reliability through reasonable fast charging station planning decisions can bring significant benefits

[21] Therefore, this paper calculates the reliability benefits of each planning scheme based on the expected value of insufficient power at the charging station, the power outage loss per unit of power, the total number of years of the planning period and the discount rate as follows:

[0092]

[0093] Where: r is the discount rate; γ health An equal installment capital recovery factor to take into account the healthy life cycle; is the terminal value of reliability benefit in the tth year after the planning scheme δ is put into implementation; are the expected power shortage values ​​in the tth year before and after the implementation of the planning scheme; Thealth The planning period for the whole life health planning cycle; T t load is the annual charging hours in the operation scenario in year t; P t load is the daily charging load demand under different operation scenarios in year t; P t hour is the hourly power supply of each station under different operation scenarios in the tth year; χ is the electricity price.

[0094] The specific calculation formula for the expected value of insufficient power considering the aging state of the charging pile equipment is as follows:

[0095]

[0096] Where: To consider the power transmission efficiency of the charging pile, the optimal load shedding amount of node j under the fault state ζ in the tth year; is the power factor of the charging pile equipment; v is the starting efficiency of the charging pile as a whole; g is the performance attenuation rate of the charging pile as a whole.

[0097] (2) Power supply profit benefit E2

[0098] For operators, the reasonable commissioning of fast charging stations can effectively improve their profitability, which is specifically reflected in the increase in electricity sales after the commissioning of fast charging stations. This article defines charging profitability as the annual value of annual profit taking into account the time value of money. The specific expression is as follows:

[0099]

[0100] Steps 1-4: Build cost metrics

[0101] Considering the time value of money, the full life cycle cost is used as the cost indicator, and the calculation formula is:

[0102] C=C I +C O +C M +C F +C D (11)

[0103] Where: C is the annual value of the full life cycle cost within the cycle planning; C I , C O , C M , C F , C D It is the annual value of initial investment cost, operating cost, repair and maintenance cost, failure cost, and decommissioning cost within the planning period.

[0104] (1) Initial investment cost CI

[0105] The initial investment cost mainly includes the equipment cost, land acquisition cost and other infrastructure costs required for the construction of new fast charging stations. At the same time, this article also needs to consider the investment cost of some equipment reaching the healthy retirement cycle too early or too late. The calculation formula is as follows:

[0106]

[0107] Where: C ev-fast are the unit capacity costs of fast charging pile equipment within the planning period; P t ev-fast is the total capacity of charging piles put into operation in the corresponding year of the tth year; ω is the infrastructure cost of new charging stations during the planning period, including cables, transformers, safety monitoring equipment, etc.; μ is the discount factor.

[0108] (2) Operating cost C O

[0109] The operating cost is mainly the cost of line losses caused by the investment in fast charging stations from the initial year, and the calculation formula is as follows:

[0110]

[0111] Where: P t loss is the network loss value in the tth year; β is the electricity purchase price.

[0112] (3) Maintenance cost C M

[0113] The inspection and maintenance cost of fast charging stations is closely related to the total cost of charging pile equipment operation, which can be calculated based on the total initial investment in the equipment. The calculation formula is as follows:

[0114]

[0115] Where: α is the maintenance cost conversion coefficient.

[0116] (4) Failure cost C F

[0117] The power shortage cost of the charging station under fault condition is considered as the fault cost, which is obtained by the expected value of power shortage when the accident occurs under the operation scenario. The calculation formula is as follows:

[0118]

[0119] (5) Decommissioning cost C D

[0120] After the charging station equipment expires, it still has residual value. The decommissioning disposal cost mainly includes the scrap asset residual value recovery income and scrap disposal management expenses, among which the equivalent annual value C of the decommissioning disposal cost is D The calculation formula is as follows:

[0121]

[0122] In the formula: q is the residual value; b is the asset management fee ratio coefficient.

[0123] Steps 1-5: Constraints

[0124] (1) Restrictions on the number of charging piles at fast charging stations.

[0125] On the one hand, considering the needs of users, on the other hand, due to the limited available area and investment cost of each fast charging station, there are upper and lower limits on the number of charging piles in a fast charging station:

[0126]

[0127] Where: are the minimum and maximum number of charging piles in charging station i; n i is the number of charging piles in charging station i.

[0128] (2) Distribution network flow equation constraints.

[0129]

[0130] The voltage constraints for each node are:

[0131] U j,min ≤U j ≤U j,max (20)

[0132] Where: U j,δ ,U k,δ are the voltages of nodes j and k under the planning scheme δ; P j,δ ,Q j,δ are the active power and reactive power injected into node j respectively; G jk and B jk is the conductance and susceptance of the system; θ jk is the voltage phase difference between nodes j and k; U j,max and U j,min are the upper and lower limits of the node voltage amplitude respectively.

[0133] Step 2: Considering the differences in fast charging station equipment, a health status assessment model for fast charging stations is proposed:

[0134] Considering that charging piles may age and break down after being used for a period of time, this paper uses the formula of aging health index widely used in Europe and the United States to describe the health status, and introduces aging health index and aging coefficient to dynamically measure the status of each station and the health status of the charging station. as follows:

[0135]

[0136] Where: represents the health assessment status of the i-th charging station in the t-th year; H t They represent the aging health index at year t0 and year t respectively; M is the aging coefficient.

[0137] H t =U i,t +η i,t +λ i,ζ (twenty two)

[0138]

[0139] Where: T ev represents the LCC life of the fast charging pile equipment, that is, the original planned retirement cycle; f1 represents the load correction factor of the component; f2 refers to the environmental correction factor of the component; U i,t is the charging voltage fluctuation rate; η i,t λ is the unavailability rate of charging pile equipment due to aging failure; i,t is the failure rate of the charging station.

[0140] (1) Charging voltage fluctuation rate U i,t

[0141] The grid input voltage has a large fluctuation, which will affect the service life of key components of the charging pile. Therefore, the voltage fluctuation is included as one of the indicators for comprehensive evaluation of the health status of the charging pile. The specific expression is as follows:

[0142]

[0143] Where: U i,t represents the charging voltage fluctuation rate of the i-th fast charging station in the t-th year.

[0144] (2) Charging pile equipment aging failure unavailability rate η i,t

[0145] As a power converter between the power grid and the electric vehicle battery, the efficiency of the charging pile will continue to decline with the frequent use of the equipment. Inefficient power conversion will cause the charging pile to extract more electricity from the power grid to meet the charging needs of the electric vehicle, which will lead to increased losses within the station and a continuous increase in the internal temperature of the charging pile.

[0146]

[0147] Where: η i,t represents the aging failure unavailability rate of the i-th charging station in the t-th year.

[0148] (3) Charging station failure rate λ i,t

[0149] Failure rate is one of the key indicators to measure the health status of charging piles.

[0150]

[0151] Where: i,t represents the charging failure rate of the i-th charging station in the t-th year; Num is the total number of simulated samples; X i Represents a collection of simulation states.

[0152] For the fault and operation states of each charging pile device, the probability characteristic of each device can be represented by a random number G between [0,1]. i To describe, assume that J represents the failure rate of device i, for the state B of the device i Then we have:

[0153]

[0154] Where: B i G is the operating status of the complete equipment of each fast charging station; i The random number of the probability characteristic of the charging station is between [0,1]; J t is the failure rate in year t; is the initial failure rate. The state of each corresponding device can be obtained through formula (28), and then the state of a system can be obtained X = (B1, B2, ..., B i ), repeat the above process N times, and you can get a set X'={X1,X2,…,X Num}.

[0155] Step 3 includes the following steps:

[0156] Step 3-1: Combined with the equipment health status, a dynamic planning cycle determination method considering the equipment health status is proposed, and the Pareto front solution set is solved by the vector order optimization algorithm, and the optimal planning method is obtained according to the normalized target:

[0157] As attached Figure 1As shown in the figure, by determining a reasonable health dynamic planning cycle, on the one hand, the retirement cycle of charging stations with good health status and surplus residual value is extended to fully improve the overall utilization of equipment and reduce additional investment; on the other hand, charging stations with poor health status and surplus residual value are retired in advance to reduce the invisible negative impact brought by charging equipment.

[0158] Different from the conventional LCC theoretical planning method, the life cycle of each fast charging station in this paper needs to consider the health status of the equipment at the previous moment to dynamically determine the operating status of the charging station equipment at the next moment. i health The determination method is as follows:

[0159] Rating based on the health status of the charging pile

[70] , judge the operating status of the equipment at the next moment, and provide a basis for judging the reasonable commissioning and retirement of charging station equipment.

[0160]

[0161] Where: T i health represents the healthy retirement cycle of the i-th fast charging station.

[0162] Different from the conventional life cycle cost planning theory, due to the different retirement times of various charging stations, some charging stations will reach healthy retirement status ahead of time, while other charging stations will still be in service. Therefore, its planning meets the following principles, as shown in the attached Figure 2 As shown:

[0163] 1) This chapter takes the longest retirement period of newly built equipment as the entire healthy dynamic planning life cycle, that is:

[0164]

[0165] 2) If any equipment reaches the end of its healthy life cycle within the original planning period, the equipment will be retired in the year when its life expires and new equipment will be put into operation again, and the planning scheme will remain unchanged.

[0166] 3) This chapter only calculates the annual costs incurred within the health planning cycle. Costs incurred outside the planning cycle will be included in the calculation of the next dynamic health planning cycle.

[0167] Step 3-2: Vector order optimization solution algorithm

[0168] Step 3-3: Input the original data and parameters, and determine the new construction / renovation plan of PV storage and charging according to the road network coupling node diagram. Randomly select N planning schemes (generally N = 1000) to form an ordered optimization representation set f(P EV ,PES ,P PV )={f1,f2,···,f n}.

[0169] Step 3-4: Based on formula (3-2)(3-11)(3-14)(3-21)(3-23)(3-25), an ordered optimization rough evaluation model is constructed. S and C are used as reverse performance indicators, and E is used as a forward performance indicator. The rough evaluation values ​​are sorted and the non-inferior solutions are stratified to obtain an ordered performance curve (OPC). The type of the optimization problem is determined based on the OPC curve.

[0170] Step 3-5: Select the first s layers of feasible solutions in the rough model evaluation results as the selected set, where s is calculated as shown in formula (31).

[0171]

[0172] Where: s is a function of k and g; k is the number of sufficiently good solutions; g indicates that the first g layers are designated as the number of truly good enough solutions; k and g are generally set artificially; e is a natural number; Z σ ,ρ, is a regression parameter, which can be determined according to the OPC type; υ is the noise component.

[0173] Step 3-6: Use equations (3-2), (3-7), and (3-16) as the order optimization exact model, and sort and stratify the selected set S to form a Pareto non-dominated solution set.

[0174] Step 3-7: Use the SEC normalization index to further sort the Pareto non-dominated solution set, and take the solution with the smallest SEC normalization index as the optimal solution. Its SEC normalization index is shown in formula (32).

[0175]

[0176] Example Description

[0177] This description selects the IEEE 33-node distribution network system as an example for simulation analysis. Its structure is shown in the attached Figure 3 Considering the growing charging load demand of electric vehicle users in an area, where the daily average load demand in the area is 6.5MW and the annual average charging load demand increases by 0.1MW, the locations where fast charging stations need to be deployed are 6, 10, 13, 18, 21, 23, and 28 nodes, and the available capacities are 300kw, 360kw, and 420kw.

[0178] In order to verify the effectiveness of the method proposed in this paper, the following three methods are designed for comparative analysis:

[0179] Method 1: Planning of charging stations considering the theoretical commissioning and retirement of the entire life cycle and the maximum load utilization hours;

[0180] Method 2: Planning of fast charging stations considering theoretical commissioning and random scenarios over the entire life cycle;

[0181] Method 3: Fast charging station planning considering the dynamic healthy retirement cycle of equipment, which is the planning method described in this article.

[0182] The economic and technical parameters of the example are shown in Table 1

[0183] Technical Parameters Numeric Economic parameters Numeric Fast charging pile power / kW 60 Single machine price / 10,000 yuan 1 Fast charging pile LCC life cycle / year 10 Infrastructure cost / 10,000 yuan 60 Initial efficiency of the whole machine / % 95 Electricity purchase price / (yuan / (kW·h)) 0.6 Overall performance attenuation efficiency / % 2 Electricity price / (yuan / (kW·h)) 1 Power conversion factor / % 98 discount rate / % 7 Initial failure rate / % 2.5 Scrap asset cost ratio 0.04 Maintenance cost conversion factor 0.2 Residual value rate / % 5 Load correction factor 0.3 Environmental correction factor 0.5 Health Factor 0.5

[0184] Example simulation results

[0185] The order optimization curve and Pareto non-dominated solution set obtained by the planning method proposed in this specification are shown in the attached Figure 4 The OPC curve and Pareto non-dominated solution set obtained by methods 1 and 2 are shown in Figure 5 ; The SEC values ​​of the Pareto non-dominated solution sets of methods 1 to 3 are shown in Appendix 1-3;

[0186] Table 1 Index values ​​of Pareto non-dominated solution set of method 1

[0187]

[0188]

[0189] Table 2 Index values ​​of Pareto non-dominated solution set of method 2

[0190] Layer number plan Safety indicators Performance index / ten thousand yuan Cost index / ten thousand yuan FSEC 1 17 0.300 286.712 205.216 0.214 1 24 0.299 285.575 205.199 0.215 1 94 0.305 287.344 205.787 0.218 1 197 0.302 289.572 208.602 0.217 1 208 0.298 285.803 205.688 0.214 1 388 0.301 284.988 204.802 0.217 1 466 0.300 288.888 209.422 0.218 1 599 0.305 289.707 209.374 0.220 1 620 0.295 287.869 206.751 0.212 1 625 0.295 286.9 206.157 0.212 1 658 0.318 289.091 208.082 0.229 1 796 0.303 286.132 203.778 0.216 1 814 0.323 289.862 209.142 0.233 1 911 0.307 287.435 206.182 0.220 1 999 0.303 286.480 204.447 0.216

[0191] Table 3 Index values ​​of Pareto non-dominated solution set of method 3

[0192]

[0193]

[0194] According to the SEC normalized index, the optimal planning implementation measures obtained by the three methods are shown in Table 4:

[0195] Table 4 Implementation measures of the optimal planning schemes of the three methods

[0196]

[0197] 3) Comparative analysis

[0198] We continue to compare and analyze the values ​​of each sub-indicator of the optimal planning schemes obtained by the three planning methods. The specific values ​​are shown in Tables 5 and 6.

[0199] (1) Comparative analysis between method 1 and method 2

[0200] The values ​​of each sub-indicator of the planning decision results of comparison methods one and two are shown in Table 5.

[0201] Table 5 Sub-indicator values ​​of Method 1 and Method 2

[0202]

[0203] As shown in Table 5, in terms of the safety index S, method 2 is better than method 1, and its value is 0.07 less than that of method 1, that is, method 2 causes lower charging voltage fluctuations to each fast charging station. The reason why method 2 can reduce the fluctuation of charging voltage deviation is that method 2 considers the randomness of charging load when simulating the operation scenario, and no longer uses the maximum charging scenario as a single simulation method. Compared with method 1, method 2 has a small short-term charging load access and low power supply, which reduces the voltage impact. In addition, compared with method 1, the total capacity of the fast charging pile equipment put into operation by method 2 is 240Kw more than that of method 1. Its planning decision-making scheme puts more charging pile equipment into operation to alleviate the charging load impact brought by users on the equipment.

[0204] In terms of performance indicators, method 1 is superior, and its annual value of performance indicators is 133,620 yuan higher than that of method 2, among which the annual value of reliability benefit is 55,830 yuan higher, and the annual value of power supply profit benefit is 77,800 yuan higher. This is because method 1 takes the maximum load as a single scenario. Since there is no significant difference in the simulated failure rate of each node, its reliability benefit is directly related to the maximum load shedding of each node, while the simulation scenario of method 1 indirectly affects the maximum load shedding of each node, and the operation and charging conditions are simulated in the maximum charging scenario every year, so its reliability benefit is higher than that of method 2; in terms of the power supply profit benefit indicator, since the average daily power supply load demand is the same in different scenario simulations, the size of its indicator value is directly related to the simulated single-hour power supply in different scenarios and the annual load charging hours. Method 1 simulates according to the maximum load scenario. Although the annual load charging hours will be slightly lower, its single-hour power supply is significantly higher than that of method 2. In general, method 1 brings higher benefits.

[0205] In terms of cost indicators, Method 1 is superior, and its LCC and other annual values ​​are 187,610 yuan lower than those of Method 2. This is because the values ​​of various cost sub-indicators of Method 1 are slightly lower than those of Method 2, including initial investment cost and other annual values ​​5,700 yuan lower, operating cost and other annual values ​​89,070 yuan lower, maintenance cost and other annual values ​​4,000 yuan lower, and failure cost and other annual values ​​88,900 yuan lower. The difference in the above indicators is mainly due to: on the one hand, compared with method one, the total capacity of the fast charging pile equipment put into operation in method two is 240Kw more than that in method one, which leads to a slightly higher annual value of initial investment cost. Similarly, since the maintenance and decommissioning disposal costs are positively correlated with the overall equipment investment cost, the two costs of method two are higher than those of method one; on the other hand, although method one simulates the maximum load charging scenario, which leads to higher short-term line losses than method two, the size of the annual value of operating cost is also related to the size of the load charging hours. In order to meet the annual average daily power supply load demand, method two has a longer charging time for electric vehicle load access, which makes its annual values ​​of operating cost and failure cost higher, which is also the main reason for the higher annual value of LCC. From the overall SEC value, the planning scheme obtained by method two is better than that of method one. The main reason is that method two takes into account the randomness of electric vehicle users' charging and does not simulate the maximum load scenario, which greatly alleviates the huge impact of electric vehicle users in a short time, reduces the short-term impact loss on charging piles, and improves their safety.

[0206] (2) Comparative analysis between method 2 and method 3 The values ​​of each sub-indicator of the planning results of method 2 and method 3 are shown in Table 6:

[0207] Table 6 Values ​​of each sub-indicator of Method 2 and Method 3

[0208]

[0209] As shown in Table 4, in terms of safety indicators, method 3 is better than method 2, and its value is 0.11 less than method 1, that is, method 3 causes lower charging voltage fluctuations on each fast charging station. The reason why method 3 can reduce the fluctuation of charging voltage deviation is that method 3 takes into account the health status of each fast charging station, so that some stations with poor health status can reasonably replace equipment within the full life cycle, and some charging stations with good health status and exceeding the original life cycle of the equipment can continue to be re-commissioned, alleviating the invisible negative charging voltage impact caused by equipment aging.

[0210] In terms of performance indicators, method three is superior, and its annual value of performance indicators is 35,270 yuan higher than that of method two, of which the annual value of reliability benefits is 20,850 yuan higher, and the annual value of power supply profit benefits is 14,430 yuan higher than that of method two. In terms of reliability benefits, when the node failure rate coefficients simulated by methods two and three are the same and the random operation scenarios simulated by methods two and three are not much different, their maximum load shedding amounts are also not much different. Method three takes into account the health status of the equipment and replaces the equipment of some sites according to the residual value of the equipment, thereby improving the overall efficiency, which decreases year by year with the increase of the operating life of the equipment, and thus improving the annual value of the reliability benefits of method three. In terms of power supply profit benefits, the difference is mainly due to the different simulations of random operation scenarios for charging load demand.

[0211] In terms of cost indicators, method three is superior, and its LCC equivalent annual value is 61,340 yuan lower than that of method two. This is because although the annual value of failure cost equivalent of method three is 3,700 yuan higher than that of method two, its initial investment cost equivalent annual value, operating cost equivalent annual value and maintenance cost equivalent annual value are 50,090 yuan, 9,020 yuan and 6,380 yuan lower than those of method two, respectively. The difference in the above indicators is mainly due to: on the one hand, compared with method three, the total capacity of fast charging pile equipment put into operation in the initial planning year of method two is 60Kw more than that of method three. Similarly, method three replaced the charging pile equipment with a capacity of 660Kw and 1020Kw in the 9th and 10th years. However, due to the consideration of time capital conversion, on the one hand, the equal installment capital recovery coefficient considering the healthy dynamic cycle is slightly lower than the equal installment capital recovery coefficient considering the LCC planning cycle, and on the other hand, its discount coefficient in the 9th and 10th years is significantly lower than that in the 1st year. Therefore, on the whole, the initial investment cost equivalent of method two is The annual value is higher than that of method three. Similarly, since the maintenance and decommissioning disposal costs are positively correlated with the overall equipment investment cost, the two costs of method two are higher than those of method three. On the other hand, in terms of operating costs, method three takes into account the health status of the equipment, which improves the overall functional efficiency of the charging stations in the area and reduces unnecessary invisible negative losses. Therefore, the annual value of the operating cost of method three is slightly lower. In terms of failure costs, the difference between the two methods is not large. This is mainly because when the simulated node failure rate coefficient is the same, the random operation scenarios simulated by methods two and three are not much different, and the difference between their maximum load shedding amounts is small.

[0212] From the perspective of the overall SEC value, the planning scheme obtained by Method 3 is better than that of Method 2. This is mainly because Method 3 takes into account the surplus and loss status of the residual value of the equipment itself during the planning process, and replaces some stations in advance and postpones the decommissioning of some stations according to their health status. As a result, the overall use value of the fast charging stations in the area is improved, the equipment utilization rate is greatly improved, the impact of electric vehicle users is alleviated, and the penetration rate is reduced. This makes Method 3 have an overall advantage in the SEC indicator.

[0213] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fast charging station planning method considering equipment health life and safety efficiency cost, characterized in that: The following steps are involved: Step 1: Build a cost planning model; Step 2: Construct a health status assessment model for charging stations; Step 3: Combined with the equipment health status, set up a dynamic planning cycle determination method that takes the equipment health status into consideration, and solve the Pareto front solution set through the vector order optimization algorithm, and obtain the optimal planning method based on the normalized objective.

2. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 1 is characterized by: Construct the objective function in step 1. The objective function is: F(P EV-fast ,T health )=min[S(P EV-fast ,T health ),-E(P EV-fast ,T health ),C(P EV-fast ,T health )] Where: P EV-fast is the total capacity vector of charging piles in the newly built fast charging station, where N is the total number of fast charging station addresses; T health is the healthy dynamic planning cycle of the fast charging station; S, E, and C are safety, efficiency, and cost indicators, respectively, where the negative sign indicates the opposite optimization direction; Construct a safety index; the sum of the voltage deviations of charging stations each year is S. The voltage fluctuations of fast charging stations will affect the safety and reliability of electric vehicles and charging pile equipment, affect their charging efficiency, and have a negative impact on the health of charging pile equipment; construct a safety index S with the sum of the voltage deviations of each station: Where: It is the maximum value of voltage deviation of each fast charging station within the planning period after the planning scheme is implemented; U is the node voltage value of each fast charging station in the initial planning year t0; i,t is the node voltage value of each fast charging station in the tth year; S indicates the sum of the maximum voltage deviations of each fast charging station; the smaller S is, the smaller the voltage deviation is, and the more stable the power supply performance and health status of the fast charging station are.

3. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 2 is characterized by: Construct the performance index E in step 1, taking the sum of the reliability benefit and charging profit benefit of the fast charging station as the performance index; E=E1+E2 Where: E1 is the reliability benefit; E2 is the power supply profit benefit; Taking into account the reliability benefit E1 of energy supply efficiency; improving the reliability of power supply through the planning decision of fast charging stations, which is used to bring benefits. The reliability benefit of each planning scheme is calculated according to the expected value of insufficient power of the charging station, the power outage loss per unit power, the total planning period and the discount rate. Where: r is the discount rate; γ health An equal installment capital recovery factor to take into account the healthy life cycle; is the terminal value of reliability benefit in the tth year after the planning scheme δ is put into implementation; are the expected power shortage values ​​in the tth year before and after the implementation of the planning scheme; T health The planning period for the whole life health planning cycle; T t load is the annual charging hours in the operation scenario in year t; P t load is the daily charging load demand under different operation scenarios in year t; P t hour is the hourly power supply of each station under different operation scenarios in year t; χ is the electricity price; The specific calculation formula for the expected value of insufficient power considering the aging state of the charging pile equipment is: Where: To consider the power transmission efficiency of the charging pile, the optimal load shedding amount of node j under the fault state ζ in the tth year; is the power factor of the charging pile equipment; v is the starting efficiency of the charging pile as a whole; g is the performance attenuation rate of the charging pile as a whole; Power supply profit benefit E2, The commissioning of fast charging stations is used to improve their profitability, which is specifically reflected in the increase in electricity sales after the commissioning of fast charging stations. The charging profitability is set to the annual value of the annual profit under the consideration of the time value of money. The specific expression is: Construct cost indicators, consider the time value of money, and use the full life cycle cost as the cost indicator. The calculation formula is: C=C I +C O +C M +C F +C D Where: C is the annual value of the full life cycle cost within the cycle planning; C I , C O , C M , C F , C D It is the annual value of initial investment cost, operating cost, repair and maintenance cost, failure cost, and decommissioning cost within the planning period.

4. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 3 is characterized by: The initial investment cost includes the equipment cost, land acquisition cost and other infrastructure costs required for the construction of new fast charging stations, and takes into account the investment cost of some equipment reaching the healthy retirement cycle too early or too late. The calculation formula is as follows: Where: C ev-fast are the unit capacity costs of fast charging pile equipment within the planning period; P t ev-fast is the total capacity of charging piles put into operation in the corresponding year in the tth year; ω is the infrastructure cost of new charging stations within the planning period, including cables, transformers, safety monitoring equipment, etc.; μ is the discount factor; The operating cost includes the cost of line losses caused by the investment in fast charging stations from the initial year, and the calculation formula is as follows: Where: P t loss is the network loss value in year t; β is the electricity purchase price; The maintenance cost is related to the total cost of the charging pile equipment, which is calculated based on the total initial investment of the equipment. The calculation formula is as follows: Where: α is the maintenance cost conversion coefficient; Failure cost: The power shortage cost of the charging station under fault condition is the failure cost, which is obtained by the expected value of insufficient power when an accident occurs under the operation scenario. The calculation formula is as follows: Decommissioning and disposal costs: After the charging station equipment expires and is scrapped, it still has residual value. The decommissioning and disposal costs include the scrap asset residual value recovery income and scrapping and disposal management expenses. The equivalent annual value calculation formula for the decommissioning and disposal costs is as follows: Where: q is the residual value; b is the asset management fee ratio coefficient.

5. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 4 is characterized by: The constraints of step 1 include the number of charging piles in the fast charging station and the power flow equation constraint of the distribution network; The constraint on the number of charging piles in the fast charging station takes into account the needs of users on the one hand, and on the other hand, due to the limited available area and investment cost of each fast charging station, there are upper and lower limits on the number of charging piles in the fast charging station: Where: are the minimum and maximum number of charging piles in charging station i; n i is the number of charging piles in charging station i; The distribution network flow equation constraint is: The voltage constraints satisfied by each node are: IN j,min ≤U j ≤U j,max Where: U j,δ ,U k,δ are the voltages of nodes j and k under the planning scheme δ; P j,δ ,Q j,δ are the active power and reactive power injected into node j respectively; G jk and B jk is the conductance and susceptance of the system; θ jk is the voltage phase difference between nodes j and k; U j,max and U j,min are the upper and lower limits of the node voltage amplitude respectively.

6. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 1 is characterized by: In the step 2, the equipment differences of fast charging stations are considered, and a health status assessment model for fast charging stations is proposed. After a period of use, the charging piles will age and be damaged. By introducing the aging health index and aging coefficient to dynamically measure the status of each station, the health status of the charging station is evaluated. as follows, Where: represents the health assessment status of the i-th charging station in the t-th year; H t0 , H t They represent the aging health index at year t0 and year t respectively; M is the aging coefficient; H t =U i,t +n i,t +λ i,ζ Where: T ev represents the LCC life of the fast charging pile equipment, that is, the original planned retirement cycle; f1 represents the load correction factor of the component; f2 refers to the environmental correction factor of the component; U i,t is the charging voltage fluctuation rate; η i,t λ is the unavailability rate of charging pile equipment due to aging failure; i,t is the failure rate of the charging station.

7. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 8 is characterized by: The charging voltage fluctuation rate, the grid input voltage has fluctuations, which affects the service life of the charging pile. The voltage fluctuation is included in the index for comprehensive evaluation of the health status of the charging pile. The specific expression is as follows: Where: U i,t represents the charging voltage fluctuation rate at the i-th fast charging station in year t; The aging failure and unavailability rate of the charging pile equipment. The charging pile is a power converter between the power grid and the electric vehicle battery. The frequent use of the equipment causes the overall efficiency of the equipment to continue to decline. Inefficient power conversion causes the charging pile to extract more electricity from the power grid to meet the charging needs of the electric vehicle, which in turn leads to increased losses in the station and a continuous increase in the internal temperature of the charging pile; Where: η i,t represents the aging failure unavailability rate of the i-th charging station in the t-th year; The charging station failure rate is one of the indicators to measure the health status of charging piles. Where: i,t represents the charging failure rate of the i-th charging station in the t-th year; Num is the total number of simulated samples; X i Represents a set of simulation states; For the fault and operation states of each charging pile device, the probability characteristic of each device can be represented by a random number G between [0,1]. i To describe, let J represent the failure rate of device i, for the state B of the device i Then we have: J t =θ·(1+g) t Where: B i G is the operating status of the complete equipment of each fast charging station; i The random number of the probability characteristic of the charging station is between [0,1]; J t is the failure rate in the tth year; θ is the initial failure rate; by obtaining the status of each corresponding device, the system status X=(B1, B2,…, B i ), repeat the above process N times to obtain a set X'={X1,X2,…,X Num }.

8. The fast charging station planning method considering equipment health life and safety efficiency cost according to claim 1 is characterized by: The step three includes the following sub-steps: Step 3-1: By determining a reasonable health dynamic planning cycle, on the one hand, the retirement cycle of charging stations with good health status and surplus residual value is extended to fully improve the overall utilization rate of equipment and reduce additional investment; on the other hand, charging stations with poor health status and surplus residual value are retired in advance to reduce the invisible negative impact of charging equipment; the life cycle of each fast charging station needs to consider the health status of the equipment at the previous moment to dynamically judge the operating status of the charging station equipment at the next moment, and the health retirement cycle of each charging station is determined as follows; according to the health status rating of the charging pile, the operating status of the equipment at the next moment is judged to provide a judgment basis for the reasonable commissioning and retirement of the charging station equipment, Where: T i health represents the healthy retirement cycle of the i-th fast charging station; Due to the different retirement times of various charging stations, some charging stations will reach the healthy retirement state ahead of time, while other charging stations will remain in service. Therefore, their planning meets the following principles, with the longest retirement time of the newly built equipment as the entire healthy dynamic planning life cycle, namely: If any equipment reaches its healthy life cycle within the original planning cycle, the equipment will be retired in the year when its life expires, and new equipment will be put into operation again, and the planning scheme will remain unchanged; and only the annual costs incurred within the healthy planning cycle will be calculated, and the costs incurred outside the planning cycle will be included in the calculation of the next dynamic healthy planning cycle; Step 3-2: Vector order optimization solution algorithm; Step 3-3: Input the original data and parameters, and determine the new construction / renovation plan of PV storage and charging according to the road network coupling node diagram; extract N planning schemes (including N = 1000) to form the ordered optimization representation set f(P EV ,P ES ,P PV )={f1,f2,···,f n }; Step 3-4: Construct a sequential optimization rough evaluation model, use S and C as the reverse index, E as the forward index, sort the rough evaluation values, stratify the non-inferior solutions to obtain the sequence curve, and determine the type of the optimization problem according to the sequence curve; Step 3-5: Select the first s layers of feasible solutions in the rough model evaluation results as the selected set, where the calculation formula for s is, Where: s is a function of k and g; k is the number of sufficiently good solutions; g indicates that the first g layers are designated as the number of truly good enough solutions; k and g are artificially set; e is a natural number; Z σ ,ρ, is the regression parameter, determined according to the type of sequence curve, υ is the noise component; Step 3-6: Sort and stratify the selected set S to form a Pareto non-dominated solution set; Step 3-7: Use the SEC normalization index to further sort the Pareto non-dominated solution set, and take the solution with the smallest SEC normalization index as the optimal solution; its SEC normalization index formula is,

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