A collaborative optimization method for on-board energy storage configuration and charging station layout

Abstract

The present invention discloses a method for collaborative optimization of on-board energy storage configuration and charging station layout, including the following steps: determining the maximum speed curve of train operation time efficiency, the maximum required power of train operation and the maximum operating energy consumption of a single section of the train; constructing a coupling model of on-board energy storage and charging station and a cost objective function of on-board energy storage and charging station; outputting the preliminary collaborative optimization results of on-board energy storage configuration and charging station layout based on a heuristic method; and performing secondary optimization on the charging station layout to output the optimal results of on-board energy storage configuration and charging station layout. The present invention reduces the overall cost while ensuring the stable operation of the train to provide a directly applicable on-board energy storage and charging solution, and performs secondary optimization on the charging station layout to further improve the stability of the train operation.

Description

Technical Field

[0001] The present invention relates to the field of train construction optimization, and in particular to a method for collaborative optimization of on-board energy storage configuration and charging station layout. Background Art

[0002] Modern energy storage trams are gradually being built and operated in many large and medium-sized cities in my country. When planning the on-board energy storage configuration and charging station layout, it is necessary to comprehensively consider the train operation needs and costs. On the one hand, if the cost reduction is considered, the train operation may be restricted. On the other hand, if the stability of train operation is considered, the cost of on-board energy storage and charging stations may be extremely high. In addition, the on-board energy storage configuration or charging station layout alone cannot achieve the optimal match between the two, and it is difficult to obtain a reasonable and low-cost design solution. Therefore, while ensuring the stable operation of the train, the on-board energy storage configuration and the charging station layout need to be coordinated and optimized to achieve the goal of stable operation of the train and reduce costs.

[0003] For the coordinated optimization of on-board energy storage configuration and charging station layout, the existing methods establish objective functions for on-board energy storage and charging stations respectively, solve multi-objective problems based on genetic algorithms, and then achieve coordinated optimization of on-board energy storage configuration and charging station layout. However, the existing methods do not establish the feasibility premise of the scheme that the train can run in the section in the constraint function, resulting in the solution of the optimization result that the train cannot run in the section. At the same time, the set objective function does not consider the equipment purchase and maintenance costs of on-board energy storage and charging stations, as well as the specific coupling relationship between the two. Therefore, it is impossible to directly obtain applicable on-board energy storage and charging solutions based on the optimization results. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for collaborative optimization of on-board energy storage configuration and charging station layout, which reduces the overall cost while ensuring stable operation of the train, provides a directly applicable on-board energy storage and charging solution, and performs secondary optimization on the charging station layout to further improve the stability of train operation.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for collaboratively optimizing vehicle-mounted energy storage configuration and charging station layout comprises the following steps:

[0007] S1. Obtain line data and train data, and determine the maximum speed curve of train running time efficiency, the maximum required power of train running, and the maximum running energy consumption of a single section of the train according to the line data and train data;

[0008] S2. Construct a coupling model of on-board energy storage and charging stations, obtain construction and maintenance cost data, and construct a cost objective function of on-board energy storage and charging stations based on the coupling model of on-board energy storage and charging stations and the construction and maintenance cost data;

[0009] S3, based on the maximum required power of the train operation in step S1, the maximum energy consumption of a single section of the train, and the cost objective function of the on-board energy storage and the charging station in step S2, output the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout based on the heuristic method;

[0010] S4. According to the preliminary collaborative optimization result of the on-board energy storage configuration and the charging station layout in step S3, the charging station layout is optimized for the second time to output the optimal result of the on-board energy storage configuration and the charging station layout.

[0011] Furthermore, step S1 includes the following sub-steps:

[0012] S11, obtaining line data, and discretizing each section of the line according to the line data to obtain line section data;

[0013] S12, obtaining train data, calculating the maximum traction capacity of the line section data in step S11 according to the train data, and determining the maximum speed curve of the running time efficiency of the entire line train according to the line section data and the maximum traction capacity;

[0014] S13, calculating the train running power requirement according to the train data in step S12 and the train running time efficiency maximum speed curve, and determining the train running maximum power requirement;

[0015] S14. Calculate the train section operation energy consumption based on the train running time efficiency maximum speed curve in sub-step S12 and the train required power in sub-step S13, and determine the train single section maximum operation energy consumption.

[0016] Furthermore, step S13 includes the following sub-steps:

[0017] S131. Calculate the train traction power according to the maximum speed curve of train running time efficiency in step S12, expressed as:

[0018] P t =(F t / η t -F b η b )v

[0019] Where: P t is the train traction power, F t is the traction force exerted by the train, η t is the traction efficiency of the traction system, F bis the electric braking force applied by the train, η b is the braking efficiency of the traction system, v is the train speed;

[0020] S132. Calculate the train running power requirement according to the train data and the train traction power in step S131, expressed as:

[0021] P need =P t +P aux

[0022] Where: P need P is the power required for train operation, t is the train traction power, P aux The power of the vehicle auxiliary system;

[0023] S133. Determine the maximum required power for train operation according to the required power for train operation in sub-step S132.

[0024] Furthermore, in sub-step S14, the train section operation energy consumption is calculated, which is expressed as:

[0025]

[0026] Where: E is the energy consumption of train section operation, P need is the power required for train operation, v is the train speed, and s is the distance of the interval.

[0027] Further, step S2 includes the following sub-steps:

[0028] S21. Construct a coupling model between on-board energy storage and charging station;

[0029] S22. Obtain construction and maintenance cost data, and calculate the cost of the charging station based on the construction and maintenance cost data and the coupling model of the on-board energy storage and the charging station in step S21, expressed as:

[0030] C cha =N cha (C c1 +C c2 +C cm )

[0031] Where: C cha is the charging station cost, N cha is the number of charging stations, C c1 is the unit equipment purchase cost of the charging station, C c2 is the construction cost of the charging station, C cm The subsequent maintenance cost of the charging station;

[0032] S23. Calculate the on-board energy storage cost based on the construction and maintenance cost data in sub-step S22, expressed as:

[0033] C bat =N bat (C b +C bm )

[0034] Where: C bat is the on-board energy storage cost, N bat Configure the capacity of the on-board energy storage, C b is the unit purchase cost of on-board energy storage, C bm The subsequent maintenance cost of on-board energy storage;

[0035] S24. According to the charging station cost function in sub-step S22 and the on-board energy storage cost in sub-step S23, a cost objective function of on-board energy storage and charging station is constructed, which is expressed as:

[0036] J1=min(C cha +C bat )

[0037] Where: J1 is the total cost of the charging station and on-board energy storage, min is the symbol for taking the minimum value, C cha is the charging station cost, C bat The cost of on-board energy storage.

[0038] Furthermore, step S21 includes the following sub-steps:

[0039] S211. Construct the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, expressed as:

[0040] I Chamax =λ1I batmax

[0041] Where: I Chamax is the maximum charging current of the charging station, λ1 is the relationship coefficient between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, I batmax The maximum charging current for on-board energy storage;

[0042] S212: Construct the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, expressed as:

[0043] P Chamax =λ2P batmax

[0044] Where: P Chamax is the maximum charging power of the charging station, λ2 is the relationship coefficient between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, P batmax The maximum charging power for on-board energy storage;

[0045] S213, based on the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage in sub-step S211 and the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage in sub-step S212, a coupling model of the on-board energy storage and the charging station is constructed, which is expressed as:

[0046] C c1 =f(I chamax ,P chamax )

[0047] Where: C c1 is the coupling model of charging capacity and cost under the association of on-board energy storage and charging station, f() is the functional relationship between equipment purchase cost and charging capacity, I chamax is the maximum charging current of the charging station, P chamax The maximum charging power of the charging station.

[0048] Further, step S3 includes the following sub-steps:

[0049] S31, determining the minimum range of on-board energy storage configuration according to the maximum required power of the train operation and the maximum energy consumption of a single section of the train in step S1;

[0050] S32: According to the cost objective function of the on-board energy storage and the charging station in step S2, determine the first fitness function, which is expressed as:

[0051]

[0052] Where: fit1 is the fitness function value of the charging station and on-board energy storage, C cha is the charging station cost, C bat The cost of on-board energy storage;

[0053] S33, encoding the on-board energy storage configuration scheme and the charging station layout, and initializing them according to the minimum range of the on-board energy storage configuration in sub-step S31, and obtaining initialization coding data;

[0054] S34, using the maximum speed curve of train running time efficiency to solve the initialized coded data in step S33 to obtain coded individuals;

[0055] S35, constructing a constraint model for on-board energy storage;

[0056] S36, judging whether the coded individual in sub-step S34 satisfies the constraint model of the on-board energy storage in sub-step S35; if so, proceeding to sub-step S37, otherwise, determining the cost objective function value and the fitness function value of the coded individual as the first set value;

[0057] S37, calculating the cost objective function value of the encoding individual according to the cost objective function, and calculating the fitness function value of the encoding individual according to the first fitness function in sub-step S34;

[0058] S38, determine whether the initial optimization end condition is met; if so, output the initial collaborative optimization result of the on-board energy storage configuration and the charging station layout, otherwise update the coded individual according to the heuristic method and jump to sub-step S36.

[0059] Further, step S4 includes the following sub-steps:

[0060] S41, determine the equivalent constraints of the number of charging stations, and construct the second-stage constraint model of on-board energy storage;

[0061] S42, determining a margin objective function, and determining a second fitness function according to the margin objective function;

[0062] S43, encoding the charging station layout according to the preliminary collaborative optimization result of the on-board energy storage configuration and the charging station layout in step S3, and initializing it to obtain the second-stage initialization encoding individual data;

[0063] S44, using the maximum speed curve of train running time efficiency to solve the second stage initialization coded individual data in step S43, and obtain the on-board energy storage state data of the second stage coded individual under the full-line maximum time efficiency operation of the train;

[0064] S45, judging whether the on-board energy storage state data of the second-stage coded individual in sub-step S44 satisfies the second-stage constraint model of the on-board energy storage in sub-step S41; if so, proceeding to sub-step S46, otherwise, determining the margin objective function value and fitness function value of the second-stage coded individual as the second set value;

[0065] S46, calculating the margin objective function value of the second-stage coding individual according to the margin objective function in sub-step S42, and calculating the fitness function value of the second-stage coding individual according to the second fitness function in sub-step S42;

[0066] S47, determine whether the second-stage optimization end condition is met; if so, output the optimal results of the on-board energy storage configuration and charging station layout, otherwise update the second-stage coded individuals according to the heuristic method and jump to sub-step S45.

[0067] Further, in sub-step S42, the margin objective function is expressed as:

[0068] J2=max(min(SOC(s)))

[0069] Where: J2 is the total margin of the charging station and the on-board energy storage, max is the maximum value symbol, min is the minimum value symbol, and SOC(s) is the state of charge value of the on-board energy storage.

[0070] The beneficial effects of the present invention are:

[0071] (1) The present invention performs on-board energy storage configuration (electric energy capacity design scheme of on-board power supply system) and charging station layout optimization (number of charging stations and distribution location setting scheme) based on the train's maximum time efficiency speed curve. The maximum optimization degree that the train can achieve in the long term corresponding to the maximum time efficiency speed curve can therefore meet the long-term operation needs of the train in the future;

[0072] (2) When constructing the cost objective function of on-board energy storage and charging stations, the present invention comprehensively considers the various costs of on-board energy storage and charging stations and the specific coupling relationship between the two, so that the final on-board energy storage and charging solutions are more feasible;

[0073] (3) The state of charge of the on-board energy storage during train operation indicates its anti-interference ability, i.e., stability. Based on this, after obtaining the preliminary collaborative optimization results of the on-board energy storage configuration and charging station layout that meet the stable operation of the train, the present invention considers that the state of charge of the on-board energy storage in some charging station intervals is low, and thus performs secondary optimization on the charging station layout. The charging station layout can be further optimized without changing the number of charging stations, thereby improving the stability of the train during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The figure is a flow chart of a method for collaborative optimization of on-board energy storage configuration and charging station layout. DETAILED DESCRIPTION

[0075] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0076] like Figure 1 As shown, a method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout includes steps S1-S4, which are specifically as follows:

[0077] S1. Obtain line data and train data, and determine the maximum speed curve of train running time efficiency, the maximum required power of train running, and the maximum running energy consumption of a single section of the train based on the line data and train data.

[0078] In an optional embodiment of the present invention, the present invention obtains line data, discretizes each section of the line to obtain line section data; the present invention obtains train data, and calculates the maximum traction capacity of the line section data based on the train data to determine the maximum speed curve of the train running time efficiency, and further determines the maximum required power of the train operation and the maximum operating energy consumption of a single section of the train based on the determined maximum speed curve of the train running time efficiency.

[0079] Step S1 includes the following sub-steps:

[0080] S11. Obtain line data, and discretize each section of the line according to the line data to obtain line section data.

[0081] Specifically, the line data acquired by the present invention includes station kilometer marks, speed limits, slopes and line curves.

[0082] S12, obtaining train data, calculating the maximum traction capacity of the line section data in step S11 according to the train data, and determining the maximum speed curve of the train running time efficiency of the entire line according to the line section data and the maximum traction capacity.

[0083] Specifically, the train data acquired by the present invention includes the power of the on-board auxiliary system, the vehicle weight, the efficiency of the traction drive system, the traction braking characteristics and the basic resistance characteristics.

[0084] S13. Calculate the train running power requirement according to the train data in sub-step S12 and the train running time efficiency maximum speed curve, and determine the train running maximum required power.

[0085] Step S13 includes the following sub-steps:

[0086] S131. Calculate the train traction power according to the maximum speed curve of train running time efficiency in step S12, expressed as:

[0087] P t =(F t / η t -F b η b )v

[0088] Where: P t is the train traction power, F t is the traction force exerted by the train, η t is the traction efficiency of the traction system, F b is the electric braking force applied by the train, η b is the braking efficiency of the traction system, and v is the train speed.

[0089] S132. Calculate the train running power requirement according to the train data and the train traction power in step S131, expressed as:

[0090] P need =P t +P aux

[0091] Where: P need P is the power required for train operation, t is the train traction power, P aux The power of the vehicle auxiliary system.

[0092] S133. Determine the maximum required power for train operation according to the required power for train operation in sub-step S132.

[0093] S14. Calculate the train section operation energy consumption based on the train running time efficiency maximum speed curve in sub-step S12 and the train required power in sub-step S13, and determine the train single section maximum operation energy consumption.

[0094] The present invention calculates the train section operation energy consumption, which is expressed as:

[0095]

[0096] Where: E is the energy consumption of train section operation, P need is the power required for train operation, v is the train speed, and s is the distance of the interval.

[0097] S2. Construct a coupling model of the on-board energy storage and the charging station, obtain construction and maintenance cost data, and construct a cost objective function of the on-board energy storage and the charging station based on the coupling model of the on-board energy storage and the charging station and the construction and maintenance cost data.

[0098] In an optional embodiment of the present invention, the present invention constructs a coupling model of on-board energy storage and charging stations, obtains construction and maintenance cost data, and constructs a cost objective function of on-board energy storage and charging stations based on the coupling model of on-board energy storage and charging stations and the construction and maintenance cost data. In the process of constructing the cost objective function of on-board energy storage and charging stations, the present invention comprehensively considers the various costs of on-board energy storage and charging stations and the specific coupling relationship between the two, so that the final obtained on-board energy storage and charging scheme is more feasible.

[0099] Step S2 includes the following sub-steps:

[0100] S21. Construct a coupling model of on-board energy storage and charging station.

[0101] Step S21 includes the following sub-steps:

[0102] S211. Construct the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, expressed as:

[0103] I Chamax =λ1I batmax

[0104] Where: I Chamax is the maximum charging current of the charging station, λ1 is the relationship coefficient between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, I batmax It is the maximum charging current of the on-board energy storage.

[0105] S212: Construct the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, expressed as:

[0106] P Chamax =λ2P batmax

[0107] Where: P Chamax is the maximum charging power of the charging station, λ2 is the relationship coefficient between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, P batmax The maximum charging power for on-board energy storage.

[0108] S213, based on the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage in sub-step S211 and the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage in sub-step S212, a coupling model of the on-board energy storage and the charging station is constructed, which is expressed as:

[0109] C c1 =f(I chamax ,P chamax )

[0110] Where: C c1 is the coupling model of charging capacity and cost under the association of on-board energy storage and charging station, f() is the functional relationship between equipment purchase cost and charging capacity, I chamax is the maximum charging current of the charging station, P chamax The maximum charging power of the charging station.

[0111] S22. Obtain construction and maintenance cost data, and calculate the cost of the charging station based on the construction and maintenance cost data and the coupling model of the on-board energy storage and the charging station in step S21, expressed as:

[0112] C cha =N cha (C c1 +C c2 +C cm )

[0113] Where: C cha is the charging station cost, Ncha is the number of charging stations, C c1 is the unit equipment purchase cost of the charging station, C c2 is the construction cost of the charging station, C cm It is the subsequent maintenance cost of the charging station.

[0114] S23. Calculate the on-board energy storage cost based on the construction and maintenance cost data in sub-step S22, expressed as:

[0115] C bat =N bat (C b +C bm )

[0116] Where: C bat is the on-board energy storage cost, N bat Configure the capacity of the on-board energy storage, C b is the unit purchase cost of on-board energy storage, C bm It is the later maintenance cost of on-board energy storage.

[0117] S24. According to the charging station cost function in sub-step S22 and the on-board energy storage cost in sub-step S23, a cost objective function of on-board energy storage and charging station is constructed, which is expressed as:

[0118] J1=min(C cha +C bat )

[0119] Where: J1 is the total cost of the charging station and on-board energy storage, min is the symbol for taking the minimum value, C cha is the charging station cost, C bat The cost of on-board energy storage.

[0120] S3. Based on the maximum required power of the train operation in step S1, the maximum energy consumption of a single section of the train, and the cost objective function of the on-board energy storage and the charging station in step S2, the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout are output based on the heuristic method.

[0121] In an optional embodiment of the present invention, the present invention determines the on-board energy storage configuration constraints according to the maximum required power of the train operation and the maximum energy consumption of a single section of the train, and outputs the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout based on the heuristic method according to the on-board energy storage configuration constraints and the cost objective function of the on-board energy storage and the charging station. In the process of determining the on-board energy storage configuration constraints, the present invention takes into account the charge capacity decay rate of the on-board energy storage within the service life, which can meet the long-term operation needs of the train in the future.

[0122] Step S3 includes the following sub-steps:

[0123] S31. Determine the minimum range of the on-board energy storage configuration according to the maximum required power of the train operation and the maximum energy consumption of a single section of the train in step S1.

[0124] Specifically, the present invention determines the on-board energy storage configuration constraint according to the maximum required power of train operation and the maximum energy consumption of a single section of the train, which is expressed as:

[0125] P batmax >P needmax ,

[0126] E batmax >αE needmax

[0127] Where: P batmax is the maximum output power of the on-board energy storage under the lowest working state, P needmax is the maximum required power of train operation, E batmax is the charge capacity of the on-board energy storage in full state, α is the charge capacity decay rate of the on-board energy storage during its service life, E needmax It is the maximum energy consumption of a single section of the train.

[0128] Then, the present invention determines the minimum range of the on-board energy storage configuration according to the on-board energy storage configuration constraint, that is, determines the minimum configuration quantity of the constituent monomers of the on-board energy storage based on the minimum power constraint of the on-board energy storage.

[0129] S32: According to the cost objective function of the on-board energy storage and the charging station in step S2, determine the first fitness function, which is expressed as:

[0130]

[0131] Where: fit1 is the fitness function value of the charging station and on-board energy storage, C cha is the charging station cost, C bat The cost of on-board energy storage.

[0132] Specifically, the first fitness function in the present invention is the inverse of the cost objective function. The larger the cost, the smaller the fitness function. Therefore, the maximum fitness function corresponds to the lowest cost.

[0133] S33, encoding the on-board energy storage configuration scheme and the charging station layout, and initializing them according to the minimum range of the on-board energy storage configuration in sub-step S31 to obtain initialization coding data.

[0134] Specifically, the on-board energy storage configuration scheme is coded in decimal, indicating the number of on-board energy storage units. The charging station layout is coded in binary, with 0 and 1 indicating whether a charging station is set up at the station. The minimum range of on-board energy storage configuration is used to limit the range of the number of codes during the initialization coding process.

[0135] S34. Use the maximum speed curve of train running time efficiency to solve the initialized coded data in step S33 to obtain coded individuals.

[0136] S35. Construct a constraint model for on-board energy storage.

[0137] The state of charge of the on-board energy storage must be within the normal operating range. The present invention constructs a constraint model for on-board energy storage, which is expressed as:

[0138] SOC min ≤SOC(s)≤SOC max

[0139] Among them: SOC min SOC(s) is the minimum state of charge for the on-board energy storage to work normally, SOC max It is the maximum state of charge for the on-board energy storage to work normally.

[0140] S36, judging whether the coded individual in sub-step S34 satisfies the constraint model of the on-board energy storage in sub-step S35; if so, proceeding to sub-step S37, otherwise, determining the cost objective function value and fitness function value of the coded individual as the first set value.

[0141] Specifically, if the coded individual does not satisfy the constraint model of the on-board energy storage, the cost objective function value of the coded individual is determined to be infinite, and the fitness function value is determined to be zero.

[0142] S37. Calculate the objective function value of the encoding individual according to the cost objective function, and calculate the fitness function value of the encoding individual according to the first fitness function in sub-step S34.

[0143] S38, determine whether the initial optimization end condition is met; if so, output the initial collaborative optimization result of the on-board energy storage configuration and the charging station layout, otherwise update the coded individual according to the heuristic method and jump to sub-step S36.

[0144] Heuristic methods include genetic algorithms, particle swarm algorithms, differential evolution algorithms, etc. Different heuristic methods have different evolution modes. The present invention can adopt any heuristic method.

[0145] S4. According to the preliminary collaborative optimization result of the on-board energy storage configuration and the charging station layout in step S3, the charging station layout is optimized for the second time to output the optimal result of the on-board energy storage configuration and the charging station layout.

[0146] In an optional embodiment of the present invention, the present invention performs secondary optimization on the charging station layout according to the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout to output the optimal results of the on-board energy storage configuration and the charging station layout. After obtaining the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout that meet the stable operation of the train, the present invention considers that the state of charge of the on-board energy storage in some charging station intervals is low, and therefore performs secondary optimization on the charging station layout. The charging station layout can be further optimized without changing the number of charging stations, thereby improving the stability of the train during operation.

[0147] Step S4 includes the following sub-steps:

[0148] S41, determine the equivalent constraint of the number of charging stations, and construct the second stage constraint model of on-board energy storage. The present invention determines the equivalent constraint of the number of charging stations, which is expressed as:

[0149] N cha2 =N cha

[0150] Where: N cha2 The number of charging station layout optimizations, N cha is the number of charging stations.

[0151] The present invention determines the number constraint of charging stations, which is expressed as:

[0152] N cha ≤N max

[0153] Where: N cha is the number of charging stations, N max is the number of train stations.

[0154] S42: Determine a margin objective function, and determine a second fitness function according to the margin objective function.

[0155] The margin objective function is expressed as:

[0156] J2=max(min(SOC(s)))

[0157] Where: J2 is the total margin of the charging station and the on-board energy storage, max is the maximum value symbol, min is the minimum value symbol, and SOC(s) is the state of charge value of the on-board energy storage.

[0158] Specifically, in the present invention, the second fitness function is equal to the margin objective function.

[0159] S43: Encode the charging station layout according to the preliminary collaborative optimization result of the on-board energy storage configuration and the charging station layout in step S3, and initialize it to obtain the second-stage initialization encoding individual data.

[0160] S44. Utilize the maximum speed curve of train operation time efficiency to solve the second-stage initialized coded individual data in step S43, and obtain the on-board energy storage status data of the second-stage coded individual under the train operation with maximum time efficiency on the entire line.

[0161] S45. Determine whether the on-board energy storage status data of the second-stage coded individual in sub-step S44 satisfies the second-stage constraint model of the on-board energy storage in sub-step S41; if so, proceed to sub-step S46, otherwise determine the margin objective function value and fitness function value of the second-stage coded individual as the second set value.

[0162] Specifically, if the second-stage coding individual does not satisfy the second-stage constraint model of the on-board energy storage, the margin objective function value of the second-stage coding individual is determined to be 0, and the fitness function value is determined to be 0.

[0163] S46. Calculate the margin objective function value of the second-stage coding individual according to the margin objective function in sub-step S42, and calculate the fitness function value of the second-stage coding individual according to the second fitness function in sub-step S42.

[0164] S47, determine whether the second-stage optimization end condition is met; if so, output the optimal results of the on-board energy storage configuration and charging station layout, otherwise update the second-stage coded individuals according to the heuristic method and jump to sub-step S45.

[0165] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout, characterized in that: The following steps are involved: S1. Obtain line data and train data, and determine the maximum speed curve of train running time efficiency, the maximum required power of train running, and the maximum running energy consumption of a single section of the train according to the line data and train data; S2. Construct a coupling model of on-board energy storage and charging stations, obtain construction and maintenance cost data, and construct a cost objective function of on-board energy storage and charging stations based on the coupling model of on-board energy storage and charging stations and the construction and maintenance cost data; Step S2 includes the following sub-steps: S21. Construct a coupling model between on-board energy storage and charging station; Step S21 includes the following sub-steps: S211. Construct the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, expressed as: in: is the maximum charging current of the charging station, is the coefficient of the maximum charging current of the charging station and the maximum charging current of the on-board energy storage, The maximum charging current for on-board energy storage; S212: Construct the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, expressed as: in: is the maximum charging power of the charging station, is the relationship coefficient between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage, The maximum charging power for on-board energy storage; S213, based on the relationship between the maximum charging current of the charging station and the maximum charging current of the on-board energy storage in sub-step S211 and the relationship between the maximum charging power of the charging station and the maximum charging power of the on-board energy storage in sub-step S212, a coupling model of the on-board energy storage and the charging station is constructed, which is expressed as: in: It is a coupling model of charging capacity and cost under the association of on-board energy storage and charging stations. is the functional relationship between equipment acquisition cost and charging capacity, is the maximum charging current of the charging station, The maximum charging power of the charging station; S22. Obtain construction and maintenance cost data, and calculate the cost of the charging station based on the construction and maintenance cost data and the coupling model of the on-board energy storage and the charging station in step S21, expressed as: in: is the cost of the charging station, is the number of charging stations, is the unit equipment purchase cost of the charging station, is the construction cost of the charging station, The subsequent maintenance cost of the charging station; S23. Calculate the on-board energy storage cost based on the construction and maintenance cost data in sub-step S22, expressed as: in: is the on-board energy storage cost, Configure capacity for on-board energy storage, is the unit purchase cost of on-board energy storage, The subsequent maintenance cost of on-board energy storage; S24. According to the charging station cost function in sub-step S22 and the on-board energy storage cost in sub-step S23, a cost objective function of on-board energy storage and charging station is constructed, which is expressed as: in: is the total cost of charging stations and on-board energy storage, To get the minimum symbol, is the cost of the charging station, The cost of on-board energy storage; S3, based on the maximum required power of the train operation in step S1, the maximum energy consumption of a single section of the train, and the cost objective function of the on-board energy storage and the charging station in step S2, output the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout based on the heuristic method; S4, according to the preliminary collaborative optimization results of the on-board energy storage configuration and the charging station layout in step S3, the charging station layout is optimized twice to output the optimal results of the on-board energy storage configuration and the charging station layout; Step S4 includes the following sub-steps: S41, determine the equivalent constraints of the number of charging stations, and construct the second-stage constraint model of on-board energy storage; The equivalent constraint for determining the number of charging stations is expressed as: in: The number of charging station layout optimizations, is the number of charging stations, is the number of train stations; S42, determining a margin objective function, and determining a second fitness function according to the margin objective function; The margin objective function is expressed as: in: is the total margin of the charging station and on-board energy storage, To obtain the maximum value symbol, To get the minimum symbol, is the state of charge value of the on-board energy storage; The second fitness function is equal to the margin objective function; S43, encoding the charging station layout according to the preliminary collaborative optimization result of the on-board energy storage configuration and the charging station layout in step S3, and initializing it to obtain the second-stage initialization encoding individual data; S44, using the maximum speed curve of train running time efficiency to solve the second stage initialization coded individual data in step S43, and obtain the on-board energy storage state data of the second stage coded individual under the full-line maximum time efficiency operation of the train; S45, judging whether the on-board energy storage state data of the second-stage coded individual in sub-step S44 satisfies the second-stage constraint model of the on-board energy storage in sub-step S41; if so, proceeding to sub-step S46, otherwise, determining the margin objective function value and fitness function value of the second-stage coded individual as the second set value; S46, calculating the margin objective function value of the second-stage coding individual according to the margin objective function in sub-step S42, and calculating the fitness function value of the second-stage coding individual according to the second fitness function in sub-step S42; S47, determine whether the second-stage optimization end condition is met; if so, output the optimal results of the on-board energy storage configuration and charging station layout, otherwise update the second-stage coded individuals according to the heuristic method and jump to sub-step S45.

2. A method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout according to claim 1, characterized in that: Step S1 includes the following sub-steps: S11, obtaining line data, and discretizing each section of the line according to the line data to obtain line section data; S12, obtaining train data, calculating the maximum traction capacity of the line section data in step S11 according to the train data, and determining the maximum speed curve of the running time efficiency of the entire line train according to the line section data and the maximum traction capacity; S13, calculating the train running power requirement according to the train data in step S12 and the train running time efficiency maximum speed curve, and determining the train running maximum power requirement; S14. Calculate the train section operation energy consumption based on the train running time efficiency maximum speed curve in sub-step S12 and the train required power in sub-step S13, and determine the train single section maximum operation energy consumption.

3. A method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout according to claim 2, characterized in that: Step S13 includes the following sub-steps: S131. Calculate the train traction power according to the maximum speed curve of train running time efficiency in step S12, expressed as: in: is the train traction power, The traction force applied to the train, is the traction efficiency of the traction system, The electric braking force applied to the train, is the braking efficiency of the traction system, is the train speed; S132. Calculate the train running power requirement according to the train data and the train traction power in step S131, expressed as: in: The power required for train operation is is the train traction power, The power of the vehicle auxiliary system; S133. Determine the maximum required power for train operation according to the required power for train operation in sub-step S132.

4. A method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout according to claim 2, characterized in that: In sub-step S14, the train section operation energy consumption is calculated, which is expressed as: in: is the train section operation energy consumption, The power required for train operation is is the train speed, is the distance between the intervals.

5. The method for collaborative optimization of vehicle-mounted energy storage configuration and charging station layout according to claim 1, characterized in that: Step S3 includes the following sub-steps: S31, determining the minimum range of on-board energy storage configuration according to the maximum required power of the train operation and the maximum energy consumption of a single section of the train in step S1; S32: According to the cost objective function of the on-board energy storage and the charging station in step S2, determine the first fitness function, which is expressed as: in: is the fitness function value of the charging station and on-board energy storage, is the cost of the charging station, The cost of on-board energy storage; S33, encoding the on-board energy storage configuration scheme and the charging station layout, and initializing them according to the minimum range of the on-board energy storage configuration in sub-step S31, and obtaining initialization coding data; S34, using the maximum speed curve of train running time efficiency to solve the initialized coded data in step S33 to obtain coded individuals; S35, constructing a constraint model for on-board energy storage; S36, judging whether the coded individual in sub-step S34 satisfies the constraint model of the on-board energy storage in sub-step S35; if so, proceeding to sub-step S37, otherwise, determining the cost objective function value and the fitness function value of the coded individual as the first set value; S37, calculating the cost objective function value of the encoding individual according to the cost objective function, and calculating the fitness function value of the encoding individual according to the first fitness function in sub-step S34; S38, determine whether the initial optimization end condition is met; if so, output the initial collaborative optimization result of the on-board energy storage configuration and the charging station layout, otherwise update the coded individual according to the heuristic method and jump to sub-step S36.

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