A multi-resource collaborative configuration method for integrated traction power supply system

Abstract

The invention discloses a multi-resource collaborative configuration method for a fusion-type traction power supply system, comprising the following steps: S1. constructing an upper-level configuration model based on minimizing annual comprehensive costs, wherein the objective function of the upper-level configuration model includes the annual investment cost of the system and the annual operation and maintenance cost of the system; S2. constructing a lower-level optimization model based on minimizing the operation and maintenance cost, wherein the objective function of the lower-level optimization model is the daily operation and maintenance cost of the system; S3. firstly assigning initial values ​​to the equipment access position and the capacity of each equipment; S4. transmitting the equipment access position and the capacity of each equipment obtained by the upper-level configuration model to the lower-level optimization model to solve the output of each equipment and the operation and maintenance cost; S5. transmitting the solved operation and maintenance cost to the upper-level configuration model to optimize the annual comprehensive cost, updating the equipment access position and the capacity of each equipment to the lower-level optimization model, and iterating S4-S5 until the optimal solution is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit resource configuration, and in particular to a multi-resource collaborative configuration method for a fusion-type traction power supply system. Background Art

[0002] As one of the major energy-consuming industries, electrified railways are in urgent need of promoting the green transformation of their power supply architecture. On the one hand, the areas along the railways are rich in renewable resources. On the other hand, electric locomotives give priority to regenerative braking when braking, which generates a large amount of regenerative braking energy. The integrated traction power supply architecture based on the railway power conditioner (RPC) can efficiently integrate new energy and energy storage, promote the local consumption of renewable energy and the efficient use of regenerative braking energy, and promote the green upgrade of the railway energy supply architecture.

[0003] The upgrade of system architecture requires reasonable resource allocation to maximize its energy efficiency and return on investment. However, existing studies have focused on the configuration of single resources such as RPC and energy storage, or the configuration of RPC-energy storage and new energy-energy storage. There is a lack of coordinated configuration of multiple resources, making it difficult to simultaneously achieve low-carbon energy supply and stable operation of the system. In addition, with the construction of a fusion-type traction power supply system, the scope of power flow has expanded from the power supply arm of a single site to multiple sites on the traction network. The energy interaction between stations can achieve efficient utilization of new energy and regenerative braking energy. In summary, it is necessary to consider the energy interaction between multiple traction substations and realize the coordinated configuration of multiple resources of a fusion-type traction power supply system with global optimization as the goal. Summary of the invention

[0004] In view of this, the present invention provides a multi-resource collaborative configuration method for an integrated traction power supply system, which is used to at least solve the problem that the traction power supply system in the prior art lacks multi-resource collaborative configuration and does not consider the coordinated optimization among multiple sites, making it difficult to ensure the overall economy of the system configuration and is not conducive to energy-friendly mutual assistance among stations.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A multi-resource collaborative configuration method for a traction power supply system of a fusion type comprises the following steps:

[0007] S1. Construct an upper configuration model based on minimizing the annual comprehensive cost. The objective function of the upper configuration model includes the annual investment cost of the system and the annual operation and maintenance cost of the system;

[0008] S2. Construct a lower-level optimization model based on minimizing the operation and maintenance cost, where the objective function of the lower-level optimization model is the system's single-day operation and maintenance cost;

[0009] S3. First, assign initial values ​​to the device access location and the capacity of each device;

[0010] S4. The device access location and capacity of each device obtained by the upper configuration model are passed to the lower optimization model to solve the output and operation and maintenance cost of each device;

[0011] S5. The solved operation and maintenance cost is passed to the upper configuration model to optimize the annual comprehensive cost, and the device access location and the capacity of each device are updated and passed to the lower optimization model. S4-S5 are iterated until the optimal solution is obtained;

[0012] The constraints of the lower optimization model include:

[0013] Power balance constraints on the DC bus:

[0014] ;

[0015] Where: is the actual power consumption of the photovoltaic power generation unit; For substation The charging and discharging power of energy storage, charging is positive and discharging is negative; and Substation RPC Harmony Phase active transmission power; and Divisions RPC Mutually, Phase active transmission power; represents the total number of traction substations; Indicates the total number of partitions.

[0016] 2) Preferably, the constraints of the lower optimization model also include:

[0017] Power balance constraints on the traction network:

[0018] When the train is in traction condition, the active power of the traction load is greater than 0. At this time, the active power on the traction network satisfies:

[0019] ;

[0020] When the train is in regenerative braking condition, the active power of the traction load is less than 0. At this time, the active power on the traction network satisfies:

[0021] ;

[0022] The reactive power on the traction network meets the following requirements:

[0023] ;

[0024] Where: and Substation of Harmony Phase traction load active power; and Substation The transformer Harmony Phase active power; and Substation RPC Harmony Phase active power; and Divisions RPC Harmony Phase active power; and Substation of Harmony Phase regenerative braking power; and Substation of Harmony Phase traction load reactive power; and Substation Transformer Harmony Phase reactive power; and Substation RPC Harmony Phase reactive power; and Divisions RPC Harmony Phase reactive power;

[0025] Equipment capacity constraints:

[0026] System transformers and RPCs must meet capacity constraints:

[0027] ;

[0028] Where: is the rated capacity of the transformer; and They are the rated capacities of the substation RPC and the substation RPC respectively;

[0029] Regardless of the feeding grid situation, the active power value of the traction transformer is non-negative:

[0030] ;

[0031] Photovoltaic output constraints:

[0032] The output of the photovoltaic power generation unit is non-negative and is constrained by its predicted value:

[0033] ;

[0034] Where: is the predicted output value of the photovoltaic power generation unit; It is a binary variable, and its value is 1, indicating that the substation The photovoltaic unit is configured in the substation. When it is 0, it indicates No photovoltaic unit is configured; for The value of the photovoltaic power generation curve at the moment; is the rated power of the photovoltaic;

[0035] Energy storage constraints:

[0036] The output power of the energy storage unit shall not exceed its rated power. To avoid overcharging or overdischarging of the energy storage element, which may lead to accelerated aging of the medium, its state of charge (SOC) is limited to the set upper and lower limits:

[0037] ;

[0038] ;

[0039] Where: It is a binary variable of the hybrid energy storage charging and discharging state. When its value is 1, it indicates that the hybrid energy storage unit is working in the charging state. When it is 0, it indicates that the hybrid energy storage unit is in the discharging state, so as to limit the hybrid energy storage unit from being in the charging and discharging state at the same time. and are the charging and discharging power of the hybrid energy storage unit respectively; It is a binary variable, and its value is 1, indicating that the substation The energy storage unit is configured in the substation. When it is 0, it indicates No energy storage unit is configured; and are the rated capacity and rated power of the hybrid energy storage respectively; Optimize the phase time scale for the upper layer; for Time Substation Energy storage state of charge, is the scheduling period, and for its minimum and maximum values; and are the charging and discharging efficiencies of the hybrid energy storage unit, respectively;

[0040] Regenerative braking power constraint:

[0041] The regenerative braking energy actually recovered by the train is non-negative and less than the regenerative braking energy generated by the train:

[0042] ;

[0043] Where: and They are Time Substation The regenerative braking power value generated by the system and the regenerative braking power value actually consumed;

[0044] Three-phase voltage unbalance constraint:

[0045] ;

[0046] Where: is the upper limit of the three-phase voltage unbalance, ; is the short-circuit capacity of the common connection point.

[0047] Preferably, the upper configuration model based on minimizing the annual comprehensive cost in S1 is:

[0048] ;

[0049] Where: It is the annual comprehensive cost of the system; is the annual investment cost of the system, It is the annual operation and maintenance fee of the system;

[0050] Including energy storage investment costs , Photovoltaic investment cost , Substation RPC investment cost and RPC investment cost of the partition :

[0051] ;

[0052] ;

[0053] Where: is the discount rate; and They are the economical applicability years of energy storage, photovoltaics, and RPC respectively; and They are the unit cost coefficients of energy storage capacity, energy storage power, photovoltaic power, and RPC capacity respectively;

[0054] for:

[0055] ;

[0056] Where: It is the daily operation and maintenance cost of the system.

[0057] Optimally, the daily operation and maintenance cost of the system in S2 for:

[0058] ;

[0059] ;

[0060] Where: The cost of purchasing electricity; Energy storage operation and maintenance costs; The cost of photovoltaic operation and maintenance; It is the power loss cost of RPC in substation; RPC power loss cost for partitions; The cost of power loss of traction transformer; Cost of energy abandonment for photovoltaic and regenerative braking; and Refers to the left and right phases of RPC, traction transformer and regenerative braking energy respectively. The left and right phases include Harmony Mutually; and The cost coefficients for electricity purchase, energy storage operation and maintenance, PV operation and maintenance, RPC power loss, traction transformer power loss, PV energy abandonment and regenerative braking energy abandonment respectively; Power transfer efficiency for RPC is the transformer load factor; Output active power to the transformer; and Transmit power for substation RPC and sub-district RPC respectively; and They are no-load loss and short-circuit loss of the transformer respectively; and They are the regenerative braking power value generated by the system and the regenerative braking power value actually consumed.

[0061] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a multi-resource collaborative configuration method for a fusion-type traction power supply system, which has the following beneficial effects:

[0062] Based on the existing coordinated configuration of rail transit resources, this invention studies the coordinated configuration of multiple resources and considers the coordinated optimization between multiple sites. It constructs a two-layer optimization model with the upper layer aiming at the lowest annual comprehensive cost of the system and the lower layer aiming at the lowest system operation and maintenance cost. It also uses the PSO-GA hybrid algorithm to achieve the hybrid optimization of discrete site selection variables and continuous constant capacity variables, and solves the photovoltaic and energy storage access locations and the capacity of each device. The proposed configuration strategy can effectively promote the low-carbonization of the system and show good investment benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flowchart of a multi-resource collaborative configuration method for a fusion-type traction power supply system disclosed in the present invention;

[0065] Figure 2 This is a schematic diagram of the algorithm flow in this implementation mode;

[0066] Figure 3 This is a schematic diagram of the architecture of the integrated traction power supply system of the multi-site cluster in this implementation mode;

[0067] Figure 4 is a schematic diagram of load data in this implementation mode, wherein Figure 4 (a) is the traction load data of substation A, Figure 4 (b) is the traction load data of substation B, Figure 4 (c) is the traction load data of substation C;

[0068] Figure 5 This is a schematic diagram of the photovoltaic power generation curve value in this implementation mode;

[0069] Figure 6 This is a schematic diagram of the algorithm iteration convergence curve in this implementation mode;

[0070] Figure 7 Schematic diagram of the photovoltaic and regenerative braking energy consumption of the system in this embodiment; wherein, Figure 7 (a) is the photovoltaic absorption value of substation 2, Figure 7 (b) is the photovoltaic power consumption value of substation 3, Figure 7 (c) is the system regenerative braking absorption value;

[0071] Figure 8 This is a schematic diagram of the RPC transmission power of the partition in this implementation mode; wherein, Figure 8 (a) RPC transmission power of partition 1, Figure 8 (b) is the RPC transmission power of partition 2;

[0072] Fig. 9 is a schematic diagram of the SOC of the energy storage unit in this embodiment; wherein, Fig. 9 (a) is the energy storage charge state of substation B, Fig. 9 (b) is the energy storage charge state of substation C;

[0073] Fig.10 Schematic diagram of system cost curve under two schemes in this implementation method. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] The present invention provides a multi-resource collaborative configuration method for a fusion-type traction power supply system, comprising the following steps:

[0076] S1. Construct an upper configuration model based on minimizing the annual comprehensive cost. The objective function of the upper configuration model includes the annual investment cost of the system and the annual operation and maintenance cost of the system;

[0077] S2. Construct a lower-level optimization model based on minimizing the operation and maintenance cost, where the objective function of the lower-level optimization model is the system's single-day operation and maintenance cost;

[0078] S3. First, assign initial values ​​to the device access location and the capacity of each device;

[0079] S4. The device access location and capacity of each device obtained by the upper configuration model are passed to the lower optimization model to solve the output and operation and maintenance cost of each device;

[0080] S5. The solved operation and maintenance cost is passed to the upper-level configuration model to optimize the annual comprehensive cost, and the device access location and the capacity of each device are updated and passed to the lower-level optimization model. S4-S5 are iterated until the optimal solution is obtained.

[0081] In order to further implement the above technical solution, the upper configuration model based on minimizing the annual comprehensive cost in S1 is:

[0082] ;

[0083] Where: It is the annual comprehensive cost of the system; is the annual investment cost of the system, It is the annual operation and maintenance fee of the system;

[0084] Including energy storage investment costs , Photovoltaic investment cost , Substation RPC investment cost and RPC investment cost of the partition :

[0085] ;

[0086] ;

[0087] Where: Indicates each substation; Indicates each sub-district office; is the discount rate; and They are the economical applicability years of energy storage, photovoltaics, and RPC respectively; and is a binary variable, and when its value is 1, it means that the substation is equipped with energy storage unit and photovoltaic unit; when it is 0, it means that the substation is not equipped with energy storage unit and photovoltaic unit; and They are the unit cost coefficients of energy storage capacity, energy storage power, photovoltaic power, and RPC capacity respectively; and are the rated capacity and rated power of the hybrid energy storage respectively; is the rated power of the photovoltaic; and They are the rated capacities of the substation RPC and the substation RPC respectively; for:

[0088] ;

[0089] Where: It is the daily operation and maintenance cost of the system.

[0090] In order to further implement the above technical solutions, the daily operation and maintenance cost of the system in S2 for:

[0091] ;

[0092] ;

[0093] Where: The cost of purchasing electricity; Energy storage operation and maintenance costs; The cost of photovoltaic operation and maintenance; It is the power loss cost of RPC in substation; RPC power loss cost for partitions; The cost of power loss of traction transformer; Cost of energy abandonment for photovoltaic and regenerative braking; and They refer to the left and right phases of RPC, traction transformer and regenerative braking energy respectively; Optimize the phase time scale for the upper layer; and The cost coefficients for electricity purchase, energy storage operation and maintenance, PV operation and maintenance, RPC power loss, traction transformer power loss, PV energy abandonment and regenerative braking energy abandonment respectively; is the RPC power transmission efficiency; is the transformer load factor; Output active power to the transformer; and are the charging and discharging power of the hybrid energy storage unit respectively; and They are respectively the predicted output value and the actual power consumption of the photovoltaic power generation unit; and Transmit power for substation RPC and sub-district RPC respectively; and They are no-load loss and short-circuit loss of the transformer respectively; and They are the regenerative braking power value generated by the system and the regenerative braking power value actually consumed.

[0094] In order to further implement the above technical solution, the constraints of the lower optimization model include:

[0095] 1) Power balance constraints on the DC bus:

[0096] ;

[0097] Where: For substation The charging and discharging power of energy storage, charging is positive and discharging is negative; and Substation RPC Harmony Phase active transmission power; and Divisions RPC Mutually, Phase active transmission power;

[0098] 2) Power balance constraints on the traction network:

[0099] When the train is in traction condition, the active power of the traction load is greater than 0. At this time, the active power on the traction network satisfies:

[0100] ;

[0101] When the train is in regenerative braking condition, the active power of the traction load is less than 0. At this time, the active power on the traction network satisfies:

[0102] ;

[0103] The reactive power on the traction network meets the following requirements:

[0104] ;

[0105] Where: and Substation of Harmony Phase traction load active power; and Substation Transformer Harmony Phase active power; and Substation RPC Harmony Phase active power; and Divisions RPC Harmony Phase active power; and Substation of Harmony Phase regenerative braking power; and Substation of Harmony Phase traction load reactive power; and Substation Transformer Harmony Phase reactive power; and Substation RPC Harmony Phase reactive power; and Divisions RPC Harmony Phase reactive power; or 3;

[0106] 3) Equipment capacity constraints:

[0107] System transformers and RPCs must meet capacity constraints:

[0108] ;

[0109] Where: is the rated capacity of the transformer; and They are the rated capacities of the substation RPC and the substation RPC respectively;

[0110] Regardless of the feeding grid situation, the active power value of the traction transformer is non-negative:

[0111] ;

[0112] 4) Photovoltaic output constraints:

[0113] The output of the photovoltaic power generation unit is non-negative and is constrained by its predicted value:

[0114] ;

[0115] Where: for The value of the photovoltaic power generation curve at the moment;

[0116] 5) Energy storage constraints:

[0117] The output power of the energy storage unit shall not exceed its rated power. To avoid overcharging or overdischarging of the energy storage element, which may lead to accelerated aging of the medium, its state of charge (SOC) is limited to the set upper and lower limits:

[0118] ;

[0119] ;

[0120] Where: It is a binary variable of the hybrid energy storage charging and discharging state. When its value is 1, it indicates that the hybrid energy storage unit is working in the charging state. When it is 0, it indicates that the hybrid energy storage unit is in the discharging state, so as to limit the hybrid energy storage unit from being in the charging and discharging state at the same time. for Time Substation Energy storage state of charge, is the scheduling period, and for its minimum and maximum values; and are the charging and discharging efficiencies of the hybrid energy storage unit, respectively;

[0121] 6) Regenerative braking power constraints:

[0122] The regenerative braking energy actually recovered by the train is non-negative and less than the regenerative braking energy generated by the train:

[0123] ;

[0124] Where: and They are Time Substation The regenerative braking power value generated by the system and the regenerative braking power value actually consumed;

[0125] 7) Three-phase voltage imbalance constraint:

[0126] ;

[0127] Where: is the upper limit of the three-phase voltage unbalance, ; is the short-circuit capacity of the common connection point.

[0128] It should be noted that:

[0129] For solving discrete variable optimization problems, GA is more suitable due to its coding advantages and higher flexibility. The PSO algorithm optimizes the model by simulating the social behavior of bird flocks, and is good at solving problems in continuous variable space. The configuration strategy proposed in this article uses 0-1 variables for site selection, continuous variables for optimization and volume determination, and the model involves the optimization of mixed variables. In order to give full play to the algorithmic advantages of GA and PSO, the GA-PSO combined algorithm is used in the embodiment of the present invention to solve the proposed configuration strategy. The algorithm flow chart is as follows: Figure 2 shown.

[0130] First, GA is used to optimize whether the discrete variable site is equipped with photovoltaic and energy storage units, while PSO is used to optimize the rated capacity and rated power of the continuous variable equipment. In order to make the initial population evenly distributed and improve the global search ability of the algorithm, the continuous variables can be initialized by the good point set in practical applications. In addition, in order to reduce the solution time of the algorithm, PARFOR is used to solve the variables in parallel. During the optimization process, the YALMIP toolbox is used to call the CPLEX commercial solver to solve the model.

[0131] In general, the present invention first obtains a discrete optimization result, namely the site selection and capacity determination result, and transmits it to the lower-level optimization model to solve the output and operation and maintenance costs of each device. The lowest operation and maintenance cost obtained is then transmitted to the upper layer to optimize the device access location and capacity, and this cycle is iterated until the optimal solution of the model is obtained.

[0132] In order to verify the effectiveness and superiority of the method involved in the present invention, this embodiment uses the measured data of three traction substations A, B, and C as examples for calculation demonstration. Figure 3 As shown. Take the optimized time scale , traction load data such as Figure 4 As shown, the photovoltaic power generation curve value is as follows Figure 5 The cost coefficients of each part in the operation optimization model are all set at the power grid price of 0.5557 yuan / kWh; in this embodiment, the system life cycle is 15 years, and the equipment discount rate is 0.08. The remaining parameters are shown in Table 1.

[0133]

[0134] The algorithm iteration convergence curve is as follows Figure 6 As shown in Table 2, the lowest total system cost is RMB 789.48 million, and the corresponding site selection result is to configure photovoltaic power generation units and energy storage units in substations B and C. The RPC rated capacity of substations 1 and 2 is 5000kW respectively, and the other configuration results are shown in Table 2.

[0135]

[0136] The photovoltaic and regenerative braking energy consumption of the system under this configuration strategy is as follows: Figure 7 As shown, the overall photovoltaic absorption rate of the system reaches 98.7%, and the regenerative braking absorption rate reaches 96.3%. The configuration strategy disclosed in the present invention effectively promotes the recovery and utilization of photovoltaic and regenerative braking energy.

[0137] like Figure 8To transmit power to the RPCs in different zones, multi-station electrified railways can achieve flexible energy allocation through power transmission of RPCs between stations. By optimizing the energy usage of the entire system, the system's energy self-sufficiency is improved, while reducing dependence on traditional power supply methods, reducing photovoltaic and regenerative braking energy waste, and promoting the global consumption of electrified railway energy.

[0138] like Fig. 9 is the SOC of the energy storage unit. At night when there is no photovoltaic output, the change in the energy storage SOC is due to the storage and utilization of regenerative braking energy by switching the charging and discharging states. During the day when the photovoltaic output is sufficient, the energy storage gives full play to its advantages and smoothes the imbalance between photovoltaic and load supply and demand. Through efficient storage and utilization, energy storage improves the absorption rate of regenerative braking and photovoltaic energy, optimizes system energy utilization, and promotes low-carbon and stable operation of the system.

[0139] In order to further verify the effectiveness of the scheme proposed in the present invention, the operating results of the system under the scheme of the present invention are compared with the operating results of the traditional "grid-to-vehicle" power supply system. The average annual cost of each part of the system is shown in Table 3.

[0140]

[0141] As shown in Table 3, the system can save 334.81 million yuan in operating costs per year under the scheme of the present invention, which is 33.06% lower than the traditional scheme; the total annual cost of the system is reduced by 224.32 million yuan, a decrease of 22.13%. The system under the scheme of the present invention shows significant economic benefits.

[0142] To further illustrate the engineering feasibility of the scheme of the present invention, an economic analysis of the system configuration investment is conducted. The system cost curves under the two schemes are as follows: Fig.10 As shown, the solution of the present invention is expected to pay back in 4.95 years. In addition, considering the service life of RPC, energy storage unit, and photovoltaic power generation unit, the present invention takes the full life cycle of the system as 15 years. In this period, the solution of the present invention can save 336.47 million yuan.

[0143] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-resource collaborative configuration method for a traction power supply system, characterized in that: The following steps are involved: S1. Construct an upper configuration model based on minimizing the annual comprehensive cost. The objective function of the upper configuration model includes the annual investment cost of the system and the annual operation and maintenance cost of the system; S2. Construct a lower-level optimization model based on minimizing the operation and maintenance cost, where the objective function of the lower-level optimization model is the system's single-day operation and maintenance cost; S3. First, assign initial values ​​to the device access location and the capacity of each device; S4. The device access location and capacity of each device obtained by the upper configuration model are passed to the lower optimization model to solve the output and operation and maintenance cost of each device; S5. The solved operation and maintenance cost is passed to the upper configuration model to optimize the annual comprehensive cost, and the device access location and the capacity of each device are updated and passed to the lower optimization model. S4-S5 are iterated until the optimal solution is obtained; The constraints of the lower optimization model include: Power balance constraints on the DC bus: Where: is the actual power consumption of the photovoltaic power generation unit; is the charging and discharging power of the energy storage of substation i, charging is positive and discharging is negative; and are the α-phase and β-phase active transmission powers of the RPC in substation i, respectively; and are the active transmission power of phase α and phase β of RPC in substation j respectively; I represents the total number of traction substations; J represents the total number of substations; Power balance constraints on the traction network: When the train is in traction condition, the active power of the traction load is greater than 0. At this time, the active power on the traction network satisfies: When the train is in regenerative braking condition, the active power of the traction load is less than 0. At this time, the active power on the traction network satisfies: Where: and are the active powers of traction loads in phase α and phase β of substation i respectively; and are the active powers of the transformer phase α and phase β of substation i respectively; and are the α-phase and β-phase regenerative braking powers of substation i respectively; Regenerative braking power constraint: The regenerative braking energy actually recovered by the train is non-negative and less than the regenerative braking energy generated by the train: Where: and are the regenerative braking power value generated by the substation i system at time t and the regenerative braking power value actually absorbed; The daily operation and maintenance cost F of the system in S2 is: Where: Δt is the time scale of the upper layer optimization stage, Τ is the scheduling period, t is any time, The cost of purchasing electricity; Energy storage operation and maintenance costs; The photovoltaic operation and maintenance cost; It is the power loss cost of RPC in substation; RPC power loss cost for partitions; The cost of power loss of traction transformer; is the cost of photovoltaic and regenerative braking energy abandonment; m, n and s refer to the RPC, traction transformer and the left and right phases of regenerative braking energy, respectively, and the left and right phases include α phase and β phase; and are the charging and discharging power of the hybrid energy storage unit; μ i is a binary variable. When its value is 1, it indicates that the energy storage unit is configured in the substation i. When it is 0, it indicates that the energy storage unit is not configured in the substation i. grid 、c ess 、c pv 、c r 、c t 、c ap and c ar The cost coefficients for electricity purchase, energy storage operation and maintenance, PV operation and maintenance, RPC power loss, traction transformer power loss, PV energy abandonment and regenerative braking energy abandonment respectively; is the RPC power transmission efficiency; is the transformer load factor; Output active power to the transformer; and Transmit power for substation RPC and sub-district RPC respectively; and They are no-load loss and short-circuit loss of the transformer respectively; is the predicted output value of the photovoltaic power generation unit; and They are the regenerative braking power value generated by the system and the regenerative braking power value actually consumed.

2. A multi-resource collaborative configuration method for a traction power supply system of a fusion type according to claim 1, characterized in that: The constraints of the lower optimization model also include: The reactive power on the traction network meets the following requirements: Where: and are the reactive powers of traction loads in phase α and phase β of substation i respectively; and are the reactive powers of transformer phase α and phase β of substation i respectively; and are the α-phase and β-phase reactive powers of the RPC of substation i, respectively; and are the α-phase and β-phase reactive powers of the RPC in zone j, respectively; Equipment capacity constraints: System transformers and RPCs must meet capacity constraints: Where: is the rated capacity of the transformer; and They are the rated capacities of the substation RPC and the substation RPC respectively; Regardless of the feeding grid situation, the active power value of the traction transformer is non-negative: Photovoltaic output constraints: The output of the photovoltaic power generation unit is non-negative and is constrained by its predicted value: Where: i is a binary variable, and when its value is 1, it means that the substation i is equipped with a photovoltaic unit, and when it is 0, it means that the substation i is not equipped with a photovoltaic unit; L t is the value of the photovoltaic power generation curve at time t; is the rated power of the photovoltaic; Energy storage constraints: The output power of the energy storage unit shall not exceed its rated power. To avoid overcharging or overdischarging of the energy storage element, which may lead to accelerated aging of the medium, its state of charge (SOC) is limited to the set upper and lower limits: Where: i,t It is a binary variable of the hybrid energy storage charging and discharging state. When its value is 1, it indicates that the hybrid energy storage unit is working in the charging state. When it is 0, it indicates that the hybrid energy storage unit is in the discharging state, so as to limit the hybrid energy storage unit from being in the charging and discharging state at the same time. and are the rated capacity and rated power of the hybrid energy storage respectively; is the energy storage charge state of substation i at time t, and for its minimum and maximum values; and are the charging and discharging efficiencies of the hybrid energy storage unit, respectively; Three-phase voltage unbalance constraint: Where: ε umax is the upper limit of the three-phase voltage unbalance, and ε umax =1.3%; is the short-circuit capacity of the common connection point.

3. A multi-resource collaborative configuration method for a traction power supply system of a fusion type according to claim 2, characterized in that: The upper configuration model based on minimizing the annual comprehensive cost in S1 is: C = min (C1 + C2); In the formula: C is the annual comprehensive cost of the system; C1 is the annual investment cost of the system; C2 is the annual operation and maintenance cost of the system; C1 includes the energy storage investment cost C e , Photovoltaic investment cost C p , Substation RPC investment cost C br and the RPC investment cost C of the partition fr : C1=C e +C p +C br +C fr ; Where: d is the discount rate; y e ,y p and b They are the economical applicability years of energy storage, photovoltaics, and RPC respectively; and They are the unit cost coefficients of energy storage capacity, energy storage power, photovoltaic power, and RPC capacity respectively; C2 is: C2=365F; Where: F is the system's daily operation and maintenance cost.

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