Parameter configuration method of power supply system, electronic equipment and readable storage medium

Abstract

The embodiment of the invention relates to the field of electrified railway traction power supply, in particular to a parameter configuration method of a power supply system, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining photovoltaic output data and traction load data of two adjacent traction stations within a preset target age limit; constructing a photovoltaic-traction load scene set based on the photovoltaic output data and the traction load data corresponding to each traction; and substituting the photovoltaic-traction load scene sets of the two traction stations into a preset planning-operation double-layer model, and solving the planning-operation double-layer model based on a double-layer alternate iteration strategy to obtain target configuration parameters of the multi-traction station power supply system. According to the parameter configuration method disclosed by the invention, the problem that the energy utilization rate of a single traction station is reduced due to the fluctuation of the traction load and the intermittency of photovoltaic power is solved, so that two adjacent traction stations realize cooperative operation and energy complementation, and the absorption rate of new energy and the utilization rate of regenerative braking are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of electrified railway traction power supply, and in particular to a parameter configuration method, electronic equipment and readable storage medium of a power supply system. Background Art

[0002] In the field of railway electrification, from the perspective of the entire life cycle, railway carbon emissions are still at a relatively high level. At the same time, my country's railway network covers a vast territory, and there are many intersections with the energy network in geographical space. In addition, there are a large number of land resources along the railway that can be connected to new energy sources, which is very suitable for the access of new energy sources such as wind turbines and photovoltaics. Due to the intermittent nature of the railway regenerative braking energy, the access to the energy storage system can suppress the occurrence of this situation. Therefore, the access to new energy has a positive effect on achieving economic reduction, efficient use of energy, and green and low-carbon operation of the traction power supply system.

[0003] At present, there are two main technical routes for the access of “photovoltaic-energy storage” to electrified railways: 1) Taking the comprehensive economic cost of the system as the optimization goal, and taking the hybrid energy storage charging and discharging conditions, photovoltaic output operation, and active power balance as the system safety operation constraints, an energy scheduling model for the access of photovoltaic and energy storage to the traction power supply system is constructed; 2) Taking into account the long slope operation conditions, through the optimal configuration of the energy storage system and the formulation of a reasonable energy management strategy, the economic efficiency and power quality indicators are improved while ensuring the maximum utilization of regenerative braking energy.

[0004] However, most existing technical routes focus on a single traction substation, and due to the volatility of traction load and the intermittent nature of photovoltaics, no matter how optimized, it is impossible to avoid the situation where solar power is abandoned and regenerative braking energy cannot be fully utilized. The existing technology does not consider the impact of the volatility of traction load and the intermittent nature of photovoltaics on the energy utilization rate of a single traction substation. Summary of the invention

[0005] The purpose of the present invention is to at least provide a parameter configuration method, an electronic device and a readable storage medium for a power supply system, which can at least reduce the impact of the volatility of traction load and the intermittent nature of photovoltaics on the energy utilization rate of a single traction station, and at least achieve energy complementarity through the coordinated operation of adjacent traction stations, thereby improving the absorption rate of new energy and the full utilization of regenerative braking.

[0006] A parameter configuration method for a power supply system is applied to a multi-traction station power supply system, wherein the multi-traction station power supply system comprises a first traction station, a second traction station and a sub-station, wherein the first traction station and the second traction station are arranged adjacent to each other, and the sub-station is used for energy exchange between the first traction station and the second traction station, comprising:

[0007] Acquire the photovoltaic output data and traction load data of the first traction station, and the photovoltaic output data and traction load data of the second traction station within a preset target period;

[0008] For each traction station, based on the PV output data and traction load data corresponding to the traction station, a PV-traction load scenario set consisting of a PV output timing curve and a traction load timing curve is constructed, wherein the PV-traction load scenario set is used to characterize the temporal matching of the PV output and the traction load;

[0009] Substituting the photovoltaic-traction load scenario set corresponding to the first traction station and the second traction station into a preset planning-operation two-layer model, solving the planning-operation two-layer model based on a two-layer alternating iteration strategy, and obtaining the target configuration parameters of the multi-traction station power supply system,

[0010] Among them, the planning-operation two-layer model includes a planning layer model and an operation layer model. The planning layer model is used to minimize the sum of the investment cost and the annual operating cost of the power supply system of the multiple traction stations as the objective function, and use the equipment capacity as the constraint condition to solve and obtain the capacity configuration parameters of each device, and send the equipment capacity configuration parameters to the operation layer model. The operation layer model is used to minimize the annual operating cost as the objective function, and use the equipment operating conditions as the constraints. By calculating the interaction energy between the first traction station and the second traction station, the operation status of each device under the equipment capacity configuration parameters is solved, and the operation status of each device is fed back to the planning layer model.

[0011] In this embodiment, by constructing a photovoltaic-traction load scenario set, the matching of the photovoltaic output timing curve and the traction load timing curve is considered. During the peak output period of the photovoltaic power station, the power supply to the traction load can be increased; during the low output period of the photovoltaic power station, the power supply to the traction load can be reduced, and the supply and demand relationship is balanced through the energy storage device to optimize energy, ensure the maximum utilization of photovoltaic energy when there is sufficient sunlight, and reduce dependence on traditional energy. At the same time, the two adjacent traction stations are connected through the partition station to achieve coordinated operation and energy complementarity, improve the impact of the volatility of the traction load and the intermittent nature of photovoltaics on the energy utilization rate of a single traction station, improve the absorption rate of new energy and the utilization rate of regenerative braking, greatly improve the economy of the entire system, and greatly reduce carbon dioxide emissions.

[0012] In one embodiment, the step of constructing a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve includes:

[0013] Divide a day into several time periods according to a preset time scale;

[0014] Taking each of the time periods as a random variable, and for the traction load data in each of the time periods in each season, determining the probability distribution of the traction load corresponding to the time period;

[0015] Based on the photovoltaic output data in each season, the probability distribution of light intensity in each season is analyzed;

[0016] Based on the probability distribution of the traction load and the probability distribution of the light intensity, a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled and obtained every day in each season using a random sampling method.

[0017] In this embodiment, by dividing a day into several time periods, the time series variation characteristics of photovoltaic output and traction load can be captured more finely, which helps to more accurately predict the photovoltaic output and traction load in each time period, thereby improving the accuracy of the prediction. Based on the probability distribution of traction load and the probability distribution of light intensity, a photovoltaic-traction load scenario set is constructed using a random sampling method. These scenario sets cover the photovoltaic output and traction load conditions under different weather conditions, operation plans and other factors, providing rich data support for optimizing resource allocation, and can more intuitively analyze the matching between photovoltaic output and traction load. It helps to formulate a more reasonable energy complementarity strategy, optimize the power distribution between the two traction stations, and achieve efficient use of energy.

[0018] In one embodiment, the sampling obtains a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve for each day in each season, including:

[0019] An initial photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled for each day in each season;

[0020] The initial photovoltaic-traction load scenario set is reduced by using a K-means clustering algorithm to obtain a reduced typical scenario set, and the typical scenario set is used as the photovoltaic-traction load scenario set.

[0021] In this embodiment, the initial scene set usually contains a large number of scenes, and directly analyzing and optimizing them will consume a lot of computing resources. By reducing the initial scene set through the K-means clustering algorithm, the number of scenes can be significantly reduced, thereby reducing the complexity of subsequent calculations and analysis. The typical scene set reduces the number of scenes and improves the representativeness of the scene set while retaining the diversity of the initial scene set. Due to the reduction in the number of scenes, results can be obtained faster in subsequent optimization scheduling, risk assessment and other work, thereby improving the overall analysis efficiency.

[0022] In one embodiment, solving the planning-operation two-layer model based on a two-layer alternating iterative strategy to obtain target configuration parameters of the multi-traction power supply system includes:

[0023] determining initial values ​​of decision variables of the planning layer model, and transmitting the initial values ​​to the operation layer model,

[0024] Solving the operation layer model based on the initial value to obtain annual operation cost and operation decision variables, and sending the annual operation cost and operation decision variables to the planning layer model;

[0025] Solving the planning layer model based on the annual operating cost and the operating decision variables to obtain updated values ​​of the decision variables;

[0026] Based on the updated values ​​of the decision variables, the operation layer model is solved to obtain the new annual operation cost and operation decision variables, and the new annual operation cost and operation decision variables are sent to the planning layer model. The above alternating iterative steps are repeated until the preset convergence conditions of the planning layer model are reached, then the iteration is stopped to obtain the target configuration parameters of the multi-traction power supply system.

[0027] In this embodiment, the planning-operation two-layer model can comprehensively consider the long-term planning and short-term operation of the power supply system, and achieve global optimization through two-layer optimization. The planning layer model is responsible for determining the configuration parameters of the system, while the operation layer model performs operation optimization based on the configuration parameters. This hierarchical optimization method can more accurately reflect the actual situation of the system and improve the accuracy of the solution. By alternating the iteration of the planning layer model and the operation layer model, the global optimal solution can be gradually approached. In each iteration, the planning layer model is updated according to the annual operating cost and operation decision variables provided by the operation layer model, and the operation layer model is recalculated based on the updated configuration parameters provided by the planning layer model. The alternating iteration method of this embodiment can accelerate the convergence process and improve the solution efficiency.

[0028] In one embodiment, the convergence condition is set to one of the following:

[0029] The absolute value of the difference between the objective functions of the planning layer model in two adjacent iterations satisfies a preset error condition;

[0030] and

[0031] The number of iterations reaches the preset upper limit.

[0032] In this embodiment, by setting the absolute value error condition of the objective function difference between two adjacent iterations, it can be ensured that the solution process stops iterating after reaching a certain accuracy, avoiding the waste of computing resources caused by excessive iterations, while ensuring the stability and reliability of the solution results. Setting an upper limit on the number of iterations can prevent the solution process from falling into an infinite loop or failing to converge for a long time. When the number of iterations reaches the upper limit, even if the objective function difference does not reach the preset error condition, the iteration will stop, thereby protecting computing resources and time. Choosing one of the two convergence conditions can improve the efficiency and flexibility of the solution.

[0033] In one embodiment, solving the operation layer model based on the initial value to obtain the annual operation cost and operation decision variables includes:

[0034] Based on a parallel solution algorithm of synchronous alternating direction multipliers, the expression of power coupling formed by the energy interaction between the first traction station and the second traction station in the operating layer model in the partition is decoupled to obtain a first decoupling model and a second decoupling model;

[0035] The photovoltaic-traction load scenario set corresponding to the first traction station is sent to the first decoupling model, and the photovoltaic-traction load scenario set corresponding to the second traction station is sent to the second decoupling model, and the first decoupling model and the second decoupling model are solved in parallel to obtain the annual operating cost and operating decision variables of the first traction station, and the annual operating cost and operating decision variables of the second traction station.

[0036] In this embodiment, the energy interaction between two adjacent traction stations is decoupled by forming power coupling in the sub-station converter, thereby converting the global problem into two sub-problems that can be solved in parallel (i.e., the first decoupling model and the second decoupling model). The two decoupled models are solved in parallel, which not only improves the solution efficiency but also reduces the communication pressure on the top-level dispatching center.

[0037] In this embodiment, the synchronous alternating direction multiplier algorithm is used to solve the first decoupling model and the second decoupling model in parallel, which further accelerates the entire solution process and can maintain a high accuracy during the solution process, ensuring that the solution results of the decoupled model are accurate and reliable.

[0038] In one embodiment, the calculation formulas of the first decoupling model and the second decoupling model are as follows:

[0039]

[0040] in, is the calculation formula of the first decoupling model, is the calculation formula of the second decoupling model, C op，m is the annual operating cost of the first traction station, Cop，n is the annual operating cost of the second traction station, ρ is the penalty factor, is the interactive power from the first traction station to the second traction station in the season j scenario s and time period t, is the optimization result of the interaction power of the first traction station in the previous iteration, is the average value of the optimization results of the interaction power of adjacent tractions in the previous iteration, u k For the operator.

[0041] In one embodiment, the annual operating cost is composed of equipment operating cost, electricity fee, feed-in penalty cost, photovoltaic abandonment cost and line loss cost.

[0042] In this embodiment, the cost of photovoltaic abandoned light and line loss is taken into consideration, which can encourage the power supply system to take more energy-saving and consumption-reducing measures to improve energy utilization efficiency. By comprehensively considering multiple cost factors, the actual operation status of the power supply system can be more accurately reflected, providing strong support for optimizing decisions.

[0043] In one embodiment, the constraints in the operation layer model using equipment operating conditions as constraints include: photovoltaic output operation constraints, energy storage output constraints, energy storage capacity constraints, internal power balance constraints between the first traction station and the second traction station, and interactive energy constraints between the first traction station and the second traction station.

[0044] In this embodiment, by setting photovoltaic output operation constraints and energy storage output constraints, it is possible to ensure that photovoltaic panels, energy storage devices and other equipment operate within their rated ranges, avoid overload or underload, and thus improve the operating stability and safety of the equipment. Internal power balance constraints and interactive energy constraints help ensure power balance within the power supply system and between different traction stations, and prevent system crashes or equipment damage caused by power imbalance. This helps to improve the stability and safety of system operation while improving energy utilization and reducing operating costs.

[0045] At least one embodiment of the present application further provides an electronic device, including:

[0046] at least one processor; and,

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above parameter configuration method.

[0049] At least one embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned parameter configuration method is implemented.

[0050] In summary, the parameter configuration method of the power supply system provided in this application has at least the following beneficial effects:

[0051] 1. By constructing a photovoltaic-traction load scenario set, the matching of the photovoltaic output timing curve and the traction load timing curve is considered. During the peak output period of the photovoltaic power station, the power supply to the traction load can be increased; during the low output period of the photovoltaic power station, the power supply to the traction load can be reduced, and the supply and demand relationship can be balanced through energy storage devices to optimize energy, ensure the maximum utilization of photovoltaic energy when there is sufficient sunlight, and reduce dependence on traditional energy. At the same time, the two adjacent traction stations are connected through the partition station to achieve coordinated operation and energy complementarity, improve the impact of the volatility of the traction load and the intermittent nature of photovoltaics on the energy utilization rate of a single traction station, improve the absorption rate of new energy and the utilization rate of regenerative braking, greatly improve the economy of the entire system, and greatly reduce carbon dioxide emissions.

[0052] 2. The energy interaction between two adjacent traction stations is decoupled by forming power coupling in the sub-station converter, thereby converting the global problem into two sub-problems that can be solved in parallel (i.e., the first decoupling model and the second decoupling model). The two decoupled models are solved in parallel, which not only improves the solution efficiency but also reduces the communication pressure of the top-level dispatching center. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0054] Figure 1 It is a flowchart of a parameter configuration method of a power supply system provided by an embodiment of the present application;

[0055] Figure 2 It is a schematic diagram of a planning-operation two-layer model solution framework provided by an embodiment of the present application;

[0056] Figure 3 It is a schematic diagram of a process of parallel solution of a synchronous alternating direction multiplier algorithm provided by an embodiment of the present application;

[0057] Figure 4 is a structural topology diagram of a power supply system provided by an embodiment of the present application;

[0058] Figure 5 It is a schematic diagram of a parameter configuration device for a power supply system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0059] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0060] In order to solve the technical problem of the impact of the volatility of traction load and the intermittent nature of photovoltaic power on the energy utilization rate of a single traction station, the present invention proposes a parameter configuration method for a power supply system. The implementation details of the parameter configuration of the power supply system of this embodiment are described in detail below. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this solution.

[0061] Embodiment 1:

[0062] The parameter configuration method of the power supply system of this embodiment can be applied to electronic devices with communication, computing and data storage capabilities. The specific process can be as follows: Figure 1 As shown:

[0063] A parameter configuration method for a power supply system is applied to a multi-traction station power supply system, wherein the multi-traction station power supply system comprises a first traction station, a second traction station and a sub-station, wherein the first traction station and the second traction station are arranged adjacent to each other, and the sub-station is used for energy exchange between the first traction station and the second traction station, and comprises:

[0064] Step 110, obtaining the photovoltaic output data and traction load data of the first traction station, and the photovoltaic output data and traction load data of the second traction station within a preset target period;

[0065] In this embodiment, in order to better understand the impact of the volatility of traction load and the intermittent nature of photovoltaic power on the energy utilization rate of a single traction station, it is first necessary to understand the actual energy supply and demand situation.

[0066] Photovoltaic output: the power of photovoltaic power generation. The size of photovoltaic output depends on many factors, including solar radiation intensity, conversion efficiency of photovoltaic modules, temperature, shadows and cloud cover. In photovoltaic power generation systems, photovoltaic output data is of great significance for evaluating system performance, optimizing energy configuration, and predicting future power generation capacity. By analyzing photovoltaic output data, we can understand the power generation of photovoltaic systems in different time periods and weather conditions, thereby providing a scientific basis for the operation and maintenance of the system.

[0067] Traction load: refers to the product of the actual kilometers traveled by the traction train on the section line and the train load, which reflects the electric power required by the electric locomotive during operation. In the operation of electrified railways, changes in vehicle density, operating speed, line conditions, environmental factors, etc. will cause the vehicle current and the total traction load to change greatly over time, and the traction load is generally difficult to show a continuous state. By analyzing the traction load data, we can understand the energy consumption of the train in different time periods and under different operating conditions.

[0068] Due to the volatility of traction load and the intermittent and volatile nature of photovoltaics, it is difficult for the power supply system to maintain a stable balance between supply and demand during operation. By obtaining photovoltaic output data and traction load data within the preset target year, we can have a more comprehensive understanding of the peak and trough conditions of photovoltaic output within a year, as well as the distribution of peak and trough conditions of traction load. This provides a reliable data basis for planning energy supply, ensuring sufficient energy supply during peak hours, and avoiding energy shortages or waste.

[0069] When the output power of photovoltaic power generation is greater than the demand of traction load, the phenomenon of abandoned light will occur; and when the demand of traction load is greater than the output power of photovoltaic power generation, additional electricity needs to be obtained from the power grid. At present, many research and optimization measures are focused on a single traction substation, aiming to improve its operating efficiency and power supply quality. However, no matter how this focus is optimized, the phenomenon of abandoned light cannot be avoided. Therefore, in this application, multiple traction substations and substations work together to form a complex power supply network, and the energy between two adjacent traction substations is complementary, which can give full play to the power supply potential of the entire system.

[0070] Step 120, for each traction station, based on the PV output data and traction load data corresponding to the traction station, construct a PV-traction load scenario set consisting of a PV output timing curve and a traction load timing curve, wherein the PV-traction load scenario set is used to characterize the temporal matching between the PV output and the traction load;

[0071] In this embodiment, the photovoltaic output timing curve represents the change of the power output power generated by the photovoltaic system over time in a certain time period. The traction load timing curve represents the change of the power load required by the traction system over time in a certain time period. By combining these two timing curves, a photovoltaic-traction load scenario set is constructed. The scenario set is actually a database containing a large amount of historical data on the matching of photovoltaic output and traction load. Each scenario represents the relative relationship between photovoltaic output and traction load in a specific time period.

[0072] Through the photovoltaic-traction load scenario set, we can intuitively see the matching between photovoltaic output and traction load, such as discovering some potential energy waste: when the photovoltaic output is high but the traction load is low, the phenomenon of abandoned light may occur. By adjusting the train operation plan or increasing the capacity of energy storage equipment, the photovoltaic power can be used more effectively. Or it can be used to predict future energy demand and photovoltaic output, providing stronger support for the access and integration of renewable energy in the traction power supply system.

[0073] Step 130, substitute the photovoltaic-traction load scenario set corresponding to the first traction station and the second traction station into the preset planning-operation two-layer model, solve the planning-operation two-layer model based on the two-layer alternating iteration strategy, and obtain the target configuration parameters of the multi-traction station power supply system.

[0074] Among them, the planning-operation two-layer model includes a planning layer model and an operation layer model. The planning layer model is used to minimize the sum of the investment cost and the annual operating cost of the power supply system of multiple traction stations as the objective function, and the equipment capacity as the constraint condition, to solve the capacity configuration parameters of each device, and send the equipment capacity configuration parameters to the operation layer model. The operation layer model is used to minimize the annual operating cost as the objective function, and the equipment operating conditions as the constraints. By calculating the interaction energy between the first traction station and the second traction station, the operation status of each device under the equipment capacity configuration parameters is solved, and the operation status of each device is fed back to the planning layer model.

[0075] In this embodiment, the planning-operation two-layer model includes two levels of optimization models. The planning layer model focuses on long-term investment decisions, such as the configuration of equipment capacity; while the operation layer model focuses on short-term operation decisions, such as the actual operation of the equipment and the calculation of interaction energy. In the operation layer model, it is necessary to calculate the interaction energy between the first traction station and the second traction station. This process reflects the mutual dependence and energy flow of the two traction stations in the power supply process. When the load demand of a traction station increases and the electric energy is insufficient, additional electric energy can be obtained from the adjacent traction station to meet its power supply demand. Similarly, when a traction station has excess electric energy, it can transmit electric energy to the adjacent traction station. Through the energy interaction between two adjacent traction stations, the load distribution of the power system can be balanced, the fluctuation and instability factors of the power grid can be reduced, and the stability of the power system can be improved.

[0076] The specific modeling process is as follows:

[0077] (1) Construction of planning layer model

[0078] 1.1. The objective function of the multi-traction coordination planning layer is to minimize the system annual comprehensive cost consisting of system investment cost and system annual operation cost, which is specifically:

[0079]

[0080] Among them, C F is the annual comprehensive cost of the system; C inv is the system investment cost; C op is the annual operation and maintenance cost of the system. inv and Y op is the decision variable.

[0081] Specifically:

[0082]

[0083] In the formula, C inv_PV is the investment cost of photovoltaic power generation during its service life; C inv_ESS is the investment cost of energy storage within its service life; C inv_COV is the investment cost of the converter during its service life; M is the number of traction stations; I is the number of inverters; r is the discount rate, Y pv , Y ESS , Y cov are the service life of photovoltaic, energy storage and inverter respectively; π pv , π ESS , π cov They are the annual unit capacity investment costs of photovoltaic, energy storage, inverter, etc. is the photovoltaic rated power, The rated capacity of the energy storage configuration, Represents the rated capacity of the substation converter and traction substation converter, X inv is the decision variable,

[0084] It should be noted that, since the power factor of the current high-speed railway is very high, close to 1, in this embodiment, reactive power can be reasonably ignored.

[0085] 1.2. Equipment capacity constraints and geographical location constraints are used as constraints for the multi-traction station system planning layer, specifically:

[0086] 1) The photovoltaic capacity is limited by the area of ​​land along the railway

[0087]

[0088] In the formula, The area where photovoltaic power can be laid along the railway. The area required to install 1MW of photovoltaic power.

[0089] 2) The rated power and rated capacity of energy storage are subject to certain restrictions, as follows:

[0090]

[0091] In the formula, γ is the minimum ratio of energy storage to photovoltaic rated power required by the local government; is the maximum configuration rated power of energy storage, δ is the continuous charging and discharging time; Maximum configuration rated power of energy storage.

[0092] 3) The converter device of the sub-district station is limited by the line capacity. The converter device of the traction station should be larger than the capacity of photovoltaic and energy storage, as follows:

[0093]

[0094] In the formula, S Line is the line capacity.

[0095] (2) Construction of the operational layer model

[0096] In one embodiment, the annual operating cost is composed of equipment operating cost, electricity fee, feed-in penalty cost, photovoltaic abandonment cost and line loss cost. Specifically as follows:

[0097]

[0098] Where, M is the number of traction stations, C op_eq is the total annual operating cost of M traction substations, C op,m is the total operating cost of the mth traction station, C op_eq,m , C ec,m, C fed,m , C dem,m , C abd,m are the equipment operation cost, electricity cost, feed-in penalty cost, maximum demand electricity cost, and photovoltaic abandonment cost of the mth traction station, respectively. ex,m is the penalty term for the interaction energy between adjacent traction stations, indicating the virtual loss cost of line loss caused by the energy interaction between adjacent traction stations; D j is the number of days in typical scenes in spring, summer, autumn and winter, where j = 1, 2, 3, 4, P j,s is the probability of occurrence of typical scenarios in each season, They are the unit operating cost of photovoltaic power generation, the unit operating cost of energy storage, the unit electricity charge, the unit feed-in penalty cost, the unit maximum demand electricity charge, the unit abandoned light cost, and the unit line loss penalty cost; Δt is the operating period, and T is the number of operating periods in a typical day; They are the maximum photovoltaic output, photovoltaic output, energy storage power, energy storage charging power, energy storage discharging power, power purchased from the grid, feed-in power, maximum demand power, and interactive power from traction station m to adjacent traction station n for the typical scenario set of traction station m in season j scenario s and time period t. The decision variable is Y op ,

[0099]

[0100] 2.2. In one embodiment, the photovoltaic output operation constraint, the energy storage output constraint, the energy storage capacity constraint, the internal power balance constraint of the first traction station and the second traction station, and the interactive energy constraint between the first traction station and the second traction station are used as constraint conditions. Specifically:

[0101] 1) Photovoltaic output operation constraints:

[0102]

[0103] In the formula, The actual output of photovoltaic operation. To standardize the output in photovoltaic scenarios.

[0104] 2) Energy storage charging and discharging cannot occur at the same time. Specific energy storage constraints include energy storage output constraints and energy storage capacity constraints as follows:

[0105] The energy storage output constraint is:

[0106]

[0107] In the formula, is the energy storage charging state variable of traction m in period t under scenario s in season j.

[0108] The energy storage capacity constraints are as follows:

[0109]

[0110] In the formula, is the energy storage capacity of traction station m in period t-1 under scenario s in season j, are the energy storage charging and discharging efficiency of traction station m in period t under scenario s in season j, is the minimum state of charge of the energy storage in traction m during period t-1 under scenario s in season j, is the maximum state of charge of the energy storage in traction m during period t-1 under scenario s in season j.

[0111] 3) Internal power balance constraints between two adjacent traction stations m and n:

[0112]

[0113] 4) The interaction energy between adjacent traction stations should be less than the rated capacity of the converter, and the sum of the input and output power of adjacent traction stations should be zero, specifically:

[0114]

[0115] In summary, this embodiment considers the matching of the photovoltaic output timing curve and the traction load timing curve by constructing a photovoltaic-traction load scenario set. During the peak output period of the photovoltaic power station, the power supply to the traction load can be increased; during the low output period of the photovoltaic power station, the power supply to the traction load can be reduced, and the supply and demand relationship can be balanced through energy storage devices to optimize energy, ensure the maximum utilization of photovoltaic energy when there is sufficient sunlight, and reduce dependence on traditional energy. At the same time, the two adjacent traction stations are connected through the partition station to achieve coordinated operation and energy complementarity, improve the impact of the volatility of the traction load and the intermittent nature of photovoltaics on the energy utilization rate of a single traction station, improve the absorption rate of new energy and the utilization rate of regenerative braking, greatly improve the economy of the entire system, and greatly reduce carbon dioxide emissions.

[0116] In one embodiment, a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is constructed, including: dividing a day into a number of time periods according to a preset time scale; taking each time period as a random variable, and determining the probability distribution of the traction load corresponding to the time period for the traction load data in each time period of each season; analyzing and obtaining the probability distribution of light intensity in each season based on the photovoltaic output data in each season; and sampling and obtaining the photovoltaic-traction load scenario set consisting of the photovoltaic output timing curve and the traction load timing curve for each day in each season using a random sampling method based on the probability distribution of the traction load and the probability distribution of the light intensity.

[0117] In this embodiment, by dividing a day into several time periods, the time series variation characteristics of photovoltaic output and traction load can be captured more finely, which helps to more accurately predict the photovoltaic output and traction load in each time period, thereby improving the accuracy of the prediction. Based on the probability distribution of traction load and the probability distribution of light intensity, a photovoltaic-traction load scenario set is constructed using a random sampling method. These scenario sets cover the photovoltaic output and traction load conditions under different weather conditions, operation plans and other factors, providing rich data support for optimizing resource allocation, and can more intuitively analyze the matching between photovoltaic output and traction load. It helps to formulate a more reasonable energy complementarity strategy, optimize the power distribution between the two traction stations, and achieve efficient use of energy.

[0118] The specific implementation steps are as follows:

[0119] (1) Traction load data preprocessing: Divide a 24-hour day into 96 time periods, take each time period as a random variable, and use non-parametric kernel density estimation to obtain the probability distribution of traction load in a single period in the four seasons of spring, summer, autumn, and winter. Specifically:

[0120]

[0121] Among them, P train_sea is the power vector set of traction load data divided by season, which is a 4*96 matrix. Represents the traction load power at each time period of each day in spring, which is a column vector of 1*120; Represents the traction load power at each time period in summer, which is a column vector of 1*120; Represents the traction load power at each time period of each day in autumn, which is a column vector of 1*120; Represents the traction load power at each time period of each day in winter, which is a column vector of 1*120.

[0122] Afterwards, the probability distribution of traction load in each period of each season is calculated according to the following formula:

[0123]

[0124] In the formula, f(P train ) is the probability distribution function of the traction load, h is the bandwidth, and K is the kernel function.

[0125] (2) Light data preprocessing: Photovoltaic output is directly related to light intensity. Based on the data of spring, summer, autumn and winter, Beta distribution is used to describe it. Specifically:

[0126]

[0127] Where f(S) is the probability distribution function of light intensity, S is the light intensity, S max is the maximum light intensity.

[0128] (3) Scenario set construction: Based on probability distribution, a scenario set consisting of the time series curves of photovoltaic output and traction load in four seasons is obtained through stratified sampling using the Latin hypercube method. The photovoltaic output is normalized based on the relationship between light intensity and photovoltaic output to facilitate the determination of photovoltaic capacity parameters.

[0129] P PV =σ PV ·S PV ·A PV

[0130]

[0131] Where P pv is the photovoltaic output calculated based on the scenario generated by the probability distribution of light intensity, p pv It is the normalized photovoltaic output, and its value is in the range of 0 to 1.

[0132] In one embodiment, a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled and obtained for each day in each season, including:

[0133] An initial photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled for each day in each season;

[0134] The initial photovoltaic-traction load scenario set is reduced by using a K-means clustering algorithm to obtain a reduced typical scenario set, and the typical scenario set is used as the photovoltaic-traction load scenario set.

[0135] In this embodiment, the initial scene set usually contains a large number of scenes, and directly analyzing and optimizing them will consume a lot of computing resources. By reducing the initial scene set through the K-means clustering algorithm, the number of scenes can be significantly reduced, thereby reducing the complexity of subsequent calculations and analysis. The typical scene set reduces the number of scenes and improves the representativeness of the scene set while retaining the diversity of the initial scene set. Due to the reduction in the number of scenes, results can be obtained faster in subsequent optimization scheduling, risk assessment and other work, thereby improving the overall analysis efficiency.

[0136] The calculation formula for the occurrence probability of the typical scenario after reduction is the product of the occurrence probability of photovoltaic and traction loads, specifically:

[0137]

[0138] In the formula, Ω s is a typical scenario set composed of photovoltaic and traction loads, Ω s_pv is a typical photovoltaic scene set, Ω strain A set of typical scenarios for traction loads.

[0139] In one embodiment, a planning-operation two-layer model is solved based on a two-layer alternating iteration strategy to obtain target configuration parameters and target operating parameters of a multi-traction power supply system, including: determining initial values ​​of decision variables of a planning layer model, transmitting the initial values ​​to an operating layer model, solving the operating layer model based on the initial values ​​to obtain annual operating costs and operating decision variables, and sending the annual operating costs and operating decision variables to the planning layer model; solving the planning layer model based on the annual operating costs and operating decision variables to obtain updated values ​​of the decision variables; solving the operating layer model based on the updated values ​​of the decision variables to obtain new annual operating costs and operating decision variables, and sending the new annual operating costs and operating decision variables to the planning layer model, repeating the above alternating iteration steps until the preset convergence conditions of the planning layer model are reached, then stopping the iteration to obtain the target configuration parameters of the multi-traction power supply system.

[0140] In this embodiment, the planning-operation two-layer model can comprehensively consider the long-term planning and short-term operation of the power supply system, and achieve global optimization through two-layer optimization. The planning layer model is responsible for determining the configuration parameters of the system, while the operation layer model performs operation optimization based on the configuration parameters. This hierarchical optimization method can more accurately reflect the actual situation of the system and improve the accuracy of the solution. By alternating the iteration of the planning layer model and the operation layer model, the global optimal solution can be gradually approached. In each iteration, the planning layer model is updated according to the annual operating cost and operation decision variables provided by the operation layer model, and the operation layer model is recalculated based on the updated configuration parameters provided by the planning layer model. The alternating iteration method of this embodiment can accelerate the convergence process and improve the solution efficiency.

[0141] like Figure 2 As shown, the specific steps are as follows:

[0142] 1) Read system parameters and initialize the maximum number of iterations P max , initialize the particle population, generate the initial population W, generate the maximum population W max The value initialized in this step is the initial value of the decision variable of the planning layer model;

[0143] 2) Solve the operation layer model: solve the decision variable X of the planning layer model inv_k As the known quantity of the operation layer model, the interaction energy of adjacent traction stations and other operation decision variables Y are solved op_k , and pass the result to the planning layer model.

[0144] 3) Solve the planning layer model: The annual operating cost C obtained from the operating layer model in this iteration is op As a known quantity, it is passed to the planning layer model and the new configuration decision variable X is obtained by solving the planning layer model. inv_k+1 , and pass the result back to the run-level model.

[0145] 4) Determine whether the model has converged according to the convergence conditions preset in the planning layer model. If converged, output the expected values ​​of the configuration parameters and their operating parameters; otherwise, p = p + 1, and repeat the above iterative calculation process.

[0146] The convergence condition is that the absolute value of the difference between the objective functions of the collaborative planning layer of two adjacent tractions meets the error, or the number of iterations reaches the maximum number of iterations P max :

[0147]

[0148] In this embodiment, by setting the absolute value error condition of the objective function difference between two adjacent iterations, it can be ensured that the solution process stops iterating after reaching a certain accuracy, avoiding the waste of computing resources caused by excessive iterations, while ensuring the stability and reliability of the solution results. Setting an upper limit on the number of iterations can prevent the solution process from falling into an infinite loop or failing to converge for a long time. When the number of iterations reaches the upper limit, even if the objective function difference does not reach the preset error condition, the iteration will stop, thereby protecting computing resources and time. Choosing one of the two convergence conditions can improve the efficiency and flexibility of the solution.

[0149] In one embodiment, an operating layer model is solved based on initial values ​​to obtain annual operating costs and operating decision variables, including: decoupling the expression of power coupling formed by energy interaction between the first traction station and the second traction station in the operating layer model in the partition based on a parallel solution algorithm of synchronous alternating direction multipliers to obtain a first decoupling model and a second decoupling model; sending the photovoltaic-traction load scenario set corresponding to the first traction station to the first decoupling model, and sending the photovoltaic-traction load scenario set corresponding to the second traction station to the second decoupling model, and solving the first decoupling model and the second decoupling model in parallel to obtain the annual operating cost and operating decision variables of the first traction station and the annual operating cost and operating decision variables of the second traction station.

[0150] In this embodiment, the energy interaction between two adjacent traction stations is decoupled by forming power coupling in the sub-station converter, thereby converting the global problem into two sub-problems that can be solved in parallel (i.e., the first decoupling model and the second decoupling model). The two decoupled models are solved in parallel, which greatly improves the solution efficiency.

[0151] The synchronous alternating direction multiplier algorithm is used to solve the first decoupling model and the second decoupling model in parallel, which further accelerates the entire solution process and can maintain a high accuracy during the solution process.

[0152] The specific decoupling iteration process is as follows:

[0153] 1) When performing optimization calculation at traction station m (i.e., the first traction station mentioned above), the revised value of the calculation result of the expected exchange power last time is selected as the reference value for the next time. The following formula is the revised formula for the exchange power of traction station m:

[0154]

[0155] In the formula, represent the reference values ​​of the interaction power of traction station n (i.e., the second traction station mentioned above) and traction station m in the current iteration, respectively, They represent the optimization results of the interaction power of adjacent traction substations in the last iteration, It represents the average value of the optimization results of the interaction power of adjacent tractions in the previous iteration.

[0156] 2) Split the objective function of the operation layer model into the following two general forms of sub-problems based on augmented Lagrangian functions, namely, the calculation formulas of the first decoupling model and the second decoupling model. Specifically:

[0157]

[0158] in, is the calculation formula of the first decoupling model, is the calculation formula of the second decoupling model, C op，m is the annual operating cost of the first traction station, C op，n is the annual operating cost of the second traction station, ρ is the penalty factor, is the interactive power from the first traction station to the second traction station in the season j scenario s and time period t, is the optimization result of the interaction power of the first traction station in the previous iteration, is the average value of the optimization results of the interaction power of adjacent tractions in the previous iteration, u k For the operator.

[0159] 3) The iteration process is as follows:

[0160]

[0161] In the formula, They represent the optimal solutions of the k+1th decision variables of two adjacent traction stations in the operating layer model, u k+1 is the k+1th operator.

[0162] 4) The original residual and the dual residual are used as the criterion for the convergence conditions of the first decoupling model and the second decoupling model, specifically:

[0163]

[0164] In the formula, α k+1 is the original residual, β k+1 is the dual residual, ε 1 and ε 2 are the thresholds for the primal residual and the dual residual, respectively.

[0165] like Figure 3 FIG. 1 is a flow chart of using a synchronous alternating direction multiplier algorithm to solve the running layer model in parallel in this embodiment, which specifically includes the following steps:

[0166] 1. Initialize the Lagrange multiplier, the interaction power of adjacent traction substations and the maximum number of iterations;

[0167] 2. Decouple the operation layer model through interactive power to obtain the decoupled models of two sub-problems, namely the first decoupled model and the second decoupled model;

[0168] 3. Solve the first decoupling model and the second decoupling model respectively, and obtain the expected values ​​of all decision variables of the operation layer model including the interactive power of adjacent traction stations;

[0169] 4. The iterative process updates each decision variable and Lagrangian operator:

[0170]

[0171] 5. Calculate the original residual and the dual residual:

[0172] 6. Determine the convergence condition, that is, whether the residual error in step 4 meets the condition or whether the maximum number of iterations is reached; if the convergence condition is met, then end; otherwise, k = k + 1, and repeat steps 2 to 6.

[0173] In one embodiment, Figure 4 As shown, a schematic diagram of the structural topology of a multi-traction power supply system is provided. The first traction station is provided with a sub-control center 1, the second traction station is provided with a sub-control center 2, and the power supply system is also provided with a main control center. The main control center stores a planning layer model, and the first decoupling model and the second decoupling model obtained after the operation layer model is decoupled are respectively decentralized to the sub-control center 1 of the first traction station and the sub-control center 2 of the second traction station.

[0174] When solving the planning-operation double-layer model, the photovoltaic-traction load scenario sets corresponding to the first traction station and the second traction station are substituted into the planning layer model, the photovoltaic-traction load scenario set of the first traction station is substituted into the first decoupling model, and the photovoltaic-traction load scenario set of the second traction station is substituted into the second decoupling model. The two decoupling models are solved in parallel using distributed computing.

[0175] The existing traction power supply system adopts centralized communication, and the data of each traction station is collected through a unified dispatching center, which easily causes communication pressure. Based on this, in this embodiment, by setting up sub-control center 1 and sub-control center 2 and making them communicate with each other, the parallel solution of two decoupled models is realized, which not only improves the solution efficiency, but also reduces the communication pressure of the top-level dispatching center.

[0176] Embodiment 2:

[0177] Another embodiment of the present application relates to a parameter configuration device for a power supply system. The implementation details of the parameter configuration device for the power supply system of this embodiment are specifically described below. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution. The schematic diagram of the parameter configuration device for the power supply system of this embodiment can be as follows Figure 5 As shown, the parameter configuration device of the power supply system is applied to a multi-traction power supply system, the multi-traction power supply system includes a first traction station, a second traction station and a sub-station, the first traction station and the second traction station are arranged adjacent to each other, and the sub-station is used for energy interaction between the first traction station and the second traction station. It includes: a data acquisition module 510, a scene set construction module 520, and an iterative calculation module 530.

[0178] The data acquisition module 510 is used to acquire the photovoltaic output data and traction load data of the first traction station, and the photovoltaic output data and traction load data of the second traction station within a preset target period.

[0179] The scenario set construction module 520 is used to construct a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve for each traction station based on the photovoltaic output data and traction load data corresponding to the traction station. The photovoltaic-traction load scenario set is used to characterize the temporal matching between the photovoltaic output and the traction load.

[0180] The iterative calculation module 530 substitutes the photovoltaic-traction load scenario set corresponding to the first traction station and the second traction station into a preset planning-operation two-layer model, solves the planning-operation two-layer model based on a two-layer alternating iteration strategy, and obtains the target configuration parameters of the multi-traction station power supply system.

[0181] Among them, the planning-operation two-layer model includes a planning layer model and an operation layer model. The planning layer model is used to minimize the sum of the investment cost and the annual operating cost of the power supply system of multiple traction stations as the objective function, and the equipment capacity as the constraint condition, to solve the capacity configuration parameters of each device, and send the equipment capacity configuration parameters to the operation layer model. The operation layer model is used to minimize the annual operating cost as the objective function, and the equipment operating conditions as the constraints. By calculating the interaction energy between the first traction station and the second traction station, the operation status of each device under the equipment capacity configuration parameters is solved, and the operation status of each device is fed back to the planning layer model.

[0182] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0183] Embodiment three:

[0184] Another embodiment of the present application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the parameter configuration method of the power supply system in the above-mentioned embodiments.

[0185] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0186] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0187] Embodiment 4:

[0188] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0189] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0190] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A parameter configuration method for a power supply system, characterized in that: Applicable to a multi-traction station power supply system, the multi-traction station power supply system includes a first traction station, a second traction station and a sub-station, the first traction station and the second traction station are arranged adjacent to each other, and the sub-station is used for energy exchange between the first traction station and the second traction station, including: Acquire the photovoltaic output data and traction load data of the first traction station, and the photovoltaic output data and traction load data of the second traction station within a preset target period; For each traction station, based on the PV output data and traction load data corresponding to the traction station, a PV-traction load scenario set consisting of a PV output timing curve and a traction load timing curve is constructed, wherein the PV-traction load scenario set is used to characterize the temporal matching of the PV output and the traction load; Substituting the photovoltaic-traction load scenario set corresponding to the first traction station and the second traction station into a preset planning-operation two-layer model, solving the planning-operation two-layer model based on a two-layer alternating iteration strategy, and obtaining the target configuration parameters of the multi-traction station power supply system, Among them, the planning-operation two-layer model includes a planning layer model and an operation layer model. The planning layer model is used to minimize the sum of the investment cost and the annual operating cost of the power supply system of the multiple traction stations as the objective function, and use the equipment capacity as the constraint condition to solve and obtain the capacity configuration parameters of each device, and send the equipment capacity configuration parameters to the operation layer model. The operation layer model is used to minimize the annual operating cost as the objective function, and use the equipment operating conditions as the constraints. By calculating the interaction energy between the first traction station and the second traction station, the operation status of each device under the equipment capacity configuration parameters is solved, and the operation status of each device is fed back to the planning layer model.

2. The parameter configuration method according to claim 1, characterized in that: The step of constructing a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve includes: Divide a day into several time periods according to a preset time scale; Taking each of the time periods as a random variable, and for the traction load data in each of the time periods in each season, determining the probability distribution of the traction load corresponding to the time period; Based on the photovoltaic output data in each season, the probability distribution of light intensity in each season is analyzed; Based on the probability distribution of the traction load and the probability distribution of the light intensity, a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled and obtained every day in each season using a random sampling method.

3. The parameter configuration method according to claim 2, characterized in that: The sampling obtains a photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve for each day in each season, including: An initial photovoltaic-traction load scenario set consisting of a photovoltaic output timing curve and a traction load timing curve is sampled for each day in each season; The initial photovoltaic-traction load scenario set is reduced by using a K-means clustering algorithm to obtain a reduced typical scenario set, and the typical scenario set is used as the photovoltaic-traction load scenario set.

4. The parameter configuration method according to claim 2, characterized in that: The step of solving the planning-operation double-layer model based on a double-layer alternating iterative strategy to obtain target configuration parameters of the multi-traction power supply system includes: determining initial values ​​of decision variables of the planning layer model, and transmitting the initial values ​​to the operation layer model, Solving the operation layer model based on the initial value to obtain annual operation cost and operation decision variables, and sending the annual operation cost and operation decision variables to the planning layer model; Solving the planning layer model based on the annual operating cost and the operating decision variables to obtain updated values ​​of the decision variables; Based on the updated values ​​of the decision variables, the operation layer model is solved to obtain the new annual operation cost and operation decision variables, and the new annual operation cost and operation decision variables are sent to the planning layer model. The above alternating iterative steps are repeated until the preset convergence conditions of the planning layer model are reached, then the iteration is stopped to obtain the target configuration parameters of the multi-traction power supply system.

5. The parameter configuration method according to claim 4, characterized in that: The convergence condition is set to one of the following: The absolute value of the difference between the objective functions of the planning layer model in two adjacent iterations satisfies a preset error condition; and The number of iterations reaches the preset upper limit.

6. The parameter configuration method according to claim 4, characterized in that: Solving the operation layer model based on the initial value to obtain the annual operation cost and operation decision variables includes: Based on a parallel solution algorithm of synchronous alternating direction multipliers, the expression of power coupling formed by the energy interaction between the first traction station and the second traction station in the operating layer model in the partition is decoupled to obtain a first decoupling model and a second decoupling model; The photovoltaic-traction load scenario set corresponding to the first traction station is sent to the first decoupling model, and the photovoltaic-traction load scenario set corresponding to the second traction station is sent to the second decoupling model, and the first decoupling model and the second decoupling model are solved in parallel to obtain the annual operating cost and operating decision variables of the first traction station, and the annual operating cost and operating decision variables of the second traction station.

7. The parameter configuration method according to any one of claim 6, characterized in that: The calculation formulas of the first decoupling model and the second decoupling model are as follows: in, is the calculation formula of the first decoupling model, is the calculation formula of the second decoupling model, C op，m is the annual operating cost of the first traction station, C op，n is the annual operating cost of the second traction station, ρ is the penalty factor, is the interactive power from the first traction station to the second traction station in the season j scenario s and time period t, is the optimization result of the interaction power of the first traction station in the previous iteration, is the average value of the optimization results of the interaction power of adjacent tractions in the previous iteration, u k For the operator.

8. The parameter configuration method according to any one of claims 1 to 7, characterized in that: The annual operating cost is composed of equipment operating cost, electricity charges, feed-in penalty cost, photovoltaic curtailment cost and line loss cost.

9. The parameter configuration method according to any one of claims 1 to 7, characterized in that: The constraints in the operation layer model using equipment operation conditions as constraints include: photovoltaic output operation constraints, energy storage output constraints, energy storage capacity constraints, internal power balance constraints between the first traction station and the second traction station, and interactive energy constraints between the first traction station and the second traction station.

10. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the parameter configuration method according to any one of claims 1 to 9.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the parameter configuration method according to any one of claims 1 to 9 is implemented.

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