Distributed scheduling method for railway traction station and peripheral micro-energy network

Abstract

The invention provides a distributed scheduling method for railway traction stations and peripheral micro-energy grids, and belongs to the technical field of energy supply of micro-energy grid groups containing the railway traction stations, and the method comprises the steps: building a micro-energy grid sharing mutual aid grid structure containing the railway traction stations, obtaining a power model of each railway traction station, and optimally configuring energy storage equipment, obtaining operation constraints of the energy storage equipment; constructing an energy sharing benefit function by taking each micro-energy network agent as a game main body and taking individual benefit maximization as a target, setting constraint conditions, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model by adopting an alternating direction multiplier algorithm to obtain a distributed energy transaction behavior result and a price result. According to the method, distributed solution is carried out on the micro-energy network energy sharing non-cooperative game model based on the alternating direction multiplier method, original centralized optimization is converted into coupling optimization among a plurality of micro-energy network agents through interactive variables, the efficiency of the whole iteration process is guaranteed, and the privacy of each energy network is protected.

Description

Technical Field

[0001] This invention relates to a distributed scheduling method for railway traction substations and surrounding microgrids, belonging to the field of microgrid group power supply technology including railway traction substations. Background Technology

[0002] With the escalating global energy crisis and the urgent need for energy conservation and emission reduction, railway traction substations, as the power core of the railway system, have received widespread attention for their energy efficiency and energy management technologies. During operation, railway traction substations generate a significant amount of braking energy. Effective recovery and reuse of this energy would greatly improve energy efficiency. Furthermore, with the development of microgrid technology, the coordinated and optimized operation of railway traction substations with surrounding microgrids has also become an important way to improve energy efficiency.

[0003] Existing research largely focuses on the multi-energy complementarity of individual micro-energy systems and demand response between microgrids and users, lacking research on energy sharing among microgrids. Individual railway traction substation systems have significant external energy demands; multi-system mutual support can directly improve energy self-sufficiency. Existing research uses the Shapley value method in cooperative game theory models to redistribute the benefits of multi-entity joint operation. However, these studies mostly consider electricity sharing among multiple integrated energy entities, lacking research on sharing mechanisms for other energy forms and their cost settlement methods. Furthermore, traditional energy management models often neglect the individual interests of each microgrid, leading to low energy utilization efficiency. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a distributed scheduling method for railway traction substations and surrounding microgrids. By establishing a non-cooperative game model that considers the maximization of benefits obtained by each microgrid individual through various energy sharing activities, the problem of low energy utilization efficiency is solved.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a distributed scheduling method for railway traction substations and surrounding microgrids, comprising: establishing a shared and mutually supportive microgrid network structure, including: multiple microgrids containing railway traction substations; obtaining the power model of each railway traction substation, and optimizing the configuration of energy storage devices based on the power model of each railway traction substation to obtain energy storage device operation constraints; constructing an energy sharing benefit function with each microgrid agent as the game subject and the goal of maximizing the individual interests of a single microgrid agent participating in energy sharing, setting energy storage device operation constraints, individual profit-seeking constraints, and energy sharing interaction constraints as constraints, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model using the alternating direction multiplier algorithm to obtain the results of distributed energy trading behavior between microgrids and the results of energy trading prices.

[0007] Furthermore, the energy storage device includes: electrochemical energy storage as a low-power recovery medium and a supercapacitor as a high-power recovery medium.

[0008] The optimization configuration of energy storage equipment based on the power model of each railway traction station includes: analyzing the measured value curve of regenerative braking power throughout the day, extracting regenerative braking sections with a power duration greater than a preset value as recyclable braking sections; analyzing each recyclable braking section, classifying all recyclable braking sections into low-power recyclable braking sections and high-power recyclable braking sections; optimizing the configuration of electrochemical energy storage based on the power of low-power recyclable braking sections, and optimizing the configuration of supercapacitors based on the power of high-power recyclable braking sections.

[0009] Furthermore, the operational constraints of the energy storage device include: operational constraints for electrochemical energy storage:

[0010] ;

[0011] in, ;

[0012] In the formula, The state of charge (SOC) at time t for electrochemical energy storage. These represent the minimum and maximum SOC for electrochemical energy storage, respectively. These are the State of Charge (SOC) at the beginning and end of the electrochemical energy storage scheduling cycle, respectively. These represent the charging and discharging states of the electrochemical energy storage at time t, respectively. Let be the charging and discharging power of the electrochemical energy storage at time t, respectively. These are the maximum charging and discharging power of electrochemical energy storage, respectively. This is the upper limit of power for the low-power regenerative braking section;

[0013] Supercapacitor operating constraints:

[0014] ;

[0015] in, ;

[0016] In the formula, The state of charge (SOC) of the supercapacitor at time t. These represent the minimum and maximum states of charge (SOC) of the supercapacitor, respectively. These are the State of Charge (SOC) at the beginning and end of the supercapacitor scheduling cycle, respectively. These represent the charging and discharging states of the supercapacitor at time t, respectively. These represent the charging and discharging power of the supercapacitor at time t, respectively. This is the upper limit of the power for the high-power regenerative braking section.

[0017] Furthermore, the constraints also include: operational constraints on the electro-gas conversion device, specifically:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] In the formula, For high heating value voltage, This indicates the operating temperature of the electrolytic cell, expressed in Kelvin (K). The voltage across the battery terminals. Indicates electrolysis efficiency. These represent total power consumption, hydrogen production power consumption, and reaction heating power consumption, respectively. This indicates the equivalent number of battery packs after series-parallel connection operations. Indicates the effective reaction area of ​​the electrolytic cell. This indicates the current density of the electrolytic cell.

[0024] Furthermore, the energy sharing benefit function is:

[0025] ;

[0026] in,

[0027] ;

[0028] In the formula, The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. Agent for t time to agents Sold the first The price of shared energy Agent for t time to agents Purchase the The price of shared energy; Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, Indicates a collection of agents. Represents a set of energy types. Indicates the scheduling period.

[0029] Furthermore, the individual profit-seeking constraint is as follows:

[0030] ;

[0031] In the formula, This indicates the operational efficiency of agent k when considering energy sharing. The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. This indicates that the operational efficiency of agent k is not considered when energy sharing is not taken into account.

[0032] Furthermore, the energy sharing interaction constraints are as follows:

[0033] ;

[0034] In the formula, Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, This represents a set of energy types.

[0035] Furthermore, the method of using the alternating direction multiplier algorithm to solve the energy-sharing non-cooperative game model includes: energy-sharing interaction constraint transformation, establishing an augmented Lagrange form of the sum of energy-sharing benefits of all agents, and handling the non-convex constraints of mixed integer problems;

[0036] Develop a strategy to update the penalty coefficient.

[0037] Furthermore, the expression for the penalty coefficient update strategy is:

[0038] ;

[0039] In the formula, Indicates the penalty coefficient The update coefficient; These represent the original residual and the dual residual, respectively. This is a multiple threshold.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0041] (1) This invention establishes a non-cooperative game model between railway traction station and surrounding microgrids with the goal of maximizing individual interests. Based on the alternating direction multiplier method, the non-cooperative game model of energy sharing of microgrids is solved in a distributed manner. The original centralized optimization is transformed into a coupled optimization between multiple microgrid agents through interactive variables. This distributed solution scheme not only ensures the efficiency of the entire iteration process, but also protects the privacy of each energy network.

[0042] (2) The present invention aims to achieve efficient storage of braking energy by refining the braking energy power classification of railway traction stations, formulating battery and supercapacitor configuration plans for each power range, and combining multi-system energy mutual assistance to improve energy utilization efficiency and reduce energy waste. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the microgrid sharing and mutual assistance network structure in Example 2;

[0044] Figure 2 This is a diagram showing the predicted wind power output and user power load within microgrids 1, 2, and 3 in Example 2;

[0045] Figure 3 The gas load curves of users in microgrids 1, 2, and 3 in Example 2 are shown.

[0046] Figure 4 The heat load curves of users in microgrids 1, 2, and 3 in Example 2 are shown.

[0047] Figure 5 This is a typical daily braking power curve of the railway traction station in Example 2;

[0048] Figure 6 The results of the analysis of braking frequency and peak power at the railway traction station in Example 2;

[0049] Figure 7 This refers to the power purchase situation between microgrids in Example 2, where power is shared between them and purchased from outside the region. Detailed Implementation

[0050] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus. The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0051] Example 1

[0052] This embodiment provides a distributed scheduling method for railway traction substations and surrounding microgrids, including:

[0053] Step 1: Establish a microgrid sharing and mutual assistance network structure.

[0054] Specifically, the microgrid sharing and mutual assistance network structure consists of several microgrids, including railway traction substations. Establishing the microgrid sharing and mutual assistance network structure includes: establishing the architecture of heating network, gas supply network and distribution network, modeling energy conversion equipment, and modeling energy storage equipment.

[0055] Generally, energy conversion equipment modeling includes modeling electro-gas conversion devices, electro-thermal conversion devices, and gas-thermal conversion devices.

[0056] Each microgrid fully considers renewable energy fluctuations and surpluses under high renewable energy penetration conditions to meet normal electricity loads and electricity-to-gas loads. When there is an energy shortage within the integrated energy distribution network, energy is purchased from external sources.

[0057] Step 2: Obtain the power model of each railway traction station, and optimize the configuration of energy storage equipment for recovering braking energy of the railway traction station based on the power model of each railway traction station, and obtain the operating constraints of the energy storage equipment.

[0058] In some specific embodiments, a hybrid energy storage system is used to recover regenerative braking energy, thereby reducing the amount of electricity purchased and lowering the electricity cost of the high-speed rail power supply system. The energy storage devices in the hybrid energy storage system include: electrochemical energy storage as a low-power recovery medium and supercapacitors as a high-power recovery medium.

[0059] The configuration of energy storage equipment needs to take into account the performance of regenerative braking power. In the embodiment of using electrochemical energy storage and supercapacitors to recover low-power and high-power regenerative braking energy separately, it is necessary to first analyze the regenerative braking energy and separate the low-power regenerative braking energy and the high-power regenerative braking energy.

[0060] Specifically, the measured value curve of regenerative braking power throughout the day is first analyzed, and the regenerative braking segment with a power duration greater than the preset value is extracted as the regenerative braking segment. For example, the regenerative braking segment with a duration greater than 10s is selected, and its braking energy is regarded as high-quality regenerative energy.

[0061] Each regenerative braking segment is analyzed and classified according to constant power return, fluctuating power return, mixed power return, and peak power return. All regenerative braking segments are classified into low-power regenerative braking segments and high-power regenerative braking segments, and the upper limits of low-power braking power and high-power braking power are obtained.

[0062] The configuration of electrochemical energy storage is optimized based on the power limit of the low-power regenerative braking segment, that is, the power limit of low-power braking is used as the charging power limit of electrochemical energy storage; the configuration of supercapacitors is optimized based on the power limit of the high-power regenerative braking segment.

[0063] In this embodiment, the operational constraints of the energy storage device include: operational constraints of electrochemical energy storage and operational constraints of supercapacitors.

[0064] Specifically, the operational constraints for electrochemical energy storage are:

[0065] ;

[0066] in, ;

[0067] In the formula, The state of charge (SOC) at time t for electrochemical energy storage. These represent the minimum and maximum SOC for electrochemical energy storage, respectively. These are the State of Charge (SOC) at the beginning and end of the electrochemical energy storage scheduling cycle, respectively. These represent the charging and discharging states of the electrochemical energy storage at time t, respectively. Let be the charging and discharging power of the electrochemical energy storage at time t, respectively. These are the maximum charging and discharging power of electrochemical energy storage, respectively. This is the upper limit of power for the low-power regenerative braking section;

[0068] The operating constraints of supercapacitors are:

[0069] ;

[0070] in, ;

[0071] In the formula, The state of charge (SOC) of the supercapacitor at time t. These represent the minimum and maximum states of charge (SOC) of the supercapacitor, respectively. These are the State of Charge (SOC) at the beginning and end of the supercapacitor scheduling cycle, respectively. These represent the charging and discharging states of the supercapacitor at time t, respectively. These represent the charging and discharging power of the supercapacitor at time t, respectively. This is the upper limit of the power for the high-power regenerative braking section.

[0072] In some specific embodiments, the energy conversion equipment includes: an electricity-to-gas conversion unit and a combined cooling, heating, and power (CCHP) unit. Correspondingly, the constraints also include: operational constraints for the electricity-to-gas conversion unit, specifically:

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] In the formula, For high heating value voltage, This indicates the operating temperature of the electrolytic cell, expressed in Kelvin (K). The voltage across the battery terminals. Indicates electrolysis efficiency. These represent total power consumption, hydrogen production power consumption, and reaction heating power consumption, respectively. This indicates the equivalent number of battery packs after series-parallel connection operations. Indicates the effective reaction area of ​​the electrolytic cell. This indicates the current density of the electrolytic cell.

[0079] The operational constraints for combined cooling, heating and power (CCHP) units are as follows:

[0080] ;

[0081] In the formula, This represents the gas turbine power generation efficiency at node m. This indicates the calorific value of natural gas.

[0082] Step 3: Using each microgrid agent as the game subject, construct an energy sharing benefit function with the goal of maximizing the individual interests of each microgrid agent participating in energy sharing. Set constraints such as energy storage equipment operation constraints, individual profit-seeking constraints, and energy sharing interaction constraints as conditions to establish a non-cooperative game model for energy sharing.

[0083] Constructing a non-cooperative game model includes establishing an energy-sharing benefit function, establishing profit-seeking constraints for energy-sharing individuals, establishing energy-sharing interaction constraints, and establishing physical constraints on energy flow.

[0084] Specifically, based on the business objective of maximizing individual interests among microgrid agents, an energy-sharing benefit function among microgrid agents based on generalized non-cooperative game theory is established, as follows:

[0085] ;

[0086] in,

[0087] ;

[0088] In the formula, The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. Agent for t time to agents Sold the first The price of shared energy Agent for t time to agents Purchase the The price of shared energy; Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, Indicates a collection of agents. Represents a set of energy types. Indicates the scheduling period.

[0089] For microgrid agents, their participation in energy sharing is contingent upon increased individual benefits, and the profit-seeking constraint for individuals in energy sharing is as follows:

[0090] ;

[0091] In the formula, This indicates the operational efficiency of agent k when considering energy sharing. The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. This indicates that the operational efficiency of agent k is not considered when energy sharing is not taken into account.

[0092] The energy sharing interaction constraints are:

[0093] ;

[0094] In the formula, Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, This represents a set of energy types.

[0095] The establishment of physical constraints on energy flow, including regional distribution network flow constraints, gas network flow constraints, and heat network transmission constraints, is not within the scope of protection of this invention and will not be elaborated here.

[0096] Step 4: The alternating direction multiplier algorithm is used to solve the energy-sharing non-cooperative game model to obtain the results of distributed energy trading behavior and energy trading prices between microgrids.

[0097] The results of distributed energy trading here include: electricity sharing, heat sharing, and natural gas sharing; the results of energy trading prices include: the trading prices of shared electricity, shared heat, and shared natural gas.

[0098] Specifically, solving non-cooperative game models for energy sharing includes:

[0099] Step 41: Transform the energy sharing interaction constraints and establish the augmented Lagrangian form of the sum of energy sharing benefits of all agents, as well as the non-convex constraint handling of the mixed integer problem.

[0100] Since the interaction variables between agents are globally coupled variables, other constraints, except for energy interaction constraints, are only related to the decision variables of each microgrid itself. For the energy sharing interaction constraints between agents, auxiliary variables are needed to transform them. Then, based on this, the compact model representing the sum of the benefits of each microgrid agent is transformed into an augmented Lagrangian form.

[0101] Step 42: Formulate the penalty coefficient update strategy.

[0102] In some specific embodiments, to accelerate the convergence process, a threshold is typically set where there exists a certain multiple between the original residual and the dual residual. In each iteration, it is determined whether this threshold has been reached, and a penalty coefficient is set accordingly. Update based on the update factor.

[0103] Specifically, the expression for the penalty coefficient update strategy is:

[0104] ;

[0105] In the formula, Indicates the penalty coefficient The update coefficient; These represent the original residual and the dual residual, respectively. This is a multiple threshold.

[0106] Example 2

[0107] Based on Example 1, this example demonstrates the performance of the present invention in a practical application scenario.

[0108] The application scenario of this embodiment is as follows: Figure 1 As shown, Figure 1 The microgrid sharing and mutual assistance network structure is an electric-gas-thermal coupled microgrid architecture that includes railway traction substations. Specifically, the distribution network structure is an improved IEEE-33-node system, the gas distribution network structure is an improved 20-node Belgian natural gas network, and the heating network structure is an 8-node heating distribution network.

[0109] Microgrids RIES1, RIES2, and RIES3 are located at nodes 9, 12, and 15 of the distribution network, nodes 7, 13, and 18 of the gas distribution network, and nodes 5, 7, and 8 of the heating network, respectively. Each microgrid fully considers renewable energy fluctuations and surpluses under high renewable energy penetration conditions to meet normal electricity loads and electricity-to-gas loads. When there is an energy shortage within the integrated distribution network, energy is purchased from external sources.

[0110] The pricing parameters are set as follows: the price of CO2, the raw material for electricity-to-gas conversion, is 60 yuan / t; the main grid electricity purchase price adopts time-of-use pricing, with 0.54 yuan / kWh for the normal period from 23:00 to 6:00 and 0.67 yuan / kWh for the remaining time as peak hours; the main grid electricity sales price is 0.15 yuan / kWh; the main grid gas supply price for the three regions is 2.74 yuan / m³. 3 2.54 yuan / m 3 2.42 yuan / m 3 The main grid gas price is 2.2 yuan / m³. 3 .

[0111] It should be noted that, considering the regional nature of heating, no external unit provides direct heating services to the microgrid sharing and mutual assistance network structure.

[0112] For shared energy storage, the initial energy storage capacity for each microgrid is set at 50% of its leased capacity from the shared energy storage. The ratio of the rated capacity to the power limit of the shared energy storage is set at 0.4. The unit cost of storage / discharge is set at 0.12 yuan / kWh, the capacity cost at 450 yuan / kW, and the power cost at 420 yuan / kW. The daily maintenance cost is 65 yuan, and the lifespan is 10 years.

[0113] Table 1 Unit Configuration within the Integrated Energy Distribution Network

[0114] Unit rated capacity RIES1 RIES2 RIES3 Fan 1500kW 650kW 900kW Cogeneration units 200kW 500kW 400kW Electrolysis cell (electric to gas conversion) 400kW 500kW 600kW

[0115] Figure 2 , 3 Figures 4 and 5 respectively show the predicted wind power output and the load curves of electricity, gas and heat users in microgrids RIES1, RIES2 and RIES3.

[0116] A typical daily braking power curve for a railway traction substation is shown below. Figure 5 As shown, according to Figure 5 After discarding braking power values ​​with a duration of less than 10 seconds, a total of 59 single braking power values ​​meeting the requirements can be extracted. Through power fluctuation analysis, it can be determined that braking power with constant power or small-range fluctuations within the power fluctuation range of 250kW~450kW accounts for approximately [percentage missing]. Figure 5 The total braking time during a typical day is shown to be 59%.

[0117] according to Figure 5 The analysis results of the braking frequency and peak power are as follows: Figure 6 As shown.

[0118] Considering that the cumulative braking energy value should meet the upper limit constraint of the hybrid energy storage system capacity, each braking cycle until traction consumption occurs is considered a braking segment. Therefore, a total of 36 traction segments and 36 braking segments are obtained. Figure 6 As shown. Generally, considering that the energy of the 36th braking segment is much higher than that of the previous 35 segments, it is not included in the daily braking energy recovery range.

[0119] Figure 7 The results of shared electricity transactions are displayed. "1-2", "1-3", and "2-3" represent the energy exchange amounts between "microgrid agents 1 to 2", "microgrid agents 1 to 3", and "microgrid agents 2 to 3", respectively. The letters "E", "G", and "H" represent electricity, natural gas, and heat, respectively. "Main" indicates energy transactions with external energy distribution networks.

[0120] Depend on Figure 7 It is evident that, considering the energy-sharing scenario, external electricity is purchased only at 18:00, indicating a very low frequency of external purchases. This suggests that IEROs will prioritize energy sharing or storage after meeting their own energy needs. Energy sharing ensures the optimal allocation of the limited energy resources in the microgrid, while energy trading achieves optimal operating costs.

[0121] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed scheduling method for railway traction substations and surrounding microgrids, characterized in that, include: Establish a microgrid sharing and mutual assistance network structure, including: multiple microgrids containing railway traction substations; The power models of each railway traction station are obtained, and the energy storage equipment is optimized and configured based on the power models of each railway traction station to obtain the operating constraints of the energy storage equipment; wherein, the energy storage equipment is used to recover the braking energy of the railway traction station. Taking each microgrid agent as the main players in the game, and aiming at maximizing the individual interests of each microgrid agent participating in energy sharing, an energy sharing benefit function is constructed. The constraints are set as energy storage equipment operation constraints, individual profit-seeking constraints, and energy sharing interaction constraints, and a non-cooperative game model for energy sharing is established. The alternating direction multiplier algorithm is used to solve the energy-sharing non-cooperative game model, and the results of distributed energy trading behavior and energy trading prices between microgrids are obtained.

2. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The energy storage device includes: an electrochemical energy storage medium as a low-power recovery medium and a supercapacitor as a high-power recovery medium; The optimization configuration of energy storage devices based on the power model of each railway traction station includes: Analyze the measured curve of regenerative braking power throughout the day, and extract the regenerative braking section with a power duration greater than the preset value as the regenerative braking section; Each regenerative braking segment was analyzed, and all regenerative braking segments were classified into low-power regenerative braking segments and high-power regenerative braking segments. Electrochemical energy storage is configured based on the power optimization of the low-power regenerative braking segment, and supercapacitors are configured based on the power optimization of the high-power regenerative braking segment.

3. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 2, characterized in that, The operational constraints of the energy storage device include: Electrochemical energy storage operation constraints: ； in, ; In the formula, The state of charge (SOC) at time t for electrochemical energy storage. These represent the minimum and maximum SOC for electrochemical energy storage, respectively. These are the State of Charge (SOC) at the beginning and end of the electrochemical energy storage scheduling cycle, respectively. These represent the charging and discharging states of the electrochemical energy storage at time t, respectively. Let be the charging and discharging power of the electrochemical energy storage at time t, respectively. These are the maximum charging and discharging power of electrochemical energy storage, respectively. This is the upper limit of power for the low-power regenerative braking section; Operating constraints of supercapacitors; ； in, ; In the formula, The state of charge (SOC) of the supercapacitor at time t. These represent the minimum and maximum states of charge (SOC) of the supercapacitor, respectively. These are the State of Charge (SOC) at the beginning and end of the supercapacitor scheduling cycle, respectively. These represent the charging and discharging states of the supercapacitor at time t, respectively. These represent the charging and discharging power of the supercapacitor at time t, respectively. This is the upper limit of the power for the high-power regenerative braking section.

4. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The constraints also include: operational constraints for the electro-gas conversion device, specifically: ； ； ； ； ； In the formula, For high heating value voltage, This indicates the operating temperature of the electrolytic cell, expressed in Kelvin (K). The voltage across the battery terminals. Indicates electrolysis efficiency. These represent total power consumption, hydrogen production power consumption, and reaction heating power consumption, respectively. This indicates the equivalent number of battery packs after series-parallel connection operations. Indicates the effective reaction area of ​​the electrolytic cell. This indicates the current density of the electrolytic cell.

5. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The energy sharing benefit function is: ； in, ； In the formula, The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. Agent for t time to agents Sold the first The price of shared energy Agent for t time to agents Purchase the The price of shared energy; Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, Indicates a collection of agents. Represents a set of energy types. Indicates the scheduling period.

6. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The individual profit-seeking constraint is: ； In the formula, This indicates the operational efficiency of agent k when considering energy sharing. The operational benefits that agent k obtains through energy sharing transactions during the scheduling period. This indicates that the operational efficiency of agent k is not considered when energy sharing is not taken into account.

7. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The energy sharing interaction constraint is: ； In the formula, Agent for t time to agents Sold the first The amount of shared energy, Agent for t time to agents Purchase the The amount of shared energy, This represents a set of energy types.

8. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 1, characterized in that, The method of solving the energy-sharing non-cooperative game model using the alternating direction multiplier algorithm includes: The transformation of energy sharing interaction constraints is carried out, and an augmented Lagrangian form of the sum of energy sharing benefits of all agents is established, as well as the non-convex constraint handling of mixed integer problems. Develop a strategy to update the penalty coefficient.

9. The distributed scheduling method for railway traction substations and surrounding microgrids according to claim 8, characterized in that, The expression for the penalty coefficient update strategy is: ； In the formula, Indicates the penalty coefficient The update coefficient; These represent the original residual and the dual residual, respectively. This is a multiple threshold.

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