Rail transit energy supply configuration method, system and equipment and computer medium

Abstract

The invention discloses a rail transit energy supply configuration method, system and device and a computer medium, and relates to the technical field of energy distribution. New energy supply information, energy storage information and energy consumption information of rail transit are obtained; generating a power supply working condition type of the rail transit according to the new energy supply information, the energy storage information and the energy consumption information; determining a target operation mode of the rail transit based on the power supply condition type; generating an optimization target model of the rail transit in the target operation mode; generating an energy supply constraint condition of the rail transit; according to the constraint condition and the optimization target model, generating candidate energy supply configuration capacity; and according to the distance information between the candidate energy supply configuration capacities, selecting a target energy supply configuration capacity from the candidate energy supply configuration capacities so as to perform energy supply configuration according to the target energy supply configuration capacity. According to the method, the reasonable target energy supply configuration capacity is generated through distance value operation according to the working state, the constraint condition and the optimization target model of the rail transit, and the configuration accuracy is high.

Description

Technical Field

[0001] The present application relates to the field of energy distribution technology, and more specifically, to a rail transit energy supply configuration method, system, device and computer medium. Background Art

[0002] With the development of new energy and energy storage technologies, they have gradually been applied to the rail transit sector. These technologies can not only effectively reduce energy consumption and operating costs, but also improve system reliability and flexibility, becoming the future development direction of rail transit.

[0003] Despite this, effectively integrating renewable energy and energy storage technologies into railway traction power systems still faces numerous technical and planning challenges. For example, how to rationally configure renewable energy generation equipment and energy storage systems to ensure the safety and stability of rail transit traction power supply in various scenarios has become a critical issue that needs to be addressed.

[0004] In summary, how to accurately adapt the energy supply of rail transit is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a rail transit energy supply configuration method, which can, to a certain extent, solve the technical problem of how to accurately adapt the energy supply of rail transit. This application also provides a rail transit energy supply configuration system, electronic equipment and computer-readable storage medium.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A rail transit energy supply configuration method, comprising:

[0008] Obtain new energy supply information, energy storage information and energy consumption information for rail transit;

[0009] generating a power supply operating condition type for rail transit according to the new energy supply information, the energy storage information, and the energy consumption information;

[0010] Determining a target operating mode of rail transit based on the power supply operating condition type;

[0011] generating an optimized target model of rail transit in the target operation mode;

[0012] Generate energy supply constraints for rail transit;

[0013] Generating a candidate energy supply configuration capacity according to the constraint conditions and the optimization target model;

[0014] According to the distance information between the candidate energy supply configuration capacities, a target energy supply configuration capacity is selected from the candidate energy supply configuration capacities, so as to perform energy supply configuration according to the target energy supply configuration capacity.

[0015] Preferably, the obtaining of new energy supply information, energy storage information and energy consumption information of rail transit includes:

[0016] Obtain the fan's real-time wind speed, fan cut-in wind speed, fan rated wind speed, fan cut-out wind speed, and fan rated power;

[0017] generating a real-time output power of the wind turbine based on the real-time wind speed of the wind turbine, the cut-in wind speed of the wind turbine, the rated wind speed of the wind turbine, the cut-out wind speed of the wind turbine, and the rated power of the wind turbine;

[0018] Obtain the rated output power, real-time solar irradiance, standard solar irradiance, real-time temperature and adjusted temperature of the photovoltaic array;

[0019] generating a photovoltaic array real-time output power based on the photovoltaic array rated output power, the real-time solar irradiance, the standard solar irradiance, the real-time temperature, and the adjustment temperature;

[0020] The real-time output power of the wind turbine and the real-time output power of the photovoltaic array are used as new energy supply information for rail transit;

[0021] Obtain energy storage working status, energy storage working power and energy storage working time;

[0022] Generate a real-time state of charge of energy storage based on the energy storage working state, the energy storage working power and the energy storage working duration;

[0023] The real-time state of charge of the energy storage, the upper limit threshold of the state of charge of the energy storage and the lower limit threshold of the state of charge of the energy storage are used as energy storage information of rail transit;

[0024] The real-time power consumption of the load of rail transit is obtained, and the real-time power consumption of the load is used as energy consumption information.

[0025] Preferably, generating the power supply operating condition type of rail transit according to the new energy supply information, the energy storage information and the energy consumption information includes:

[0026] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type representing power demand of the grid and green power curtailment is generated;

[0027] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than or equal to the upper limit threshold of the state of charge of the energy storage, generating a power supply condition type representing power supply and power abandonment by the power grid;

[0028] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than the lower limit threshold of the energy storage state of charge, generating a power supply condition type indicating that the energy storage is shut down and the power grid meets the energy consumption of the load;

[0029] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than or equal to the lower limit threshold of the energy storage state of charge, generating a power supply condition type representing that the energy storage cooperates with the new energy and the power grid to meet the energy consumption of the load;

[0030] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type is generated, indicating that new energy meets the energy consumption of the load and green power is abandoned;

[0031] In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than or equal to the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type is generated, indicating that the power grid does not supply energy, new energy sources meet the energy consumption of the load, and green electricity is abandoned;

[0032] In response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than the lower limit threshold of the energy storage state of charge, a power supply condition type is generated that characterizes that the energy storage cooperates with new energy to meet the energy consumption of the load.

[0033] Preferably, determining the target operation mode of rail transit based on the power supply operating condition type includes:

[0034] In response to the power supply operating condition type reflecting that the new energy sources sequentially guarantee the energy consumption of the load and the energy storage, and the power grid consumes green electricity while supplying power, determining that the target operating mode of the rail transit is the first operating mode;

[0035] In response to the power supply operating condition type reflecting unidirectional power supply by the power grid and the power grid providing energy guarantee when the supply of new energy and energy storage is insufficient, determining the target operating mode of the rail transit to be the second operating mode;

[0036] In response to the power supply operating condition type reflecting that the electric energy of the rail transit comes from new energy and energy storage, the target operating mode of the rail transit is determined to be the third operating mode.

[0037] Preferably, generating an optimized target model of rail transit in the target operation mode includes:

[0038] Generate rail transit investment data, operation and maintenance data, electricity purchase data, electricity sales data, replacement data and residual value data;

[0039] In response to the target operating mode being the first operating mode, generating an economic optimization target corresponding to the first operating mode based on the investment data, the operation and maintenance data, the power purchase data, the power sales data, the replacement data, and the residual value data;

[0040] In response to the target operating mode being the second operating mode, generating an economic optimization target corresponding to the second operating mode based on the investment data, the operation and maintenance data, the power purchase data, the replacement data, and the residual value data;

[0041] In response to the target operating mode being the third operating mode, generating an economic optimization target corresponding to the third operating mode based on the investment data, the operation and maintenance data, the replacement data, and the residual value data;

[0042] Generate environmental optimization targets for rail transit in the target operation mode based on grid emissions;

[0043] Based on the total amount of renewable energy power generation and the total amount of energy consumption, generate a self-consistency optimization target for rail transit in the target operation mode;

[0044] The economic optimization target, the environmental optimization target and the self-consistency optimization target are used as an optimization target model;

[0045] The energy supply constraints for generating rail transit include:

[0046] Generate energy and power constraints for rail transit;

[0047] Generate renewable energy power generation constraints and energy storage state of charge constraints;

[0048] The energy power constraint condition, the new energy power generation constraint condition and the energy storage charge state constraint condition are used as energy supply constraint conditions.

[0049] Preferably, generating candidate energy supply configuration capacities according to the constraint conditions and the optimization target model includes:

[0050] Initialize system parameters, including light intensity, temperature, wind speed, real-time load power consumption, set parameters, upper and lower limits of configuration capacity;

[0051] Converting the optimization target model into the optimization target space of the population, randomly generating a configuration capacity as the initial population, and starting iteration counting;

[0052] Perform non-dominated sorting on the population according to the constraints, and calculate the objective function value of each individual in the population through guided crossover and mutation;

[0053] Determine whether the population level classification and crowding calculation are completed;

[0054] If not completed, stratify the individuals in the population, compare the dominance and non-domination relationships between individuals, and mark the non-dominated individual population as the first-level non-dominated layer; ignore the marked individuals, and return to the step of performing non-dominated sorting of the population according to the constraints until the population is stratified;

[0055] If completed, calculate the crowding density of individuals in the same level, and determine the optimal individual with the target value based on the individual crowding density; use guided crossover and mutation operations to generate the offspring population, and the offspring population is influenced by the optimal parent population, and the evolution direction is the optimal direction of the group;

[0056] Merge the initial population and the offspring population, calculate the objective function value; perform fast non-dominated sorting on the newly merged population; select individuals to generate a new generation of population;

[0057] Determine whether the termination condition is met based on the number of iterations;

[0058] If satisfied, the output population is the candidate energy supply configuration capacity;

[0059] If not satisfied, increase the number of iterations and return to the step of performing non-dominated sorting on the population according to the constraint condition.

[0060] Preferably, the selecting a target energy supply configuration capacity from the candidate energy supply configuration capacities according to the distance information between the candidate energy supply configuration capacities includes:

[0061] Normalizing the candidate energy supply configuration capacity to obtain a normalized energy supply configuration capacity;

[0062] Screening out a maximum energy supply configuration capacity solution set and a minimum energy supply configuration capacity solution set from the normalized energy supply configuration capacity;

[0063] generating, according to the entropy weight of the optimization target model, a first distance value between the normalized energy supply configuration capacity and the maximum value solution set of the energy supply configuration capacity, and generating a second distance value between the normalized energy supply configuration capacity and the minimum value solution set of the energy supply configuration capacity;

[0064] generating a distance evaluation index based on the first distance value and the second distance value;

[0065] The candidate energy supply configuration capacity corresponding to the distance evaluation index is used as the target energy supply configuration capacity.

[0066] A rail transit energy supply configuration system, comprising:

[0067] The first acquisition module is used to obtain new energy supply information, energy storage information and energy consumption information of rail transit;

[0068] A first generating module is configured to generate a power supply operating condition type of rail transit according to the new energy supply information, the energy storage information, and the energy consumption information;

[0069] A first determining module is configured to determine a target operating mode of rail transit based on the power supply operating condition type;

[0070] A second generating module is used to generate an optimized target model of rail transit in the target operation mode;

[0071] The third generation module is used to generate energy supply constraints for rail transit;

[0072] a fourth generating module, configured to generate a candidate energy supply configuration capacity according to the constraint conditions and the optimization target model;

[0073] The first selection module is configured to select a target energy supply configuration capacity from the candidate energy supply configuration capacities according to distance information between the candidate energy supply configuration capacities, so as to perform energy supply configuration according to the target energy supply configuration capacity.

[0074] An electronic device, comprising:

[0075] memory for storing computer programs;

[0076] A processor is used to implement the steps of any of the above-mentioned rail transit energy supply configuration methods when executing the computer program.

[0077] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned rail transit energy supply configuration methods.

[0078] The present application provides a rail transit energy supply configuration method, which obtains new energy supply information, energy storage information and energy consumption information of the rail transit; generates the power supply condition type of the rail transit based on the new energy supply information, energy storage information and energy consumption information; determines the target operation mode of the rail transit based on the power supply condition type; generates an optimization target model of the rail transit in the target operation mode; generates energy supply constraints for the rail transit; generates candidate energy supply configuration capacities based on the constraints and the optimization target model; selects the target energy supply configuration capacity from the candidate energy supply configuration capacities based on the distance information between the candidate energy supply configuration capacities, and performs energy supply configuration according to the target energy supply configuration capacity. The present application can convert the new energy supply information, energy storage information and energy consumption information of rail transit into power supply condition types, and further convert them into target operation modes, so that the working status of rail transit can be determined with the help of power supply condition types and target operation modes, providing a good data basis for subsequent energy supply configuration; and in the energy supply configuration process, it is necessary to generate an optimization target model of rail transit in the target operation mode, so as to use the optimization target model to guide the configuration direction of energy supply configuration, generate energy supply constraints for rail transit, and use the energy supply constraints to avoid energy supply configuration exceeding the limit. In this way, reasonable candidate energy supply configuration capacities can be generated according to the constraints and the optimization target model, and finally, according to the distance information between the candidate energy supply configuration capacities, the target energy supply configuration capacity closest to the ideal configuration capacity is selected from the candidate energy supply configuration capacities, so as to realize the generation of reasonable target energy supply configuration capacity for energy supply configuration by distance value calculation according to the working status, constraints and optimization target model of rail transit, thereby improving the accuracy of energy supply adaptation for rail transit. The rail transit energy supply configuration system, electronic device and computer-readable storage medium provided by the present application also solve the corresponding technical problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0080] Figure 1 A flow chart of a rail transit energy supply configuration method provided in an embodiment of the present application;

[0081] Figure 2 Schematic diagram of the topology of the self-consistent system;

[0082] Figure 3 Schematic diagram of working condition division;

[0083] Figure 4This is the energy flow diagram for operating condition 1 in the first operating mode;

[0084] Figure 5 This is the energy flow diagram for working condition 2 in the first operating mode;

[0085] Figure 6 This is the energy flow diagram for working condition three in the first operating mode;

[0086] Figure 7 This is the energy flow diagram for working condition 4 in the first operating mode;

[0087] Figure 8 This is the energy flow diagram for working condition 1 in the second operating mode;

[0088] Figure 9 This is the energy flow diagram for working condition 2 in the second operating mode;

[0089] Figure 10 This is the energy flow diagram for working condition three in the second operating mode;

[0090] Figure 11 This is the energy flow diagram of working condition 4 in the second operating mode;

[0091] Figure 12 This is the energy flow diagram for working condition 5 in the third operating mode;

[0092] Figure 13 This is the energy flow diagram for working condition 6 in the third operating mode;

[0093] Figure 14 This is the energy flow diagram for working condition seven in the third operating mode;

[0094] Figure 15 generating schematic diagrams of the capacity of candidate energy supply configurations;

[0095] Figure 16 A schematic diagram of the structure of a rail transit energy supply configuration system provided in an embodiment of the present application;

[0096] Figure 17 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0097] Figure 18 Another structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0098] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0099] See also Figure 1 , Figure 1 A flow chart of a rail transit energy supply configuration method provided in an embodiment of the present application.

[0100] A rail transit energy supply configuration method provided in an embodiment of the present application may include the following steps:

[0101] Step S101: Acquire new energy supply information, energy storage information, and energy consumption information of rail transit.

[0102] To facilitate understanding of the solution, the rail transit physical terminal form is defined as the four key elements of "source-grid-vehicle-storage", and its topological architecture combination relationship is as follows: Figure 2 As shown. The "source" primarily consists of new energy sources. New energy sources are the energy-based power generation units of rail transit assets, such as photovoltaic and wind power, built according to local conditions. They are the primary source of electricity for the integrated development of energy and transportation. The "grid" comprises the interconnected microgrid and the power grid. The interconnected microgrid utilizes primary energy and a secondary communication network to connect the four key elements within the system. The power grid not only replenishes energy for the self-consistent system but also, in specific scenarios, assists the self-consistent system in absorbing green electricity. The "vehicle" includes electric locomotives, communication and control equipment related to auxiliary locomotive operation in traction stations, and electrically powered loads in traction stations. These loads can be divided into traction and non-traction loads based on their energy consumption, and are energy-consuming units within the microgrid system. The "storage" comprises electrochemical energy storage units, which not only enable green electricity consumption but also reduce reliance on external energy sources through energy time-shifting when no external power supply is available. The self-consistent system monitors the energy demand of the "vehicle", the output of the "source", the real-time charge status of the "storage" and other information in real time through the energy management system, and coordinates the "source", "grid", "vehicle" and "storage" to achieve real-time energy balance of the self-consistent system.

[0103] In actual application, during the implementation of this application, it is necessary to first obtain the new energy supply information, energy storage information and energy consumption information of rail transit. For example, the real-time wind speed of the wind turbine can be obtained. , fan cut-in wind speed, fan rated wind speed, fan cut-out wind speed and fan rated power; based on the fan real-time wind speed, fan cut-in wind speed , fan rated wind speed , fan cut-out wind speed and fan rated power , generate real-time output power of wind turbine ; Get the rated output power of the photovoltaic array , real-time solar irradiance , standard solar irradiance , real-time temperature and adjust the temperature Generate the real-time output power of the photovoltaic array based on the rated output power of the photovoltaic array, real-time solar irradiance, standard solar irradiance, real-time temperature and adjustment temperature ; Use the real-time output power of wind turbines and photovoltaic arrays as new energy supply information for rail transit; obtain the working status of energy storage , energy storage working power and energy storage working time; based on the energy storage working status, energy storage working power and energy storage working time, generate the real-time state of charge of the energy storage ; Set the energy storage real-time charge state and energy storage charge state upper limit threshold and the lower limit threshold of energy storage charge state As the energy storage information of rail transit; obtain the real-time power consumption of the load of rail transit, and use the real-time power consumption of the load as the energy consumption information.

[0104] In an exemplary embodiment, the formula for generating the real-time output power of the wind turbine may be:

[0105] .

[0106] In an exemplary embodiment, the formula for generating the real-time output power of the photovoltaic array may be:

[0107] ;

[0108] in, Indicates the power attenuation coefficient.

[0109] In an exemplary embodiment, the formula for generating the real-time state of charge of the energy storage device may be:

[0110] ;

[0111] in, Indicates the moment; When it is less than 0, the energy storage unit is charged. When it is greater than 0, the energy storage unit discharges; Indicates the real-time charging power of the energy storage system; Indicates the real-time discharge power of the energy storage system; Indicates the working time of charging or discharging state; Indicates the self-discharge rate of energy storage.

[0112] Step S102: Generate the power supply condition type of rail transit according to the new energy supply information, energy storage information and energy consumption information.

[0113] In actual applications, after obtaining the new energy supply information, energy storage information and energy consumption information of rail transit, the power supply condition type of rail transit can be generated based on the new energy supply information, energy storage information and energy consumption information, so as to reflect the energy working status of rail transit with the help of the power supply condition type.

[0114] In an exemplary embodiment, as Figure 3 As shown, in the process of generating the power supply condition type of rail transit according to the new energy supply information, energy storage information and energy consumption information, in response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load , and the energy storage real-time state of charge is greater than the energy storage state of charge upper threshold, that is, , , a power supply condition type representing the power demand of the grid and the abandonment of green power is generated. At this time, the real-time output of new energy units such as wind power and photovoltaic power is greater than the real-time energy consumption of the load, and the charge state of the energy storage unit has reached the set threshold upper limit. The new energy unit has priority in meeting the load energy consumption.

[0115] Accordingly, in response to the sum of the wind turbine real-time output power and the photovoltaic array real-time output power being greater than the load real-time power consumption, and the energy storage real-time state of charge being less than or equal to the energy storage state of charge upper limit threshold, that is, , , then a power supply condition type representing power supply and power abandonment of the power grid is generated; at this time, the real-time output of the new energy unit is greater than the real-time load energy consumption, the charge state of the energy storage unit has not reached the set threshold upper limit, and the new energy unit meets the charging needs of the load and energy storage system in sequence.

[0116] Correspondingly, in response to the sum of the wind turbine real-time output power and the photovoltaic array real-time output power being less than or equal to the load real-time power consumption, and the energy storage real-time charge state being less than the energy storage charge state lower limit threshold, that is, , , then a power supply condition type is generated that represents the energy storage shutdown and the power grid meeting the load energy demand; at this time, the real-time output of the new energy unit is difficult to meet the real-time energy demand of the load, and the energy storage unit has reached the lower limit of the charge state.

[0117] Correspondingly, in response to the sum of the wind turbine real-time output power and the photovoltaic array real-time output power being less than or equal to the load real-time power consumption, and the energy storage real-time charge state being greater than or equal to the energy storage charge state lower limit threshold, that is, , , then a power supply condition type is generated that represents the energy storage cooperating with new energy and the power grid to meet the load energy demand; at this time, it is difficult to meet the load energy demand by relying solely on the output of the new energy unit, and the energy storage unit has not yet reached the lower limit of the charge state.

[0118] Accordingly, in response to the sum of the wind turbine's real-time output power and the photovoltaic array's real-time output power being greater than the load's real-time power consumption, and the energy storage's real-time state of charge being greater than the energy storage's state of charge upper threshold, that is, , , then a power supply condition type is generated, which represents that new energy meets the load energy demand and abandons part of the green electricity; this condition is similar to condition 1, but there is no grid absorption.

[0119] Accordingly, in response to the sum of the wind turbine real-time output power and the photovoltaic array real-time output power being greater than the load real-time power consumption, and the energy storage real-time state of charge being less than or equal to the energy storage state of charge upper limit threshold, that is, , , then a power supply condition type is generated, which represents that the power grid does not supply energy and new energy meets the load energy demand and abandons part of the green electricity;

[0120] Correspondingly, in response to the sum of the wind turbine real-time output power and the photovoltaic array real-time output power being less than or equal to the load real-time power consumption, and the energy storage real-time charge state being less than the energy storage charge state lower limit threshold, that is, , , then a power supply condition type is generated that represents the coordinated use of energy storage and new energy to meet the energy needs of the load; at this time, the real-time output of the new energy unit is no longer able to meet the real-time energy needs of the load.

[0121] Step S103: Determine the target operation mode of rail transit based on the power supply operating condition type.

[0122] In practical applications, after generating the power supply condition type of rail transit, the target operation mode of rail transit can be determined based on the power supply condition type, that is, the power supply condition type can be converted into the target operation mode for subsequent energy supply configuration.

[0123] In an exemplary embodiment, as shown in Table 1, in the process of determining the target operating mode of rail transit based on the power supply condition type, in response to the power supply condition type reflecting that new energy guarantees load energy consumption and energy storage energy consumption in sequence, and the power grid consumes green electricity while supplying power, the target operating mode of rail transit is determined to be the first operating mode. This first operating mode is operated in a grid-connected scenario, and new energy power generation guarantees load and energy storage energy consumption in sequence. The power grid can not only supply power but also consume additional green electricity. Referring to the management and control architecture, the energy flow diagram of this mode is as follows Figure 4As shown in Figure 7, it can be adapted to stations, traction substations, etc.; in response to the power supply condition type reflecting the unidirectional power supply of the power grid, and the power grid provides energy guarantee when the supply of new energy and energy storage is insufficient, the target operation mode of rail transit is determined to be the second operation mode. This second operation mode can be operated in a grid-connected scenario. The power grid can only provide unidirectional power supply, but the power supply stability is good. When the power supply of new energy and energy storage units is insufficient, the power grid can provide real-time guarantee. Referring to the management and control architecture, the energy flow diagram of this mode is as follows Figures 8 to 11 , which can be adapted to stations, traction substations and other places; in response to the power supply condition type reflecting that the electric energy of rail transit comes from new energy and energy storage, the target operation mode of rail transit is determined to be the third operation mode. This third operation mode can be operated in an off-grid scenario. The main sources of all electric energy in the scenario are new energy and energy storage units. Referring to the control architecture, the energy flow diagram of this mode is as follows Figures 12 to 14 , which can be adapted to the traction power supply system in non-electrified sections.

[0124] Table 1 System operation mode table

[0125]

[0126] Step S104: Generate an optimized target model of rail transit in the target operation mode.

[0127] Step S105: Generate energy supply constraints for rail transit.

[0128] In actual applications, during the energy supply configuration process, it is also necessary to generate an optimization target model for rail transit in the target operating mode and generate energy supply constraints for rail transit, so as to use the optimization target model to guide the configuration direction and use the energy supply constraints to avoid configuration exceeding the actual situation of rail transit.

[0129] In an exemplary embodiment, in the process of generating an optimization target model for rail transit in a target operation mode, economic, environmental, and system targets can be used as evaluation targets, and system targets can be used as constraint targets, that is, investment data for rail transit can be generated. , operation and maintenance data , electricity purchase data , electricity sales data , replace data and residual value data In response to the target operation mode being the first operation mode, the economic optimization target corresponding to the first operation mode is generated based on the investment data, operation and maintenance data, power purchase data, power sales data, replacement data and residual value data. , In response to the target operation mode being the second operation mode, the economic optimization target corresponding to the second operation mode is generated based on the investment data, operation and maintenance data, power purchase data, replacement data and residual value data. , In response to the target operation mode being the third operation mode, the economic optimization target corresponding to the third operation mode is generated based on the investment data, operation and maintenance data, replacement data and residual value data. , ; Based on the emissions of the power grid, generate the environmental protection optimization target of rail transit in the target operation mode; based on the total amount of new energy power generation and the total energy consumption, generate the self-consistency optimization target of rail transit in the target operation mode; use the economic optimization target, environmental protection optimization target and self-consistency optimization target as the optimization target model.

[0130] In this way, this application scheme has good economic promotion prospects, that is, through the optimal configuration of new energy and energy storage unit capacity, it effectively improves the utilization rate of renewable energy in rail transit energy supply, reduces dependence on external power grids or fossil fuels, thereby achieving significant savings in the overall energy cost of the system and improving economic benefits; it has long-term economic benefit potential. As the operating time increases, the utilization efficiency of renewable energy continues to improve, and the energy cost further decreases. The electricity cost can be reduced in the electrified section, and the frequency of fuel use can be reduced in the non-electrified section. In addition, in areas with strong power grid coverage, additional income can be achieved through the "surplus power grid" mechanism, further enhancing the economic benefits of the system. Moreover, this application scheme has advantages in terms of the environment. That is, by adopting renewable energy on a large scale and reducing traditional energy consumption, it can reduce the carbon emissions of the rail transit system in electrified and non-electrified sections, help achieve the "dual carbon" goals, and alleviate regional climate and environmental pressures; promote the green and sustainable development of rail transit, that is, this scheme follows the concept of sustainable development, and by building an energy-self-sufficient, intelligent and efficient rail transit energy system, it not only saves resources and protects the environment, but also provides a feasible solution and promotion path for the green upgrade and transformation of the rail transit industry.

[0131] In specific application scenarios, the economic goal of the self-consistent system is to minimize the annual cost of the entire life cycle. The entire scenario includes investment and construction, operation and maintenance, power purchase, power sales, replacement and residual value costs. The discount rate method is used to convert future payments into present value payments to coordinate the total cost of the system, that is, the investment data , operation and maintenance data , electricity purchase data , electricity sales data , replace data and residual value data The generation formula can be:

[0132] ;

[0133] ;

[0134] ; ;

[0135] ;

[0136] ;

[0137] in, represents the discount rate; 、 and They are the operating years of the self-consistent system, the replacement years of new energy sources and the replacement years of the energy storage system; 、 、 They are wind power, photovoltaic and energy storage configuration capacity respectively; 、 and are the unit capacity purchase cost coefficients of wind power, photovoltaic power, and energy storage respectively; and are the purchased and sold electricity powers at time t respectively; 、 are the purchase and sale electricity prices at time t respectively; 、 、 are the average annual operation and maintenance cost coefficients for wind power, photovoltaic power, and energy storage respectively; 、 、 These are the ratio coefficients of the remaining residual value cost of wind power, photovoltaic power and energy storage after the system reaches its operating life to the purchase cost.

[0138] In specific application scenarios, the environmental protection goal is to minimize greenhouse and polluting gas emissions. When energy is supplied through the grid, greenhouse and polluting gas emissions such as carbon oxides, nitrogen oxides, and sulfides will be indirectly generated. Therefore, the environmental protection optimization goal in all operating modes is to minimize greenhouse and polluting gas emissions. The generation formula can be:

[0139] ;

[0140] ;

[0141] in, is the average annual greenhouse and pollutant gas emissions of the self-consistent system; The greenhouse gas and pollutant gas emissions per kilowatt-hour of electricity from the power grid; 、 、 They are the emissions of carbon oxides, nitrogen oxides and sulphides per kilowatt-hour of electricity from the power grid.

[0142] In specific application scenarios, the self-consistency optimization target in all operating modes is The generation formula can be:

[0143] ;

[0144] in, The maximum total annual power generation of wind, solar and other new energy units in the self-consistent system; It is the total annual power consumption of the energy-consuming unit. When the system is running, it can achieve complete self-consistency in meeting its own electricity demand through new energy power generation units and energy storage units.

[0145] In an exemplary embodiment, in the process of generating energy supply constraints for rail transit, energy power constraints for rail transit can be generated; renewable energy power generation constraints and energy storage state of charge constraints can be generated; and energy power constraints, renewable energy power generation constraints and energy storage state of charge constraints can be used as energy supply constraints.

[0146] In specific application scenarios, the energy power constraint of rail transit can be expressed as:

[0147] ;

[0148] The constraints on renewable energy power generation can be expressed as:

[0149] ; ;

[0150] The energy storage state of charge constraint can be expressed as:

[0151] ; ;

[0152] in, Indicates the upper limit threshold of the energy storage system's charging and discharging power; Indicates the lower threshold of the charging and discharging power of the energy storage system.

[0153] Step S106: Generate candidate energy supply configuration capacities based on the constraint conditions and the optimization target model.

[0154] Step S107: selecting a target energy supply configuration capacity from the candidate energy supply configuration capacities based on the distance information between the candidate energy supply configuration capacities, and performing energy supply configuration according to the target energy supply configuration capacity.

[0155] In practical applications, after obtaining the constraints and optimization target model, candidate energy supply configuration capacities can be generated based on the constraints and optimization target model; then, based on the distance information between the candidate energy supply configuration capacities, the target energy supply configuration capacity can be selected from the candidate energy supply configuration capacities to perform energy supply configuration according to the target energy supply configuration capacity.

[0156] In specific application scenarios, in the process of generating candidate energy supply configuration capacity according to the constraints and optimization target model, an intelligent algorithm can be used to solve the fitness function. For example, the population-guided crossover method is used to improve the non-dominated sorting genetic algorithm with elite strategy to optimize the energy unit configuration capacity of the self-consistent system. The solution process is as follows: Figure 15 , including the following steps: initializing system parameters, including illumination, temperature, wind speed, real-time power consumption of load, setting parameters, configuration capacity upper limit, configuration capacity lower limit, and setting parameters can be basic parameters of the algorithm; converting the optimization target model into the optimization target space of the population, randomly generating the configuration capacity as the initial population, and starting the iteration count; sorting the population non-dominated according to the constraints, and calculating the objective function value of each individual in the population through guided crossover and mutation; judging whether the population level classification and crowding calculation are completed; if not, stratifying the individuals in the population, comparing the dominant and non-dominated relationships between individuals, and marking the non-dominated individual population as the first-level non-dominated layer; ignoring the marked individuals, and returning to execute according to the constraints The steps of performing non-dominated sorting on the population are repeated until the population is stratified; if the stratification is completed, the crowding density of individuals in the same level is calculated, and the individual with the optimal target value is determined based on the individual crowding density, such as by using the tournament selection method to determine the individual with the optimal target value; the offspring population is generated by guided crossover and mutation operations, and the offspring population is affected by the optimal parent population, and the evolution direction is the optimal direction of the group; the initial population and the offspring population are merged, and the objective function value is calculated; a fast non-dominated sort is performed on the newly merged population; individuals are selected to generate a new generation population; whether the termination condition is met is determined based on the number of iterations; if it is met, the output population is the candidate energy supply configuration capacity; if not, the number of iterations is increased, and the step of performing non-dominated sorting on the population according to the constraint conditions is returned.

[0157] In a specific application scenario, the process of generating a progeny population through the guided crossover method can be as follows:

[0158] As parent population Better than , and the resulting populations are and ;

[0159] The genetic difference between the parent populations is set to ;

[0160] The same gene of the parent population is set as ;

[0161] The evolutionary directions of the offspring population are set as follows:

[0162] ;

[0163] Where, 、 、 、 、 、 and To set the parameters, It can take values ​​of 0.8, It can take the value 1.5. It can take the value 0.5. 、 、 and A random number between 0 and 1;

[0164] The offspring population is generated as:

[0165] .

[0166] In the exemplary embodiment, the candidate energy supply configuration capacity obtained includes different capacity configuration schemes of wind-solar-storage. In order to make the self-consistent system optimization configuration method have universal value for various typical scenarios, an entropy weight Topsis selection method is defined for the selection of ideal solutions: based on different network attributes, economic, system and environmental protection goals are weighted, and the optimal configuration capacity of wind-solar-storage-diesel in different scenarios is evaluated by the degree of approximation to the ideal solution. That is, in the process of selecting the target energy supply configuration capacity from the candidate energy supply configuration capacities based on the distance information between the candidate energy supply configuration capacities, the candidate energy supply configuration capacities can be normalized to obtain the normalized energy supply configuration capacity; the maximum value solution set of the energy supply configuration capacity and the minimum value solution set of the energy supply configuration capacity are screened out from the normalized energy supply configuration capacity; according to the entropy weight of the optimization target model, a first distance value is generated between the normalized energy supply configuration capacity and the maximum value solution set of the energy supply configuration capacity, and a second distance value is generated between the normalized energy supply configuration capacity and the minimum value solution set of the energy supply configuration capacity; based on the first distance value and the second distance value, a distance evaluation index is generated; and the candidate energy supply configuration capacity corresponding to the distance evaluation index is used as the target energy supply configuration capacity.

[0167] To understand this process, assume To solve the problem, the number of solution sets for generating candidate energy supply configuration capacity is calculated. To optimize the target quantity of the self-consistent system model, the solution set of candidate energy supply configuration capacity is expressed as , , ;

[0168] Then the normalized energy supply configuration capacity obtained after normalization and dimension elimination is expressed as:

[0169] ;

[0170] The maximum solution set of energy supply configuration capacity selected from the normalized energy supply configuration capacity Expressed as:

[0171] ;

[0172] The minimum solution set of energy supply configuration capacity selected from the normalized energy supply configuration capacity Expressed as:

[0173] ;

[0174] First distance value Expressed as:

[0175] ;

[0176] Second distance value Expressed as:

[0177] ;

[0178] Distance evaluation index Expressed as:

[0179] .

[0180] It should be noted that the entropy weight This model characterizes the importance of the economic, system, and environmental objectives in different scenarios. This can be selected based on the application scenario. For example, the entropy weighting principle can be considered as follows: Operating Mode 1 is often located in economically developed regions, focusing on project investment and returns, so the economic objective entropy weight is high; Operating Mode 2 is suitable for most regions, minimizing power curtailment and ensuring system energy consistency, so the system objective entropy weight is high; Operating Mode 3 is often located in remote areas, prioritizing economic investment and environmental protection, so the economic and environmental objective entropy weights are high. The entropy weighting can then be assigned as shown in Table 2.

[0181] Table 2 Entropy weight assignment reference table

[0182]

[0183] The entropy weight value can be set quantitatively, for example, the high value is not less than 0.4, the median value is greater than 0.2 and less than 0.4, and the low value is not greater than 0.2; and the sum of the economic, system, and environmental entropy weights in the same scenario is 1.

[0184] In this way, the configuration method proposed in this application achieves energy self-sufficiency and efficient utilization by constructing a self-consistent energy system that is suitable for electrified and non-electrified sections of rail transit, solving the problem of traditional rail transit systems being highly dependent on a single power source or fossil energy for energy supply. This method significantly improves energy utilization efficiency and reduces carbon emissions. It is also widely applicable, that is, it proposes flexible operating modes and optimized configuration strategies for different power supply conditions in electrified and non-electrified sections. It can be applied to various rail transit scenarios such as traction substations, stations, and maintenance stations, and has good versatility and engineering application value.

[0185] The present application provides a rail transit energy supply configuration method, which obtains new energy supply information, energy storage information and energy consumption information of the rail transit; generates the power supply condition type of the rail transit based on the new energy supply information, energy storage information and energy consumption information; determines the target operation mode of the rail transit based on the power supply condition type; generates an optimization target model of the rail transit in the target operation mode; generates energy supply constraints for the rail transit; generates candidate energy supply configuration capacities based on the constraints and the optimization target model; selects the target energy supply configuration capacity from the candidate energy supply configuration capacities based on the distance information between the candidate energy supply configuration capacities, and performs energy supply configuration according to the target energy supply configuration capacity. The present application can convert the new energy supply information, energy storage information and energy consumption information of rail transit into power supply condition types, and further into target operation modes, so that the working conditions of rail transit can be determined with the help of power supply condition types and target operation modes, providing a good data basis for subsequent energy supply configuration; and in the energy supply configuration process, it is necessary to generate an optimization target model of rail transit in the target operation mode, so as to use the optimization target model to guide the configuration direction of energy supply configuration, generate energy supply constraints for rail transit, and use the energy supply constraints to avoid energy supply configuration exceeding the limit. In this way, reasonable candidate energy supply configuration capacities can be generated according to the constraints and the optimization target model, and finally, according to the distance information between the candidate energy supply configuration capacities, the target energy supply configuration capacity that is closest to the ideal configuration capacity is selected from the candidate energy supply configuration capacities, thereby realizing the generation of reasonable target energy supply configuration capacity through distance value calculation according to the working status, constraints and optimization target model of rail transit for energy supply configuration, thereby improving the accuracy of energy supply adaptation for rail transit.

[0186] See also Figure 16 , Figure 16 A schematic structural diagram of a rail transit energy supply configuration system provided in an embodiment of the present application.

[0187] An embodiment of the present application provides a rail transit energy supply configuration system, which may include:

[0188] The first acquisition module 101 is used to obtain new energy supply information, energy storage information and energy consumption information of rail transit;

[0189] The first generating module 102 is used to generate a power supply condition type of rail transit based on the new energy supply information, energy storage information and energy consumption information;

[0190] A first determining module 103 is configured to determine a target operating mode of rail transit based on a power supply operating condition type;

[0191] The second generating module 104 is used to generate an optimized target model of rail transit in a target operation mode;

[0192] The third generating module 105 is used to generate energy supply constraints for rail transit;

[0193] The fourth generating module 106 is used to generate a candidate energy supply configuration capacity according to the constraint conditions and the optimization target model;

[0194] The first selection module 107 is configured to select a target energy supply configuration capacity from the candidate energy supply configuration capacities according to the distance information between the candidate energy supply configuration capacities, so as to perform energy supply configuration according to the target energy supply configuration capacity.

[0195] The embodiment of the present application provides a rail transit energy supply configuration system, the first acquisition module can be specifically used to: obtain the real-time wind speed of the fan, the cut-in wind speed of the fan, the rated wind speed of the fan, the cut-out wind speed of the fan and the rated power of the fan; generate the real-time output power of the fan based on the real-time wind speed of the fan, the cut-in wind speed of the fan, the rated wind speed of the fan, the cut-out wind speed of the fan and the rated power of the fan; obtain the rated output power of the photovoltaic array, the real-time solar irradiance, the standard solar irradiance, the real-time temperature and the adjusted temperature; based on the rated output power of the photovoltaic array, the real-time solar irradiance, the standard solar irradiance, the real-time The real-time temperature and the adjustment temperature are used to generate the real-time output power of the photovoltaic array; the real-time output power of the wind turbine and the real-time output power of the photovoltaic array are used as the new energy supply information of the rail transit; the energy storage working status, energy storage working power and energy storage working time are obtained; based on the energy storage working status, energy storage working power and energy storage working time, the real-time charge state of the energy storage is generated; the real-time charge state of the energy storage, the upper limit threshold of the energy storage charge state and the lower limit threshold of the energy storage charge state are used as the energy storage information of the rail transit; the real-time power consumption of the rail transit load is obtained, and the real-time power consumption of the load is used as the energy consumption information.

[0196] An embodiment of the present application provides a rail transit energy supply configuration system, in which the first generation module can be specifically used to: in response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage is greater than the upper limit threshold of the energy storage state of charge, generate a power supply condition type that characterizes the power demand of the power grid and the green power abandonment; in response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than or equal to the upper limit threshold of the energy storage state of charge, generate a power supply condition type that characterizes the power supply of the power grid and the power abandonment; in response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than the lower limit threshold of the energy storage state of charge, generate a power supply condition type that characterizes the energy storage shutdown and the power grid meeting the load energy consumption; in response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is less than or equal to the real-time power consumption of the load rate, and the real-time state of charge of the energy storage is greater than or equal to the lower limit threshold of the energy storage state of charge, then a power supply condition type is generated that characterizes that the energy storage cooperates with the new energy and the power grid to meet the energy consumption of the load; in response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage is greater than the upper limit threshold of the energy storage state of charge, then a power supply condition type is generated that characterizes that the new energy meets the energy consumption of the load and abandons green power; in response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than or equal to the upper limit threshold of the energy storage state of charge, then a power supply condition type is generated that characterizes that the power grid does not supply energy and the new energy meets the energy consumption of the load and abandons green power; in response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than the lower limit threshold of the energy storage state of charge, then a power supply condition type is generated that characterizes that the energy storage cooperates with the new energy to meet the energy consumption of the load.

[0197] In a rail transit energy supply configuration system provided by an embodiment of the present application, a first determination module can be specifically used to: in response to the power supply operating condition type reflecting that new energy guarantees load energy consumption and energy storage energy consumption in sequence, and the power grid consumes green electricity while supplying electricity, determine that the target operating mode of the rail transit is the first operating mode; in response to the power supply operating condition type reflecting that the power grid supplies power in one direction, and the power grid provides energy guarantee when the supply of new energy and energy storage is insufficient, determine that the target operating mode of the rail transit is the second operating mode; in response to the power supply operating condition type reflecting that the electric energy of the rail transit comes from new energy and energy storage, determine that the target operating mode of the rail transit is the third operating mode.

[0198] An embodiment of the present application provides a rail transit energy supply configuration system, in which the second generation module can be specifically used to: generate investment data, operation and maintenance data, electricity purchase data, electricity sales data, replacement data and residual value data for rail transit; in response to the target operation mode being the first operation mode, generate the economic optimization target corresponding to the first operation mode based on the investment data, operation and maintenance data, electricity purchase data, electricity sales data, replacement data and residual value data; in response to the target operation mode being the second operation mode, generate the economic optimization target corresponding to the second operation mode based on the investment data, operation and maintenance data, electricity purchase data, replacement data and residual value data; in response to the target operation mode being the third operation mode, generate the economic optimization target corresponding to the third operation mode based on the investment data, operation and maintenance data, replacement data and residual value data; generate the environmental protection optimization target of rail transit in the target operation mode based on the power grid emissions; generate the self-consistency optimization target of rail transit in the target operation mode based on the total amount of new energy power generation and the total energy consumption; use the economic optimization target, environmental protection optimization target and self-consistency optimization target as the optimization target model;

[0199] The third generation module can be specifically used to: generate energy power constraints for rail transit; generate new energy power generation constraints and energy storage state of charge constraints; and use energy power constraints, new energy power generation constraints and energy storage state of charge constraints as energy supply constraints.

[0200] The embodiment of the present application provides a rail transit energy supply configuration system, in which the fourth generation module can be specifically used to: initialize system parameters, which include light, temperature, wind speed, real-time load power consumption, set parameters, upper limit of configuration capacity, and lower limit of configuration capacity; convert the optimization target model into the optimization target space of the population, randomly generate the configuration capacity as the initial population, and start iterative counting; perform non-dominated sorting on the population according to the constraints, and calculate the objective function value of each individual in the population through guided crossover and mutation; determine whether the population level classification and congestion calculation are completed; if not, stratify the individuals in the population, compare the dominant and non-dominated relationships between individuals, and mark the non-dominated individual population as a first-level non-dominated layer; ignore Ignore the marked individuals and return to the step of performing non-dominated sorting on the population according to the constraints until the population is stratified; if it is completed, calculate the crowding density of individuals in the same level and determine the individual with the optimal target value based on the individual crowding density; use guided crossover and mutation operations to generate the offspring population, and the offspring population is influenced by the optimal parent population, and the evolution direction is the optimal direction of the group; merge the initial population and the offspring population, calculate the value of the objective function; perform fast non-dominated sorting on the newly merged population; select individuals to generate a new generation population; determine whether the termination condition is met based on the number of iterations; if so, output the population as the candidate energy supply configuration capacity; if not, increase the number of iterations and return to the step of performing non-dominated sorting on the population according to the constraints.

[0201] An embodiment of the present application provides a rail transit energy supply configuration system, in which the first selection module can be specifically used to: normalize the candidate energy supply configuration capacity to obtain a normalized energy supply configuration capacity; screen out a maximum energy supply configuration capacity solution set and a minimum energy supply configuration capacity solution set from the normalized energy supply configuration capacity; generate a first distance value between the normalized energy supply configuration capacity and the maximum energy supply configuration capacity solution set, and generate a second distance value between the normalized energy supply configuration capacity and the minimum energy supply configuration capacity solution set based on the entropy weight of the optimization target model; generate a distance evaluation index based on the first distance value and the second distance value; and use the candidate energy supply configuration capacity corresponding to the distance evaluation index as the target energy supply configuration capacity.

[0202] This application also provides an electronic device and a computer-readable storage medium, both of which have the corresponding effects of the rail transit energy supply configuration method provided in the embodiment of this application. Figure 17 , Figure 17 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0203] An electronic device provided in an embodiment of the present application includes a memory 201 and a processor 202. The memory 201 stores a computer program, and when the processor 202 executes the computer program, the steps of the rail transit energy supply configuration method described in any of the above embodiments are implemented.

[0204] See also Figure 18 Another electronic device provided in an embodiment of the present application may further include: an input port 203 connected to the processor 202 for transmitting commands inputted from the outside to the processor 202; a display unit 204 connected to the processor 202 for displaying the processing results of the processor 202 to the outside world; and a communication module 205 connected to the processor 202 for enabling communication between the electronic device and the outside world. The display unit 204 may be a display panel, a laser scanning display, etc. The communication method adopted by the communication module 205 includes but is not limited to Mobile High-Definition Link (MHL), Universal Serial Bus (USB), High-Definition Multimedia Interface (HDMI), wireless connection: Wireless Fidelity (WiFi), Bluetooth communication technology, Bluetooth low energy communication technology, and communication technology based on IEEE802.11s.

[0205] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the rail transit energy supply configuration method described in any of the above embodiments are implemented.

[0206] The computer-readable storage medium involved in this application includes random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs (Compact Disc Read-Only Memory), or any other form of storage medium known in the technical field.

[0207] For descriptions of the relevant portions of the rail transit energy supply configuration system, electronic device, and computer-readable storage medium provided in the embodiments of this application, please refer to the detailed description of the corresponding portions in the rail transit energy supply configuration method provided in the embodiments of this application, and no further description is given here. In addition, portions of the above-mentioned technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0208] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0209] The above description of the disclosed embodiments will enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rail transit energy supply configuration method, characterized in that: include: Obtain new energy supply information, energy storage information and energy consumption information for rail transit; generating a power supply operating condition type for rail transit according to the new energy supply information, the energy storage information, and the energy consumption information; Determining a target operation mode of rail transit based on the power supply operating condition type; generating an optimized target model of rail transit in the target operation mode; Generate energy supply constraints for rail transit; Generating a candidate energy supply configuration capacity according to the constraint conditions and the optimization target model; According to the distance information between the candidate energy supply configuration capacities, a target energy supply configuration capacity is selected from the candidate energy supply configuration capacities, so as to perform energy supply configuration according to the target energy supply configuration capacity.

2. The method according to claim 1, characterized in that The acquisition of new energy supply information, energy storage information and energy consumption information of rail transit includes: Obtain the fan's real-time wind speed, fan cut-in wind speed, fan rated wind speed, fan cut-out wind speed, and fan rated power; generating a real-time output power of the wind turbine based on the real-time wind speed of the wind turbine, the cut-in wind speed of the wind turbine, the rated wind speed of the wind turbine, the cut-out wind speed of the wind turbine, and the rated power of the wind turbine; Obtain the rated output power, real-time solar irradiance, standard solar irradiance, real-time temperature and adjusted temperature of the photovoltaic array; generating a photovoltaic array real-time output power based on the photovoltaic array rated output power, the real-time solar irradiance, the standard solar irradiance, the real-time temperature, and the adjustment temperature; The real-time output power of the wind turbine and the real-time output power of the photovoltaic array are used as new energy supply information for rail transit; Obtain energy storage working status, energy storage working power and energy storage working time; Generate a real-time state of charge of energy storage based on the energy storage working state, the energy storage working power and the energy storage working duration; The real-time state of charge of the energy storage, the upper limit threshold of the state of charge of the energy storage and the lower limit threshold of the state of charge of the energy storage are used as energy storage information of rail transit; The real-time power consumption of the load of rail transit is obtained, and the real-time power consumption of the load is used as energy consumption information.

3. The method according to claim 2, characterized in that The generating of the power supply operating condition type of rail transit according to the new energy supply information, the energy storage information, and the energy consumption information includes: In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type representing power demand of the grid and green power curtailment is generated; In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than or equal to the upper limit threshold of the state of charge of the energy storage, generating a power supply condition type representing power supply and power abandonment by the power grid; In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than the lower limit threshold of the energy storage state of charge, generating a power supply condition type indicating that the energy storage is shut down and the power grid meets the energy consumption of the load; In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than or equal to the lower limit threshold of the energy storage state of charge, generating a power supply condition type representing that the energy storage cooperates with the new energy and the power grid to meet the energy consumption of the load; In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being greater than the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type is generated, indicating that new energy meets the energy consumption of the load and green power is abandoned; In response to the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array being greater than the real-time power consumption of the load, and the real-time state of charge of the energy storage being less than or equal to the upper limit threshold of the state of charge of the energy storage, a power supply operating condition type is generated, indicating that the power grid does not supply energy, new energy sources meet the energy consumption of the load, and green electricity is abandoned; In response to the fact that the sum of the real-time output power of the wind turbine and the real-time output power of the photovoltaic array is less than or equal to the real-time power consumption of the load, and the real-time state of charge of the energy storage is less than the lower limit threshold of the energy storage state of charge, a power supply condition type is generated that characterizes that the energy storage cooperates with new energy to meet the energy consumption of the load.

4. The method according to claim 3, characterized in that The determining of a target operation mode of rail transit based on the power supply operating condition type includes: In response to the power supply operating condition type reflecting that the new energy sources sequentially guarantee the energy consumption of the load and the energy storage, and the power grid consumes green electricity while supplying power, determining that the target operating mode of the rail transit is the first operating mode; In response to the power supply operating condition type reflecting unidirectional power supply by the power grid and the power grid providing energy guarantee when the supply of new energy and energy storage is insufficient, determining the target operating mode of the rail transit to be the second operating mode; In response to the power supply operating condition type reflecting that the electric energy of the rail transit comes from new energy and energy storage, the target operating mode of the rail transit is determined to be the third operating mode.

5. The method according to claim 4, characterized in that Generating an optimized target model of rail transit in the target operation mode includes: Generate rail transit investment data, operation and maintenance data, electricity purchase data, electricity sales data, replacement data and residual value data; In response to the target operating mode being the first operating mode, generating an economic optimization target corresponding to the first operating mode based on the investment data, the operation and maintenance data, the power purchase data, the power sales data, the replacement data, and the residual value data; In response to the target operating mode being the second operating mode, generating an economic optimization target corresponding to the second operating mode based on the investment data, the operation and maintenance data, the power purchase data, the replacement data, and the residual value data; In response to the target operating mode being the third operating mode, generating an economic optimization target corresponding to the third operating mode based on the investment data, the operation and maintenance data, the replacement data, and the residual value data; Generate environmental optimization targets for rail transit in the target operation mode based on grid emissions; Based on the total amount of renewable energy power generation and the total amount of energy consumption, generate a self-consistency optimization target for rail transit in the target operation mode; The economic optimization target, the environmental optimization target and the self-consistency optimization target are used as an optimization target model; The energy supply constraints for generating rail transit include: Generate energy and power constraints for rail transit; Generate renewable energy power generation constraints and energy storage state of charge constraints; The energy power constraint condition, the new energy power generation constraint condition and the energy storage charge state constraint condition are used as energy supply constraint conditions.

6. The method according to claim 5, characterized in that Generating candidate energy supply configuration capacities according to the constraint conditions and the optimization target model includes: Initialize system parameters, including light intensity, temperature, wind speed, real-time load power consumption, set parameters, upper and lower limits of configuration capacity; Converting the optimization target model into the optimization target space of the population, randomly generating a configuration capacity as the initial population, and starting iteration counting; Perform non-dominated sorting on the population according to the constraints, and calculate the objective function value of each individual in the population through guided crossover and mutation; Determine whether the population level classification and crowding calculation are completed; If not completed, stratify the individuals in the population, compare the dominance and non-domination relationships between individuals, and mark the non-dominated individual population as the first-level non-dominated layer; ignore the marked individuals, and return to the step of performing non-dominated sorting of the population according to the constraints until the population is stratified; If completed, calculate the crowding density of individuals in the same level, and determine the optimal individual with the target value based on the individual crowding density; use guided crossover and mutation operations to generate the offspring population, and the offspring population is influenced by the optimal parent population, and the evolution direction is the optimal direction of the group; Merge the initial population and the offspring population, calculate the objective function value; perform fast non-dominated sorting on the newly merged population; select individuals to generate a new generation of population; Determine whether the termination condition is met based on the number of iterations; If satisfied, the output population is the candidate energy supply configuration capacity; If not satisfied, increase the number of iterations and return to the step of performing non-dominated sorting on the population according to the constraint condition.

7. The method according to claim 6, characterized in that The selecting a target energy supply configuration capacity from the candidate energy supply configuration capacities according to the distance information between the candidate energy supply configuration capacities includes: Normalizing the candidate energy supply configuration capacity to obtain a normalized energy supply configuration capacity; Screening out a maximum energy supply configuration capacity solution set and a minimum energy supply configuration capacity solution set from the normalized energy supply configuration capacity; generating, according to the entropy weight of the optimization target model, a first distance value between the normalized energy supply configuration capacity and the maximum value solution set of the energy supply configuration capacity, and generating a second distance value between the normalized energy supply configuration capacity and the minimum value solution set of the energy supply configuration capacity; generating a distance evaluation index based on the first distance value and the second distance value; The candidate energy supply configuration capacity corresponding to the distance evaluation index is used as the target energy supply configuration capacity.

8. A rail transit energy supply configuration system, characterized in that: include: The first acquisition module is used to obtain new energy supply information, energy storage information and energy consumption information of rail transit; A first generating module is configured to generate a power supply operating condition type of rail transit according to the new energy supply information, the energy storage information, and the energy consumption information; A first determining module is configured to determine a target operating mode of rail transit based on the power supply operating condition type; A second generating module is used to generate an optimized target model of rail transit in the target operation mode; The third generation module is used to generate energy supply constraints for rail transit; a fourth generating module, configured to generate a candidate energy supply configuration capacity according to the constraint conditions and the optimization target model; The first selection module is configured to select a target energy supply configuration capacity from the candidate energy supply configuration capacities according to distance information between the candidate energy supply configuration capacities, so as to perform energy supply configuration according to the target energy supply configuration capacity.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the rail transit energy supply configuration method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the rail transit energy supply configuration method according to any one of claims 1 to 7.

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