A method and system for controlling railway regenerative braking energy and new energy access based on maximum demand optimization.

Abstract

This invention provides a method and system for controlling railway regenerative braking energy and new energy access based on maximum demand optimization. The method includes: monitoring the real-time power output from the grid on the high-voltage side of the traction substation and the power output from the traction bus on the low-voltage side, and calculating the average power output within a preset rolling time window; determining historical load data, regenerative braking energy data, and configuration parameters of the energy storage system for the traction substation; determining a discharge power threshold based on a preset maximum demand reduction value; comparing the average power output with the discharge power threshold; determining a discharge control command when the average power output reaches or exceeds the discharge power threshold; and executing the discharge control command to control the energy storage system to release the stored regenerative braking energy and / or the accessed new energy power. This invention enables efficient synergistic utilization of regenerative braking energy and new energy sources, significantly reducing railway operating electricity costs while improving system reliability.

Description

Technical Field

[0001] This application relates to the field of electrified railway traction power supply technology, and more specifically, to an energy storage control method and system for railway regenerative braking energy and new energy access based on maximum demand optimization. Background Technology

[0002] The waste of regenerative braking energy in electrified railways and the high maximum demand electricity costs of traction substations are long-standing pain points in the industry. Existing energy storage solutions have two major limitations:

[0003] First, there are shortcomings in system reliability and control economy. As a primary load related to train operation safety, the electrified railway traction power supply system imposes extremely high reliability requirements on all connected equipment, including energy storage systems, far exceeding those in general industrial applications. These requirements are specifically reflected in:

[0004] The demanding nature of uninterrupted continuous operation: Traction substations require 24-hour uninterrupted operation. Any power outage or deterioration in power quality caused by equipment failure can directly affect the normal operation of trains and even cause safety accidents. This means that the energy storage system must have extremely high online availability, be able to adapt to the harsh conditions of drastic fluctuations in railway load, and achieve stable operation over a long period of time.

[0005] Extremely low fault tolerance: Existing centralized or simply parallel energy storage architectures are prone to single-point failures. A failure in core power conversion or a storage unit often leads to the shutdown of the entire energy storage system, not only losing its energy-saving and cost-saving functions but also becoming a new point of failure in the power supply system. This contradicts the high reliability standards required by railway power supply departments. Furthermore, their control strategies are often limited to load smoothing or simple peak shaving and valley filling, failing to proactively optimize for the core of electricity billing—the monthly maximum demand. The timing of discharge is disconnected from the formation process of maximum demand, resulting in poor investment economics.

[0006] Second, there is insufficient capacity for unified scheduling and optimization of multiple energy sources. Although new energy sources such as photovoltaics and wind power have been deployed in railway scenarios, they are often managed independently or simply connected in parallel with regenerative braking energy in existing systems. There is a lack of a unified control platform with the core economic goal of reducing maximum demand, which fails to achieve optimal combination scheduling of multiple energy sources under a unified strategy, thus limiting further improvement in overall energy efficiency and return on investment.

[0007] Therefore, there is an urgent need for an energy storage control method and system that combines high reliability and high economy, and can uniformly dispatch regenerative braking energy and new energy. Summary of the Invention

[0008] The present invention aims to overcome the shortcomings of the prior art and provide a railway regenerative braking energy and new energy access energy storage control method and system based on maximum demand optimization. The method and system achieve efficient synergistic utilization of regenerative braking energy and new energy by using an intelligent control strategy with the direct optimization objective of reducing monthly maximum demand and combining it with modular and redundant hardware design. This significantly reduces railway operating electricity costs while improving system reliability.

[0009] In a first aspect, the present invention provides an energy storage control method for railway regenerative braking energy and new energy access based on maximum demand optimization, the method comprising: Monitor the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and calculate the average power of the power taken from the grid within a preset rolling time window; Determine the historical load data, regenerative braking energy data, and configuration parameters of the energy storage system for the traction substation; Based on the historical load data, the regenerative braking energy data, and the configuration parameters, and in conjunction with the preset maximum demand reduction value, the discharge power threshold is determined; The average power value is compared with the discharge power threshold. When it is determined that the average power value reaches or exceeds the discharge power threshold, a discharge control command is determined by combining the real-time power on the low-voltage side with the state of charge of the energy storage system. The discharge control command is executed to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

[0010] Preferably, determining the configuration parameters of the energy storage system includes: Multiple candidate values ​​for reverse energy utilization rate are set, wherein the reverse energy utilization rate is the proportion of regenerative braking energy absorbed by the energy storage system and used for discharge to the total regenerative braking energy. For each candidate value, based on the regenerative braking energy data, the performance indicators of the energy storage system under the preset charging and discharging logic are simulated and calculated. Using the electricity required to meet the maximum demand reduction value as a constraint, the configuration parameter combination that optimizes the total life cycle cost of the energy storage system is selected from the simulation results corresponding to each candidate value as the final configuration.

[0011] Preferably, determining the discharge power threshold based on the historical load data, the regenerative braking energy data, and the configuration parameters, combined with a preset maximum demand reduction value, includes: Based on the maximum demand reduction value, calculate the corresponding target total peak shaving power demand and the target maximum peak shaving power demand for a single operation; Based on the historical load data and the regenerative braking energy data, a candidate discharge power threshold is determined; Verify whether the energy storage system, based on the configuration parameters, can meet the target total peak shaving power requirement and the target maximum peak shaving power requirement for a single operation; When the verification is successful, the candidate discharge power threshold is determined as the discharge power threshold.

[0012] Preferably, verifying whether the energy storage system can meet the target total peak shaving power requirement and the target maximum peak shaving power requirement per cycle based on the configuration parameters includes: Determine whether the target peak-shaving total power demand is less than or equal to the total power that the energy storage system can release under the set reverse power utilization rate; In addition, it is determined whether the target maximum peak-shaving power demand is less than or equal to the single discharge capacity of the energy storage system. The verification is successful when both conditions are met.

[0013] Preferably, the method further includes: The operating mode of the energy storage system is automatically switched according to the state of charge of the energy storage system, the real-time regenerative braking power and the traction load power. The operating modes include charging mode, discharging mode, and standby mode.

[0014] Thirdly, the present invention provides an energy storage system for railway regenerative braking energy and new energy access based on maximum demand optimization, wherein the energy storage system is used to perform the method described in any one of the first aspects, including: A maximum demand optimization controller is configured to execute the method. And an energy storage execution unit, connected between the maximum demand optimization controller and the power supply bus or substation bus of the traction substation, for performing energy absorption or release operations in response to instructions from the maximum demand optimization controller; The energy storage execution unit includes at least one power conversion module and at least one energy storage battery module; The maximum demand optimization controller is connected to the measurement and control units on the high-voltage side and low-voltage side of the traction substation to obtain the corresponding power signals.

[0015] Preferably, the power conversion module and the energy storage battery module adopt a modular redundant design, each containing multiple sub-units that are electrically and physically independent and can be switched in parallel or independently. The maximum demand optimization controller is also configured to: in the event of a failure in a single subunit, control the isolation of that subunit from its module and coordinate the continued operation of the remaining subunits.

[0016] Preferably, the power conversion module is a bidirectional converter; The DC side of the bidirectional converter is connected to the energy storage battery module, and its AC side is connected to the different power supply arm buses of the traction substation or the two end buses of the section substation through a transformer.

[0017] Preferably, the maximum demand optimization controller includes: The monitoring module monitors the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and calculates the average power taken from the grid within a preset rolling time window. The data determination module is used to determine the historical load data, regenerative braking energy data, and configuration parameters of the energy storage system of the traction substation. The discharge power threshold determination module is used to determine the discharge power threshold based on the historical load data, the regenerative braking energy data, and the configuration parameters, combined with a preset maximum demand reduction value. The discharge control command determination module is used to compare the average power value and the discharge power threshold; when it is determined that the average power value reaches or exceeds the discharge power threshold, the discharge control command is determined by combining the real-time power on the low-voltage side and the state of charge of the energy storage system. An execution module is used to execute the discharge control command to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

[0018] Preferably, the discharge power threshold determination module is configured to perform iterative optimization calculations when determining the discharge power threshold: With the preset maximum demand reduction value as the optimization objective, under the power and capacity constraints of the energy storage system, iterative simulation is performed by adjusting the candidate discharge power threshold. In each iteration, based on the historical load data and the regenerative braking energy data, the actual maximum demand reduction that can be achieved when using the current candidate discharge power threshold and the corresponding energy storage system charge and discharge plan are evaluated. When the actual maximum demand reduction reaches the optimization target and the charging and discharging plan meets the operating constraints of the energy storage system, the candidate discharge power threshold in the current iteration is output as the determined discharge power threshold.

[0019] Thirdly, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0020] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0021] This invention provides a railway regenerative braking energy and new energy access energy storage control method and system based on maximum demand optimization. The control method directly aims to reduce monthly maximum demand electricity costs. By calculating the optimal discharge threshold, it precisely intervenes at key billing points, directly linking energy storage discharge behavior to electricity cost savings, resulting in a clear return on investment. The system hardware adopts a modular and redundant design, allowing independent switching of power conversion and energy storage units. A single module failure does not affect overall operation, meeting the power supply reliability requirements of railway primary loads and facilitating maintenance. A unified control platform is provided, enabling coordinated scheduling of regenerative braking energy and new energy sources such as photovoltaic and wind power, achieving complementary and optimized utilization of multiple energy sources in time and space, increasing the proportion of green electricity and the overall system energy efficiency. The system supports two mainstream topologies, which can be flexibly selected according to line characteristics, investment budget, and other factors, combining engineering practicality with technological advancement.

[0022] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0023] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic diagram of an energy storage control method for railway regenerative braking energy and new energy access based on maximum demand optimization, provided in an embodiment of the present invention; Figure 2 A schematic diagram of an energy storage system based on maximum demand optimization for railway regenerative braking energy and new energy access, provided as an embodiment of the present invention; Figure 3 A schematic diagram of a power-free through-type main circuit topology for an energy storage system application based on maximum demand optimization for railway regenerative braking energy and new energy access, provided as an embodiment of the present invention; Figure 4 A schematic diagram of another power-through main circuit topology for an energy storage system application based on maximum demand optimization for railway regenerative braking energy and new energy access, provided as an embodiment of the present invention. Figure 5 A schematic diagram of another power-through main circuit topology for an energy storage system application based on maximum demand optimization for railway regenerative braking energy and new energy access, provided as an embodiment of the present invention. Figure 6This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0026] The waste of regenerative braking energy in electrified railways and the high maximum demand electricity costs of traction substations are long-standing pain points in the industry. Existing energy storage solutions have two major limitations:

[0027] First, there are shortcomings in system reliability and control economy. As a primary load related to train operation safety, the electrified railway traction power supply system imposes extremely high reliability requirements on all connected equipment, including energy storage systems, far exceeding those in general industrial applications. These requirements are specifically reflected in:

[0028] The demanding nature of uninterrupted continuous operation: Traction substations require 24-hour uninterrupted operation. Any power outage or deterioration in power quality caused by equipment failure can directly affect the normal operation of trains and even cause safety accidents. This means that the energy storage system must have extremely high online availability, be able to adapt to the harsh conditions of drastic fluctuations in railway load, and achieve stable operation over a long period of time.

[0029] Extremely low fault tolerance: Existing centralized or simply parallel energy storage architectures are prone to single-point failures. A failure in core power conversion or a storage unit often leads to the shutdown of the entire energy storage system, not only losing its energy-saving and cost-saving functions but also becoming a new point of failure in the power supply system. This contradicts the high reliability standards required by railway power supply departments. Furthermore, their control strategies are often limited to load smoothing or simple peak shaving and valley filling, failing to proactively optimize for the core of electricity billing—the monthly maximum demand. The timing of discharge is disconnected from the formation process of maximum demand, resulting in poor investment economics.

[0030] Second, there is insufficient capacity for unified scheduling and optimization of multiple energy sources. Although new energy sources such as photovoltaics and wind power have been deployed in railway scenarios, they are often managed independently or simply connected in parallel with regenerative braking energy in existing systems. There is a lack of a unified control platform with the core economic goal of reducing maximum demand, which fails to achieve optimal combination scheduling of multiple energy sources under a unified strategy, thus limiting further improvement in overall energy efficiency and return on investment.

[0031] Therefore, there is an urgent need for an energy storage control method and system that combines high reliability and high economy, and can uniformly dispatch regenerative braking energy and new energy.

[0032] In view of this, the present invention provides an energy storage control method for railway regenerative braking energy and new energy access based on maximum demand optimization. See also Figure 1 The image shows a specific embodiment of the energy storage control method for railway regenerative braking energy and new energy access based on maximum demand optimization provided by the present invention. In this embodiment, the energy storage control method for railway regenerative braking energy and new energy access based on maximum demand optimization includes:

[0033] Step 101: Monitor the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and calculate the average power taken from the grid within a preset rolling time window; This embodiment is applied to the traction substation of electrified railways. By real-time monitoring of the power taken from the power grid, intelligent analysis of historical data, and precise control of the energy storage system, a closed-loop optimization system is constructed with the direct economic goal of reducing the monthly maximum demand electricity cost, so as to realize the efficient and coordinated utilization of regenerative braking energy and the access of new energy sources.

[0034] Specifically, this step forms the basis of data sensing in this embodiment. A high-precision power monitoring device (measurement and control unit) is installed at the high-voltage metering point (e.g., the 110 kV or 220 kV incoming line side) where the traction substation purchases electricity from the public grid. This device continuously measures and records the instantaneous value of active power (grid power consumption) obtained by the traction substation from the grid. A preset rolling time window is a key parameter; its length must strictly match the billing cycle for calculating the maximum monthly demand by the local power supply department, typically set to 15 minutes. The system calculates in real time the arithmetic mean of all instantaneous power values ​​within this recently concluded continuous time window; this value is called the rolling time window's power average. This average dynamically reflects the substation's overall load level in the short term and is a direct basis for determining whether intervention is needed to suppress maximum demand growth.

[0035] In practical systems, the measurement and control unit or monitoring device can comprehensively detect information such as the grid power taken from the high-voltage side of the traction substation, the real-time power on the low-voltage side (traction grid side), and the state of charge (SOC) of the energy storage system. Among these, the high-voltage side power is the direct basis for calculating and optimizing maximum demand; the low-voltage side power and SOC are used to achieve precise and smooth switching of operating modes and fine-tuning of control strategies. In this embodiment, the high-voltage side refers to the connection point between the traction substation and the public power grid (usually the 110kV or 220kV incoming line side). Monitoring the power taken from this point is the direct and sole basis for calculating and optimizing the monthly maximum demand. The low-voltage side refers to the traction bus output side of the traction substation (usually 27.5kV). Monitoring the real-time power at this point aims to obtain instantaneous changes in traction load and regenerative braking energy, which assists the controller in quickly and smoothly switching operating modes and finely adjusting control strategies to improve the overall dynamic performance and efficiency of the system.

[0036] Step 102: Determine the historical load data, regenerative braking energy data, and configuration parameters of the energy storage system for the traction substation; Furthermore, this step provides historical data and system capability boundaries for subsequent optimization decisions. First, the system retrieves three core data points from the integrated historical database or energy management platform: one is historical load data, i.e., detailed load curves of the high-voltage side of the traction substation during representative periods (such as typical days and months); the second is regenerative braking energy data, including the regenerative power curve generated by train braking during the corresponding period and the total electrical energy fed back to the grid daily. Second, the process of determining the energy storage system configuration parameters is executed. Specifically, this includes:

[0037] Parameter Definition and Candidates: Several candidate values ​​for "reverse energy utilization rate" are set. This utilization rate is defined as the percentage of regenerative braking energy that the energy storage system can successfully capture, store, and utilize for subsequent discharge, relative to the total regenerative braking energy during the same period. It is a key indicator for measuring the energy conversion economy of the energy storage system.

[0038] Performance simulation: For each candidate utilization rate, based on the actual measured regenerative braking energy data, the preset charging and discharging logic is run in the simulation environment to calculate the key performance indicators of the energy storage system under this configuration, such as the average daily effective absorbed electricity, system operating losses, and the actual releaseable electricity that can be used for peak shaving.

[0039] Economic Optimization and Determination: The total peak-shaving power required to meet the "preset maximum demand reduction value" (e.g., 8000 kW in this embodiment) is used as a rigid technical constraint. The initial investment, operation and maintenance costs, and electricity cost savings of different candidate solutions are comprehensively evaluated. Through a full life-cycle cost analysis, the optimal configuration parameter combination is selected as the implementation plan, including the total rated power of the energy storage system (e.g., 3600 kW), the total rated capacity (e.g., 3600 kWh), and the final adopted reverse power utilization rate. Under this configuration, the system can actually release approximately 10400 kWh of electricity per day for peak shaving.

[0040] In this embodiment, the "maximum demand reduction value" refers to a pre-set target value that is expected to reduce the maximum billing demand of the traction substation within an electricity billing cycle. This value is the optimization target and constraint of the entire control strategy; for example, in this embodiment, this value is set to 8000 kilowatts.

[0041] Step 103: Based on historical load data, regenerative braking energy data, and configuration parameters, and in conjunction with the preset maximum demand reduction value, determine the discharge power threshold; Furthermore, the purpose of this step is to determine an optimal discharge power threshold through calculation. Initiating discharge when the real-time monitored average power reaches this threshold ensures that the preset maximum demand reduction target is achieved most effectively within the energy storage system's capacity.

[0042] Demand Quantification (Calculation of Target Peak Shaving Power): Based on the preset maximum demand reduction value (8000 kW), historical load curves are simulated and analyzed. The specific objective is to quantify: the total power required to be "removed" from historical load peaks to reduce the monthly maximum demand by this value (target total peak shaving power demand), and the maximum power required to be "removed" in each 15-minute window (target single maximum peak shaving power demand). This calculation is based on a theoretical integral model, integrating all power portions in the historical curve that exceed the "expected maximum demand reduction value" to obtain the total demand. For example, in this embodiment, the calculated target total peak shaving power demand is 9940 kWh per day, and the target single maximum peak shaving power demand is 2608 kWh.

[0043] System capability verification: The energy storage system configuration parameters determined in step 102 are compared and verified with the calculated power demand.

[0044] Total energy verification: Determine whether the target peak shaving total power demand (9940 kWh / day) is less than or equal to the total power that the energy storage system can release daily under the selected reverse power utilization rate (approximately 10400 kWh / day).

[0045] Single-cycle energy and power capability verification: This involves determining whether the target maximum peak-shaving power demand (2608 kWh) is less than the rated capacity of the energy storage system (3600 kWh); simultaneously, ensuring that the required discharge power at any point during the simulation does not exceed the system's rated power (3600 kW). In this embodiment, all verification conditions were met, confirming that the configured energy storage system is capable of supporting the 8000 kW reduction target.

[0046] Iterative Threshold Optimization: After passing capability verification, an iterative optimization algorithm is used to determine the final discharge power threshold. The execution logic of this algorithm is as follows:

[0047] Initialization: Set an initial candidate discharge power threshold.

[0048] Iterative simulation: In each iteration, the candidate threshold is used, combined with historical load data and regenerative braking data, to rerun the complete charge and discharge process simulation.

[0049] Effect evaluation: Calculate the actual reduction in monthly maximum demand under the control of the candidate threshold, as obtained from the simulation.

[0050] Comparison and Adjustment: Compare the actual demand reduction value obtained from the simulation with the preset maximum demand reduction value (8000 kW). If the former is less than the latter, it indicates that the current threshold setting is too high and the discharge start is too late, and the threshold needs to be lowered to intervene earlier; otherwise, the threshold needs to be raised. At the same time, check whether the charging and discharging plan in the entire simulation process exceeds the power and capacity constraints of the energy storage system.

[0051] Convergence Determination: Repeat the above steps until a candidate threshold is found: when used for simulation, the actual maximum demand reduction obtained is exactly equal to the preset target (8000 kW), and all system constraints are satisfied throughout the process. At this point, the candidate value is determined as the final discharge power threshold. In this embodiment, the threshold is calculated to be approximately 5732 kW using this algorithm.

[0052] Step 104: Compare the average power value and the discharge power threshold; when it is determined that the average power value reaches or exceeds the discharge power threshold, combine the real-time power on the low-voltage side with the state of charge of the energy storage system to determine the discharge control command; Furthermore, this step is the core of the system's online intelligent judgment and response. The system continuously and rapidly compares the real-time updated high-voltage side power average calculated in step 101 with the discharge power threshold determined in step 103. The core decision-making logic is: once the high-voltage side power average is detected to reach or exceed the threshold, it is determined that the current situation is a critical moment that may push up the monthly maximum demand record, and the system must initiate discharge intervention.

[0053] In some cases, the decision of "when to discharge" needs to be combined with fine-grained control of "how to discharge." After determining that discharge needs to be initiated, the system does not immediately discharge at a fixed power, but instead enters a multi-parameter fusion command generation phase:

[0054] Core safety boundary verification: First, the system checks the real-time state of charge (SOC) of the energy storage system. The discharge command generation process is only allowed to continue when the SOC is higher than the preset discharge safety lower limit (e.g., 20%), ensuring battery operation safety.

[0055] Auxiliary operating condition perception and command fine-tuning: Under the premise that safety conditions are met, the system synchronously reads the real-time power of the low-voltage side traction bus. The low-voltage side power directly reflects the instantaneous operating condition of the traction network.

[0056] If the real-time power display on the low-voltage side shows significant regenerative braking energy feedback, the system can slightly increase the discharge power setting when generating the discharge command. This is because some of the discharge energy can be partially offset or supplemented by the regenerative energy generated in real time, thereby optimizing the battery energy consumption rate while achieving more efficient peak shaving.

[0057] If the low-pressure side power display shows that the traction load is rising sharply, the system can determine that the demand pressure is urgent, and the priority and rate of command generation and execution will be further improved to ensure timely response.

[0058] By sensing the dynamics of low-voltage power, the system can dynamically match key parameters in the discharge control command (such as the initial set value of discharge power, power ramp-up rate, expected duration, etc.) with the instantaneous demand of the power grid, achieving a leap from "discharge as soon as conditions are met" to "optimized discharge based on operating conditions".

[0059] Ultimately, the system synthesizes all the above judgments and automatically generates a highly customized discharge control command. This command not only clarifies the inevitability of discharge (determined by comparing the average value on the high-voltage side with the threshold), but also optimizes the specific discharge strategy (with real-time power-assisted fine-tuning on the low-voltage side), and adheres to the safety boundaries of the energy storage device throughout the entire process (guaranteed by the state of charge). This mechanism greatly improves the accuracy of control, dynamic adaptability, and overall system intelligence.

[0060] Step 105: Execute the discharge control command to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

[0061] Furthermore, this step is the final execution of the control action and the energy output stage. The system sends the generated discharge control command to the energy storage execution unit. The power conversion system in the energy storage execution unit responds immediately, efficiently converting the electrical energy stored in the energy storage battery into industrial frequency AC power, which is then fed into the traction power supply network. The released energy preferentially uses the stored regenerative braking energy. If the system connects to new energy power generation units such as photovoltaic and wind power during this period and has effective output, this portion of new energy power will also be preferentially called according to the unified dispatch strategy to achieve coordinated discharge with the stored energy. This process directly and in real time offsets part of the power that originally needed to be drawn from the public grid, thereby effectively suppressing the peak value of the load curve. After applying the method of this embodiment, the load fluctuation coefficient (the ratio of maximum demand to average load) of the traction substation can be significantly reduced, achieving the core economic goal of reducing basic electricity costs.

[0062] In some cases, the operating mode of an energy storage system can automatically switch based on continuous monitoring and logical judgment of three key real-time operating parameters. These three parameters are: the state of charge (SBC) of the energy storage system, the real-time regenerative braking feedback power, and the traction load power. The SBC of the energy storage system: This parameter is usually expressed as a percentage and is defined as the ratio of the current available charge of the energy storage battery cell to its maximum available capacity (rated capacity). It accurately reflects the real-time energy reserve level of the energy storage system. To protect battery health, extend service life, and ensure the system always has a certain emergency capability, the control system presets a safe operating range for the SBC, typically setting an upper charging limit (e.g., 95%) and a lower discharging limit (e.g., 20%). The SBC is the fundamental condition for determining whether the system can charge or discharge. Real-time regenerative braking feedback power: This parameter refers to the instantaneous value of the active power generated by the train's electric braking and fed back to the traction power supply network at the current moment. It is a directional quantity; a positive value indicates that regenerative energy is flowing from the traction network to the energy storage system or the grid, while zero or negative values ​​indicate no regenerative energy feedback. This signal is the source indication that triggers the energy storage system to capture energy. Traction load power: This parameter refers to the instantaneous value of active power drawn from the public power grid by the high-voltage side of the traction substation at the current moment, directly reflecting the real-time total demand of all online train traction and other related loads. In the control logic of this method, the more crucial criterion is its 15-minute rolling average value (i.e., the "power average value" calculated in step 101). The comparison between this value and the preset discharge power threshold is the key to determining whether to initiate peak-shaving discharge.

[0063] The automatic mode switching logic based on the above parameters is as follows: The criteria for switching to charging mode are as follows: When the system simultaneously detects that the real-time regenerative braking reverse power is greater than zero (indicating that there is regenerative energy available for recovery) and the state of charge of the energy storage system is lower than the set charging upper limit (indicating that the battery has room to absorb energy), the system automatically switches from other modes to charging mode. In this mode, the power conversion module is controlled to rectify, converting the regenerative braking AC power on the traction network into DC power, which is stored in the energy storage battery until the reverse power disappears or the state of charge reaches the upper limit.

[0064] The criteria for switching to discharge mode are as follows: When the system simultaneously meets the following conditions: the 15-minute rolling average of the traction load power reaches or exceeds the discharge power threshold determined in step 103 (indicating that the current load level may push up the monthly maximum demand), and the state of charge of the energy storage system is higher than the set lower discharge limit (indicating that the battery has sufficient energy to release), the system automatically switches to discharge mode. In this mode, the power conversion module is controlled to invert, converting the DC power in the energy storage battery into AC power with the same frequency and phase as the traction network, injecting it into the grid to offset part of the power taken out, until the average load is lower than the threshold or the state of charge drops to the lower limit.

[0065] Conditions for switching to standby mode: When none of the above-mentioned conditions for triggering charging or discharging are met, the system automatically enters standby mode. In this mode, the energy storage system neither charges nor discharges, the power conversion module operates at low power or is turned off, but the control unit continuously monitors all sensor data, assesses the system status in real time, and is always ready to respond to changes in conditions and switch to the corresponding operating mode.

[0066] As can be seen from the above technical solutions, the beneficial effects of this embodiment are as follows: First, through high-precision real-time monitoring and rolling calculation, a load data foundation strictly synchronized with electricity metering rules is provided for the system, ensuring the timeliness and accuracy of control. Second, parameter optimization design based on historical data and full life-cycle economic analysis ensures the technical feasibility and investment economy of energy storage system configuration from the source. The core lies in quantifying the macro-level "cost reduction target" into specific electricity demand and using iterative algorithms to determine the optimal discharge threshold, so that every discharge behavior of the energy storage system directly serves the core economic goal of suppressing the maximum monthly demand, achieving "discharge on demand" and "precise peak shaving". Finally, combined with multi-energy collaborative scheduling and automatic mode switching, it not only maximizes the local consumption rate of regenerative braking energy, but also effectively integrates new energy power, significantly reducing basic electricity costs while enhancing the automation, intelligence level and overall energy efficiency of system operation, providing a high-economic, high-reliability and green benefit energy storage control solution for railway traction power supply systems.

[0067] This invention also provides an energy storage system for railway regenerative braking energy and new energy access based on maximum demand optimization. See also Figure 2 The image shows a specific embodiment of an energy storage system for railway regenerative braking energy and new energy access based on maximum demand optimization, provided by the present invention. This embodiment of the system is used to execute... Figure 1 The physical apparatus of the method. Its technical solution is essentially the same as the embodiments described above, and the corresponding descriptions in the embodiments above also apply to this embodiment. The system includes:

[0068] The maximum demand optimization controller 10 is configured to execute the method described in the above embodiments; and an energy storage execution unit 20 is connected between the maximum demand optimization controller 10 and the power supply bus or substation bus of the traction substation, for performing energy absorption or release operations in response to instructions from the maximum demand optimization controller 10; wherein the energy storage execution unit 20 includes at least one power conversion module 210 and at least one energy storage battery module 220. The maximum demand optimization controller 10 is connected to the measurement and control units on the high-voltage side and low-voltage side of the traction substation to obtain corresponding power signals.

[0069] The power conversion module 210 and the energy storage battery module 220 each adopt a modular redundant design, each containing multiple electrically and physically independent sub-units that can be switched in parallel or independently. The maximum demand optimization controller 10 is also configured to isolate a single sub-unit from its module and coordinate the continued operation of the remaining sub-units when a single sub-unit fails. The power conversion module 210 is a bidirectional converter; the DC side of the bidirectional converter is connected to the energy storage battery module 220, and its AC side is connected to the different power supply arm buses of the traction substation or the two end buses of the section substation via a transformer.

[0070] The maximum demand optimization controller 10 includes: The monitoring module 110 is used to monitor the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and to calculate the average power taken from the grid within a preset rolling time window. The data determination module 120 is used to determine the historical load data, regenerative braking energy data and configuration parameters of the energy storage system of the traction substation. The discharge power threshold determination module 130 is used to determine the discharge power threshold based on historical load data, regenerative braking energy data and configuration parameters, and in combination with the preset maximum demand reduction value. The discharge control command determination module 140 is used to compare the average power value and the discharge power threshold. When it is determined that the average power value reaches or exceeds the discharge power threshold, the discharge control command is determined by combining the real-time power on the low-voltage side and the state of charge of the energy storage system. The execution module 150 is used to execute discharge control commands to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

[0071] When determining the discharge power threshold, the discharge power threshold determination module 130 is configured to perform iterative optimization calculations: with a preset maximum demand reduction value as the optimization objective, under the power and capacity constraints of the energy storage system, iterative simulation is performed by adjusting the candidate discharge power threshold; in each iteration, based on historical load data and regenerative braking energy data, the actual maximum demand reduction value that can be achieved by using the current candidate discharge power threshold and the corresponding energy storage system charge and discharge plan are evaluated; when the actual maximum demand reduction value reaches the optimization objective and the charge and discharge plan meets the operating constraints of the energy storage system, the candidate discharge power threshold in the current iteration is output as the determined discharge power threshold.

[0072] Specifically, this system is a physical device integrating intelligent decision-making and highly reliable execution. Its core design goal is to incorporate the direct economic objective of "reducing monthly peak demand electricity costs" into its hardware architecture and control software. The system essentially consists of two core components:

[0073] The Maximum Demand Optimization Controller 10 is an intelligent control hub embedded with advanced algorithms. It doesn't simply execute charge-discharge cycles, but continuously implements an optimization strategy aimed at economic efficiency. Its core task is to dynamically calculate and determine an optimal discharge power threshold. This threshold is the key criterion for deciding when the energy storage system should start discharging to avoid creating higher monthly maximum demand. Through real-time monitoring, historical data analysis, and iterative simulation, the controller ensures that each discharge command is accurate and effective, directly serving the overall goal of reducing electricity costs.

[0074] Energy storage execution unit 20: This is a physical energy exchange device connected between the controller and the railway traction power supply network, responsible for executing the controller's commands to absorb or release energy. Its design specifically emphasizes the extreme reliability and maintainability requirements of railway applications.

[0075] Modular Redundant Architecture: The core of the execution unit—the power conversion module 210 and the energy storage battery module 220—is not a single unit, but rather composed of multiple electrically and physically independent sub-units arranged in parallel. This redundancy design means that the system can allow some sub-units to fail without affecting the overall function.

[0076] Online fault handling capability: The maximum demand optimization controller 10 is assigned system management responsibilities and can monitor the status of each sub-unit. When a fault is detected in a single sub-unit, it can automatically isolate it from its module and reallocate the workload of the remaining healthy sub-units, thereby achieving seamless continuous operation in the event of a fault and supporting online maintenance.

[0077] Flexible grid interface: The system's power conversion module 210 specifically employs a bidirectional converter. The DC side of this converter is connected to the energy storage battery module 220, while its AC side has the flexibility to connect to the grid via a transformer. Depending on engineering needs, it can be connected to different power supply arm buses of the traction substation or the two busbars at both ends of the section substation. This feature directly leads to two typical system implementations with different functional focuses.

[0078] It should be noted that the bidirectional converter referred to in this embodiment generally refers to a power electronic conversion device capable of realizing bidirectional energy flow between the AC and DC sides. Its specific circuit topology can be selected according to engineering needs, including but not limited to two-level, three-level, or modular multilevel converters. In the preferred embodiment requiring power ride-through functionality, the bidirectional converter adopts a "back-to-back" connection method consisting of two AC-DC converter units connected via a common DC bus. This connection method allows the two AC ports to be controlled independently and can directly exchange power through the common DC bus, which is key to achieving cross-bus energy exchange.

[0079] like Figure 3 The diagram shows a power-free bypass main circuit topology for the energy storage system provided by this invention. Specifically, the energy storage execution unit 20 is connected to the railway traction power supply system through a single electrical connection point. This connection point is typically a phase busbar on the load side of the traction substation, or a busbar on one side of the sectioning station. The entire energy storage system interacts with energy only through this designated busbar.

[0080] A measurement and control unit is installed in the traction substation (or section substation) to measure the power drawn from the upstream power grid or the power of a designated bus load and provide electrical protection. The power data at this measurement point is transmitted in real time to the monitoring module 110 of the maximum demand optimization controller 10. The AC power drawn from this bus first passes through a transformer for voltage matching and electrical isolation. After the transformer, it is connected to the core of the energy storage execution unit 20—the power conversion module 210, i.e., the bidirectional converter. In this embodiment, the converter uses only one of its AC side ports. The DC side of the converter is connected to the energy storage battery module 220. Both the power conversion module 210 and the energy storage battery module 220 exhibit a modular and redundant physical form, for example, consisting of multiple parallel converter sub-cabinets and battery clusters. The controller's monitoring module 110 continuously monitors the bus power and calculates its average value within a preset rolling time window (such as 15 minutes for demand billing). The data determination module 120 calls upon the historical load data, regenerative braking energy data, and configuration parameters (such as capacity and power) of the energy storage system from the traction substation. The discharge power threshold determination module 130, based on the aforementioned data, parameters, and the user-defined "maximum demand reduction" target, performs iterative optimization calculations to ultimately determine the current optimal discharge power threshold. The discharge control command determination module 140 compares the real-time calculated average power value with this dynamic threshold. Once the average value reaches or exceeds the threshold, the module generates a discharge control command. The execution module 150 then issues this command to the energy storage execution unit 20. Upon receiving the discharge command, the execution unit activates the power conversion module 210, converting the DC power (from previously captured regenerative braking energy or accessed new energy sources) stored in the energy storage battery module 220 into AC power at the industrial frequency, which is then injected into the target power supply bus via a transformer. This directly supplements the power demand of train traction on that bus, thereby instantly reducing the power drawn from the high-voltage grid and effectively suppressing the formation of the monthly maximum demand peak. When regenerative braking energy is detected to be fed back onto the bus, the controller instructs the execution unit to switch to charging mode to store the excess energy.

[0081] This embodiment has a relatively simple structure and low investment. It focuses on solving the problems of peak shaving and valley filling and braking energy recovery within a single power supply arm, and is suitable for scenarios with relatively independent load characteristics and limited budgets.

[0082] Figure 2 The embodiments shown are merely basic examples of the system of the present invention. Other preferred embodiments of the system can be obtained by making certain optimizations and extensions based on them.

[0083] like Figure 4The diagram shows another power-through-type main circuit topology for the energy storage system application provided by this invention. In this embodiment, the system adopts a more powerful power-through-type main circuit topology. The key feature is that the power conversion module 210 in the energy storage execution unit 20 is a bidirectional converter with dual AC ports. Specifically, in this embodiment, the two bidirectional converters are connected back-to-back, with their two AC side ports (corresponding to the left and right connection points) independently connected to two electrical connection points in the traction power supply network via transformers. These two connection points can be two different power supply arms of the same traction substation, or power supply arms connecting two adjacent traction substations at a section substation, while their common DC side is connected to the energy storage battery module 220. This specific bidirectional converter configuration allows the power conversion module 210 to achieve flexible power allocation and direct transfer (i.e., power ride-through) between the two connection points. The system monitoring and control unit simultaneously monitors the high and low voltage power status of the traction substations at both ends of the section substation. These two data streams are synchronously uploaded to the maximum demand optimization controller 10, providing it with a more comprehensive awareness of the grid status. The controller's internal modules (calculation, data determination, threshold determination, instruction determination, and execution) need to handle more complex logic. They must not only consider the power and demand at each of the two connection points separately, but also assess the power imbalance between the two connection points. Under this architecture, the system can execute three advanced energy dispatch modes, all decided uniformly by the maximum demand optimization controller 10:

[0084] Mode A: Single-sided charging and discharging: Similar to the control logic of the single-point access type (the power-free pass-through type in the previous embodiment), the energy storage system performs independent charging or discharging operations only on one connection point (such as the left substation). For example, when the average power value on the high-voltage side of the left substation is detected to exceed the discharge threshold, the controller instructs the energy storage system to discharge to that substation.

[0085] Mode B: Power Ride-Through Interoperability: When the system detects regenerative braking energy feedback at one connection point (e.g., the left substation) while the other connection point (e.g., the right substation or section substation) simultaneously has traction load demand, the controller instructs the system to enter "power ride-through" mode. At this time, the electrical energy from the left side, after being converted on the corresponding side of the converter, is directly inverted and fed into the right connection point via the common DC bus inside the converter, achieving cross-substation, instantaneous, and direct utilization of electrical energy.

[0086] Mode C: Coordinated Charge and Discharge: The controller can coordinate the energy storage system to charge from both the left and right connection points simultaneously (e.g., absorbing new energy or regenerative braking energy from both locations), or discharge to both simultaneously, in order to achieve coordinated peak shaving and load balancing over a wider range.

[0087] It should be further noted that the "power-through" energy storage system provided by this invention is not limited to applications within the same traction substation. For example... Figure 5 As shown, its topology can be extended to more complex railway power supply networks.

[0088] exist Figure 5 In the illustrated embodiment, the energy storage system acts as a regional energy dispatch center. Its two AC ports on the bidirectional converter can be flexibly connected to different critical nodes in the railway power supply system. For example, one port can connect to a traction substation, while the other port can connect to an adjacent section substation or another traction substation. Under this configuration, the system can achieve:

[0089] Cross-substation / regional energy sharing: Surplus regenerative braking energy from one substation is directly transferred to adjacent substations or regional substations via the DC bus of the energy storage system for their traction loads, significantly improving energy utilization efficiency. Through unified control, energy storage units and renewable energy sources such as photovoltaics connected to different nodes are coordinated and dispatched, optimizing maximum demand at the regional power grid level to achieve global economic and energy efficiency optimization. In complex network structures, this system can act as a power buffer and support node, smoothing local power fluctuations and improving power supply quality and reliability.

[0090] Therefore, the “left connection point” and “right connection point” in this invention can broadly represent any two independent nodes (such as substations, sectioning stations, etc.) in the railway power supply network that are electrically connected and require energy management and optimization.

[0091] This embodiment has a relatively complex structure and higher investment, but it achieves global energy optimization. It not only possesses all the functions of the previous embodiment, but also enables energy sharing and load balancing across power supply arms through power ride-through, significantly improving the direct utilization rate of regenerative braking energy. Furthermore, it can collaboratively optimize maximum demand at the entire station level, resulting in superior cost savings and improved power quality.

[0092] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0093] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, and other types. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0094] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0095] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into main memory and then executes them. Alternatively, it may obtain the corresponding execution instructions from other devices to logically form an energy storage system based on maximum demand optimization for railway regenerative braking energy and new energy access. The processor executes the execution instructions stored in the memory to implement the energy storage control method based on maximum demand optimization for railway regenerative braking energy and new energy access provided in any embodiment of the present invention.

[0096] The above is as described in the present invention. Figure 2The method for implementing a railway regenerative braking energy and new energy access energy storage system based on maximum demand optimization, as shown in the embodiment, can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0097] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0098] This invention also proposes a readable medium storing execution instructions. When these instructions are executed by a processor of an electronic device, the device can perform an energy storage control method based on maximum demand optimization for railway regenerative braking energy and new energy access, as provided in any embodiment of this invention. Specifically, it is used to perform actions such as... Figure 1 The method shown.

[0099] The electronic devices in the foregoing embodiments may be computers.

[0100] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0101] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0102] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0103] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for controlling railway regenerative braking energy and new energy access based on maximum demand optimization, characterized in that, The method includes: Monitor the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and calculate the average power of the power taken from the grid within a preset rolling time window; Determine the historical load data, regenerative braking energy data, and configuration parameters of the energy storage system for the traction substation; Based on the historical load data, the regenerative braking energy data, and the configuration parameters, and in conjunction with the preset maximum demand reduction value, the discharge power threshold is determined; The average power value is compared with the discharge power threshold. When it is determined that the average power value reaches or exceeds the discharge power threshold, a discharge control command is determined by combining the real-time power on the low-voltage side with the state of charge of the energy storage system. The discharge control command is executed to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

2. The method according to claim 1, characterized in that, Determining the configuration parameters of the energy storage system includes: Multiple candidate values ​​for reverse energy utilization rate are set, wherein the reverse energy utilization rate is the proportion of regenerative braking energy absorbed by the energy storage system and used for discharge to the total regenerative braking energy. For each candidate value, based on the regenerative braking energy data, the performance indicators of the energy storage system under the preset charging and discharging logic are simulated and calculated. Using the electricity required to meet the maximum demand reduction value as a constraint, the configuration parameter combination that optimizes the total life cycle cost of the energy storage system is selected from the simulation results corresponding to each candidate value as the final configuration.

3. The method according to claim 1, characterized in that, The determination of the discharge power threshold based on the historical load data, the regenerative braking energy data, and the configuration parameters, combined with a preset maximum demand reduction value, includes: Based on the maximum demand reduction value, calculate the corresponding target total peak shaving power demand and the target maximum peak shaving power demand for a single operation; Based on the historical load data and the regenerative braking energy data, a candidate discharge power threshold is determined; Verify whether the energy storage system, based on the configuration parameters, can meet the target total peak shaving power requirement and the target maximum peak shaving power requirement for a single operation; When the verification is successful, the candidate discharge power threshold is determined as the discharge power threshold.

4. The method according to claim 3, characterized in that, The verification of whether the energy storage system, based on the configuration parameters, can meet the target total peak shaving power requirement and the target maximum peak shaving power requirement for a single operation includes: Determine whether the target peak-shaving total power demand is less than or equal to the total power that the energy storage system can release under the set reverse power utilization rate; In addition, it is determined whether the target maximum peak-shaving power demand is less than or equal to the single discharge capacity of the energy storage system. The verification is successful when both conditions are met.

5. The method according to claim 1, characterized in that, The method further includes: The operating mode of the energy storage system is automatically switched according to the state of charge of the energy storage system, the real-time regenerative braking power and the traction load power. The operating modes include charging mode, discharging mode, and standby mode.

6. An energy storage system for railway regenerative braking energy and new energy access based on maximum demand optimization, said energy storage system being used to execute the method described in any one of claims 1 to 5, characterized in that, include: A maximum demand optimization controller is configured to execute the method. And an energy storage execution unit, connected between the maximum demand optimization controller and the power supply bus or substation bus of the traction substation, for performing energy absorption or release operations in response to instructions from the maximum demand optimization controller; The energy storage execution unit includes at least one power conversion module and at least one energy storage battery module; The maximum demand optimization controller is connected to the measurement and control units on the high-voltage side and low-voltage side of the traction substation to obtain the corresponding power signals.

7. The system according to claim 6, characterized in that, The power conversion module and the energy storage battery module adopt a modular redundancy design, each containing multiple sub-units that are electrically and physically independent and can be switched in parallel or independently. The maximum demand optimization controller is also configured to: in the event of a failure in a single subunit, control the isolation of that subunit from its module and coordinate the continued operation of the remaining subunits.

8. The system according to claim 6, characterized in that, The power conversion module is a bidirectional converter; The DC side of the bidirectional converter is connected to the energy storage battery module, and its AC side is connected to the different power supply arm buses of the traction substation or the two end buses of the section substation through a transformer.

9. The system according to claim 6, characterized in that, The maximum demand optimization controller includes: The monitoring module monitors the power taken from the grid on the high-voltage side of the traction substation and the real-time power of the traction bus on the low-voltage side, and calculates the average power taken from the grid within a preset rolling time window. The data determination module is used to determine the historical load data, regenerative braking energy data, and configuration parameters of the energy storage system of the traction substation. The discharge power threshold determination module is used to determine the discharge power threshold based on the historical load data, the regenerative braking energy data, and the configuration parameters, combined with a preset maximum demand reduction value. The discharge control command determination module is used to compare the average power value and the discharge power threshold; when it is determined that the average power value reaches or exceeds the discharge power threshold, the discharge control command is determined by combining the real-time power on the low-voltage side and the state of charge of the energy storage system. An execution module is used to execute the discharge control command to control the energy storage system to release the stored regenerative braking energy and / or the connected new energy power.

10. The system according to claim 9, characterized in that, When determining the discharge power threshold, the discharge power threshold determination module is configured to perform iterative optimization calculations: With the preset maximum demand reduction value as the optimization objective, under the power and capacity constraints of the energy storage system, iterative simulation is performed by adjusting the candidate discharge power threshold. In each iteration, based on the historical load data and the regenerative braking energy data, the actual maximum demand reduction that can be achieved when using the current candidate discharge power threshold and the corresponding energy storage system charge and discharge plan are evaluated. When the actual maximum demand reduction reaches the optimization target and the charging and discharging plan meets the operating constraints of the energy storage system, the candidate discharge power threshold in the current iteration is output as the determined discharge power threshold.

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