Rail transit hub multi-electric energy complementary structure and management and control method

Abstract

The application discloses a rail transit hub multi-electricity complementary structure and a control method, the structure comprising an external incoming line power supply module, a photovoltaic power generation module, a regenerative braking energy absorption module, an alternating current traction substation module, an alternating current power distribution station module, a direct current traction step-down hybrid substation module and an energy storage module, power bidirectional flow between multiple types of substations being realized through a unified direct current bus; the control method adopts a double-time-scale hierarchical coordination architecture, a day-ahead scheduling layer solving an energy storage charging and discharging plan through rolling optimization based on a train operation timetable and photovoltaic prediction data, a real-time correction layer calculating output distribution amounts of each module according to a hierarchical priority order and dynamically correcting in combination with a proportional-integral deviation corrector to form a closed loop feedback, the application realizing unified complementary utilization of multiple types of railway electricities in a four-network fusion scene, and improving renewable energy consumption rate, regenerative braking energy recovery rate and energy storage full life cycle operation benefit.

Description

Technical Field

[0001] This invention belongs to the field of rail transit electrical engineering, and in particular relates to a multi-energy complementary structure and control method for rail transit hubs. Background Technology

[0002] With increasingly strained corridor resources, achieving physical transfers and interconnections between trunk railways, intercity railways, suburban railways, and urban rail transit at hub stations has become a crucial issue. However, the convergence of multiple lines and various rail transit systems at a single hub significantly increases the types and scale of train traction loads, station power distribution loads, and auxiliary loads. The fundamental differences in electrical architecture, voltage levels, and load characteristics among the power supply systems of different railway systems result in complex characteristics of the hub's overall power demand, including multiple voltage levels, AC / DC mixing, and significant load fluctuations. How to achieve complementary utilization and coordinated management of multiple forms of electrical energy within the hub, while ensuring the independent power supply security of each railway system, and reducing the power supply pressure on the external power grid and operational energy consumption, has become a key technical challenge restricting energy conservation and emission reduction in rail transit hubs.

[0003] In existing technologies, some solutions for comprehensive energy utilization in the rail transit sector propose connecting photovoltaic power generation systems to traction substations, utilizing energy storage devices to recover regenerative braking energy from trains, and regulating power between traction loads and distribution loads. Specifically, existing methods typically employ real-time energy management strategies based on fixed threshold rules. This involves collecting traction network voltage or load power and comparing it with preset thresholds to control the energy storage device to discharge under traction conditions and charge under braking conditions, thereby achieving local consumption of regenerative braking energy and peak shaving and valley filling of traction loads. Regarding capacity configuration, existing technologies mainly calculate energy storage capacity based on the maximum power deficit or maximum regenerative braking energy feedback under a single extreme condition, and use fixed discharge depth coefficients and charge depth coefficients as lifespan constraints. However, these solutions are mostly designed for single-type railways or single traction substations, failing to fully consider the multi-type load coordination and scheduling needs arising from differences in load characteristics after multiple types of railways converge at the same hub, and lacking the ability to decouple energy supply and demand fluctuations at different time scales.

[0004] The existing technologies mentioned above have the following shortcomings when applied to multi-energy complementary scenarios in rail transit hubs. Firstly, in terms of topology, existing solutions lack a unified energy complementary architecture connecting four different power supply systems: trunk railways, intercity railways, suburban railways, and urban rail transit. Flexible allocation of photovoltaic power generation, regenerative braking energy, and energy storage among various types of substations lacks structural support. Secondly, regarding energy management methods, real-time rules based on fixed thresholds can only respond to the current supply and demand relationship. They cannot utilize future photovoltaic irradiance prediction data and train timetable information for forward-looking scheduling of energy storage charging and discharging, resulting in low energy storage utilization and delayed response when photovoltaic output fluctuates drastically or load changes rapidly. Thirdly, in terms of energy storage capacity calculation, the fixed coefficients DOD and DOC do not consider the aging characteristics of batteries throughout their entire life cycle, leading to conservative capacity configuration in the early stages of operation and insufficient margin at the end of operation. Therefore, a multi-energy complementary structure and management method for rail transit hubs need to be designed to address these issues. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a multi-energy complementary structure and management method for rail transit hubs. It aims to overcome the shortcomings of existing rail transit hubs where multiple types of railway power are independent and cannot be shared and complemented, and where energy management lacks forward-looking scheduling and adaptive adjustment of energy storage throughout its entire life cycle. It features flexible allocation of power from multiple types of substations through a unified DC bus, a dual-time-scale hierarchical coordination architecture to achieve predictive optimization and real-time correction closed-loop coordination, and optimal operation throughout its entire life cycle based on dynamic adaptive thresholds of multiple energy storage factors.

[0006] To achieve the above technical solution, the technical solution adopted by the present invention is as follows: A multi-energy complementary structure for a rail transit hub includes an external incoming power module, which is connected to an AC traction substation module, an AC power distribution substation module, and a DC traction step-down hybrid substation module and provides them with power energy. The photovoltaic power generation module connects the AC traction substation module and the AC power distribution substation module to provide power energy for AC traction loads and AC power distribution loads. The regenerative braking energy absorption module connects the AC traction substation module and the DC traction step-down hybrid substation module, providing the above modules with the recovered train regenerative braking energy; The energy storage module is connected to a 10kV or 35kV AC ring network through a converter. It achieves bidirectional power flow through the AC ring network to AC traction substation modules, AC power distribution substation modules, and DC traction step-down hybrid substation modules. It is used to store and release electrical energy between substations under the control of the energy management module. The energy management module collects the power supply from the external incoming power module via the communication bus. Output power of photovoltaic power generation modules The recovery power of the regenerative braking energy absorption module Load power of AC traction substation modules Load power of AC power distribution substation modules Load power of DC traction step-down hybrid substation module It also records the state of charge and current available capacity of the energy storage modules, and issues charging and discharging power commands to the energy storage modules and converter power scheduling commands to each substation module.

[0007] Furthermore, the AC traction substation module and the AC power distribution substation module are respectively connected to the DC bus via AC / DC converters, and the DC traction step-down hybrid substation module is directly connected to the DC bus, realizing bidirectional power flow between AC traction load, AC power distribution load, DC hybrid load and energy storage module.

[0008] Preferably, a management and control method for a multi-energy complementary structure in a rail transit hub includes the following steps: Step A: Real-time acquisition of the output power of the photovoltaic power generation module The recovery power of the regenerative braking energy absorption module AC traction load power demand AC power distribution load demand power DC mixed load power demand Current available capacity of energy storage modules and the power supply of the external incoming power module ; Step B: Calculate the real-time supply-demand difference ,in The charging and discharging power of the energy storage module, during discharge Take a positive value during charging. Take the negative value; Step C: Based on photovoltaic output Based on the relationship between the power demand of each load and the sum of their respective power requirements, the current supply and demand range is determined; the output allocation of each energy module is calculated according to the following priority order: photovoltaic output is prioritized for supplying AC traction loads. Secondly, it supplies AC power to the distribution load. Resupply DC mixed load The power supply deficit of each load is first supplemented by the regenerative braking energy absorption module. When the regenerative braking energy is insufficient, it is supplemented by the energy storage module. When the available capacity of the energy storage module is insufficient, it is supplemented by the external incoming power supply module.

[0009] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All of it is supplied to AC traction load; Regenerative braking output distribution Priority should be given to supplementing the AC traction load deficit. If there is any surplus, it will be added to the AC power distribution load in sequence. and DC mixed load ; Energy storage output allocation: when hour, Discharge to replenish the remaining load demand; External power supply output distribution: when hour, ;when At that time, there is excess regenerative braking energy, and the excess amount It is absorbed and stored by the energy storage module.

[0010] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supply AC traction load, Supply AC power to the load; Regenerative braking output distribution Priority should be given to supplementing the AC power distribution load deficit. When there is surplus, supplement the DC mixed load. ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ;when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module.

[0011] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supplying AC traction loads and AC distribution loads, Supplying DC mixed loads; Regenerative braking output distribution Used to supplement DC mixed load deficit ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ;when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module; when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All three types of loads are supplied by photovoltaic power. Solar surplus output All of it is absorbed and stored by the energy storage module; Regenerative braking energy All energy is absorbed and stored by the energy storage module, where 0.8 is a fixed coefficient.

[0012] Furthermore, the coefficient 0.8 in the energy storage module output allocation calculation is the capacity utilization coefficient corresponding to the optimal lifespan of the energy storage battery. Its value is determined as follows: this coefficient represents the percentage of rated capacity that can be utilized in a single continuous discharge under the premise of ensuring that the energy storage battery operates within the optimal cycle life range; for lithium iron phosphate batteries, the value of this coefficient ranges from 0.75 to 0.85; for lithium titanate batteries, the value of this coefficient ranges from 0.80 to 0.90.

[0013] Preferably, the capacity of the energy storage module Determine using the following method: Step S1: Calculate the output power of the energy storage module To meet the energy support constraints under extreme shortage conditions: ; in, The unit is the output power of the energy storage module, expressed in kWh. This is the start time of the energy storage module's discharge. Rated discharge duration of the energy storage module, in hours; DOD is the maximum depth of discharge coefficient corresponding to the optimal lifespan of the energy storage battery, ranging from 0.8 to 0.9. , , These represent the peak power requirements of AC traction load, AC distribution load, and DC mixed load within one operating cycle, in kW. , These are the power outputs of the photovoltaic power generation module and the regenerative braking energy absorption module at their lowest points during one operating cycle, in kW. Step S2: Calculate the input power of the energy storage module To meet the energy consumption constraints under extreme excess operating conditions: ; in, Input power to the energy storage module, in kWh; DOC is the maximum depth of charge coefficient corresponding to the optimal lifespan of the energy storage battery, ranging from 0.8 to 0.9; , , These represent the power demand of AC traction load, AC distribution load, and DC mixed load during a low-temperature period within one operating cycle, in kW. , These are the peak outputs of the photovoltaic power generation module and the regenerative braking energy absorption module, respectively, in kW; Step S3: Take and The larger value is taken as the required capacity of the energy storage module: ; in, The required capacity for the energy storage module is expressed in kWh. The depth of discharge coefficient (DOD) and depth of charge coefficient (DOC) are replaced with values ​​that vary with the number of equivalent cycles. Dynamically adjusted value: ; ; in, The maximum depth of discharge coefficient is set to a reference value of 0.85 when the energy storage is initially put into operation. The maximum charging depth coefficient is set as a baseline value of 0.85 when the energy storage is initially put into operation. This represents the equivalent number of full charge and discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the depth attenuation coefficient, with a value ranging from 0.05 to 0.15.

[0014] Furthermore, the values ​​of the depth of discharge coefficient (DOD) and depth of charge coefficient (DOC) ranging from 0.8 to 0.9 are suitable for lithium iron phosphate batteries; for lithium titanate batteries, the values ​​of DOD and DOC range from 0.85 to 0.95; and for supercapacitor energy storage, the values ​​of DOD and DOC range from 0.90 to 0.98.

[0015] Preferably, the energy management adopts a dual-time-scale hierarchical coordination architecture, including a day-ahead scheduling layer and a real-time correction layer: The daytime scheduling layer uses To optimize window duration for scrolling, Take a timeframe of 15 to 60 minutes, at the start of each window, based on future... Using train timetables, predicted photovoltaic irradiance, and historical load data for a given period, we can calculate the future energy storage module's performance. Planned charging and discharging power values ​​within ; The real-time correction layer performs the following operations in each control cycle, with the control cycle ranging from 1 second to 10 seconds: It collects the real-time power data at the current moment and calculates the output allocation of each module according to the hierarchical priority order described in step C, where the actual charging and discharging power of the energy storage module is included. From the planned value of the day With real-time deviation correction The deviation correction is obtained by superposition based on the current supply and demand difference. and the current state of charge of energy storage Sure; When calculating the planned charging and discharging power of the energy storage modules in the day-ahead scheduling layer, the objective function is to minimize the daily operating cost of the hub. The expression for the objective function is: ; in, For the daily operating cost of the hub; This represents the total number of daily scheduling periods. for Electricity price per kWh for the specified time period; for External incoming power supply power during the specified time period, in kW; The duration of the scheduling period, in hours (h). This is the demand-based electricity price, in yuan / kW; This represents the maximum power demand during the day. Cycle aging cost per unit charge / discharge capacity of energy storage, expressed in yuan / kWh; constraints include: Power balance constraints: ; The superscript pred indicates the predicted value; Energy storage state of charge constraints: ; in , ; Energy storage charging and discharging power constraints: ; Hierarchical priority constraints: If and only if .

[0016] Preferably, the real-time deviation correction amount Calculate using the following formula: ; in, This is the proportional correction factor. This is the integral correction factor. This is the length of the integration time window; This represents the real-time supply and demand difference at the current moment. The proportional correction factor and integral correction factor Tuning using the attenuation curve method: Under pure proportional control, gradually increase Continue until the supply-demand difference response curve exhibits a 4:1 decaying oscillation, and record the critical proportional coefficient at this point. and oscillation period .Pick , ; The integration time window length Take the rated discharge time of the energy storage module 0.1 to 0.3 times.

[0017] Furthermore, the real-time correction layer in each After the window ends, the actual charge and discharge capacity, average state of charge, and deviation statistics of the actual and predicted load values ​​of the energy storage modules within this window will be fed back to the day-ahead dispatching layer as the basis for the next... The initial state of the window scrolling optimization and the prediction bias correction input form a closed-loop feedback.

[0018] Preferably, the fixed coefficient 0.8 in the determination condition for the energy storage module to be put into power supply is replaced with a dynamic adaptive coefficient. That is, the condition for energy storage to meet the remaining load demand is ,in This refers to the rated capacity of the energy storage module. Current state of charge Rate of change of state of charge and energy storage equivalent cycle number The function is calculated using the following formula: ; in, The basic threshold coefficient; It is the state of charge adjustment factor; As a factor for the decline in health status; This is a penalty factor for charge / discharge rates; Rated capacity of the energy storage module, in kWh.

[0019] Preferably, the basic threshold coefficient The value is 0.8, consistent with the fixed capacity utilization factor corresponding to the optimal lifespan of the energy storage battery; the state of charge adjustment factor Define segments as follows: ; in, The minimum state of charge allowed for energy storage, This represents the highest permissible state of charge for energy storage. This represents the lower limit of the optimal operating range for energy storage. This represents the upper limit of the optimal operating range for energy storage. The value is 0.3. The value is 0.7; The value is 0.1. The value is 0.9; The health status decay factor Calculate using the following formula: ; in, This represents the equivalent number of full charge / discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the aging degradation coefficient, with a value ranging from 0.1 to 0.3; The charge / discharge rate penalty factor Calculate using the following formula: ; in, It is the absolute value of the rate of change of the state of charge; The threshold value for the optimal charge / discharge rate of energy storage is set to [value]. , The rated charge / discharge rate for energy storage; This is the rate penalty coefficient, with a value ranging from 0.05 to 0.15.

[0020] The beneficial effects of this invention are as follows: 1. This invention addresses the shortcomings of existing technologies where power resources for trunk railways, intercity railways, suburban railways, and urban rail transit systems are independent and cannot be shared or complementary after convergence at a hub. This is achieved by constructing a multi-energy complementary structure that interconnects external incoming power modules, photovoltaic power generation modules, regenerative braking energy absorption modules, AC traction substation modules, AC power distribution substation modules, DC traction step-down hybrid substation modules, and energy storage modules. This structure enables bidirectional power flow between AC traction loads, AC distribution loads, and DC hybrid loads via a unified DC bus, allowing for free allocation of photovoltaic power generation, regenerative braking energy, and energy storage among various substation types. This significantly improves the local consumption ratio of renewable energy within the hub and the cross-system utilization rate of regenerative braking energy, providing a structural foundation for energy conservation and emission reduction in rail transit hubs under the four-network convergence scenario.

[0021] 2. This invention employs a dual-timescale hierarchical coordinated control architecture. At the day-ahead scheduling layer, train timetables, predicted photovoltaic irradiance, and historical load data are used as inputs. A rolling optimization model solves for the planned energy storage charging and discharging power. At the real-time correction layer, with a control cycle of 1 to 10 seconds, a hierarchical priority rule combined with a proportional-integral deviation corrector dynamically corrects the planned value based on the real-time supply-demand difference. The execution results are then fed back to the day-ahead layer to form a closed loop, overcoming the shortcomings of existing technologies that only offer real-time single-layer judgment and lack forward-looking scheduling capabilities. Through the synergistic cooperation of day-ahead layer prediction optimization and real-time layer rule correction, the energy storage charging and discharging behavior anticipates load change trends, effectively mitigating the impact of the dual uncertainties of photovoltaic output and traction load on system stability. Simultaneously, by explicitly embedding hierarchical priority scheduling constraints into the optimization model, consistency between the optimization results and operating rules is ensured, avoiding scheduling instructions that may not conform to engineering realities arising from pure optimization.

[0022] 3. This invention replaces the fixed capacity utilization coefficient with a dynamic adaptive threshold coefficient based on state of charge (DOC), health status, and charge / discharge rate. It also establishes a dynamic model for the depth of discharge (DOD) and depth of charge (DOC) that decays with the number of equivalent cycles. This addresses the shortcomings of existing fixed-coefficient methods in dynamically balancing capacity utilization and aging rate throughout the entire lifecycle of energy storage. The DOD adjustment factor automatically adjusts the available capacity window based on the current DOD level. The health status decay factor gradually lowers the overall allowable utilization depth as the number of cycles increases. The charge / discharge rate penalty factor actively limits energy storage output during periods of severe power fluctuation. The combined effect of these three factors enables the energy storage module to automatically switch operating strategies under different operating conditions and aging stages. The dynamic DOD and DOC models ensure that the capacity calculation formula maintains adaptive margin throughout the entire lifecycle, suppressing excessive conservatism in the early stages of operation and insufficient margin in the later stages from the source of capacity configuration, thus comprehensively extending the effective service life of the energy storage system. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of a multi-energy complementary structure for a rail transit hub in an embodiment of the present invention; Figure 2 This is a schematic diagram of the control method flow in an embodiment of the present invention. Detailed Implementation

[0024] Example 1: like Figure 1 As shown, a multi-energy complementary structure for a rail transit hub includes an external incoming power module, which is connected to an AC traction substation module, an AC power distribution substation module, and a DC traction step-down hybrid substation module and provides them with power energy. The photovoltaic power generation module connects the AC traction substation module and the AC power distribution substation module to provide power energy for AC traction loads and AC power distribution loads. The regenerative braking energy absorption module connects the AC traction substation module and the DC traction step-down hybrid substation module, providing the above modules with the recovered train regenerative braking energy; The energy storage module is connected to a 10kV or 35kV AC ring network through a converter. It achieves bidirectional power flow through the AC ring network to AC traction substation modules, AC power distribution substation modules, and DC traction step-down hybrid substation modules. It is used to store and release electrical energy between substations under the control of the energy management module. The energy management module collects the power supply from the external incoming power module via the communication bus. Output power of photovoltaic power generation modules The recovery power of the regenerative braking energy absorption module Load power of AC traction substation modules Load power of AC power distribution substation modules Load power of DC traction step-down hybrid substation module It also records the state of charge and current available capacity of the energy storage modules, and issues charging and discharging power commands to the energy storage modules and converter power scheduling commands to each substation module.

[0025] Furthermore, the AC traction substation module and the AC power distribution substation module are respectively connected to the DC bus via AC / DC converters, and the DC traction step-down hybrid substation module is directly connected to the DC bus, realizing bidirectional power flow between AC traction load, AC power distribution load, DC hybrid load and energy storage module.

[0026] like Figure 2 The aforementioned management and control method for a multi-energy complementary structure in a rail transit hub includes the following steps: Step A: Real-time acquisition of the output power of the photovoltaic power generation module The recovery power of the regenerative braking energy absorption module AC traction load power demand AC power distribution load demand power DC mixed load power demand Current available capacity of energy storage modules and the power supply of the external incoming power module ; Step B: Calculate the real-time supply-demand difference ,in The charging and discharging power of the energy storage module, during discharge Take a positive value during charging. Take the negative value; Step C: Based on photovoltaic output Based on the relationship between the power demand of each load and the sum of their respective power requirements, the current supply and demand range is determined; the output allocation of each energy module is calculated according to the following priority order: photovoltaic output is prioritized for supplying AC traction loads. Secondly, it supplies AC power to the distribution load. Resupply DC mixed load The power supply deficit of each load is first supplemented by the regenerative braking energy absorption module. When the regenerative braking energy is insufficient, it is supplemented by the energy storage module. When the available capacity of the energy storage module is insufficient, it is supplemented by the external incoming power supply module.

[0027] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All of it is supplied to AC traction load; Regenerative braking output distribution Priority should be given to supplementing the AC traction load deficit. If there is any surplus, it will be added to the AC power distribution load in sequence. and DC mixed load ; Energy storage output allocation: when hour, Discharge to replenish the remaining load demand; External power supply output distribution: when hour, ;when At that time, there is excess regenerative braking energy, and the excess amount It is absorbed and stored by the energy storage module.

[0028] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supply AC traction load, Supply AC power to the load; Regenerative braking output distribution Priority should be given to supplementing the AC power distribution load deficit. When there is surplus, supplement the DC mixed load. ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ;when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module.

[0029] Preferably, in step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supplying AC traction loads and AC distribution loads, Supplying DC mixed loads; Regenerative braking output distribution Used to supplement DC mixed load deficit ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ;when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module; when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All three types of loads are supplied by photovoltaic power. Solar surplus output All of it is absorbed and stored by the energy storage module; Regenerative braking energy All energy is absorbed and stored by the energy storage module, where 0.8 is a fixed coefficient.

[0030] Furthermore, the coefficient 0.8 in the energy storage module output allocation calculation is the capacity utilization coefficient corresponding to the optimal lifespan of the energy storage battery. Its value is determined as follows: this coefficient represents the percentage of rated capacity that can be utilized in a single continuous discharge under the premise of ensuring that the energy storage battery operates within the optimal cycle life range; for lithium iron phosphate batteries, the value of this coefficient ranges from 0.75 to 0.85; for lithium titanate batteries, the value of this coefficient ranges from 0.80 to 0.90.

[0031] Preferably, the capacity of the energy storage module Determine using the following method: Step S1: Calculate the output power of the energy storage module To meet the energy support constraints under extreme shortage conditions: ; in, The unit is the output power of the energy storage module, expressed in kWh. This is the start time of the energy storage module's discharge. Rated discharge duration of the energy storage module, in hours; DOD is the maximum depth of discharge coefficient corresponding to the optimal lifespan of the energy storage battery, ranging from 0.8 to 0.9. , , These represent the peak power requirements of AC traction load, AC distribution load, and DC mixed load within one operating cycle, in kW. , These are the power outputs of the photovoltaic power generation module and the regenerative braking energy absorption module at their lowest points during one operating cycle, in kW. Step S2: Calculate the input power of the energy storage module To meet the energy consumption constraints under extreme excess operating conditions: ; in, Input power to the energy storage module, in kWh; DOC is the maximum depth of charge coefficient corresponding to the optimal lifespan of the energy storage battery, ranging from 0.8 to 0.9; , , These represent the power demand of AC traction load, AC distribution load, and DC mixed load during a low-temperature period within one operating cycle, in kW. , These are the peak outputs of the photovoltaic power generation module and the regenerative braking energy absorption module, respectively, in kW; Step S3: Take and The larger value is taken as the required capacity of the energy storage module: ; in, The required capacity for the energy storage module is expressed in kWh. The depth of discharge coefficient (DOD) and depth of charge coefficient (DOC) are replaced with values ​​that vary with the number of equivalent cycles. Dynamically adjusted value: ; ; in, The maximum depth of discharge coefficient is set to a reference value of 0.85 when the energy storage is initially put into operation. The maximum charging depth coefficient is set as a baseline value of 0.85 when the energy storage is initially put into operation. This represents the equivalent number of full charge and discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the depth attenuation coefficient, with a value ranging from 0.05 to 0.15.

[0032] Furthermore, the values ​​of the depth of discharge coefficient (DOD) and depth of charge coefficient (DOC) ranging from 0.8 to 0.9 are suitable for lithium iron phosphate batteries; for lithium titanate batteries, the values ​​of DOD and DOC range from 0.85 to 0.95; and for supercapacitor energy storage, the values ​​of DOD and DOC range from 0.90 to 0.98.

[0033] Preferably, the energy management adopts a dual-time-scale hierarchical coordination architecture, including a day-ahead scheduling layer and a real-time correction layer: The daytime scheduling layer uses To optimize window duration for scrolling, Take a timeframe of 15 to 60 minutes, at the start of each window, based on future... Using train timetables, predicted photovoltaic irradiance, and historical load data for a given period, we can calculate the future energy storage module's performance. Planned charging and discharging power values ​​within ; The real-time correction layer performs the following operations in each control cycle, with the control cycle ranging from 1 second to 10 seconds: It collects the real-time power data at the current moment and calculates the output allocation of each module according to the hierarchical priority order described in step C, where the actual charging and discharging power of the energy storage module is included. From the planned value of the day With real-time deviation correction The deviation correction is obtained by superposition based on the current supply and demand difference. and the current state of charge of energy storage Sure; When calculating the planned charging and discharging power of the energy storage modules in the day-ahead scheduling layer, the objective function is to minimize the daily operating cost of the hub. The expression for the objective function is: ; in, For the daily operating cost of the hub; This represents the total number of daily scheduling periods. for Electricity price per kWh for the specified time period; for External incoming power supply power during the specified time period, in kW; The duration of the scheduling period, in hours (h). This is the demand-based electricity price, in yuan / kW; This represents the maximum power demand during the day. Cycle aging cost per unit charge / discharge capacity of energy storage, in yuan / kWh; constraints include: Power balance constraints: ; The superscript pred indicates the predicted value; Energy storage state of charge constraints: ; in , ; Energy storage charging and discharging power constraints: ; Hierarchical priority constraints: If and only if .

[0034] Preferably, the real-time deviation correction amount Calculate using the following formula: ; in, This is the proportional correction factor. This is the integral correction factor. This is the length of the integration time window; This represents the real-time supply and demand difference at the current moment. The proportional correction factor and integral correction factor Tuning using the attenuation curve method: Under pure proportional control, gradually increase Continue until the supply-demand difference response curve exhibits a 4:1 decaying oscillation, and record the critical proportional coefficient at this point. and oscillation period .Pick , ; The integration time window length Take the rated discharge time of the energy storage module 0.1 to 0.3 times.

[0035] Furthermore, the real-time correction layer in each After the window ends, the actual charge and discharge capacity, average state of charge, and deviation statistics of the actual and predicted load values ​​of the energy storage modules within this window will be fed back to the day-ahead dispatching layer as the basis for the next... The initial state of the window scrolling optimization and the prediction bias correction input form a closed-loop feedback.

[0036] Preferably, the fixed coefficient 0.8 in the determination condition for the energy storage module to be put into power supply is replaced with a dynamic adaptive coefficient. That is, the condition for energy storage to meet the remaining load demand is ,in This refers to the rated capacity of the energy storage module. Current state of charge Rate of change of state of charge and energy storage equivalent cycle number The function is calculated using the following formula: ; in, The basic threshold coefficient; It is the state of charge adjustment factor; As a factor for the decline in health status; This is a penalty factor for charge / discharge rates; Rated capacity of the energy storage module, in kWh.

[0037] Preferably, the basic threshold coefficient The value is 0.8, consistent with the fixed capacity utilization factor corresponding to the optimal lifespan of the energy storage battery; the state of charge adjustment factor Define segments as follows: ; in, The minimum state of charge allowed for energy storage, This represents the highest permissible state of charge for energy storage. This represents the lower limit of the optimal operating range for energy storage. This represents the upper limit of the optimal operating range for energy storage. The value is 0.3. The value is 0.7; The value is 0.1. The value is 0.9; The health status decay factor Calculate using the following formula: ; in, This represents the equivalent number of full charge / discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the aging degradation coefficient, with a value ranging from 0.1 to 0.3; The charge / discharge rate penalty factor Calculate using the following formula: ; in, It is the absolute value of the rate of change of the state of charge; The threshold value for the optimal charge / discharge rate of energy storage is set to [value]. , The rated charge / discharge rate for energy storage; This is the rate penalty coefficient, with a value ranging from 0.05 to 0.15.

[0038] Example 2: This embodiment discloses a specific application example of this method in a large rail transit hub in a central city. This hub integrates one trunk railway, two intercity railways, one suburban railway, and three urban rail transit lines. Within the hub area, the multi-energy complementary structure described in claim 1 is deployed: photovoltaic power generation modules with an installed capacity of 8MWp, using monocrystalline silicon modules, tilted at 30°, and facing due south, are arranged on the roof of the hub station building and the depot roof; regenerative braking energy absorption modules collect braking energy through inverter feedback devices in the traction substations of each line; energy storage modules use lithium iron phosphate battery packs with a rated capacity of 5MWh and a rated charge / discharge power of 3MW, connected to the hub's unified DC bus via bidirectional DC / DC converters; AC traction substation modules and AC power distribution substation modules are connected to the DC bus via AC / DC converters, and DC traction step-down hybrid substation modules are directly connected to the DC bus; external incoming power modules are taken from a 110kV / 10kV regional substation.

[0039] The energy management module operates according to the dual-time-scale hierarchical coordination architecture described in claim 11: day-ahead scheduling layer with rolling optimization window. Set a time interval of 30 minutes, with 48 windows per day, and a scheduling time step size. Take 5 minutes, electricity price The electricity price is based on peak-valley time-of-use pricing; the real-time correction layer control cycle is set to 5 seconds, and the PI deviation corrector is tuned using the attenuation curve method. , Integral window Take 10 minutes.

[0040] The dynamic adaptive coefficient is set as follows: , , , , , , , .

[0041] The dynamic depth factor is set as follows: , Energy storage nominal cycle life Next, current equivalent loop count The data acquisition system collected data continuously for 24 hours with a sampling period of 5 seconds, totaling 17,280 sampling points, which were then aggregated into 12 2-hour time periods for statistical summary. The photovoltaic irradiance forecast was obtained by combining numerical weather prediction with historical similar day corrections, while the load forecast was generated based on the operating schedules of each power line. The comparison method was the traditional independent power supply mode, and the specific comparative operating data is shown in Table 1 below.

[0042] Table 1: Comparison of quantitative performance indicators;

[0043] As shown in Table 1 above, the method of the present invention achieves substantial improvements over existing technical solutions in three dimensions: energy utilization efficiency, external grid stability, and healthy operation of energy storage through the synergistic effect of a dual-time-scale hierarchical architecture and dynamic adaptive parameters of energy storage multi-factors.

Claims

1. A multi-energy complementary structure for rail transit hubs, characterized in that, It includes an external power supply module, which is connected to the AC traction substation module, the AC power distribution substation module, and the DC traction step-down hybrid substation module and provides them with power. The photovoltaic power generation module connects the AC traction substation module and the AC power distribution substation module to provide power energy for AC traction loads and AC power distribution loads. The regenerative braking energy absorption module connects the AC traction substation module and the DC traction step-down hybrid substation module, providing the above modules with the recovered train regenerative braking energy; The energy storage module is connected to a 10kV or 35kV AC ring network through a converter. It achieves bidirectional power flow through the AC ring network to AC traction substation modules, AC power distribution substation modules, and DC traction step-down hybrid substation modules. It is used to store and release electrical energy between substations under the control of the energy management module. The energy management module collects the power supply from the external incoming power module via the communication bus. Output power of photovoltaic power generation module The recovery power of the regenerative braking energy absorption module Load power of AC traction substation modules Load power of AC power distribution substation modules Load power of DC traction step-down hybrid substation module It also records the state of charge and current available capacity of the energy storage modules, and issues charging and discharging power commands to the energy storage modules and converter power scheduling commands to each substation module.

2. The control method for a multi-energy complementary structure in a rail transit hub according to claim 1, characterized in that, Includes the following steps: Step A: Real-time acquisition of the output power of the photovoltaic power generation module The recovery power of the regenerative braking energy absorption module AC traction load power demand AC power distribution load demand power DC mixed load power demand Current available capacity of energy storage modules and the power supply of the external incoming power module ; Step B: Calculate the real-time supply-demand difference ,in The charging and discharging power of the energy storage module, during discharge Take a positive value during charging. Take the negative value; Step C: Based on photovoltaic output The relationship between the sum of the power demands of each load and the current supply and demand range is determined. The power output allocation of each energy module is calculated according to the following priority order: photovoltaic power is prioritized for supplying AC traction loads. Secondly, it supplies AC power to the distribution load. Resupply DC mixed load The power supply deficit of each load is first supplemented by the regenerative braking energy absorption module. When the regenerative braking energy is insufficient, it is supplemented by the energy storage module. When the available capacity of the energy storage module is insufficient, it is supplemented by the external incoming power supply module.

3. The control method for a multi-energy complementary structure in a rail transit hub according to claim 2, characterized in that, In step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All of it is supplied to AC traction load; Regenerative braking output distribution Priority should be given to supplementing the AC traction load deficit. If there is any surplus, it will be added to the AC power distribution load in sequence. and DC mixed load ; Energy storage output allocation: when hour, Discharge to replenish the remaining load demand; External power supply output distribution: when hour, ; when At that time, there is excess regenerative braking energy, and the excess amount It is absorbed and stored by the energy storage module.

4. The control method for a multi-energy complementary structure of a rail transit hub according to claim 3, characterized in that, In step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supply AC traction load, Supply AC power to the load; Regenerative braking output distribution Priority should be given to supplementing the AC power distribution load deficit. When there is surplus, supplement the DC mixed load. ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ; when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module.

5. The control method for a multi-energy complementary structure of a rail transit hub according to claim 4, characterized in that, In step C, when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation Supplying AC traction loads and AC distribution loads, Supplying DC mixed loads; Regenerative braking output distribution Used to supplement DC mixed load deficit ; Energy storage output allocation: when hour, ; External power supply output distribution: when hour, ; when At that time, excess regenerative braking energy is absorbed and stored by the energy storage module; when At that time, the output distribution of each module is determined as follows: Photovoltaic power output allocation All three types of loads are supplied by photovoltaic power. Solar surplus output All of it is absorbed and stored by the energy storage module; Regenerative braking energy All energy is absorbed and stored by the energy storage module.

6. The control method for a multi-energy complementary structure in a rail transit hub according to claim 2, characterized in that, The capacity of the energy storage module Determine using the following method: Step S1: Calculate the output power of the energy storage module To meet the energy support constraints under extreme shortage conditions: ； in, Output power to the energy storage module; This is the start time of the energy storage module's discharge. The rated discharge duration of the energy storage module; DOD is the maximum depth of discharge coefficient corresponding to the optimal lifespan of the energy storage battery. , , These represent the peak power requirements of AC traction load, AC distribution load, and DC mixed load within one operating cycle. , These represent the power outputs of the photovoltaic power generation module and the regenerative braking energy absorption module at their lowest points during a single operating cycle. Step S2: Calculate the input power of the energy storage module To meet the energy consumption constraints under extreme excess operating conditions: ； in, Input power to the energy storage module; DOC is the maximum depth of charge coefficient corresponding to the optimal lifespan of the energy storage battery; , , These represent the power demand of AC traction load, AC distribution load, and DC mixed load when they are at their lowest point during an operating cycle. , These are the peak outputs of the photovoltaic power generation module and the regenerative braking energy absorption module, respectively, within one operating cycle. Step S3: Take and The larger value is taken as the required capacity of the energy storage module: ； in, The required capacity for energy storage modules; The depth of discharge coefficient (DOD) and depth of charge coefficient (DOC) are replaced with values ​​that vary with the number of equivalent cycles. Dynamically adjusted value: ； ； in, The maximum depth of discharge coefficient is set to a reference value of 0.85 when the energy storage is initially put into operation. This is the baseline value for the maximum depth of charging coefficient at the initial commissioning of energy storage; This represents the equivalent number of full charge and discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the depth attenuation coefficient.

7. The control method for a multi-energy complementary structure of a rail transit hub according to claim 2, characterized in that, The energy management adopts a dual-time-scale hierarchical coordination architecture, including a day-ahead scheduling layer and a real-time correction layer: The daytime scheduling layer uses To optimize window duration for scrolling, at the start of each window, based on future... Using train timetables, predicted photovoltaic irradiance, and historical load data for a given period, we can calculate the future energy storage module's performance. Planned charging and discharging power values ​​within ; The real-time correction layer performs the following operations in each control cycle: collects real-time power data at the current moment, calculates the output allocation of each module according to the hierarchical priority order described in step C, wherein the actual charging and discharging power of the energy storage module is included. From the planned value of the day With real-time deviation correction The deviation correction is obtained by superposition based on the current supply and demand difference. and the current state of charge of energy storage Sure; In the day-ahead scheduling layer, when solving for the planned charging and discharging power values ​​of the energy storage modules, the objective function is to minimize the daily operating cost of the hub. The expression for the objective function is: ； in, For the daily operating cost of the hub; This represents the total number of daily scheduling periods. for Electricity price for a given time period; for External incoming power supply power during the specified time period; The duration of the scheduling period; For demand-based electricity pricing, This represents the maximum power demand during the day. Cyclic aging cost per unit charge / discharge capacity of energy storage; constraints include: Power balance constraints: ； The superscript pred indicates the predicted value; Energy storage state of charge constraints: ； Energy storage charging and discharging power constraints: ； Hierarchical priority constraints: If and only if .

8. The control method for a multi-energy complementary structure of a rail transit hub according to claim 7, characterized in that, The real-time deviation correction amount Calculate using the following formula: ； in, This is the proportional correction factor. This is the integral correction factor. This is the length of the integration time window; This represents the real-time supply and demand difference at the current moment. The proportional correction factor and integral correction factor Tuning using the attenuation curve method: Under pure proportional control, gradually increase Continue until the supply-demand difference response curve exhibits a 4:1 decaying oscillation, and record the critical proportional coefficient at this point. and oscillation period .

9. The control method for a multi-energy complementary structure in a rail transit hub according to claim 5, characterized in that, Replace the fixed coefficient 0.8 in the criteria for determining whether the energy storage module is in operation with a dynamic adaptive coefficient. That is, the condition for energy storage to meet the remaining load demand is ,in The rated capacity of the energy storage module; Current state of charge Rate of change of state of charge and energy storage equivalent cycle number The function is calculated using the following formula: ； in, The basic threshold coefficient; It is the state of charge adjustment factor; As a factor for the decline in health status; This is a penalty factor for charge / discharge rates; Rated capacity of the energy storage module, in kWh.

10. The control method for a multi-energy complementary structure of a rail transit hub according to claim 9, characterized in that: The state of charge adjustment factor Define segments as follows: ； in, The minimum state of charge allowed for energy storage, This represents the highest permissible state of charge for energy storage. This represents the lower limit of the optimal operating range for energy storage. This represents the upper limit of the optimal operating range for energy storage. The health status decay factor Calculate using the following formula: ； in, This represents the equivalent number of full charge / discharge cycles for the energy storage module. This refers to the nominal cycle life of the energy storage module. This is the aging degradation coefficient; The charge / discharge rate penalty factor Calculate using the following formula: ； in, It is the absolute value of the rate of change of the state of charge; The threshold value for the optimal charge / discharge rate of energy storage is set to [value]. , The rated charge / discharge rate for energy storage; This is the rate penalty coefficient.

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