High-speed rail hybrid power system optical storage collaborative planning method and system

Abstract

The invention relates to an optical storage collaborative planning method and system for a high-speed rail hybrid power system. The method comprises the steps of firstly collecting and analyzing meteorological data along a high-speed rail to obtain a solar radiation rate, then calculating output power of a photovoltaic system by using the solar radiation rate, then constructing an energy storage model, calculating load demand power according to working conditions, and finally establishing a target function by using the output power of the photovoltaic system, the energy storage model and the load demand power, and under constraint conditions, an IGWO-WOA hybrid optimization algorithm is adopted to optimize the energy storage model, so that optimal configuration of energy storage can be obtained, and high-speed rail hybrid power system light storage collaborative planning can be completed. Compared with the prior art, the system has the advantages of reducing carbon emission, reducing the total energy consumption of the train, improving the power generation efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the field of power supply technology, and in particular to a method and system for photovoltaic and storage collaborative planning of a high-speed rail hybrid power system. Background Art

[0002] With the rapid development of photovoltaic technology, photovoltaic power generation has begun to be integrated into the power systems of various mobile vehicles. The introduction of photovoltaics aims to reduce dependence on traditional energy and improve energy efficiency. However, photovoltaic power generation is unstable and easily affected by environmental factors such as weather, resulting in output power fluctuations. This uncertainty poses a challenge to the stable operation of the system.

[0003] The invention patent with publication number CN117335497A discloses a photovoltaic power generation grid-connected plus energy storage system. The patent proposes a complete system architecture, including a photovoltaic module array, a photovoltaic controller, a battery pack, a battery management system, an inverter, and a corresponding energy storage power station joint control and dispatching system. The patent describes four working modes of the energy storage device, including grid-connected charging, off-grid charging, off-grid independent discharge, and off-grid auxiliary discharge to adapt to different operating needs. Although the system design takes into account the changes in sunlight intensity, it does not fully consider the impact of other environmental factors such as temperature and weather conditions on system performance. The intelligent controller mentioned in the patent also needs further research and development to improve its adaptability and control accuracy to complex grid conditions. At the same time, although the patent proposes the design of an energy storage system, it cannot achieve high energy utilization under various working conditions, resulting in large carbon emissions. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for photovoltaic and storage collaborative planning of a high-speed rail hybrid power system.

[0005] The purpose of the present invention can be achieved by the following technical solutions: According to one aspect of the present invention, a method for photovoltaic storage coordinated planning of a high-speed railway hybrid power system is provided, the method comprising the following steps: S1. Collect and analyze meteorological data along the high-speed railway to obtain solar radiation rate; S2. Establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate; S3, building an energy storage model; S4. Calculate the load power requirement of the train according to different working conditions of the train; S5. Use the output power of the photovoltaic system, the energy storage model and the load demand power of the train to establish the objective function, and use the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model under the set constraints to obtain the optimal configuration of energy storage. Combine the photovoltaic system and the optimized energy storage model to provide photovoltaic and energy storage synergistic power supply for the train.

[0006] As a preferred technical solution, collecting and analyzing meteorological data along the high-speed rail line in S1 specifically refers to: first, selecting a high-speed rail line that meets the preset span, then using a clustering algorithm to select meteorological data for one day in each of the four seasons on the route, and finally integrating the selected meteorological data to obtain solar radiation rate information.

[0007] As a preferred technical solution, the photovoltaic system in S2 is a photovoltaic array horizontally laid on the top of the train. The specific formula for calculating the output power of the photovoltaic system using solar radiation rate is as follows: P PV(t) =η PV ×A PV ×I(t) Where P PV(t) is the output power of the photovoltaic system; η PV is the instantaneous power generation efficiency of the photovoltaic generator; A PV is the total area of ​​PV panels used in the PV power generation system; I(t) is the total solar radiation.

[0008] As a preferred technical solution, the energy storage model in S3 includes: energy storage device capacity, energy storage device power and energy storage device charge state; among them, the energy storage device capacity optimization interval is set to 0~1500 kWh, the energy storage device power optimization interval is set to 0~1500 kW, and the specific formula of the energy storage device charge state is as follows: SOC(t)=E(t) / E b Where SOC(t) is the state of charge of the energy storage device at time t; E(t) is the energy of the energy storage device at time t; E b is the total energy storage capacity.

[0009] As a preferred technical solution, the operating conditions in S4 include traction, cruising, coasting and braking. According to different operating conditions, the changes in the high-speed rail train load are analyzed from a kinematic perspective to calculate the load demand power of the train accordingly.

[0010] As a preferred technical solution, the objective function in S5 is constructed based on carbon emissions, and its specific formula is as follows: min Carbon=E Grid ÷Factor Carbon E grid =P grid(t)×t P Grid (t) = P Load (t) –P PV (t)–P ch (t)–P dis (t) In the formula, Carbon is carbon emissions; E Grid is the power consumption of the power grid; Factor Carbon is the carbon emission factor; P Grid (t) is the output power of the power grid at time t; P Load (t) is the load power demand of the train at time t; P PV (t) is the output power of the photovoltaic device at time t; P ch (t) is the charging power of the energy storage device at time t; P dis (t) is the discharge power of the energy storage device at time t.

[0011] As a preferred technical solution, the constraint conditions in S5 include constraints C1 to C4; wherein C1 and C2 are restrictions on the capacity and power of the energy storage device, C3 is a restriction on the energy of the energy storage device, and C4 is a restriction on the state of charge SOC of the energy storage device; the specific formula is as follows: C1:0≤|P ch(t) |≤Pb≤P max C2:0≤|P dis(t) |≤Pb≤P max C3:0<0.2Eb≤E(t)≤Eb≤E max C4:0.2≤SOC(t)≤0.8 Where P ch(t) is the charging power of the energy storage device at time t; P dis(t) is the discharge power of the energy storage device at time t; Pb is the unknown energy storage capacity; P max is the upper limit of the energy storage device capacity, which is 1500KW; Eb is the unknown energy storage energy; E(t) represents the energy of the energy storage device at time t; E max The energy limit of the energy storage device is 1500KWh.

[0012] As a preferred technical solution, S5 adopts the IGWO-WOA hybrid optimization algorithm. In the process of optimizing the energy storage model, a hybrid mutation strategy is applied, and the potential configuration of energy storage is used as the individual position. Several individuals form a population. The specific optimization process is as follows: S51, initialize the energy storage model, population size, maximum number of iterations, ratio of WOA to GWO, convergence factor and search radius of the WOA spiral; S52, initializing the positions of individuals in the population and calculating the alpha parameter to iteratively update the ratio of WOA to GWO, the convergence factor and the search radius of the WOA spiral; S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals according to the reference individuals and the objective function, calculate the new position of the individuals, and then adjust and determine the new position of the best individuals by combining the ratio of WOA and GWO; S54, determine whether the maximum number of iterations has been reached, if so, stop and return to the optimal individual new position as the optimal configuration of energy storage; if not, return to step S53.

[0013] As a preferred technical solution, the hybrid mutation strategy includes Gaussian mutation, uniform mutation, and random reset mutation; in step S51, Gaussian mutation is introduced to increase population diversity; in step S52, uniform mutation is introduced to balance exploration and development; in step S53, random reset mutation is introduced to prevent the result from falling into local optimality.

[0014] According to one aspect of the present invention, a high-speed rail hybrid power system photovoltaic storage collaborative planning system is provided, the system applies the above-mentioned high-speed rail hybrid power system photovoltaic storage collaborative planning method to work, the system includes a data collection module, a model construction module and a model optimization module; The data collection module collects and analyzes meteorological data along the high-speed railway to obtain the solar radiation rate; The model building module is used to establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate. It is also used to build an energy storage model and calculate the load demand power of the train according to different working conditions of the train. The model optimization module uses the output power of the photovoltaic system, the energy storage model and the load demand power of the train to establish the objective function, and uses the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model under the set constraints to obtain the optimal configuration of energy storage. The photovoltaic system and the optimized energy storage model are combined to provide photovoltaic and energy storage synergistic power supply for the train.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The output power of the photovoltaic system, the energy storage model and the load demand power are used to establish the objective function in the present invention, and the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model under the constraint condition to obtain the optimal configuration of energy storage and complete the photovoltaic storage coordinated planning of the high-speed rail hybrid power system. The present invention greatly reduces the dependence on traditional power grid electricity through the combination of photovoltaic power generation and energy storage system, thereby reducing the carbon emissions of the entire system. Compared with the prior art, the system's carbon emissions are significantly reduced, ensuring the low-carbon and efficient operation of the train.

[0016] 2. The photovoltaic system established in the present invention is a photovoltaic array on the top of the train, which is laid horizontally; and the output power of the photovoltaic system is calculated using the solar radiation rate, which is the product of the instantaneous power generation efficiency of the photovoltaic generator, the total area of ​​the photovoltaic modules used in the photovoltaic power generation system, and the total solar radiation rate. Unlike the traditional fixed photovoltaic array design, the present invention maximizes the efficiency of photovoltaic power generation based on the high-speed movement characteristics of the train. Thereby, the output stability of photovoltaic power generation is greatly improved, especially when the train is running at high speed, by reducing the fluctuation of power generation caused by changes in light angle, a higher energy utilization rate is achieved. The power generation efficiency is increased by an average of 15%-20%, which not only reduces the system's dependence on the power grid, but also reduces the total energy consumption of the train.

[0017] 3. The improved Grey Wolf Algorithm-Whale Algorithm (IGWO-WOA) hybrid optimization algorithm is used, combined with the advantages of lithium iron phosphate battery energy storage technology, to optimize the capacity and working power of the energy storage device. This algorithm not only takes into account the uncertainty of photovoltaic power generation, but also effectively balances the needs of energy management and system costs, ensuring the stable operation of the system under complex weather conditions. Compared with the traditional single optimization method, this hybrid optimization algorithm performs better in improving overall efficiency and reducing system operating costs.

[0018] 4. The load demand power is calculated according to the working conditions in the present invention, and the working conditions considered include traction, cruising, coasting and braking. Modeling is performed through load changes under working conditions, ensuring low-carbon and high-efficiency operation under various operating conditions.

[0019] 5. In the present invention, multiple constraints C1~C4 are used. C1 and C2 are the capacity and power limits of the energy storage device. The limits of energy storage power and capacity are taken into consideration, making the system closer to the actual situation. C3 is the limit on the energy of the energy storage device, and C4 is the limit on the state of charge SOC of the energy storage device. This ensures that the energy storage maintains a healthy charge and discharge state, prolongs its service life, and ensures that the SOC of the energy storage device is between 0.2-0.8. Extreme charge and discharge states can be avoided, making the method more practical and closer to the actual situation.

[0020] 6. In the process of optimizing the energy storage model of the present invention, a hybrid mutation strategy is applied, which includes Gaussian mutation, uniform mutation, and random reset mutation; in step S51, the introduction of Gaussian mutation can increase population diversity; in step S52, the introduction of uniform mutation can balance exploration and development; in step S53, the introduction of random reset mutation can prevent the result from falling into the local optimum. By applying the hybrid mutation strategy, the accuracy and reliability of the optimal solution can be improved, and it can be prevented from falling into the local optimum, and the optimal energy storage configuration that has been fully considered can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1A schematic diagram of the steps of a method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems in the present invention; Figure 2 A schematic diagram of the system structure of a high-speed rail hybrid power system in an embodiment; Figure 3 This is a flow chart of the photovoltaic storage coordinated planning of the high-speed rail hybrid power system in the embodiment; Figure 4 The figure is a flow chart of optimizing the energy storage model by using the IGWO-WOA hybrid optimization algorithm in the embodiment. DETAILED DESCRIPTION

[0022] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0023] With the rapid development of photovoltaic technology, photovoltaic power generation has begun to be integrated into the power systems of various mobile vehicles. The introduction of photovoltaics aims to reduce dependence on traditional energy and improve energy efficiency. However, photovoltaic power generation is unstable and easily affected by environmental factors such as weather, resulting in output power fluctuations. This uncertainty poses a challenge to the stable operation of the system. In the power system, energy storage technology is widely regarded as the key to solving energy management problems. Energy storage systems can smooth the intermittent output of photovoltaic power generation, reduce instantaneous demand fluctuations on the power grid, and thus improve the stability and reliability of the entire system. At present, hybrid energy storage systems combining supercapacitors and batteries have been proposed and applied to the optimization of high-speed rail power systems. These systems achieve efficient storage and release of energy by combining different types of energy storage devices, thereby improving energy management efficiency.

[0024] Example 1 In this embodiment, a method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems is applied. Figure 1 As shown, the following steps are included: S1. Collect and analyze meteorological data along the high-speed railway to obtain solar radiation rate; S2. Establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate; S3, building an energy storage model; S4. Calculate the load power requirement according to the working conditions; S5. Use the output power of the photovoltaic system, the energy storage model and the load demand power to establish the objective function, and under the constraints, use the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model to obtain the optimal configuration of energy storage and complete the photovoltaic storage coordinated planning of the high-speed rail hybrid power system.

[0025] In this embodiment, the specific implementation is as follows: In this embodiment, a high-speed rail hybrid power system is first built. The system is a control system that integrates a DC / DC converter and a DC / AC converter. The system is designed to provide a stable power supply for different types of loads. Nowadays, most high-speed rail power systems do not include photovoltaic equipment and large-capacity energy storage batteries. In this solution, renewable energy is used through photovoltaic equipment and energy storage batteries, which reduces the use of grid energy and improves environmental and economic benefits.

[0026] In this embodiment, the structure of the high-speed rail hybrid power system is as follows: Figure 2 As shown, it includes: a control system, a DC-DC converter (DC / DC), a DC-AC converter (DC / AC), a transformer, a DC load (DC LOAD) and an AC load (AC LOAD).

[0027] Among them, the control system is located at the center of the entire system and is responsible for monitoring and regulating the entire power conversion process. It receives feedback signals from the load and adjusts the working state of the DC / DC and DC / AC converters according to these signals to ensure the stability of the output voltage and current. The DC-AC converter is responsible for converting the DC power supply to a voltage level suitable for the train system. These converters are essential to maintain the stable operation of the train electrical system. The DC-AC converter converts the DC power supply to AC power for use by the AC load. In the high-speed rail system, this conversion is necessary to drive AC motors and other AC equipment. The transformer is used to increase or decrease the voltage to a level suitable for a specific load, while providing electrical isolation and increasing the safety of the system. DC loads are devices on the train that are powered by DC power, including control systems, lighting systems, infotainment systems, etc. AC loads are devices on the train that are powered by AC power, including traction motors, air conditioners, etc.

[0028] In this embodiment, after the high-speed rail hybrid power system is built, the photovoltaic storage coordinated planning of the high-speed rail hybrid power system is carried out, and the process is as follows: Figure 3 As shown, firstly, the meteorological data along the high-speed railway are collected and analyzed to obtain the solar radiation rate.

[0029] In this embodiment, a high-speed rail line with complex weather conditions and a preset span is selected; Solcast collects meteorological data of each station along the high-speed rail line at the time when the train passes within two years; a clustering algorithm is then used to select the date with the most representative meteorological characteristics in each season, and finally the selected meteorological data is integrated to obtain the corresponding solar radiation rate information. In this embodiment, the preset span is 2000km.

[0030] Then establish a photovoltaic system and use the solar radiation rate to calculate the output power of the photovoltaic system.

[0031] In this embodiment, the Fuxing CR400BF high-speed train was selected, and the arrangement of the power car and the trailer car and the total number of cars were determined first. The total area of ​​the photovoltaic array on the top of the train is 2,000 square meters, and the photovoltaic array is laid horizontally.

[0032] Then establish a photovoltaic system, and the specific formula for calculating the output power of the photovoltaic system using solar radiation rate is as follows: P PV(t) =η PV ×A PV ×I(t) Where P PV(t) is the output power of the photovoltaic system; η PV is the instantaneous power generation efficiency of the photovoltaic generator; A PV is the total area of ​​PV panels used in the PV power generation system; I(t) is the total solar radiation.

[0033] Then, build the energy storage model.

[0034] In this embodiment, a lithium iron phosphate battery is used as an energy storage device, and the constructed energy storage model includes: energy storage device capacity, energy storage device power and energy storage device state of charge; wherein, the energy storage device capacity optimization interval is set to 0~1500kWh, the energy storage device power optimization interval is set to 0~1500 kW, and the specific formula of the energy storage device state of charge is as follows: SOC(t)=E(t) / E b Where SOC(t) is the state of charge of the energy storage device at time t; E(t) is the energy of the energy storage device at time t; E b is the total energy storage capacity.

[0035] Next, calculate the load power requirement according to different working conditions.

[0036] In this embodiment, the high-speed train operating conditions are divided into four types: traction, cruising, coasting, and braking. The changes in the high-speed train load are analyzed from a kinematic perspective according to different operating conditions, and the average power load of the four operating conditions is determined, which are: 10400kw, 6100kw, 0kw, and 0kw. The power generated by renewable energy under braking conditions is calculated.

[0037] Finally, the objective function is established using the output power of the photovoltaic system, the energy storage model and the load demand power. Under the constraints, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model to obtain the optimal configuration of energy storage and complete the photovoltaic-storage coordinated planning of the high-speed rail hybrid power system.

[0038] In this embodiment, the objective function is constructed based on carbon emissions, and its specific formula is as follows: min Carbon=E Grid ÷Factor Carbon E grid =P grid (t)×t P Grid (t) = P Load (t) –P PV (t)–P ch (t)–P dis (t) In the formula, Carbon is carbon emissions; E Grid is the power consumption of the power grid; Factor Carbon is the carbon emission factor; P Grid (t) is the output power of the power grid at time t; P Load (t) is the load power demand of the train at time t; P PV (t) is the output power of the photovoltaic device at time t; P ch (t) is the charging power of the energy storage device at time t; P dis (t) is the discharge power of the energy storage device at time t.

[0039] In this embodiment, the constraints include constraints C1~C4; among them, C1 and C2 are restrictions on the capacity and power of the energy storage device. Considering the restrictions on the energy storage power and capacity, the system is closer to the actual situation. C3 is a restriction on the energy of the energy storage device, and C4 is a restriction on the state of charge SOC of the energy storage device, which ensures that the energy storage maintains a healthy charging and discharging state, prolongs its service life, ensures that the SOC of the energy storage device is between 0.2-0.8, and avoids extreme charging and discharging states; the specific formula is as follows:

[0040] C1:0≤|P ch(t) |≤Pb≤P max C2: 0≤|P dis(t) |≤Pb≤P max C3:0<0.2Eb≤E(t)≤Eb≤E max C4:0.2≤SOC(t)≤0.8 Where P ch(t) represents the charging power of the energy storage device at time t; P dis(t) represents the discharge power of the energy storage device at time t; Pb is the unknown energy storage capacity, the unit is KW; P max is the upper limit of the energy storage device capacity, which is 1500KW; Eb is the unknown energy storage energy, in KWh; E(t) represents the energy of the energy storage device at time t; E maxThe energy limit of the energy storage device is 1500KWh.

[0041] In this embodiment, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model, and a hybrid mutation strategy is applied during the optimization process. The optimization process is as follows: S51, initialize the energy storage model, population size, maximum number of iterations, ratio of WOA to GWO, convergence factor and search radius of the WOA spiral; S52, initializing the positions of individuals in the population and calculating the alpha parameter to iteratively update the ratio of WOA to GWO, the convergence factor and the search radius of the WOA spiral; S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals according to the reference individuals and the objective function, calculate the new position of the individuals, and then adjust and determine the new position of the best individuals by combining the ratio of WOA and GWO; S54, determine whether the maximum number of iterations has been reached, if so, stop and return the optimal individual new position as the final solution; if not, return to step S53.

[0042] Specific optimization process such as Figure 4 As shown, at the beginning of the algorithm, the algorithm population parameters are first initialized, including the population size, the maximum number of iterations, and the parameters a and bb.

[0043] In this embodiment, the population size represents the number of potential solutions of the model, which is set to 50 in this solution. The maximum number of iterations is set to 1000, and a sufficient number of iterations can fully explore the optimal solution. Parameter a represents the convergence factor, and its value decreases from 2 to 0. Parameter bb represents the radius of the WOA spiral search, and its value decreases from 1 to 0. In the initialization stage, the output power of the high-speed rail power grid, the working power of the energy storage device, and the capacity of the energy storage device must also be set.

[0044] Secondly, in each round of iteration, the alpha parameter is first calculated. This parameter is nonlinearly adjusted using a logarithmic function according to the current number of iterations. As the iteration alpha parameter decreases, the parameters a and bb change at different speeds during the optimization process. The change of the alpha parameter affects the update speed of the nonlinear adaptive parameters a and bb, as well as the ratio r of WOA to GWO.

[0045] After iteration, the top three best individuals are selected from the current population, and these individuals will be used as reference points for subsequent updates. Perform WOA spiral update on each individual, calculate the distance from the individual to the best individual, and calculate the adjustment term da of the individual's new position based on this, which represents the spiral update term based on WOA. For each individual in the population, the new position of the individual is calculated based on the top three best individuals, and the new position of the individual is determined based on the ratio r combined with GWO and WOA adjustments. At the same time, the method is called to ensure that the new position of the individual is within its search space.

[0046] In the optimization process, a hybrid mutation strategy is applied, which includes Gaussian mutation, uniform mutation, and random reset mutation. In the early stage, such as step S51, Gaussian mutation is introduced by introducing Gaussian noise to increase population diversity. In the mid-term stage, such as step S52, uniform mutation is introduced to balance exploration and development. In the late stage, such as step S53, random reset mutation is introduced by performing random reset to increase population diversity and prevent falling into local optimality.

[0047] Finally, determine whether the maximum number of iterations has been reached. If so, the algorithm stops and returns the optimal new position of the individual as the final solution; if not, restart from the step of initializing the position of the individual in the population.

[0048] The final output is the optimal energy storage configuration, that is, the energy storage capacity and energy storage power. In this embodiment, the output energy storage capacity is 295.7kwh and the energy storage power is 998.5kw. This result is the optimal energy storage configuration of this solution.

[0049] In this embodiment, in order to demonstrate the beneficial effects of this solution, the optimal energy storage configuration calculated by the optimization algorithm, i.e., the energy storage capacity of 295.7 kwh and the power data of 998.5 kw, are substituted into the simulation model together with the existing method, and the carbon emission factor is used to calculate the total carbon emissions of the system from one to ten years; and the carbon emissions of the two methods are compared to analyze the environmental benefits of the method proposed in this application.

[0050] In this embodiment, taking a ten-year cycle as an example, the total carbon emissions of the existing method in ten years are 20203560.3Kg; while the total carbon emissions of the present technical solution in ten years are 17643468.9Kg; the present solution reduces carbon emissions by 2560091.4Kg compared with the existing method. In addition, the present solution also achieves a higher energy utilization rate, and its power generation efficiency is increased by an average of 15%-20%.

[0051] In summary, this solution can maximize the efficiency of photovoltaic power generation, reduce the total energy consumption of the train, and thus reduce the carbon emissions of the entire system. Compared with existing technologies, the system's carbon emissions are significantly reduced, and by modeling the load changes under different working conditions, low-carbon and efficient operation is guaranteed under various operating conditions. Data shows that compared with traditional systems, carbon emissions have been reduced by about 12.7% in ten years.

[0052] Example 2 In this embodiment, a high-speed rail hybrid power system photovoltaic storage collaborative planning system is applied, which includes a data collection module, a model construction module and a model optimization module; Among them, the data collection module collects and analyzes meteorological data along the high-speed rail to obtain the solar radiation rate; the model construction module is used to establish a photovoltaic system, use the solar radiation rate to calculate the output power of the photovoltaic system, and is used to build an energy storage model and calculate the load demand power under different working conditions; the model optimization module uses the output power of the photovoltaic system, the energy storage model and the load demand power to establish the objective function, and under the constraints, uses the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model to obtain the optimal configuration of energy storage and complete the photovoltaic storage coordinated planning of the high-speed rail hybrid power system.

[0053] In this embodiment, the specific implementation of applying the system is as follows: In this embodiment, a high-speed rail hybrid power system is first built. The system is a control system that integrates a DC / DC converter and a DC / AC converter. The system is designed to provide a stable power supply for different types of loads. Nowadays, most high-speed rail power systems do not include photovoltaic equipment and large-capacity energy storage batteries. In this solution, renewable energy is used through photovoltaic equipment and energy storage batteries, which reduces the use of grid energy and improves environmental and economic benefits.

[0054] In this embodiment, the structure of the high-speed rail hybrid power system is as follows: Figure 2 As shown, it includes: a control system, a DC-DC converter (DC / DC), a DC-AC converter (DC / AC), a transformer, a DC load (DC LOAD) and an AC load (AC LOAD).

[0055] Among them, the control system is located at the center of the entire system and is responsible for monitoring and regulating the entire power conversion process. It receives feedback signals from the load and adjusts the working state of the DC / DC and DC / AC converters according to these signals to ensure the stability of the output voltage and current. The DC-AC converter is responsible for converting the DC power supply to a voltage level suitable for the train system. These converters are essential to maintain the stable operation of the train electrical system. The DC-AC converter converts the DC power supply to AC power for use by the AC load. In the high-speed rail system, this conversion is necessary to drive AC motors and other AC equipment. The transformer is used to increase or decrease the voltage to a level suitable for a specific load, while providing electrical isolation and increasing the safety of the system. DC loads are devices on the train that are powered by DC power, including control systems, lighting systems, infotainment systems, etc. AC loads are devices on the train that are powered by AC power, including traction motors, air conditioners, etc.

[0056] In this embodiment, after the high-speed rail hybrid power system is built, the high-speed rail hybrid power system photovoltaic storage collaborative planning system is used to perform the high-speed rail hybrid power system photovoltaic storage collaborative planning. The process is as follows: Figure 3 As shown, firstly, the meteorological data along the high-speed railway are collected and analyzed to obtain the solar radiation rate.

[0057] In this embodiment, a high-speed rail line with complex weather conditions and a preset span is selected; Solcast collects meteorological data of each station along the high-speed rail line at the time when the train passes within two years; a clustering algorithm is then used to select the date with the most representative meteorological characteristics in each season, and finally the selected meteorological data is integrated to obtain the corresponding solar radiation rate information. In this embodiment, the preset span is 2000km.

[0058] Then establish a photovoltaic system and use the solar radiation rate to calculate the output power of the photovoltaic system.

[0059] In this embodiment, the Fuxing CR400BF high-speed train was selected, and the arrangement of the power car and the trailer car and the total number of cars were determined first. The total area of ​​the photovoltaic array on the top of the train is 2,000 square meters, and the photovoltaic array is laid horizontally.

[0060] Then establish a photovoltaic system, and the specific formula for calculating the output power of the photovoltaic system using solar radiation rate is as follows: P PV(t) =η PV ×A PV ×I(t) Where P PV(t) is the output power of the photovoltaic system; η PV is the instantaneous power generation efficiency of the photovoltaic generator; A PVis the total area of ​​PV panels used in the PV power generation system; I(t) is the total solar radiation.

[0061] Then, build the energy storage model.

[0062] In this embodiment, a lithium iron phosphate battery is used as an energy storage device, and the constructed energy storage model includes: energy storage device capacity, energy storage device power and energy storage device state of charge; wherein, the energy storage device capacity optimization interval is set to 0~1500kWh, the energy storage device power optimization interval is set to 0~1500 kW, and the specific formula of the energy storage device state of charge is as follows: SOC(t)=E(t) / E b Where SOC(t) is the state of charge of the energy storage device at time t; E(t) is the energy of the energy storage device at time t; E b is the total energy storage capacity.

[0063] Next, calculate the load power requirement according to different working conditions.

[0064] In this embodiment, the high-speed train operating conditions are divided into four types: traction, cruising, coasting, and braking. The changes in the high-speed train load are analyzed from a kinematic perspective according to different operating conditions, and the average power load of the four operating conditions is determined, which are: 10400kw, 6100kw, 0kw, and 0kw. The power generated by renewable energy under braking conditions is calculated.

[0065] Finally, the objective function is established using the output power of the photovoltaic system, the energy storage model and the load demand power. Under the constraints, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model to obtain the optimal configuration of energy storage and complete the photovoltaic-storage coordinated planning of the high-speed rail hybrid power system.

[0066] In this embodiment, the objective function is constructed based on carbon emissions, and its specific formula is as follows: min Carbon=E Grid ÷Factor Carbon E grid =P grid (t)×t P Grid (t) = P Load (t) –P PV (t)–P ch (t)–P dis (t) In the formula, Carbon is carbon emissions; E Grid is the power consumption of the power grid; Factor Carbon is the carbon emission factor; P Grid(t) is the output power of the power grid at time t; P Load (t) is the load power demand of the train at time t; P PV (t) is the output power of the photovoltaic device at time t; P ch (t) is the charging power of the energy storage device at time t; P dis (t) is the discharge power of the energy storage device at time t.

[0067] In this embodiment, the constraints include constraints C1~C4; among them, C1 and C2 are restrictions on the capacity and power of the energy storage device. Considering the restrictions on the energy storage power and capacity, the system is closer to the actual situation. C3 is a restriction on the energy of the energy storage device, and C4 is a restriction on the state of charge SOC of the energy storage device, which ensures that the energy storage maintains a healthy charging and discharging state, prolongs its service life, ensures that the SOC of the energy storage device is between 0.2-0.8, and avoids extreme charging and discharging states; the specific formula is as follows:

[0068] C1:0≤|P ch(t) |≤Pb≤P max C2:0≤|P dis(t) |≤Pb≤P max C3:0<0.2Eb≤E(t)≤Eb≤E max C4:0.2≤SOC(t)≤0.8 Where P ch(t) represents the charging power of the energy storage device at time t; P dis(t) represents the discharge power of the energy storage device at time t; Pb is the unknown energy storage capacity, the unit is KW; P max is the upper limit of the energy storage device capacity, which is 1500KW; Eb is the unknown energy storage energy, in KWh; E(t) represents the energy of the energy storage device at time t; E max The energy limit of the energy storage device is 1500KWh.

[0069] In this embodiment, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model, and a hybrid mutation strategy is applied during the optimization process. The optimization process is as follows: S51, initialize the energy storage model, population size, maximum number of iterations, ratio of WOA to GWO, convergence factor and search radius of the WOA spiral; S52, initializing the positions of individuals in the population and calculating the alpha parameter to iteratively update the ratio of WOA to GWO, the convergence factor and the search radius of the WOA spiral; S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals according to the reference individuals and the objective function, calculate the new position of the individuals, and then adjust and determine the new position of the best individuals by combining the ratio of WOA and GWO; S54, determine whether the maximum number of iterations has been reached, if so, stop and return the optimal individual new position as the final solution; if not, return to step S53.

[0070] Specific optimization process such as Figure 4 As shown, at the beginning of the algorithm, the algorithm population parameters are first initialized, including the population size, the maximum number of iterations, and the parameters a and bb.

[0071] In this embodiment, the population size represents the number of potential solutions of the model, which is set to 50 in this solution. The maximum number of iterations is set to 1000, and a sufficient number of iterations can fully explore the optimal solution. Parameter a represents the convergence factor, and its value decreases from 2 to 0. Parameter bb represents the radius of the WOA spiral search, and its value decreases from 1 to 0. In the initialization stage, the output power of the high-speed rail power grid, the working power of the energy storage device, and the capacity of the energy storage device must also be set.

[0072] Secondly, in each round of iteration, the alpha parameter is first calculated. This parameter is nonlinearly adjusted using a logarithmic function according to the current number of iterations. As the iteration alpha parameter decreases, the parameters a and bb change at different speeds during the optimization process. The change of the alpha parameter affects the update speed of the nonlinear adaptive parameters a and bb, as well as the ratio r of WOA to GWO.

[0073] After iteration, the top three best individuals are selected from the current population, and these individuals will be used as reference points for subsequent updates. Perform WOA spiral update on each individual, calculate the distance from the individual to the best individual, and calculate the adjustment term da of the individual's new position based on this, which represents the spiral update term based on WOA. For each individual in the population, the new position of the individual is calculated based on the top three best individuals, and the new position of the individual is determined based on the ratio r combined with GWO and WOA adjustments. At the same time, the method is called to ensure that the new position of the individual is within its search space.

[0074] In the optimization process, a hybrid mutation strategy is applied, which includes Gaussian mutation, uniform mutation, and random reset mutation. In the early stage, such as step S51, Gaussian mutation is introduced by introducing Gaussian noise to increase population diversity. In the mid-term stage, such as step S52, uniform mutation is introduced to balance exploration and development. In the late stage, such as step S53, random reset mutation is introduced by performing random reset to increase population diversity and prevent falling into local optimality.

[0075] Finally, determine whether the maximum number of iterations has been reached. If so, the algorithm stops and returns the optimal new position of the individual as the final solution; if not, restart from the step of initializing the position of the individual in the population.

[0076] The final output is the optimal energy storage configuration, that is, the energy storage capacity and energy storage power. In this embodiment, the output energy storage capacity is 295.7kwh and the energy storage power is 998.5kw. This result is the optimal energy storage configuration of this solution.

[0077] In this embodiment, in order to demonstrate the beneficial effects of this solution, the optimal energy storage configuration calculated by the optimization algorithm, i.e., the energy storage capacity of 295.7 kwh and the power data of 998.5 kw, are substituted into the simulation model together with the existing method, and the carbon emission factor is used to calculate the total carbon emissions of the system from one to ten years; and the carbon emissions of the two methods are compared to analyze the environmental benefits of the method proposed in this application.

[0078] In this embodiment, taking a ten-year cycle as an example, the total carbon emissions of the existing method in ten years are 20203560.3Kg; while the total carbon emissions of the present technical solution in ten years are 17643468.9Kg; the present solution reduces carbon emissions by 2560091.4Kg compared with the existing method. In addition, the present solution also achieves a higher energy utilization rate, and its power generation efficiency is increased by an average of 15%-20%.

[0079] In summary, this solution can maximize the efficiency of photovoltaic power generation, reduce the total energy consumption of the train, and thus reduce the carbon emissions of the entire system. Compared with existing technologies, the system's carbon emissions are significantly reduced, and by modeling the load changes under different working conditions, low-carbon and efficient operation is guaranteed under various operating conditions. Data shows that compared with traditional systems, carbon emissions have been reduced by about 12.7% in ten years.

Claims

1. A method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems, characterized in that: The method comprises the following steps: S1. Collect and analyze meteorological data along the high-speed railway to obtain solar radiation rate; S2. Establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate; S3, building an energy storage model; S4. Calculate the load power requirement of the train according to different working conditions of the train; S5. Use the output power of the photovoltaic system, the energy storage model and the load demand power of the train to establish the objective function, and use the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model under the set constraints to obtain the optimal configuration of energy storage. Combine the photovoltaic system and the optimized energy storage model to provide photovoltaic and energy storage synergistic power supply for the train.

2. According to claim 1, a method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems is characterized in that: The collection and analysis of meteorological data along the high-speed rail line in S1 specifically refers to: first, a high-speed rail line that meets a preset span is selected, then a clustering algorithm is used to select meteorological data of one day in each of the four seasons on the route, and finally the selected meteorological data is integrated to obtain solar radiation rate information.

3. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: The photovoltaic system in S2 is a photovoltaic array horizontally laid on the top of the train. The specific formula for calculating the output power of the photovoltaic system using solar radiation rate is as follows: P PV(t) =h PV ×A PV ×I(t) Where P PV(t) is the output power of the photovoltaic system; η PV is the instantaneous power generation efficiency of the photovoltaic generator; A PV is the total area of ​​PV panels used in the PV power generation system; I(t) is the total solar radiation.

4. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: The energy storage model in S3 includes: energy storage device capacity, energy storage device power and energy storage device charge state; wherein, the energy storage device capacity optimization interval is set to 0~1500 kWh, the energy storage device power optimization interval is set to 0~1500 kW, and the specific formula of the energy storage device charge state is as follows: SOC(t)=E(t) / E b Where SOC(t) is the state of charge of the energy storage device at time t; E(t) is the energy of the energy storage device at time t; E b is the total energy storage capacity.

5. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: The operating conditions in S4 include traction, cruising, coasting and braking. According to different operating conditions, the change of the high-speed train load is analyzed from a kinematic perspective to calculate the load demand power of the train accordingly.

6. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: The objective function in S5 is constructed based on carbon emissions, and its specific formula is as follows: my Carbon=E Grid ÷Factor Carbon E grid = P grid (t)×t P Grid (t)= P Load (t)–P PV (t)–P ch (t)–P dis (t) In the formula, Carbon is carbon emissions; E Grid is the power consumption of the power grid; Factor Carbon is the carbon emission factor; P Grid (t) is the output power of the power grid at time t; P Load (t) is the load power demand of the train at time t; P PV (t) is the output power of the photovoltaic device at time t; P ch (t) is the charging power of the energy storage device at time t; P dis (t) is the discharge power of the energy storage device at time t.

7. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: The constraints in S5 include constraints C1 to C4; wherein C1 and C2 are restrictions on the capacity and power of the energy storage device, C3 is a restriction on the energy of the energy storage device, and C4 is a restriction on the state of charge SOC of the energy storage device; the specific formula is as follows: C1:0≤|P ch(t) |≤Pb≤P max C2:0≤|P dis(t) |≤Pb≤P max C3:0<0.2Eb≤E(t)≤Eb≤E max C4:0.2≤SOC(t)≤0.8 Where P ch(t) is the charging power of the energy storage device at time t; P dis(t) is the discharge power of the energy storage device at time t; Pb is the unknown energy storage capacity; P max is the upper limit of the energy storage device capacity, which is 1500KW; Eb is the unknown energy storage energy; E(t) represents the energy of the energy storage device at time t; E max The energy limit of the energy storage device is 1500KWh.

8. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1 is characterized in that: In S5, the IGWO-WOA hybrid optimization algorithm is used. In the process of optimizing the energy storage model, a hybrid mutation strategy is applied, and the potential configuration of energy storage is used as the individual position. Several individuals form a population. The specific optimization process is as follows: S51, initialize the energy storage model, population size, maximum number of iterations, ratio of WOA to GWO, convergence factor and search radius of the WOA spiral; S52, initializing the positions of individuals in the population and calculating the alpha parameter to iteratively update the ratio of WOA to GWO, the convergence factor and the search radius of the WOA spiral; S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals according to the reference individuals and the objective function, calculate the new position of the individuals, and then adjust and determine the new position of the best individuals by combining the ratio of WOA and GWO; S54, determine whether the maximum number of iterations has been reached, if so, stop and return to the optimal individual new position as the optimal configuration of energy storage; if not, return to step S53.

9. A method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 8, characterized in that: The hybrid mutation strategy includes Gaussian mutation, uniform mutation, and random reset mutation; in step S51, Gaussian mutation is introduced to increase population diversity; in step S52, uniform mutation is introduced to balance exploration and development; in step S53, random reset mutation is introduced to prevent the result from falling into local optimality.

10. A high-speed rail hybrid power system photovoltaic storage collaborative planning system, characterized in that: The system works by applying a method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems as described in any one of claims 1 to 9, and the system includes a data collection module, a model building module and a model optimization module; The data collection module collects and analyzes meteorological data along the high-speed railway to obtain solar radiation rate; The model building module is used to establish a photovoltaic system, calculate the output power of the photovoltaic system using the solar radiation rate, and is also used to build an energy storage model and calculate the load demand power of the train according to different working conditions of the train; The model optimization module uses the output power of the photovoltaic system, the energy storage model and the load demand power of the train to establish the objective function, and uses the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model under the set constraints to obtain the optimal configuration of energy storage. The photovoltaic system and the optimized energy storage model are combined to provide photovoltaic and energy storage coordinated power supply for the train.

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