A method and system for collaborative planning of photovoltaic and storage systems for high-speed rail hybrid power systems

Abstract

The present invention relates to a method and system for synergistic planning of photovoltaic and energy storage for high-speed rail hybrid power systems. The method first collects and analyzes meteorological data along the high-speed rail line to obtain solar radiation rate, then uses the solar radiation rate to calculate the output power of the photovoltaic system. An energy storage model is then constructed, and the load power demand is calculated based on the operating conditions. Finally, an objective function is established using the output power of the photovoltaic system, the energy storage model, and the load power demand. Under constraints, the energy storage model is optimized using the IGWO-WOA hybrid optimization algorithm to obtain the optimal energy storage configuration and complete the synergistic planning of photovoltaic and energy storage for the high-speed rail hybrid power system. Compared with the existing technology, the present invention has the advantages of reducing carbon emissions, reducing the total energy consumption of trains, and improving power generation efficiency.

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, PV power generation has begun to be integrated into the power systems of various mobile vehicles. The introduction of PV aims to reduce dependence on traditional energy sources and improve energy efficiency. However, PV power generation is unstable and susceptible to environmental factors such as weather, resulting in power output fluctuations. This uncertainty poses challenges 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 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 to complex grid conditions and control accuracy. At the same time, although the patent proposes a design for an energy storage system, it cannot achieve high energy utilization under various working conditions, resulting in still large carbon emissions. Summary of the Invention

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

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a method for synergistic planning of photovoltaic and storage systems for high-speed rail hybrid power systems is provided, the method comprising the following steps:

[0007] S1. Collect and analyze meteorological data along the high-speed rail line to obtain solar radiation rate;

[0008] S2. Establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate;

[0009] S3, building an energy storage model;

[0010] S4. Calculate the train's load power requirements according to different train operating conditions;

[0011] S5. Establish an objective function using the output power of the photovoltaic system, the energy storage model, and the load power demand of the train. Under the set constraints, use the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model to obtain the optimal energy storage configuration. Combined with the photovoltaic system and the optimized energy storage model, provide photovoltaic and energy storage synergistic power supply for the train.

[0012] As a preferred technical solution, the collection and analysis of 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.

[0013] As a preferred technical solution, the photovoltaic system in S2 is a photovoltaic array laid horizontally 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:

[0014] P PV(t) =η PV ×A PV ×I(t)

[0015] 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 rate.

[0016] As a preferred technical solution, the energy storage model in S3 includes: energy storage device capacity, energy storage device power, and energy storage device state of charge. The energy storage device capacity optimization range is set to 0-1500 kWh, the energy storage device power optimization range is set to 0-1500 kW, and the energy storage device state of charge is calculated as follows:

[0017] SOC(t)=E(t) / E b

[0018] 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.

[0019] 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 corresponding load power demand of the train.

[0020] As a preferred technical solution, the objective function in S5 is constructed based on carbon emissions, and its specific formula is as follows:

[0021] min Carbon=EGrid ÷Factor Carbon

[0022] E grid = P grid (t)×t

[0023] P Grid (t) = P Load (t) –P PV (t)–P ch (t)–P dis (t)

[0024] Where, 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 equipment 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.

[0025] As a preferred technical solution, the constraints in S5 include constraints C1 to C4; 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 formulas are as follows:

[0026] C1:0≤|P ch(t) |≤Pb≤P max

[0027] C2:0≤|P dis(t) |≤Pb≤P max

[0028] C3:0<0.2Eb≤E(t)≤Eb≤E max

[0029] C4:0.2≤SOC(t) ≤0.8

[0030] 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 capacity; 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.

[0031] 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. The potential configuration of energy storage is used as the individual position, and several individuals form a population. The specific optimization process is as follows:

[0032] 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;

[0033] 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;

[0034] S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals based on the reference individuals and the objective function, calculate the new positions of the individuals, and then adjust and determine the new positions of the best individuals by combining the ratio of WOA and GWO;

[0035] 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 for energy storage; if not, return to step S53.

[0036] 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.

[0037] According to one aspect of the present invention, a system for synergistic planning of photovoltaic and storage systems for high-speed rail hybrid power systems is provided. The system applies the above-mentioned method for synergistic planning of photovoltaic and storage systems for high-speed rail hybrid power systems. The system includes a data collection module, a model construction module, and a model optimization module.

[0038] The data collection module collects and analyzes meteorological data along the high-speed rail line to obtain the solar radiation rate;

[0039] The model building module is used to establish a photovoltaic system and calculate the output power of the photovoltaic system using solar radiation rate. It is also used to build an energy storage model and calculate the load power demand of the train according to different operating conditions;

[0040] The model optimization module uses the output power of the photovoltaic system, the energy storage model, and the load power demand of the train to establish the objective function. Under the set constraints, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model to obtain the optimal energy storage configuration. Combined with the photovoltaic system and the optimized energy storage model, it provides photovoltaic and energy storage synergistic power supply for the train.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This invention uses the output power of the photovoltaic system, the energy storage model, and the load demand power to establish an objective function. Under constraints, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model to obtain the optimal energy storage configuration and complete the photovoltaic-storage coordinated planning of the high-speed rail hybrid power system. By combining photovoltaic power generation with an energy storage system, this invention significantly reduces dependence on traditional grid power, thereby reducing the carbon emissions of the entire system. Compared with existing technologies, the system's carbon emissions are significantly reduced, ensuring low-carbon and efficient operation of the train.

[0043] 2. The photovoltaic system established in the present invention is a photovoltaic array on the roof of the train, which is laid horizontally. 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 traditional fixed photovoltaic array designs, the present invention maximizes the efficiency of photovoltaic power generation based on the high-speed movement of the train. This significantly improves the output stability of photovoltaic power generation, especially when the train is traveling at high speeds, by reducing the fluctuations in power generation caused by changes in light angle, thereby achieving higher energy utilization. The power generation efficiency has increased by an average of 15%-20%, which not only reduces the system's dependence on the power grid but also reduces the train's total energy consumption.

[0044] 3. A hybrid optimization algorithm, combining an improved Grey Wolf algorithm and a Whale Algorithm (IGWO-WOA), leverages the advantages of lithium iron phosphate battery energy storage technology to optimize the capacity and operating power of energy storage devices. This algorithm not only accounts for the uncertainty of photovoltaic power generation but also effectively balances energy management and system cost requirements, ensuring stable system operation in complex weather conditions. Compared to traditional single optimization methods, this hybrid optimization algorithm significantly improves overall efficiency and reduces system operating costs.

[0045] 4. The present invention calculates load power requirements based on operating conditions, including towing, cruising, coasting, and braking. Modeling is performed based on load variations under these conditions, ensuring low-carbon and high-efficiency operation under various operating conditions.

[0046] 5. The present invention utilizes multiple constraints, C1 to C4. C1 and C2 limit the capacity and power of the energy storage device, taking into account power and capacity limitations, making the system more realistic. C3 limits the energy storage device's energy, and C4 limits its state of charge (SOC). These constraints ensure healthy charge and discharge conditions, extending its service life and ensuring the SOC of the energy storage device is between 0.2 and 0.8, avoiding extreme charge and discharge conditions. This makes the present method more practical and more realistic.

[0047] 6. The present invention applies a hybrid mutation strategy during the energy storage model optimization process. 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 exploitation; and in step S53, random reset mutation is introduced to prevent the result from falling into a local optimum. By applying the hybrid mutation strategy, the accuracy and reliability of the optimal solution can be improved, preventing it from falling into a local optimum, and obtaining a fully considered optimal energy storage configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic diagram of the steps of a method for collaborative planning of photovoltaic and storage systems for high-speed rail hybrid power systems in the present invention;

[0049] Figure 2 Schematic diagram of the system structure of the high-speed rail hybrid power system in the embodiment;

[0050] Figure 3 This is a flow chart of the photovoltaic storage coordinated planning for the high-speed rail hybrid power system in the embodiment;

[0051] Figure 4 FIG. 1 is a flow chart of optimizing the energy storage model using the IGWO-WOA hybrid optimization algorithm in the embodiment. DETAILED DESCRIPTION

[0052] The present invention is described in detail below with reference to 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.

[0053] With the rapid development of photovoltaic technology, PV power generation has begun to be integrated into the power systems of various mobile vehicles. The introduction of PV aims to reduce dependence on traditional energy sources and improve energy efficiency. However, PV power generation is unstable and easily affected by environmental factors such as weather, resulting in output power fluctuations. This uncertainty poses challenges to the stable operation of the system. In power systems, energy storage technology is widely considered to be the key to solving energy management problems. Energy storage systems can smooth the intermittent output of PV power generation, reduce instantaneous demand fluctuations on the power grid, and thus improve the stability and reliability of the entire system. Currently, hybrid energy storage systems combining supercapacitors and batteries have been proposed and applied to the optimization of high-speed rail power systems. These systems combine different types of energy storage devices to achieve efficient energy storage and release, thereby improving energy management efficiency.

[0054] Example 1

[0055] In this embodiment, a method for synergistic planning of photovoltaic and storage systems for high-speed rail hybrid power systems is applied. Figure 1 As shown, the following steps are included:

[0056] S1. Collect and analyze meteorological data along the high-speed rail line to obtain solar radiation rate;

[0057] S2. Establish a photovoltaic system and calculate the output power of the photovoltaic system using the solar radiation rate;

[0058] S3, building an energy storage model;

[0059] S4. Calculate the load power requirement according to the working conditions;

[0060] S5. Use the output power of the photovoltaic system, the energy storage model, and the load demand power to establish the objective function. 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.

[0061] In this embodiment, the specific implementation is as follows:

[0062] In this example, a high-speed rail hybrid power system is first constructed. This system is a control system that integrates a DC / DC converter and a DC / AC converter. This system is designed to provide a stable power source for various loads. Currently, most high-speed rail power systems do not include photovoltaic equipment or large-capacity energy storage batteries. However, this solution utilizes renewable energy through photovoltaic equipment and energy storage batteries, reducing grid energy consumption and improving environmental and economic benefits.

[0063] In this embodiment, the structure of the high-speed rail hybrid power system is as follows: Figure 2As shown, it includes: control system, DC-DC converter (DC / DC), DC-AC converter (DC / AC), transformer, DC load (DC LOAD) and AC load (AC LOAD).

[0064] The control system sits at the heart of the system, responsible for monitoring and regulating the entire power conversion process. It receives feedback from the loads and adjusts the operating conditions of the DC / DC and DC / AC converters accordingly to ensure stable output voltage and current. The DC-AC converter converts DC power to a voltage level suitable for the train system. These converters are crucial for maintaining stable operation of the train's electrical system. The DC-AC converter converts DC power to AC power for AC loads. In high-speed rail systems, this conversion is essential for driving AC motors and other AC devices. Transformers step up or down the voltage to a level suitable for specific loads while also providing electrical isolation and enhancing system safety. DC loads are devices on the train powered by DC power, including control systems, lighting, and infotainment systems. AC loads are devices on the train powered by AC power, including traction motors and air conditioners.

[0065] In this embodiment, after the high-speed rail hybrid power system is built, the high-speed rail hybrid power system photovoltaic storage collaborative planning is carried out, and the process is as follows: Figure 3 As shown, firstly, the meteorological data along the high-speed rail line are collected and analyzed to obtain the solar radiation rate.

[0066] In this example, a high-speed rail route with complex weather conditions and a preset span was selected. Solcast was then used to collect two years of meteorological data from various stations along the route at the time trains passed through. A clustering algorithm was then used to select dates with the most representative meteorological characteristics for each season. Finally, the selected meteorological data was integrated to obtain the corresponding solar radiation information. In this example, the preset span was 2000 km.

[0067] Then establish a photovoltaic system and use the solar radiation rate to calculate the output power of the photovoltaic system.

[0068] In this example, a Fuxing CR400BF high-speed train was selected. The arrangement of the power and trailer cars, as well as the total number of cars, was first determined. The total area of ​​the photovoltaic array on the train roof was 2,000 square meters, and the array was laid out horizontally.

[0069] Then, a photovoltaic system is established. The specific formula for calculating the output power of the photovoltaic system using solar radiation is as follows:

[0070] P PV(t) =η PV ×A PV ×I(t)

[0071] 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 rate.

[0072] Then, build the energy storage model.

[0073] In this embodiment, a lithium iron phosphate battery is used as the energy storage device. The constructed energy storage model includes: energy storage device capacity, energy storage device power, and energy storage device state of charge. The energy storage device capacity optimization range is set to 0-1500 kWh, the energy storage device power optimization range is set to 0-1500 kW, and the energy storage device state of charge is calculated as follows:

[0074] SOC(t)=E(t) / E b

[0075] 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.

[0076] Next, calculate the load power requirement according to different working conditions.

[0077] In this example, the high-speed train operating conditions are divided into four categories: traction, cruising, coasting, and braking. The changes in the high-speed train load are analyzed from a kinematic perspective based on these different operating conditions. The average power loads for the four conditions are determined to be 10,400 kW, 6,100 kW, 0 kW, and 0 kW, respectively. The power generated by renewable energy under the braking condition is calculated.

[0078] 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.

[0079] In this embodiment, the objective function is constructed based on carbon emissions, and its specific formula is as follows:

[0080] min Carbon=E Grid ÷Factor Carbon

[0081] E grid = P grid (t)×t

[0082] P Grid (t) = P Load(t) –P PV (t)–P ch (t)–P dis (t)

[0083] Where, 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 equipment 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.

[0084] In this embodiment, the constraints include constraints C1 to C4. C1 and C2 are limits on the capacity and power of the energy storage device. Considering the limits on energy storage power and capacity makes the system more realistic. C3 is a limit on the energy storage device's energy, and C4 is a limit on the state of charge (SOC) of the energy storage device. This ensures that the energy storage device maintains a healthy charge and discharge state, extending its service life and ensuring that the SOC of the energy storage device is between 0.2 and 0.8, avoiding extreme charge and discharge states. The specific formula is as follows:

[0085] C1:0≤|P ch(t) |≤Pb≤P max

[0086] C2: 0≤|P dis(t) |≤Pb≤P max

[0087] C3:0<0.2Eb≤E(t)≤Eb≤E max

[0088] C4:0.2≤SOC(t) ≤0.8

[0089] 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.

[0090] In this embodiment, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model. A hybrid mutation strategy is applied during the optimization process. The optimization process is as follows:

[0091] 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;

[0092] 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;

[0093] S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals based on the reference individuals and the objective function, calculate the new positions of the individuals, and then adjust and determine the new positions of the best individuals by combining the ratio of WOA and GWO;

[0094] 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.

[0095] 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.

[0096] In this example, the population size represents the number of potential solutions to the model and is set to 50 in this solution. The maximum number of iterations is set to 1000; a sufficient number of iterations allows for the optimal solution to be fully discovered. 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. During the initialization phase, the output power of the high-speed rail power grid, the operating power of the energy storage device, and the capacity of the energy storage device must also be set.

[0097] Second, in each iteration, the alpha parameter is calculated and nonlinearly adjusted using a logarithmic function based on the current iteration number. As the alpha parameter decreases with each iteration, the parameters a and bb change at different rates during the optimization process. This change in the alpha parameter affects the update speed of the nonlinear adaptive parameters a and bb, as well as the ratio r of the WOA to the GWO.

[0098] After the iteration, the top three best individuals in the current population are selected and used as reference points for subsequent updates. A WOA spiral update is performed on each individual, calculating the distance from the individual to the best individual. Based on this distance, an adjustment term da for the individual's new position is calculated, representing the WOA-based spiral update term. For each individual in the population, a new position is calculated based on the top three best individuals. This new position is then adjusted based on the ratio r, combining GWO and WOA. A method is then called to ensure that the individual's new position is within its search space.

[0099] During the optimization process, a hybrid mutation strategy is applied, including 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-stage, such as step S52, uniform mutation is introduced to balance exploration and exploitation. 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.

[0100] 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, it restarts from the step of initializing the individual positions of the population.

[0101] The final output is the optimal energy storage configuration, namely 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 for this solution.

[0102] 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., energy storage capacity of 295.7 kWh and power data of 998.5 kw, is 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. The carbon emissions of the two methods are then compared to analyze the environmental benefits of the method proposed in this application.

[0103] In this example, taking a ten-year period as an example, the total carbon emissions of the existing method over a ten-year period were 2,020,3560.3 kg; while the total carbon emissions of this technical solution were 17,643,468.9 kg, representing a carbon emission reduction of 25,600,91.4 kg compared to the existing method. Furthermore, this solution achieves higher energy utilization, with an average increase in power generation efficiency of 15%-20%.

[0104] In summary, this solution maximizes photovoltaic power generation efficiency, reduces the train's total energy consumption, and thus reduces the system's carbon emissions. Compared to existing technologies, this system significantly reduces carbon emissions. By modeling load variations under different operating conditions, it ensures low-carbon and efficient operation under various operating conditions. Data shows that compared to traditional systems, carbon emissions have been reduced by approximately 12.7% over ten years.

[0105] Example 2

[0106] 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;

[0107] 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 and storage coordinated planning of the high-speed rail hybrid power system.

[0108] In this embodiment, the specific implementation of applying the system is as follows:

[0109] In this example, a high-speed rail hybrid power system is first constructed. This system is a control system that integrates a DC / DC converter and a DC / AC converter. This system is designed to provide a stable power source for various loads. Currently, most high-speed rail power systems do not include photovoltaic equipment or large-capacity energy storage batteries. However, this solution utilizes renewable energy through photovoltaic equipment and energy storage batteries, reducing grid energy consumption and improving environmental and economic benefits.

[0110] In this embodiment, the structure of the high-speed rail hybrid power system is as follows: Figure 2 As shown, it includes: control system, DC-DC converter (DC / DC), DC-AC converter (DC / AC), transformer, DC load (DC LOAD) and AC load (AC LOAD).

[0111] The control system sits at the heart of the system, responsible for monitoring and regulating the entire power conversion process. It receives feedback from the loads and adjusts the operating conditions of the DC / DC and DC / AC converters accordingly to ensure stable output voltage and current. The DC-AC converter converts DC power to a voltage level suitable for the train system. These converters are crucial for maintaining stable operation of the train's electrical system. The DC-AC converter converts DC power to AC power for AC loads. In high-speed rail systems, this conversion is essential for driving AC motors and other AC devices. Transformers step up or down the voltage to a level suitable for specific loads while also providing electrical isolation and enhancing system safety. DC loads are devices on the train powered by DC power, including control systems, lighting, and infotainment systems. AC loads are devices on the train powered by AC power, including traction motors and air conditioners.

[0112] 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 carry out the high-speed rail hybrid power system photovoltaic storage collaborative planning. The process is as follows: Figure 3 As shown, firstly, meteorological data along the high-speed rail line are collected and analyzed to obtain the solar radiation rate.

[0113] In this example, a high-speed rail route with complex weather conditions and a preset span was selected. Solcast was then used to collect two years of meteorological data from various stations along the route at the time trains passed through. A clustering algorithm was then used to select dates with the most representative meteorological characteristics for each season. Finally, the selected meteorological data was integrated to obtain the corresponding solar radiation information. In this example, the preset span was 2000 km.

[0114] Then establish a photovoltaic system and use the solar radiation rate to calculate the output power of the photovoltaic system.

[0115] In this example, a Fuxing CR400BF high-speed train was selected. The arrangement of the power and trailer cars, as well as the total number of cars, was first determined. The total area of ​​the photovoltaic array on the train roof was 2,000 square meters, and the array was laid out horizontally.

[0116] Then, a photovoltaic system is established. The specific formula for calculating the output power of the photovoltaic system using solar radiation is as follows:

[0117] P PV(t) =η PV ×A PV ×I(t)

[0118] Where, P PV(t) is the output power of the photovoltaic system; η PV is the instantaneous power generation efficiency of the photovoltaic generator; APV is the total area of ​​PV panels used in the PV power generation system; I(t) is the total solar radiation rate.

[0119] Then, build the energy storage model.

[0120] In this embodiment, a lithium iron phosphate battery is used as the energy storage device. The constructed energy storage model includes: energy storage device capacity, energy storage device power, and energy storage device state of charge. The energy storage device capacity optimization range is set to 0-1500 kWh, the energy storage device power optimization range is set to 0-1500 kW, and the energy storage device state of charge is calculated as follows:

[0121] SOC(t)=E(t) / E b

[0122] 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.

[0123] Next, calculate the load power requirement according to different working conditions.

[0124] In this example, the high-speed train operating conditions are divided into four categories: traction, cruising, coasting, and braking. The changes in the high-speed train load are analyzed from a kinematic perspective based on these different operating conditions. The average power loads for the four conditions are determined to be 10,400 kW, 6,100 kW, 0 kW, and 0 kW, respectively. The power generated by renewable energy under the braking condition is calculated.

[0125] 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.

[0126] In this embodiment, the objective function is constructed based on carbon emissions, and its specific formula is as follows:

[0127] min Carbon=E Grid ÷Factor Carbon

[0128] E grid = P grid (t)×t

[0129] P Grid (t) = P Load (t) –P PV (t)–P ch (t)–P dis (t)

[0130] Where, 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 equipment 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.

[0131] In this embodiment, the constraints include constraints C1 to C4. C1 and C2 are limits on the capacity and power of the energy storage device. Considering the limits on energy storage power and capacity makes the system more realistic. C3 is a limit on the energy storage device's energy, and C4 is a limit on the state of charge (SOC) of the energy storage device. This ensures that the energy storage device maintains a healthy charge and discharge state, extending its service life and ensuring that the SOC of the energy storage device is between 0.2 and 0.8, avoiding extreme charge and discharge states. The specific formula is as follows:

[0132] C1:0≤|P ch(t) |≤Pb≤P max

[0133] C2:0≤|P dis(t) |≤Pb≤P max

[0134] C3:0<0.2Eb≤E(t)≤Eb≤E max

[0135] C4:0.2≤SOC(t) ≤0.8

[0136] 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.

[0137] In this embodiment, the IGWO-WOA hybrid optimization algorithm is used to optimize the energy storage model. A hybrid mutation strategy is applied during the optimization process. The optimization process is as follows:

[0138] 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;

[0139] 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;

[0140] S53, select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals based on the reference individuals and the objective function, calculate the new positions of the individuals, and then adjust and determine the new positions of the best individuals by combining the ratio of WOA and GWO;

[0141] 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.

[0142] 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.

[0143] In this example, the population size represents the number of potential solutions to the model and is set to 50 in this solution. The maximum number of iterations is set to 1000; a sufficient number of iterations allows for the optimal solution to be fully discovered. 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. During the initialization phase, the output power of the high-speed rail power grid, the operating power of the energy storage device, and the capacity of the energy storage device must also be set.

[0144] Second, in each iteration, the alpha parameter is calculated and nonlinearly adjusted using a logarithmic function based on the current iteration number. As the alpha parameter decreases with each iteration, the parameters a and bb change at different rates during the optimization process. This change in the alpha parameter affects the update speed of the nonlinear adaptive parameters a and bb, as well as the ratio r of the WOA to the GWO.

[0145] After the iteration, the top three best individuals in the current population are selected and used as reference points for subsequent updates. A WOA spiral update is performed on each individual, calculating the distance from the individual to the best individual. Based on this distance, an adjustment term da for the individual's new position is calculated, representing the WOA-based spiral update term. For each individual in the population, a new position is calculated based on the top three best individuals. This new position is then adjusted based on the ratio r, combining GWO and WOA. A method is then called to ensure that the individual's new position is within its search space.

[0146] During the optimization process, a hybrid mutation strategy is applied, including 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-stage, such as step S52, uniform mutation is introduced to balance exploration and exploitation. 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.

[0147] 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, it restarts from the step of initializing the individual positions of the population.

[0148] The final output is the optimal energy storage configuration, namely 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 for this solution.

[0149] 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., energy storage capacity of 295.7 kWh and power data of 998.5 kw, is 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. The carbon emissions of the two methods are then compared to analyze the environmental benefits of the method proposed in this application.

[0150] In this example, taking a ten-year period as an example, the total carbon emissions of the existing method over a ten-year period were 2,020,3560.3 kg; while the total carbon emissions of this technical solution were 17,643,468.9 kg, representing a carbon emission reduction of 25,600,91.4 kg compared to the existing method. Furthermore, this solution achieves higher energy utilization, with an average increase in power generation efficiency of 15%-20%.

[0151] In summary, this solution maximizes photovoltaic power generation efficiency, reduces the train's total energy consumption, and thus reduces the system's carbon emissions. Compared to existing technologies, this system significantly reduces carbon emissions. By modeling load variations under different operating conditions, it ensures low-carbon and efficient operation under various operating conditions. Data shows that compared to traditional systems, carbon emissions have been reduced by approximately 12.7% over ten years.

Claims

1. A method for collaborative planning of photovoltaic and storage systems for high-speed rail hybrid power systems, characterized in that: The method comprises the following steps: S1. Collect and analyze meteorological data along the high-speed rail line 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 train's load power requirements according to different train operating conditions; S5. Establish an objective function using the PV system's output power, the energy storage model, and the train's load power requirements. Under the set constraints, use the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model to obtain the optimal energy storage configuration. Combined with the PV system and the optimized energy storage model, this provides PV-storage synergistic power supply for the train. The objective function in step 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) Where, 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 equipment 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; In step S5, the IGWO-WOA hybrid optimization algorithm is adopted, and the hybrid mutation strategy is applied in the process of optimizing the energy storage model. The potential configuration of energy storage is used as the individual position, and 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, where the population size represents the number of potential solutions of the energy storage model, the convergence factor is reduced from 2 to 0, and the search radius of the WOA spiral is reduced from 1 to 0. In addition, the output power of the high-speed rail power grid, the operating power of the energy storage device, and the capacity of the energy storage device are set, and Gaussian mutation is introduced in this process to increase population diversity; S52, initialize the position of individuals in the population and calculate the alpha parameter to iteratively update the ratio of WOA to GWO, the convergence factor and the search radius of the WOA spiral. In this process, uniform mutation is introduced to balance exploration and exploitation. Specifically, in each round of iteration, the alpha parameter is first calculated. The alpha parameter is nonlinearly adjusted using a logarithmic function according to the current number of iterations. As the alpha parameter decreases with the iteration, the convergence factor and the search radius of the WOA spiral change at different speeds during the optimization process. The change of the alpha parameter affects the update speed of the nonlinear adaptive parameter convergence factor and the search radius of the WOA spiral, as well as the ratio r of WOA to GWO. S53. Select the three best individuals in the population as reference individuals, perform WOA spiral update on the remaining individuals based on the reference individuals and the objective function, calculate the new positions of the individuals, and then adjust and determine the new positions of the best individuals by combining the ratio of WOA and GWO. In this process, random reset variation is introduced to prevent the results from falling into local optimality. Specifically, after each round of iteration, the top three best individuals are selected from the current population. These individuals will be used as reference points for subsequent updates. A WOA spiral update is performed on each individual, and the distance from the individual to the best individual is calculated. Based on this, the adjustment term da of the individual's new position is calculated. The adjustment term da 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 then the new position of the best individual is determined by adjusting it according to the ratio r. 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 for energy storage; if not, return to step S53.

2. The method for collaborative planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1, characterized in that: The collection and analysis of 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.

3. The method for synergistic planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1, 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) =the 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 rate.

4. The method for collaborative planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1, characterized in that: The energy storage model in S3 includes: energy storage device capacity, energy storage device power and energy storage device state of charge; wherein, the energy storage device capacity optimization range is set to 0-1500kWh, the energy storage device power optimization range is set to 0-1500kW, and the energy storage device state of charge specific formula 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 collaborative planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1, characterized in that: The operating conditions in S4 include traction, cruising, coasting and braking. According to different operating conditions, the changes in the high-speed train load are analyzed from a kinematic perspective to calculate the load demand power of the train accordingly.

6. The method for collaborative planning of photovoltaic and storage systems for high-speed railway hybrid power systems according to claim 1, characterized in that: The constraints in S5 include constraints C1 to C4; 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 capacity; 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.

7. A high-speed rail hybrid power system photovoltaic storage collaborative planning system, characterized by: The system applies a high-speed rail hybrid power system photovoltaic storage collaborative planning method as described in any one of claims 1-6, and 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 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 power demand of the train according to different operating conditions of the train; The model optimization module uses the output power of the photovoltaic system, the energy storage model, and the load power demand of the train to establish an objective function. Under the set constraints, it uses the IGWO-WOA hybrid optimization algorithm to optimize the energy storage model to obtain the optimal energy storage configuration. Combined with the photovoltaic system and the optimized energy storage model, it provides photovoltaic and energy storage synergistic power supply for the train.

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