Rail transit green energy system optimization operation method

Abstract

The invention discloses an optimized operation method for a rail transit green energy system, and relates to the technical field of comprehensive energy systems, and the method comprises the steps: building a regenerative braking energy utilization model through the energy storage maximization regenerative braking energy utilization rate of a supercapacitor; performing compensation processing on a non-traction load based on the regenerative braking energy utilization model to obtain utilization efficiency and economic benefits of regenerative braking energy; establishing an improved light robust optimization model based on the utilization efficiency of the regenerative braking energy, the economic benefit and the distributed photovoltaic output uncertainty; and performing load verification according to the improved light robust optimization model to obtain an optimized operation result of the rail transit green energy system. According to the method, challenges caused by different time resolutions of distributed photovoltaic power generation and traction load are effectively solved, collaborative optimization of risk cost and operation cost is realized, and the overall operation efficiency and reliability of a rail transit green energy system are improved.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology and involves many fields such as electrical engineering, energy and power, transportation, and low-carbon economy. In particular, it relates to an optimized operation method for a green energy system for rail transit. Background Technology

[0002] As a major carbon emitter in the transportation sector, the railway industry faces increasingly severe pressure to reduce emissions due to its high carbon footprint resulting from traditional energy consumption patterns. How to promote the green and low-carbon development of the railway industry and achieve efficient energy utilization has become a key issue that urgently needs to be addressed for the industry's development.

[0003] Deploying distributed photovoltaic power generation systems at traction substations and along railway lines provides an effective solution to this challenge. This initiative not only fully utilizes the abundant natural resources surrounding the railway for green power generation, significantly improving energy self-sufficiency, but also ensures the continuous operation of the railway in special circumstances such as grid failures, powerfully promoting the achievement of "dual-carbon" goals. Simultaneously, configuring hybrid energy storage systems on demand enables efficient recovery and utilization of regenerative braking energy, effectively maintaining a balance between power supply and demand, and further optimizing energy efficiency.

[0004] As a key intermediate link in the rail transit energy system, power electronic equipment plays an important role in power conversion and aggregation. It can introduce clean energy such as distributed photovoltaics into the traction network, while meeting the power needs of non-traction loads such as station air conditioning, lighting, communication and signaling systems. This promotes the gradual transformation and upgrading of the traditional traction power supply system into a green energy system for rail transit, realizing coordinated energy supply from the grid to the source to the storage to the vehicle.

[0005] However, in actual operation, distributed photovoltaic (PV) power output is significantly uncertain due to factors such as sunlight intensity and weather conditions, and its time resolution differs from that of traction load. This poses a significant challenge to the operation and scheduling of green energy systems in rail transit. Existing technologies struggle to effectively address the time-scale mismatch between distributed PV power generation and traction load, failing to achieve synergistic optimization of risk and operating costs. Consequently, the overall operational efficiency and reliability of green energy systems in rail transit fail to meet practical requirements. Therefore, an optimized operation method for green energy systems in rail transit is urgently needed to address the shortcomings of existing technologies. Summary of the Invention

[0006] The purpose of this invention is to propose an optimized operation method for a green energy system for rail transit. With the goal of minimizing the total daily cost of the green energy system for rail transit, an improved lightweight robust optimization model is constructed that takes into account the uncertainty risk of distributed photovoltaic power output. The operation results are corrected on a second-level time scale through a two-round load verification method, thereby achieving synergistic optimization of risk cost and operating cost.

[0007] To achieve the above objectives, the present invention provides a method for optimizing the operation of a green energy system for rail transit, comprising the following steps:

[0008] S1. Establish a regenerative braking energy utilization model by maximizing the regenerative braking energy utilization rate through supercapacitor energy storage.

[0009] S2. Based on the regenerative braking energy utilization model, the non-traction load is compensated to obtain the utilization efficiency and economic benefits of regenerative braking energy.

[0010] S3. Based on the utilization efficiency of the regenerative braking energy and the economic benefits, and taking into account the uncertainty of distributed photovoltaic power output, an improved lightly robust optimization model is established.

[0011] S4. Based on the improved lightweight robust optimization model, perform load verification to obtain the optimized operation results of the rail transit green energy system.

[0012] Optionally, S1, establish a regenerative braking energy utilization model by maximizing the regenerative braking energy utilization rate through supercapacitor energy storage, including:

[0013] Regenerative braking energy is obtained by storing energy in supercapacitors;

[0014] Based on the regenerative braking energy and considering the time-of-use electricity price, the objective function under braking conditions is to maximize the utilization rate of regenerative braking energy by setting the supercapacitor energy storage as the regenerative braking energy storage.

[0015] Based on the objective function under the braking condition, a regenerative braking energy utilization model is constructed using the constraints of supercapacitor energy storage operation.

[0016] Optionally, the objective function under the braking condition is calculated as follows:

[0017]

[0018] in, This refers to the time-of-use electricity price for period T, where T represents data with a time resolution of 15 minutes. The supercapacitor energy storage operating power is denoted by T, where ΔT represents a time resolution of 15 minutes.

[0019] Optionally, the operating constraints of the supercapacitor energy storage include the state of charge constraint of the supercapacitor energy storage, the daily charge and discharge balance constraint, and the charge and discharge power constraint.

[0020] The formula for calculating the state-of-charge constraint of the supercapacitor energy storage is as follows:

[0021]

[0022] The formula for calculating the daily charge-discharge balance constraint of the supercapacitor energy storage is as follows:

[0023]

[0024] The formula for calculating the charging and discharging power constraint of the supercapacitor energy storage is as follows:

[0025]

[0026] in, This represents the state of charge limit for energy storage in supercapacitors. The rated capacity for energy storage of a supercapacitor. This represents the energy stored in the supercapacitor during time period T, where T indicates data with a time resolution of 15 minutes. This represents the upper limit of the state of charge (SOC) for energy storage in a supercapacitor. The initial daily charge of the supercapacitor is T', which represents the time period not exceeding T. The supercapacitor energy storage operating power is represented by T', where ΔT is a time resolution of 15 minutes. The final charge of energy stored in a supercapacitor. The supercapacitor's energy storage power during time period T is limited under braking conditions. The supercapacitor energy storage operating power during time period T. The rated power for energy storage in a supercapacitor.

[0027] Optionally, S2, based on the regenerative braking energy utilization model, compensation processing is performed on the non-traction load to obtain the utilization efficiency and economic benefits of regenerative braking energy, including:

[0028] Based on the aforementioned regenerative braking energy utilization model, supercapacitor energy storage and regenerative braking energy are collected and utilized.

[0029] The supercapacitor stores regenerative braking energy and uses an energy feed system to compensate for the non-traction load, thus obtaining the compensated non-traction load.

[0030] The utilization efficiency and economic benefits of regenerative braking energy are obtained based on the non-traction load and the compensated non-traction load.

[0031] The formula for calculating the utilization efficiency of the regenerative braking energy is as follows:

[0032]

[0033] The formula for calculating the economic benefits is as follows:

[0034]

[0035] Where η represents the utilization efficiency of regenerative braking energy, and T represents data with a time resolution of 15 minutes. The non-traction load power before compensation in time period T. P represents the non-traction load power after compensation during time period T, where ΔT is a time resolution of 15 minutes. t RB0 Let t represent the regenerative braking power during time period t, Δt be the time resolution of 1 second, and C0 be the economic benefit generated by the supercapacitor's energy storage and utilization of regenerative braking energy. For time-of-use electricity pricing during period T, The operating cost per MWh for supercapacitor energy storage The operating power of the supercapacitor energy storage during time period T.

[0036] Optionally, the calculation formula for compensating non-traction loads by utilizing regenerative braking energy stored in the supercapacitor through the energy feed system is as follows:

[0037]

[0038] Where T represents data with a time resolution of 15 minutes. The non-traction load power after compensation during time period T. The non-traction load power before compensation in time period T. The operating power of the supercapacitor energy storage during time period T.

[0039] Optionally, S3, an improved lightweight robust optimization model is established based on the utilization efficiency of the regenerative braking energy, the economic benefits, and the uncertainty of distributed photovoltaic power output, including:

[0040] Based on the utilization efficiency of the regenerative braking energy and the economic benefits, the objective function for minimizing the total daily cost of the green energy system for rail transit under traction conditions is set.

[0041] An improved lightweight robust optimization model is established based on the objective function under the aforementioned traction conditions and the distributed photovoltaic power output constraint.

[0042] The minimized daily total cost of the green energy system for rail transit includes operating costs and risk costs. The operating costs include electricity costs, lithium-ion battery energy storage operating costs, distributed photovoltaic power generation subsidies, and the economic benefits.

[0043] The objective function under the traction condition is calculated as follows:

[0044]

[0045] The formula for calculating the distributed photovoltaic power output constraint is as follows:

[0046]

[0047] Among them, C total To minimize the total daily cost of green energy systems for rail transit, C operation For operating costs, C risk C1 represents the cost of electricity, C2 represents the operating cost of lithium-ion battery energy storage, C3 represents the subsidy for distributed photovoltaic power generation, C0 represents the economic benefit, T represents the data with a time resolution of 15 minutes, and G represents the risk cost. T The electricity price for time period T. The power purchased from the grid by the green energy system of rail transit during time period T, where ΔT is a time resolution of 15 minutes. The operating cost per MWh for lithium-ion battery energy storage The operating power of lithium-ion battery energy storage during time period T. The operating revenue per MWh of lithium-ion battery energy storage during time period T. Subsidies for each MWh of distributed photovoltaic power generation. For the distributed photovoltaic power generation system during time period T, ω T γ represents the weighting coefficient of the slack variable at time T. T For time period T, slack variables Let j be the output of distributed photovoltaic unit j during time period T, and k be the total number of distributed photovoltaic units.

[0048] Optionally, establishing an improved lightweight robust optimization model based on the objective function under the traction condition and the distributed photovoltaic power output constraint further includes:

[0049] An improved, slightly robust optimization model is established based on system power balance constraints, traction transformer power constraints, state of charge constraints, daily charge-discharge balance constraints, and charge-discharge power constraints for lithium-ion battery energy storage.

[0050] The calculation formula for the system power balance constraint of the lithium-ion battery energy storage is as follows:

[0051]

[0052] The formula for calculating the power constraint of the traction transformer for lithium-ion battery energy storage is as follows:

[0053]

[0054] The formula for calculating the state of charge constraint of the lithium-ion battery energy storage is as follows:

[0055]

[0056] The formula for calculating the daily charge-discharge balance constraint of the lithium-ion battery energy storage is as follows:

[0057]

[0058] The formula for calculating the charge and discharge power constraint of the lithium-ion battery energy storage is as follows:

[0059]

[0060] in, For the green energy system of rail transit during time period T, the power purchased from the grid. The operating power of lithium-ion battery energy storage during time period T. To provide power for the distributed photovoltaic power generation system during time period T. The traction load power during time period T. The non-traction load power after compensation during time period T. This refers to the rated power of the traction transformer. This represents the state of charge limit for lithium-ion battery energy storage. The rated capacity for energy storage in lithium-ion batteries. This represents the energy charge stored in the lithium-ion battery during time period T. This represents the upper limit of the state of charge (SOC) for energy storage in lithium-ion batteries. The initial daily charge of the lithium-ion battery energy storage is represented by T, where T represents data with a time resolution of 15 minutes, and T' represents the time period not exceeding T. The operating power of the lithium-ion battery energy storage during time period T' is given, where ΔT represents a time resolution of 15 minutes. The final charge capacity for energy storage in lithium-ion batteries. Rated power for energy storage in lithium-ion batteries.

[0061] Optionally, S4, load verification is performed based on the improved lightweight robust optimization model to obtain the optimized operation results of the rail transit green energy system, including:

[0062] The improved lightweight robust optimization model is used to perform the first round of load verification on the compensated non-traction load to obtain the time resolution of the distributed photovoltaic power generation and the compensated non-traction load power as the first round of operation results.

[0063] Based on the results of the first round of operation, the grid output power under the maximum discharge power of the lithium-ion battery energy storage is obtained using the system power balance constraint of the lithium-ion battery energy storage.

[0064] The adjusted maximum discharge power of the lithium-ion battery is obtained by adjusting the grid output power under the maximum discharge power of the lithium-ion battery energy storage.

[0065] Based on the adjusted maximum discharge power of the lithium-ion battery energy storage, the time resolution of the lithium-ion battery energy storage power is obtained using the improved lightweight robust optimization model as the result of the second round of operation.

[0066] Based on the results of the second round of operation, a second round of load verification was conducted to obtain the amount of abandoned solar power as the result of optimized operation of the green energy system for rail transit.

[0067] Compared with the closest existing technology, the present invention has the following advantages:

[0068] This invention proposes an optimized operation method for a green energy system for rail transit, taking into account the uncertainty risk of distributed photovoltaic power output. An improved lightweight robust optimization model is constructed, and the operation results are corrected on a second-level time scale through a two-round load verification method. This effectively solves the challenges brought about by the different time resolutions of distributed photovoltaic power generation and traction load, and achieves synergistic optimization of risk cost and operating cost, thereby improving the overall operating efficiency and reliability of the green energy system for rail transit. Attached Figure Description

[0069] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0070] Figure 1 This is a flowchart illustrating an optimized operation method for a green energy system in rail transit, as described in an embodiment of the present invention.

[0071] Figure 2 This is a schematic diagram of a typical structure of a green energy system for rail transit proposed in an embodiment of the present invention;

[0072] Figure 3 This is a flowchart of the model solving strategy proposed in an embodiment of the present invention;

[0073] Figure 4 This is a measured traction load curve of a traction substation proposed in an embodiment of the present invention;

[0074] Figure 5 This is a graph showing the expected power of a distributed photovoltaic unit in a traction substation versus the non-traction load, as proposed in an embodiment of the present invention.

[0075] Figure 6 This is a risk distribution diagram of the relaxation variables for distributed photovoltaic power output proposed in an embodiment of the present invention;

[0076] Figure 7 This is a distribution diagram of the total fluctuation of distributed photovoltaic power output proposed in an embodiment of the present invention;

[0077] Figure 8 This is a schematic diagram of the electricity price in the time-of-use pricing and two-part pricing schemes proposed in this embodiment of the invention;

[0078] Figure 9 The following are statistical charts showing the utilization efficiency, economic benefits, and CO2 emission reduction of the regenerative braking energy utilization model proposed in this embodiment of the invention. Among them, (a) is a statistical chart of three different uses of regenerative braking energy, (b) is a statistical chart of the utilization efficiency of the regenerative braking energy utilization model, (c) is a statistical chart of economic benefits, and (d) is a statistical chart of CO2 emission reduction.

[0079] Figure 10 This is a schematic diagram showing the solution results of the regenerative braking energy utilization model proposed in this embodiment of the invention;

[0080] Figure 11 The diagram shows the typical time period operation results proposed in the embodiments of the present invention, wherein (a) is the operation result of the time period from 12:30 to 12:45, (b) is the operation result of the time period from 12:30 to 12:31, (c) is the operation result of the time period from 12:35 to 12:40, (d) is the operation result of the time period from 12:32 to 12:33, and (e) is the operation result of the time period from 12:42 to 12:43. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0082] The terminology used in the embodiments section of this invention is for the purpose of explaining specific embodiments of the invention only, and is not intended to limit the invention.

[0083] Example 1

[0084] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for optimizing the operation of a green energy system for rail transit, including the following steps:

[0085] S1. Establish a regenerative braking energy utilization model by maximizing the regenerative braking energy utilization rate through supercapacitor energy storage.

[0086] S2. Based on the regenerative braking energy utilization model, the non-traction load is compensated to obtain the utilization efficiency and economic benefits of regenerative braking energy.

[0087] S3. Based on the utilization efficiency of the regenerative braking energy and the economic benefits, and taking into account the uncertainty of distributed photovoltaic power output, an improved lightly robust optimization model is established.

[0088] S4. Based on the improved lightweight robust optimization model, perform load verification to obtain the optimized operation results of the rail transit green energy system.

[0089] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps;

[0090] Regenerative braking energy is obtained by storing energy in supercapacitors;

[0091] Based on the regenerative braking energy and considering the time-of-use electricity price, the objective function under braking conditions is to maximize the utilization rate of regenerative braking energy by setting the supercapacitor energy storage as the regenerative braking energy storage.

[0092] Based on the objective function under the braking condition, a regenerative braking energy utilization model is constructed using the constraints of supercapacitor energy storage operation.

[0093] Furthermore, the objective function under the braking condition is calculated as follows:

[0094]

[0095] in, This refers to the time-of-use electricity price for period T, where T represents data with a time resolution of 15 minutes. The supercapacitor energy storage operating power is denoted by T, where ΔT represents a time resolution of 15 minutes.

[0096] Furthermore, the operating constraints of the supercapacitor energy storage include the state of charge constraint of the supercapacitor energy storage, the daily charge and discharge balance constraint, and the charge and discharge power constraint.

[0097] The formula for calculating the state-of-charge constraint of the supercapacitor energy storage is as follows:

[0098]

[0099] The formula for calculating the daily charge-discharge balance constraint of the supercapacitor energy storage is as follows:

[0100]

[0101]

[0102] The formula for calculating the charging and discharging power constraint of the supercapacitor energy storage is as follows:

[0103]

[0104] in, This represents the state of charge limit for energy storage in supercapacitors. The rated capacity for energy storage of a supercapacitor. This represents the energy stored in the supercapacitor during time period T, where T indicates data with a time resolution of 15 minutes. This represents the upper limit of the state of charge (SOC) for energy storage in a supercapacitor. The initial daily charge of the supercapacitor is T', which represents the time period not exceeding T. The supercapacitor energy storage operating power is represented by T', where ΔT is a time resolution of 15 minutes. The final charge of energy stored in a supercapacitor. The supercapacitor's energy storage power during time period T is limited under braking conditions. The supercapacitor energy storage operating power during time period T. The rated power for energy storage in a supercapacitor.

[0105] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0106] Based on the aforementioned regenerative braking energy utilization model, supercapacitor energy storage and regenerative braking energy are collected and utilized.

[0107] The supercapacitor stores regenerative braking energy and uses an energy feed system to compensate for the non-traction load, thus obtaining the compensated non-traction load.

[0108] The utilization efficiency and economic benefits of regenerative braking energy are obtained based on the non-traction load and the compensated non-traction load.

[0109] The formula for calculating the utilization efficiency of the regenerative braking energy is as follows:

[0110]

[0111] The formula for calculating the economic benefits is as follows:

[0112]

[0113] Where η represents the utilization efficiency of regenerative braking energy, and T represents data with a time resolution of 15 minutes. The non-traction load power before compensation in time period T. P represents the non-traction load power after compensation during time period T, where ΔT is a time resolution of 15 minutes. t RB0 Let t represent the regenerative braking power during time period t, Δt be the time resolution of 1 second, and C0 be the economic benefit generated by the supercapacitor's energy storage and utilization of regenerative braking energy. For time-of-use electricity pricing during period T, The operating cost per MWh for supercapacitor energy storage The operating power of the supercapacitor energy storage during time period T.

[0114] Furthermore, the calculation formula for compensating non-traction loads by utilizing regenerative braking energy stored in the supercapacitor through the energy feed system is as follows:

[0115]

[0116] Where T represents data with a time resolution of 15 minutes. The non-traction load power after compensation during time period T. The non-traction load power before compensation in time period T. The operating power of the supercapacitor energy storage during time period T.

[0117] As one possible implementation, in the above embodiments, step S3 may specifically include the following steps:

[0118] Based on the utilization efficiency of the regenerative braking energy and the economic benefits, the objective function for minimizing the total daily cost of the green energy system for rail transit under traction conditions is set.

[0119] An improved lightweight robust optimization model is established based on the objective function under the aforementioned traction conditions and the distributed photovoltaic power output constraint.

[0120] The minimized daily total cost of the green energy system for rail transit includes operating costs and risk costs. The operating costs include electricity costs, lithium-ion battery energy storage operating costs, distributed photovoltaic power generation subsidies, and the economic benefits.

[0121] The objective function under the traction condition is calculated as follows:

[0122]

[0123] The formula for calculating the distributed photovoltaic power output constraint is as follows:

[0124]

[0125] Among them, C total To minimize the total daily cost of green energy systems for rail transit, C operation For operating costs, C risk C1 represents the cost of electricity, C2 represents the operating cost of lithium-ion battery energy storage, C3 represents the subsidy for distributed photovoltaic power generation, C0 represents the economic benefit, T represents the data with a time resolution of 15 minutes, and G represents the risk cost. T The electricity price for time period T. The power purchased from the grid by the green energy system of rail transit during time period T, where ΔT is a time resolution of 15 minutes. The operating cost per MWh for lithium-ion battery energy storage The operating power of lithium-ion battery energy storage during time period T. The operating revenue per MWh of lithium-ion battery energy storage during time period T. Subsidies for each MWh of distributed photovoltaic power generation. For the distributed photovoltaic power generation system during time period T, ω T γ represents the weighting coefficient of the slack variable at time T. T For time period T, slack variables Let j be the output of distributed photovoltaic unit j during time period T, and k be the total number of distributed photovoltaic units.

[0126] Furthermore, the improved lightweight robust optimization model based on the objective function under the traction condition and the distributed photovoltaic power output constraint also includes:

[0127] An improved, slightly robust optimization model is established based on system power balance constraints, traction transformer power constraints, state of charge constraints, daily charge-discharge balance constraints, and charge-discharge power constraints for lithium-ion battery energy storage.

[0128] The calculation formula for the system power balance constraint of the lithium-ion battery energy storage is as follows:

[0129]

[0130] The formula for calculating the power constraint of the traction transformer for lithium-ion battery energy storage is as follows:

[0131]

[0132] The formula for calculating the state of charge constraint of the lithium-ion battery energy storage is as follows:

[0133]

[0134] The formula for calculating the daily charge-discharge balance constraint of the lithium-ion battery energy storage is as follows:

[0135]

[0136] The formula for calculating the charge and discharge power constraint of the lithium-ion battery energy storage is as follows:

[0137]

[0138] in, For the green energy system of rail transit during time period T, the power purchased from the grid. The operating power of lithium-ion battery energy storage during time period T. To provide power for the distributed photovoltaic power generation system during time period T. The traction load power during time period T. The non-traction load power after compensation during time period T. This refers to the rated power of the traction transformer. This represents the state of charge limit for lithium-ion battery energy storage. The rated capacity for energy storage in lithium-ion batteries. This represents the energy charge stored in the lithium-ion battery during time period T. This represents the upper limit of the state of charge (SOC) for energy storage in lithium-ion batteries. The initial daily charge of the lithium-ion battery energy storage is represented by T, where T represents data with a time resolution of 15 minutes, and T' represents the time period not exceeding T. The operating power of the lithium-ion battery energy storage during time period T' is given, where ΔT represents a time resolution of 15 minutes. The final charge capacity for energy storage in lithium-ion batteries. Rated power for energy storage in lithium-ion batteries.

[0139] As one possible implementation, in the above embodiments, step S4 may specifically include the following steps:

[0140] The improved lightweight robust optimization model is used to perform the first round of load verification on the compensated non-traction load to obtain the time resolution of the distributed photovoltaic power generation and the compensated non-traction load power as the first round of operation results.

[0141] Based on the results of the first round of operation, the grid output power under the maximum discharge power of the lithium-ion battery energy storage is obtained using the system power balance constraint of the lithium-ion battery energy storage.

[0142] The adjusted maximum discharge power of the lithium-ion battery is obtained by adjusting the grid output power under the maximum discharge power of the lithium-ion battery energy storage.

[0143] Based on the adjusted maximum discharge power of the lithium-ion battery energy storage, the time resolution of the lithium-ion battery energy storage power is obtained using the improved lightweight robust optimization model as the result of the second round of operation.

[0144] Based on the results of the second round of operation, a second round of load verification was conducted to obtain the amount of abandoned solar power as the result of optimized operation of the green energy system for rail transit.

[0145] Example 2

[0146] This embodiment considers the uncertainty risk of distributed photovoltaic power output and provides an optimized operation method for a green energy system in rail transit. A typical structure of such a green energy system in rail transit is as follows: Figure 2 As shown, it includes the following steps:

[0147] Step A: Maximize the utilization rate of regenerative braking energy by storing energy in supercapacitors, and establish a regenerative braking energy utilization model. Specifically:

[0148] Trains generate a significant amount of regenerative braking energy during braking, which can be collected through supercapacitor storage. Considering the impact of time-of-use pricing on the efficiency and economic benefits of regenerative braking energy utilization, the objective function under braking conditions is set as maximizing the regenerative braking energy utilization rate through supercapacitor storage under the guidance of time-of-use pricing. The calculation formula is as follows:

[0149]

[0150] in, Let ΔT be the time-of-use electricity price for period T, where T represents data with a time resolution of 15 minutes, and ΔT is the time resolution of 15 minutes. This represents the operating power of the supercapacitor energy storage during time period T, with a negative value for charging and a positive value for discharging.

[0151] Under braking conditions, the supercapacitor's state of charge, daily charge-discharge balance, and charge-discharge power constraints must be met. The constraint equations are shown in equations (2)-(5).

[0152]

[0153]

[0154] in, This represents the state of charge limit for energy storage in supercapacitors. The rated capacity for energy storage of a supercapacitor. This represents the charge stored in the supercapacitor during time period T. This represents the upper limit of the state of charge (SOC) for energy storage in a supercapacitor. The initial daily charge of energy stored in the supercapacitor. The supercapacitor energy storage operating power is defined as the time period T', where T' is the time period not exceeding T. The final charge of energy stored in a supercapacitor. The supercapacitor's energy storage power during time period T is limited under braking conditions. The rated power for energy storage in a supercapacitor.

[0155] Considering that the charging power of the supercapacitor should not exceed the regenerative braking energy within the corresponding time period, The calculation formulas are shown in equations (6)-(7):

[0156]

[0157] Where n is the conversion ratio of time resolution, Δt is the time resolution of 1 second, t represents the data with a time resolution of 1 second, and P t RB P represents the limited charging power of the supercapacitor stored during braking conditions in time period t.t RB0 The regenerative braking power during time period t is negative under traction load.

[0158] Step B: The collected regenerative braking energy is used to compensate for non-traction loads through an energy feed system. The utilization efficiency of the regenerative braking energy and the resulting economic benefits are further calculated. Specifically:

[0159] The regenerative braking energy collected by supercapacitors can compensate for non-traction loads such as air conditioning, lighting, communication, and signaling systems in stations through an energy feeder system. The calculation formula is as follows:

[0160]

[0161] in, The non-traction load power after compensation during time period T. This represents the non-traction load power before compensation during time period T.

[0162] The regenerative braking energy utilization efficiency is defined as the percentage of regenerative braking energy utilized by supercapacitors relative to the total regenerative braking energy in the traction load. The calculation formula is as follows:

[0163]

[0164] Where η is the regenerative braking energy utilization efficiency.

[0165] The economic benefits of supercapacitor energy storage utilizing regenerative braking energy are calculated as follows:

[0166]

[0167] Wherein, C0 represents the economic benefit generated by supercapacitor energy storage and utilization of regenerative braking energy. Operating cost per MWh for supercapacitor energy storage.

[0168] Step C: Considering the uncertainty of distributed photovoltaic power output, an improved lightweight robust optimization model is established, and lithium-ion battery energy storage is used to meet the power supply and demand balance under traction conditions. Specifically:

[0169] Considering the uncertainty of distributed photovoltaic power output and the resulting operational risks, an improved lightweight robust optimization model is established, requiring lithium-ion battery energy storage to meet the power supply and demand balance under traction conditions. The traction load adopts the time-of-use electricity price from the two-part tariff for large industrial users instead of the time-of-use tariff. The objective function under traction conditions is set as minimizing the total daily cost C of the green energy system for rail transit. total C total Operating cost C operation and risk cost C risk It consists of two parts. Among them, C...operation This mainly includes electricity cost C1, lithium-ion battery energy storage operating cost C2, distributed photovoltaic power generation subsidy C3, and the economic benefits C0 and C4 generated by supercapacitor energy storage utilizing regenerative braking energy. risk This indicates that the constraint of risk cost is violated due to the uncertainty of distributed photovoltaic power output. The specific calculation formula is as follows:

[0170]

[0171] Among them, G T The electricity price for time period T. For the green energy system of rail transit during time period T, the power purchased from the grid. The operating cost per MWh for lithium-ion battery energy storage This represents the operating power of the lithium-ion battery energy storage during time period T, with a negative state of charge and a positive state of discharge. The operating revenue per MWh of lithium-ion battery energy storage during time period T. Subsidies for each MWh of distributed photovoltaic power generation. For the distributed photovoltaic power generation system during time period T, ω T γT represents the weighting coefficient of the slack variable in time period T. The larger the value, the higher the cost of constraint violation. γT represents the slack variable in time period T, indicating the degree of constraint violation.

[0172] To effectively guide lithium-ion battery energy storage in peak shaving and valley filling, Make the following settings:

[0173]

[0174] in, The traction load power during time period T. This represents the average traction load power during the day. This serves as a benchmark for the operating revenue per MWh of lithium-ion battery energy storage. When the traction load is at its peak... For a positive value, lithium-ion battery energy storage should be in a discharged state to generate revenue; when the traction load is at its lowest point, If the value is negative, the lithium-ion battery energy storage should be in a charging state to generate revenue.

[0175] Based on the distribution characteristics of distributed photovoltaic (PV) power generation systems along railway lines, the output of a single PV system can be decomposed into the sum of the outputs of multiple distributed PV units. The calculation formula for the constraint equation of distributed PV output is as follows:

[0176]

[0177] in, Let j be the output of distributed photovoltaic unit j during time period T, and k be the total number of distributed photovoltaic units.

[0178] Considering the uncertainty of distributed photovoltaic (PV) output, the output of distributed PV units can be expressed as an expected value plus a fluctuation term, and the total fluctuation for different time periods can be set. The calculation formula is as follows:

[0179]

[0180] in, Let j be the expected power of the distributed photovoltaic unit during time period T. ζ represents the maximum fluctuation range of the output of distributed photovoltaic unit j during time period T. j,T Γ represents the output fluctuation ratio of distributed photovoltaic unit j during time period T. T This represents the total fluctuation during period T.

[0181] Based on the sorting truncation method, the distributed photovoltaic power output constraint is transformed into a linear correspondence of the improved lightweight robust model under the budget uncertainty set, and the calculation formula is as follows:

[0182]

[0183] Among them, |Γ T |not exceeding Γ T The largest integer, The sequence after descending order The elements in [-ΔP] T ΔP T [T] represents the fluctuation range of the overall output of the distributed photovoltaic power generation system during time period T.

[0184] Generally, the larger the slack variable, the higher the risk cost, leading to an increase in the weighting coefficient when the slack variable increases. Considering the difficulty in obtaining the true probability distribution of distributed photovoltaic (PV) output, the confidence interval of distributed PV output is divided into low-risk and high-risk zones based on the expected power. When distributed PV output is in the low-risk zone, a smaller weighting coefficient is assigned to encourage distributed PV consumption; while when it is in the high-risk zone, a larger weighting coefficient is assigned to increase the cost of constraint violation. The risk cost calculation formula for time period T is as follows:

[0185]

[0186] in, The weighting coefficient for the low-risk area during time period T. b1 is the weighting coefficient for high-risk areas during time period T, b2 is the risk ratio coefficient for distributed photovoltaic power output in low-risk areas, and b3 is the risk ratio coefficient for distributed photovoltaic power output in high-risk areas.

[0187] In addition to the aforementioned uncertainties in distributed photovoltaic power output, under traction conditions, the system power balance constraint, traction transformer power constraint, state of charge constraint of lithium-ion battery energy storage, daily charge-discharge balance constraint, and charge-discharge power constraint must also be met. The constraint equations are as follows:

[0188]

[0189] in, This refers to the rated power of the traction transformer. This represents the state of charge limit for lithium-ion battery energy storage. The rated capacity for energy storage in lithium-ion batteries. This represents the energy charge stored in the lithium-ion battery during time period T. This represents the upper limit of the state of charge (SOC) for energy storage in lithium-ion batteries. The initial daily charge of lithium-ion batteries for energy storage. The operating power of the lithium-ion battery energy storage during time period T'. The final charge capacity for energy storage in lithium-ion batteries. Rated power for energy storage in lithium-ion batteries.

[0190] Step D: Propose a two-round load verification method to correct the running results on a second-level time scale. Specifically:

[0191] The time resolution of distributed photovoltaic power generation prediction data is 15 minutes, while the time resolution of actual traction load measurement data is mostly 1 second, resulting in a time resolution mismatch. To address this issue, a two-round load verification method is proposed to correct the operating results on a second-level time scale.

[0192] Under braking conditions, the regenerative braking energy utilization model is a linear programming problem with a time resolution of 15 minutes. Supercapacitor energy storage, as a power-type energy storage method, has a rapid response and can meet the 1-second time resolution constraint, allowing for efficient solution using commercial optimization software. After solving, the compensated non-traction load is used as input to the improved lightweight robust optimization model.

[0193] Under traction conditions, the objective function of the improved lightweight robust optimization model with a time resolution of 15 minutes includes nonlinear risk costs. Lithium-ion battery energy storage, as an energy-type storage system, has a slow response speed and may not meet the 1-second time resolution constraint, making it difficult to solve directly using commercial optimization software.

[0194] Assuming distributed photovoltaic power output is in a low-risk zone, based on 0≤γT≤ΔP T The improved lightweight robust optimization model can be transformed into a linear programming problem, and then the slack variable γ can be obtained. T The solution to '. Based on γ T The risk cost of distributed photovoltaic power output is adjusted as follows:

[0195]

[0196] Given γ T It has been confirmed that the model can be transformed into a linear programming problem, and the results can be obtained at a time resolution of 15 minutes.

[0197] When the traction load with a time resolution of 1 second is higher or slightly lower than its average value over 15 minutes, the system power balance constraint can be met by increasing or decreasing the grid output power. Since the load change is within a controllable range, no constraint violation will occur. When the traction load with a time resolution of 1 second is much lower than its average value over 15 minutes, simply reducing the grid output power is insufficient to maintain system power balance, and a constraint violation will occur. The first round of load verification aims to adjust the maximum discharge power constraint of lithium-ion battery energy storage; the second round of load verification aims to resolve constraint violations exceeding the regulation range of the grid and lithium-ion battery energy storage, and, if necessary, to perform curtailment to ensure stable system operation. Details are as follows:

[0198] First, the time resolution of the distributed photovoltaic power generation and the compensated non-traction load power in the first round of operation results is converted from 15 minutes to 1 second. Then, the grid output power under the maximum discharge power of lithium-ion battery energy storage is calculated according to the system power balance constraint. The calculation formula is as follows:

[0199]

[0200] Among them, P t G,Ι P represents the grid output power during time period t in the first round of load verification. t PV,Ι Let Ⅰ represent the distributed photovoltaic power generation during time period t in the first round of load verification, and D represent the first round of load verification. t The total load power during time period t is obtained by adding the traction load power and the compensated non-traction load power.

[0201] When P t G,Ι When P ≥ 0, it indicates that there is no constraint violation, and the maximum discharge power constraint of lithium-ion battery energy storage does not need to be adjusted during time period t. t G,Ι When the value is less than 0, it indicates that the discharge power of the lithium-ion battery energy storage is too high, affecting the absorption of distributed photovoltaic power, thus violating the constraint. Therefore, it is necessary to adjust the maximum discharge power of the lithium-ion battery energy storage during time period t. The calculation formula is as follows:

[0202]

[0203] The adjusted maximum discharge power constraint of the lithium-ion battery energy storage was introduced into the improved lightweight robust optimization model for the second round of solution. Furthermore, the time resolution of the lithium-ion battery energy storage power in the second round results was changed from 15 minutes to 1 second. However, when... At that time, it is necessary to order And adjust the power of other time periods t to ensure that the average power of time period T is equal to Among them, P t LBES The operating power of the lithium-ion battery energy storage during time period t.

[0204] Subsequently, in the second round of load verification, the power grid output expression is as follows:

[0205] P t G,ΙΙ =D t -P t PV,ΙΙ -P t LBES (26)

[0206] Among them, P t G,ΙΙ P represents the grid output power during time period t in the second round of load verification. t PV,ΙΙ Ⅱ represents the distributed photovoltaic power generation during time period t in the second round of load verification, and Ⅱ indicates the second round of load verification.

[0207] Similar to the first round of load verification, when P t G,ΙΙ A constraint violation will occur when the value is less than 0. The amount of light discarded, P, during time interval t. t PV ,curtail The calculation formula is as follows:

[0208] P t PV,curtail =-P t G,ΙΙ P t G,ΙΙ <0 (27)

[0209] Finally, the time resolution of the abandoned light power was changed from 1 second to 15 minutes.

[0210] In summary, the model solution strategy process is as follows: Figure 3 As shown.

[0211] This embodiment demonstrates the rationality and effectiveness of the model established in this invention through numerical examples, and proves that the proposed method can achieve synergistic optimization of risk costs and operating costs, effectively improving the operating efficiency and reliability of green energy systems for rail transit. Specifically:

[0212] 1. Optimization Operation Method and Model Parameter Setting for Green Energy Systems in Rail Transit Taking into Account the Uncertainty Risk of Distributed Photovoltaic Output

[0213] Using a traction substation of a heavy-haul railway in China as the test object, the rationality and effectiveness of the model and algorithm established in this invention are verified. The rated power and capacity of the supercapacitor energy storage under braking conditions are set to 3MW and 1MWh, respectively; the rated power and capacity of the lithium-ion battery energy storage under traction conditions are set to 3MW and 2MWh, respectively; the operating cost per MWh of supercapacitor energy storage is 50 yuan; the operating cost per MWh of lithium-ion battery energy storage is 50 yuan; the benchmark operating revenue per MWh of lithium-ion battery energy storage is 80 yuan; the subsidy for distributed photovoltaic power generation is 50 yuan per MWh; the risk ratio coefficients for distributed photovoltaic power output in low-risk and high-risk areas are 0.8 and 1.2, respectively; the total regenerative braking energy in the traction load is 6.17MWh; and the CO2 emission reduction for every 1MWh of electricity utilized is 0.858t. The measured traction load curve of the traction substation is shown below. Figure 4 As shown; the curves of expected power and non-traction load of distributed photovoltaic units in traction substations are as follows. Figure 5 As shown; the risk distribution of slack variables in distributed photovoltaic power output is as follows: Figure 6 As shown; the total fluctuation of distributed photovoltaic power output is as follows: Figure 7 As shown; the electricity price in time-of-use pricing and two-part pricing is as follows: Figure 8 As shown in the figure; the optimized operation results are shown in Table 1.

[0214] Table 1

[0215] Operating cost / yuan Risk cost / yuan Total cost / yuan 258880 1040 259920

[0216] 2. Analysis of Regenerative Braking Energy Utilization Model

[0217] Three sets of supercapacitors with different rated power and capacity (referred to as sets 1-3) were used for energy storage, namely (1MW, 1MWh), (3MW, 1MWh), and (3MW, 2MWh).

[0218] Analysis shows that, from Figure 9 (a)- Figure 9 (d) It can be seen that the larger the rated power and capacity of the supercapacitor energy storage, the greater the utilization efficiency, economic benefits, and CO2 emission reduction of the regenerative braking energy utilization model. Furthermore, due to the influence of time-of-use pricing, groups 2 and 3 show similar utilization efficiencies, but different economic benefits. From... Figure 10 It can be seen that as the rated capacity of supercapacitor energy storage increases, the discharge power of supercapacitor energy storage becomes more concentrated during peak electricity price periods, which improves the economic benefits of the regenerative braking energy utilization model while compensating for non-traction loads.

[0219] 3. Analysis of the operational results of load verification at the second-level time scale

[0220] The operation results of load verification at the second-level time scale were analyzed using the typical period from 12:30 to 12:45. The average comprehensive load was 27.17 MW. The operation results for the typical period are as follows: Figure 11 (a)- Figure 11 As shown in (e).

[0221] Analysis shows that, from Figure 11 As can be seen from (b) and (c), when the comprehensive load with a time resolution of 1 second is higher or slightly lower than its average value within 15 minutes, simply increasing or decreasing the grid output is sufficient to meet the system's power balance constraints. At this time, the lithium-ion battery energy storage discharges at its rated power, and the distributed photovoltaic power is fully absorbed. From... Figure 11 As can be seen from (d) and (e), when the comprehensive load with a time resolution of 1 second is much lower than its average value within 15 minutes, it is not enough to maintain the system power balance simply by reducing the grid output. When the grid output drops to 0, the lithium-ion battery energy storage rapidly transitions from a discharging state to a charging state. If this exceeds the adjustment range of the grid and the lithium-ion battery energy storage, then solar power is curtailed.

[0222] Further comparative analysis was conducted on the results of the first round of model solving and the second round of load verification during the aforementioned typical time periods. Table 2 shows a comparison of the results for each typical time period.

[0223] Table 2

[0224]

[0225]

[0226] Analysis shows that, as can be seen from Table 2, after the second round of load verification, due to the sudden drop in the overall load, the amount of abandoned light during the typical period was 32.5 kWh.

[0227] The above embodiments have provided a detailed description of the technical solution of the present invention. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various modifications, but any modifications that are equivalent to or similar to the present invention fall within the scope of protection of the present invention.

[0228] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0229] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0230] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0231] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0232] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a green energy system for rail transit, characterized in that, Specifically, the following steps are included: S1. Establish a regenerative braking energy utilization model by maximizing the regenerative braking energy utilization rate through supercapacitor energy storage. S2. Based on the regenerative braking energy utilization model, the non-traction load is compensated to obtain the utilization efficiency and economic benefits of regenerative braking energy. S3. Based on the utilization efficiency of the regenerative braking energy and the economic benefits, and taking into account the uncertainty of distributed photovoltaic power output, an improved lightly robust optimization model is established. S4. Based on the improved lightweight robust optimization model, perform load verification to obtain the optimized operation results of the rail transit green energy system.

2. The method for optimizing the operation of a green energy system for rail transit according to claim 1, characterized in that, S1. Establish a regenerative braking energy utilization model by maximizing the regenerative braking energy utilization rate through supercapacitor energy storage, including: Regenerative braking energy is obtained by storing energy in supercapacitors; Based on the regenerative braking energy and considering the time-of-use electricity price, the objective function under braking conditions is to maximize the utilization rate of regenerative braking energy by setting the supercapacitor energy storage as the regenerative braking energy storage. Based on the objective function under the braking condition, a regenerative braking energy utilization model is constructed using the constraints of supercapacitor energy storage operation.

3. The method for optimizing the operation of a green energy system for rail transit according to claim 2, characterized in that, The objective function under the braking condition is calculated as follows: in, This refers to the time-of-use electricity price for period T, where T represents data with a time resolution of 15 minutes. The supercapacitor energy storage operating power is denoted by T, where ΔT represents a time resolution of 15 minutes.

4. The optimized operation method for a green energy system in rail transit according to claim 2, characterized in that, The constraints on the operation of supercapacitor energy storage include the state of charge constraint, the daily charge and discharge balance constraint, and the charge and discharge power constraint. The formula for calculating the state-of-charge constraint of the supercapacitor energy storage is as follows: The formula for calculating the daily charge-discharge balance constraint of the supercapacitor energy storage is as follows: The formula for calculating the charging and discharging power constraint of the supercapacitor energy storage is as follows: in, This represents the state of charge limit for energy storage in supercapacitors. The rated capacity for energy storage of a supercapacitor. This represents the energy stored in the supercapacitor during time period T, where T indicates data with a time resolution of 15 minutes. This represents the upper limit of the state of charge (SOC) for energy storage in a supercapacitor. The initial daily charge of the supercapacitor is T', which represents the time period not exceeding T. The supercapacitor energy storage operating power is represented by T', where ΔT is a time resolution of 15 minutes. The final charge of energy stored in a supercapacitor. The supercapacitor's energy storage power during time period T is limited under braking conditions. The supercapacitor energy storage operating power during time period T. The rated power for energy storage in a supercapacitor.

5. The optimized operation method for a green energy system in rail transit according to claim 1, characterized in that, S2. Based on the regenerative braking energy utilization model, compensation processing is performed on the non-traction load to obtain the utilization efficiency and economic benefits of regenerative braking energy, including: Based on the aforementioned regenerative braking energy utilization model, supercapacitor energy storage and regenerative braking energy are collected and utilized. The supercapacitor stores regenerative braking energy and uses an energy feed system to compensate for the non-traction load, thus obtaining the compensated non-traction load. The utilization efficiency and economic benefits of regenerative braking energy are obtained based on the non-traction load and the compensated non-traction load. The formula for calculating the utilization efficiency of the regenerative braking energy is as follows: The formula for calculating the economic benefits is as follows: Where η represents the utilization efficiency of regenerative braking energy, and T represents data with a time resolution of 15 minutes. The non-traction load power before compensation in time period T. P represents the non-traction load power after compensation during time period T, where ΔT is a time resolution of 15 minutes. t RB0 Let t represent the regenerative braking power during time period t, Δt be the time resolution of 1 second, and C0 be the economic benefit generated by the supercapacitor's energy storage and utilization of regenerative braking energy. For time-of-use electricity pricing during period T, The operating cost per MWh for supercapacitor energy storage The operating power of the supercapacitor energy storage during time period T.

6. The method for optimizing the operation of a green energy system for rail transit according to claim 5, characterized in that, The calculation formula for using regenerative braking energy stored in the supercapacitor to compensate for non-traction loads through the energy feed system is as follows: Where T represents data with a time resolution of 15 minutes. The non-traction load power after compensation during time period T. The non-traction load power before compensation in time period T. The operating power of the supercapacitor energy storage during time period T.

7. The optimized operation method for a green energy system in rail transit according to claim 5, characterized in that, S3. Based on the utilization efficiency of the regenerative braking energy and the economic benefits, and taking into account the uncertainty of distributed photovoltaic power output, an improved lightweight robust optimization model is established, including: Based on the utilization efficiency of the regenerative braking energy and the economic benefits, the objective function for minimizing the total daily cost of the green energy system for rail transit under traction conditions is set. An improved lightweight robust optimization model is established based on the objective function under the aforementioned traction conditions and the distributed photovoltaic power output constraint. The minimized daily total cost of the green energy system for rail transit includes operating costs and risk costs. The operating costs include electricity costs, lithium-ion battery energy storage operating costs, distributed photovoltaic power generation subsidies, and the economic benefits. The objective function under the traction condition is calculated as follows: The formula for calculating the distributed photovoltaic power output constraint is as follows: Among them, C total To minimize the total daily cost of green energy systems for rail transit, C operation For operating costs, C risk C1 represents the cost of electricity, C2 represents the operating cost of lithium-ion battery energy storage, C3 represents the subsidy for distributed photovoltaic power generation, C0 represents the economic benefit, T represents the data with a time resolution of 15 minutes, and G represents the risk cost. T The electricity price for time period T. The power purchased from the grid by the green energy system of rail transit during time period T, where ΔT is a time resolution of 15 minutes. The operating cost per MWh for lithium-ion battery energy storage The operating power of lithium-ion battery energy storage during time period T. The operating revenue per MWh of lithium-ion battery energy storage during time period T. Subsidies for each MWh of distributed photovoltaic power generation. For the distributed photovoltaic power generation system during time period T, ω T γ represents the weighting coefficient of the slack variable at time T. T For time period T, slack variables Let j be the output of distributed photovoltaic unit j during time period T, and k be the total number of distributed photovoltaic units.

8. The method for optimizing the operation of a green energy system for rail transit according to claim 7, characterized in that, The improved lightweight robust optimization model based on the objective function under the aforementioned traction condition and the distributed photovoltaic power output constraint also includes: An improved lightweight robust optimization model is established based on system power balance constraints, traction transformer power constraints, state of charge constraints, daily charge and discharge balance constraints, and charge and discharge power constraints of lithium-ion battery energy storage. The calculation formula for the system power balance constraint of the lithium-ion battery energy storage is as follows: The formula for calculating the power constraint of the traction transformer for lithium-ion battery energy storage is as follows: The formula for calculating the state of charge constraint of the lithium-ion battery energy storage is as follows: The formula for calculating the daily charge-discharge balance constraint of the lithium-ion battery energy storage is as follows: The formula for calculating the charge and discharge power constraint of the lithium-ion battery energy storage is as follows: in, For the green energy system of rail transit during time period T, the power purchased from the grid. The operating power of lithium-ion battery energy storage during time period T. To provide power for the distributed photovoltaic power generation system during time period T. The traction load power during time period T. The non-traction load power after compensation during time period T. This refers to the rated power of the traction transformer. This represents the state of charge limit for lithium-ion battery energy storage. The rated capacity for energy storage of lithium-ion batteries. This represents the energy charge stored in the lithium-ion battery during time period T. This represents the upper limit of the state of charge (SOC) for energy storage in lithium-ion batteries. The initial daily charge of the lithium-ion battery energy storage is represented by T, where T represents data with a time resolution of 15 minutes, and T' represents the time period not exceeding T. The operating power of the lithium-ion battery energy storage during time period T' is given, where ΔT represents a time resolution of 15 minutes. The final charge capacity for energy storage in lithium-ion batteries. Rated power for energy storage in lithium-ion batteries.

9. The method for optimizing the operation of a green energy system for rail transit according to claim 8, characterized in that, S4. Obtain the optimized operation results of the rail transit green energy system by performing load verification based on the improved lightweight robust optimization model, including: The improved lightweight robust optimization model is used to perform the first round of load verification on the compensated non-traction load to obtain the time resolution of the distributed photovoltaic power generation and the compensated non-traction load power as the first round of operation results. Based on the results of the first round of operation, the grid output power under the maximum discharge power of the lithium-ion battery energy storage is obtained using the system power balance constraint of the lithium-ion battery energy storage. The adjusted maximum discharge power of the lithium-ion battery is obtained by adjusting the grid output power under the maximum discharge power of the lithium-ion battery. Based on the adjusted maximum discharge power of the lithium-ion battery energy storage, the time resolution of the lithium-ion battery energy storage power is obtained using the improved lightweight robust optimization model as the result of the second round of operation. Based on the results of the second round of operation, a second round of load verification was conducted to obtain the amount of abandoned solar power as the result of optimized operation of the green energy system for rail transit.

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