Optical storage collaborative optimization configuration method for rail transit green energy system

Abstract

The invention discloses a rail transit green energy system optical storage collaborative optimization configuration method, and relates to the technical field of integrated energy systems, and the method comprises the steps: building a rail transit green energy system optical storage collaborative optimization configuration model; the method comprises the following steps: establishing a min-max-min two-stage distribution robust optimization model by utilizing distributed photovoltaic output uncertainty according to a rail transit green energy system optical storage collaborative optimization configuration model; the min-max-min two-stage distribution robust optimization model is decomposed, and a planning stage main problem and an operation stage sub-problem are obtained; and based on the planning stage main problem and the operation stage sub-problems, utilizing an imprecise column sum constraint generation algorithm to obtain a rail transit green energy system optical storage collaborative optimization configuration result. According to the method, collaborative optimization of the investment cost and the operation cost is realized, safe operation of the system is guaranteed, the new energy permeability is improved, and a solution with economical efficiency and robustness is provided for construction of a rail transit green energy system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, which is highly interdisciplinary and involves many fields such as electrical engineering, energy and power, transportation, and low-carbon economy. In particular, it relates to a method for the coordinated optimization configuration of photovoltaic and energy storage in a green energy system for rail transit. Background Technology

[0002] With the rapid expansion of my country's high-speed railway network, the issues of electricity consumption and carbon emissions have become increasingly prominent. The railway industry is responding proactively, focusing its research and development on energy efficiency improvement and energy supply, and vigorously promoting new energy sources and intelligent grid-connected solutions to enhance the green, environmentally friendly, and intelligent level of transportation energy infrastructure and promote the deep synergistic development of "source, grid, load, and storage." Against this backdrop, the traction power supply system, as the energy hub of rail transit, is facing both an urgent need and significant challenges in its clean energy transformation.

[0003] Studies have shown that rail transit lines possess abundant resources and have enormous potential for asset-based energy conversion. Connecting distributed photovoltaic power generation to traction substations and utilizing energy storage devices to recover regenerative braking energy, thereby constructing an efficient, resilient, clean, and diversified green energy system for rail transit, has become a key approach to improving energy utilization and reducing energy consumption.

[0004] Therefore, there is an urgent need for a method for the coordinated optimization of photovoltaic and energy storage configuration in green energy systems for rail transit, in order to address the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to propose a method for the coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit. The method aims to minimize the average daily total cost of the green energy system for rail transit. It takes the safe operation of each unit of "grid-source-storage-vehicle" at 1-minute intervals as the boundary, constructs a two-stage sub-Bruker optimization model of min-max-min that takes into account the uncertainty of distributed photovoltaic output, and solves it using an inaccurate column and constraint generation algorithm to obtain the optimal configuration scheme of photovoltaic and energy storage capacity under the probability distribution of the worst scenario.

[0006] To achieve the above objectives, this invention provides a method for the coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit, comprising the following steps:

[0007] S1. Establish a photovoltaic-storage synergistic optimization configuration model for green energy systems in rail transit;

[0008] S2. Based on the photovoltaic-storage synergistic optimization configuration model of green energy system for rail transit, and utilizing the uncertainty of distributed photovoltaic output, a two-stage sub-Bruker optimization model of min-max-min is established.

[0009] S3. Decompose the min-max-min two-stage sub-Bruker optimization model to obtain the main problem in the planning stage and the sub-problems in the running stage;

[0010] S4. Based on the main problem of the planning stage and the sub-problems of the operation stage, the non-precise column and constraint generation algorithm is used to obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit.

[0011] Optionally, S1, establish a photovoltaic-storage synergistic optimization configuration model for green energy systems in rail transit, including:

[0012] Minimize the average daily total cost of the green energy system for rail transit to obtain the minimum average daily total cost of the green energy system for rail transit.

[0013] With the goal of minimizing the average daily total cost of the green energy system for rail transit and with safe operation as the boundary, a photovoltaic-storage collaborative optimization configuration model for the green energy system for rail transit is established.

[0014] Optionally, the total daily cost of the green energy system for rail transit includes the equivalent daily investment cost and the typical daily operating cost;

[0015] The equivalent daily investment cost includes the equivalent daily investment cost of distributed photovoltaic and its supporting converter equipment, the equivalent daily investment cost of lithium-ion battery energy storage and its supporting converter equipment, and the equivalent daily investment cost of supercapacitor energy storage and its supporting converter equipment.

[0016] The typical daily operating cost includes electricity cost, demand cost, and operation and maintenance cost of the photovoltaic and energy storage system.

[0017] Optionally, the safe operation includes distributed photovoltaic planning and configuration constraints, hybrid energy storage system planning and configuration constraints, maximum demand power constraints, system power balance constraints, traction substation power constraints, back-to-back converter power constraints, hybrid energy storage system operation constraints, and grid-side three-phase voltage imbalance constraints.

[0018] The planning and configuration constraints for distributed photovoltaic systems are as follows:

[0019]

[0020] The planning and configuration constraints for the hybrid energy storage system are as follows:

[0021]

[0022] The maximum power demand constraint is:

[0023]

[0024] The system power balance constraint is:

[0025]

[0026] The power constraint of the traction substation is:

[0027]

[0028] The power constraint of the back-to-back converter is:

[0029]

[0030] The constraint on the three-phase voltage imbalance on the grid side is:

[0031]

[0032] in, This represents the upper limit of the capacity for distributed photovoltaic systems. This represents the lower limit for the capacity of distributed photovoltaic systems. Configure power for distributed photovoltaic systems. Configure power for lithium-ion battery energy storage. The power configuration for supercapacitor energy storage is given, where κ is the lower limit for the energy storage ratio in new energy projects. This represents the lower limit of the configuration power for lithium-ion battery energy storage. The upper limit of the configuration power for lithium-ion battery energy storage. This refers to the lower limit of the configuration power for supercapacitor energy storage. The upper limit of the configuration power for supercapacitor energy storage. Configure capacity for lithium-ion battery energy storage. Configure the capacity for supercapacitor energy storage, τ LBES τ is the continuous energy storage duration of a lithium-ion battery at its rated power. SCES This refers to the continuous energy storage time of a supercapacitor at its rated power. This represents the maximum power demand within the month. These represent the power purchased from the grid by the green energy system of rail transit during time period t. The power fed back to the grid by the green energy system of the rail transit system during time period t. These represent the forward and reverse active power of the α-phase transformer during time period t. These represent the forward and reverse active power of the β-phase transformer during time period t. These represent the forward and reverse active power of the α-phase converter during time period t, respectively. These represent the forward and reverse active power of the β-phase converter during time period t, respectively. These represent the active load power of the α-phase and β-phase power supply arms during time period t. These represent the reactive power of the α-phase and β-phase transformers during time period t. These represent the reactive power of the α-phase and β-phase converters during time period t, respectively. The reactive load power of phase α and phase β power supply arms during time period t are respectively, b grid,t Let t be the active power state variable of the tie line during time period t. These are the rated capacities of the α-phase and β-phase transformers, respectively. These are the active power state variables of the α-phase and β-phase transformers during time period t, respectively. These are the active power state variables of the α-phase and β-phase converters during time period t, respectively. These represent the rated capacities of the α-phase and β-phase converters respectively during time period t, where I2 is the negative sequence current, and I... α I is the current of the α-phase power supply arm. β For the β-phase power supply arm current, The phase angle difference between the current of phase α supply arm and the voltage of phase A on the primary side. N represents the phase angle difference between the β-phase power supply arm current and the primary side B-phase voltage. T This refers to the voltage ratio of the traction transformer.

[0033] Optionally, the operating constraints of the hybrid energy storage system include charge and discharge power constraints, state of charge constraints, and intraday charge and discharge balance constraints.

[0034] The charging and discharging power constraint is:

[0035]

[0036] The state of charge constraint is as follows:

[0037]

[0038] The intraday charge / discharge balance constraint is:

[0039]

[0040] in, These represent the charging and discharging power of the lithium-ion battery energy storage during time period t. These represent the charging and discharging power of the supercapacitor stored during time period t, and b. LBES,t b SCES,t These represent the charge / discharge state variables of lithium-ion battery energy storage and supercapacitor energy storage during time period t, respectively. The power configurations for lithium-ion battery energy storage and supercapacitor energy storage are respectively, S LBES,t S SCES,t These represent the state of charge of lithium-ion battery energy storage and supercapacitor energy storage at time t, respectively. These are the initial state of charge (SOC) during the day for lithium-ion battery energy storage and supercapacitor energy storage, respectively. The respective capacities are configured for lithium-ion battery energy storage and supercapacitor energy storage. These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. These represent the charging and discharging power of the lithium-ion battery energy storage during time period t', respectively. These represent the charging and discharging efficiencies of supercapacitor energy storage, respectively. Δt represents the charging and discharging power of the supercapacitor during time period t', respectively, where Δt is the time interval. These represent the upper and lower limits of the state of charge (SOC) for lithium-ion battery energy storage, respectively. These represent the upper and lower limits of the state of charge (SOC) for supercapacitor energy storage, respectively.

[0041] Optionally, S2, based on the photovoltaic-storage synergistic optimization configuration model of the green energy system for rail transit, utilizes the uncertainty of distributed photovoltaic output to establish a two-stage sub-Bruker optimization model (min-max-min), including:

[0042] Based on the photovoltaic-storage synergistic optimization configuration model of the green energy system for rail transit, the target configuration capacity and target operating power are obtained.

[0043] Using the target configuration capacity as the decision variable for the min problem and the target operating power as the decision variable for the max-min problem, the predicted dynamic traction load power is obtained;

[0044] Using comprehensive norm constraints, a fuzzy set of probability distribution for distributed photovoltaic power output is constructed;

[0045] Based on the predicted dynamic traction load power and the fuzzy set of the probability distribution of distributed photovoltaic power output, a two-stage sub-Bruker optimization model of min-max-min is established.

[0046] Optionally, the fuzzy set of the distributed photovoltaic power output probability distribution is:

[0047]

[0048] Where F is the fuzzy set of distributed photovoltaic power output probability distribution, ρ is the actual power output probability distribution matrix of distributed photovoltaic power of arbitrary capacity, and ρ t,n Let T be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, where t is time. Light Let N be the set of periods with sunlight, and N be the total number of discrete scenarios with distributed photovoltaic power output. Let θ1 and θ2 be the discrete probabilities corresponding to the nth initial output scenario of distributed photovoltaic power generation in time period t. ∞ These are the permissible deviations of the probability distribution under 1-norm and ∞-norm constraints, respectively.

[0049] Optionally, the main problem in the planning phase is:

[0050]

[0051] Where y is the decision variable for the first-stage optimization problem, and C inv Let L be the daily value of the investment cost, η be the introduced auxiliary variable, and L be the daily value of the investment cost. B The lower bound of the constraint on the optimal value of the main problem, C ope For typical daily operating costs, x k The simulated operating power of each unit in the k-th iteration is... Let represent the probability distribution of distributed photovoltaic power output calculated from the subproblem at the k-th iteration, where * denotes a known quantity, k is the number of previous iterations, and j is the number of the current iteration.

[0052] Optionally, the sub-problem of the running phase is:

[0053]

[0054] Among them, C inv For the cost of investment on the same day, ρ t,n Let F be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, F be the fuzzy set of distributed photovoltaic power generation probability distribution, T be the scheduling period, N be the total number of discrete scenarios of distributed photovoltaic power generation, and C be the discrete probability of distributed photovoltaic power generation in time period t. ope The figure represents the typical daily operating cost, and * indicates a known quantity. * For, x n For the nth distributed photovoltaic power output scenario, the simulated operating power of each unit is given. This refers to the nth distributed photovoltaic power output scenario during time period t.

[0055] Optionally, S4, based on the main problem of the planning phase and the sub-problems of the operation phase, using an imprecise column and constraint generation algorithm, obtain the photovoltaic-storage synergistic optimization configuration results of the rail transit green energy system, including:

[0056] Given a scenario probability distribution, the main problem of the planning stage is solved within the optimization gap to obtain the lower and upper bounds of the optimal value of the main problem;

[0057] Using the lower bound of the optimal value of the main problem and the constraint lower bound of the optimal value of the main problem, update the constraint lower bound of the optimal value of the main problem to be equal to the upper bound of the optimal value of the main problem;

[0058] The main problem of the planning phase is used to solve the sub-problems of the operation phase, the scenario probability distribution obtained from the solutions of the sub-problems of the operation phase is obtained, and the effective upper bound of the optimal value of the original problem is updated.

[0059] Use the effective upper and lower bounds of the optimal value of the original problem to determine whether the convergence criterion is met. If yes, obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit; otherwise, execute the first operation.

[0060] The first operation is as follows: using the effective upper bound of the optimal value of the original problem and the upper bound of the optimal value of the main problem to determine whether the imprecise criterion is satisfied; if so, then set the historical iteration count, the constraint lower bound of the optimal value of the main problem, and the optimization gap, and return the given scenario probability distribution; within the optimization gap, solve the main problem of the planning stage to obtain the lower bound and upper bound of the optimal value of the main problem; otherwise, return the scenario probability distribution obtained from the subproblems of the running stage to the given scenario probability distribution, solve the main problem of the planning stage within the optimization gap to obtain the lower bound and upper bound of the optimal value of the main problem, add variables and constraints, and update the iteration index.

[0061] Compared with the closest existing technology, the present invention has the following advantages:

[0062] This invention constructs an optimization model with the objective of minimizing the average daily total cost and the boundary condition of safe operation. The problem is reconstructed into a two-stage min-max-min biblical optimization model, further decomposed into a main planning problem and operational subproblems, and solved using an imprecise column and constraint generation algorithm. By constructing a fuzzy set of the probability distribution of distributed photovoltaic power output through comprehensive norm constraints, the uncertainty boundary is quantified. Under the worst-case scenario, the photovoltaic and energy storage capacity configuration is optimized, achieving synergistic optimization of investment and operating costs. This ensures the safe operation of the system and increases the penetration rate of new energy sources, providing an economical and robust solution for the construction of green energy systems in rail transit. Attached Figure Description

[0063] 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.

[0064] Figure 1 This is a flowchart illustrating a method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit, according to an embodiment of the present invention.

[0065] 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;

[0066] Figure 3 The flowchart below shows the algorithm for generating and solving inaccurate columns and constraints proposed in this embodiment of the invention.

[0067] Figure 4 This is a dynamic traction load power prediction curve diagram for high-speed railway traction substations proposed in an embodiment of the present invention;

[0068] Figure 5 This is a graph showing the discrete probability distribution of distributed photovoltaic power output in high-speed railway traction substations and the expected power prediction curve for typical days, as proposed in an embodiment of the present invention. Detailed Implementation

[0069] 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.

[0070] 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.

[0071] Example 1

[0072] like Figure 1 As shown, this embodiment provides a method for the coordinated optimization configuration of photovoltaic and energy storage in a green energy system for rail transit, including:

[0073] S1. Establish a photovoltaic-storage synergistic optimization configuration model for green energy systems in rail transit;

[0074] S2. Based on the photovoltaic-storage synergistic optimization configuration model of green energy system for rail transit, and utilizing the uncertainty of distributed photovoltaic output, a two-stage sub-Bruker optimization model of min-max-min is established.

[0075] S3. Decompose the min-max-min two-stage sub-Bruker optimization model to obtain the main problem in the planning stage and the sub-problems in the running stage;

[0076] S4. Based on the main problem of the planning stage and the sub-problems of the operation stage, the non-precise column and constraint generation algorithm is used to obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit.

[0077] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps:

[0078] Minimize the average daily total cost of the green energy system for rail transit to obtain the minimum average daily total cost of the green energy system for rail transit.

[0079] With the goal of minimizing the average daily total cost of the green energy system for rail transit and with safe operation as the boundary, a photovoltaic-storage collaborative optimization configuration model for the green energy system for rail transit is established.

[0080] Furthermore, the total daily cost of the green energy system for rail transit includes the equivalent daily investment cost and the typical daily operating cost;

[0081] The equivalent daily investment cost includes the equivalent daily investment cost of distributed photovoltaic and its supporting converter equipment, the equivalent daily investment cost of lithium-ion battery energy storage and its supporting converter equipment, and the equivalent daily investment cost of supercapacitor energy storage and its supporting converter equipment.

[0082] The typical daily operating cost includes electricity cost, demand cost, and operation and maintenance cost of the photovoltaic and energy storage system.

[0083] Furthermore, the safe operation includes distributed photovoltaic planning and configuration constraints, hybrid energy storage system planning and configuration constraints, maximum demand power constraints, system power balance constraints, traction substation power constraints, back-to-back converter power constraints, hybrid energy storage system operation constraints, and grid-side three-phase voltage imbalance constraints.

[0084] The planning and configuration constraints for distributed photovoltaic systems are as follows:

[0085]

[0086] The planning and configuration constraints for the hybrid energy storage system are as follows:

[0087]

[0088] The maximum power demand constraint is:

[0089]

[0090] The system power balance constraint is:

[0091]

[0092] The power constraint of the traction substation is:

[0093]

[0094] The power constraint of the back-to-back converter is:

[0095]

[0096] The constraint on the three-phase voltage imbalance on the grid side is:

[0097]

[0098] in, This represents the upper limit of the capacity for distributed photovoltaic systems. This represents the lower limit for the capacity of distributed photovoltaic systems. Configure power for distributed photovoltaic systems. Configure power for lithium-ion battery energy storage. The power configuration for supercapacitor energy storage is given, where κ is the lower limit for the energy storage ratio in new energy projects. This represents the lower limit of the configuration power for lithium-ion battery energy storage. The upper limit of the configuration power for lithium-ion battery energy storage. This refers to the lower limit of the configuration power for supercapacitor energy storage. The upper limit of the configuration power for supercapacitor energy storage. Configure capacity for lithium-ion battery energy storage. Configure the capacity for supercapacitor energy storage, τ LBES τ is the continuous energy storage duration of a lithium-ion battery at its rated power. SCES This refers to the continuous energy storage time of a supercapacitor at its rated power. This represents the maximum power demand within the month. These represent the power purchased from the grid by the green energy system of rail transit during time period t. The power fed back to the grid by the green energy system of the rail transit system during time period t. These represent the forward and reverse active power of the α-phase transformer during time period t. These represent the forward and reverse active power of the β-phase transformer during time period t. These represent the forward and reverse active power of the α-phase converter during time period t, respectively. These represent the forward and reverse active power of the β-phase converter during time period t, respectively. These represent the active load power of the α-phase and β-phase power supply arms during time period t. These represent the reactive power of the α-phase and β-phase transformers during time period t. These represent the reactive power of the α-phase and β-phase converters during time period t, respectively. The reactive load power of phase α and phase β power supply arms during time period t are respectively, b grid,t Let t be the active power state variable of the tie line during time period t. These are the rated capacities of the α-phase and β-phase transformers, respectively. These are the active power state variables of the α-phase and β-phase transformers during time period t, respectively. These are the active power state variables of the α-phase and β-phase converters during time period t, respectively. These represent the rated capacities of the α-phase and β-phase converters respectively during time period t, where I2 is the negative sequence current, and I... α I is the current of the α-phase power supply arm. β For the β-phase power supply arm current, The phase angle difference between the current of phase α supply arm and the voltage of phase A on the primary side. N represents the phase angle difference between the β-phase power supply arm current and the primary side B-phase voltage. T This refers to the voltage ratio of the traction transformer.

[0099] Furthermore, the operational constraints of the hybrid energy storage system include charge and discharge power constraints, state of charge constraints, and intraday charge and discharge balance constraints.

[0100] The charging and discharging power constraint is:

[0101]

[0102] The state of charge constraint is as follows:

[0103]

[0104] The intraday charge / discharge balance constraint is:

[0105]

[0106] in, These represent the charging and discharging power of the lithium-ion battery energy storage during time period t. These represent the charging and discharging power of the supercapacitor stored during time period t, and b. LBES,t b SCES,t These represent the charge / discharge state variables of lithium-ion battery energy storage and supercapacitor energy storage during time period t, respectively. The power configurations for lithium-ion battery energy storage and supercapacitor energy storage are respectively, S LBES,t S SCES,t These represent the state of charge of lithium-ion battery energy storage and supercapacitor energy storage at time t, respectively. These are the initial state of charge (SOC) during the day for lithium-ion battery energy storage and supercapacitor energy storage, respectively. The respective capacities are configured for lithium-ion battery energy storage and supercapacitor energy storage. These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. These represent the charging and discharging power of the lithium-ion battery energy storage during time period t', respectively. These represent the charging and discharging efficiencies of supercapacitor energy storage, respectively. Δt represents the charging and discharging power of the supercapacitor during time period t', respectively, where Δt is the time interval. These represent the upper and lower limits of the state of charge (SOC) for lithium-ion battery energy storage, respectively. These represent the upper and lower limits of the state of charge (SOC) for supercapacitor energy storage, respectively.

[0107] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0108] Based on the photovoltaic-storage synergistic optimization configuration model of the green energy system for rail transit, the target configuration capacity and target operating power are obtained.

[0109] Using the target configuration capacity as the decision variable for the min problem and the target operating power as the decision variable for the max-min problem, the predicted dynamic traction load power is obtained.

[0110] Using comprehensive norm constraints, a fuzzy set of probability distribution for distributed photovoltaic power output is constructed;

[0111] Based on the predicted dynamic traction load power and the fuzzy set of the probability distribution of distributed photovoltaic power output, a two-stage sub-Bruker optimization model of min-max-min is established.

[0112] The target configuration capacity is the configuration capacity of distributed photovoltaic, lithium-ion battery energy storage and supercapacitor energy storage, and the target operating power is the operating power of each unit in the "grid-source-storage-vehicle" system.

[0113] Furthermore, the fuzzy set of the distributed photovoltaic power output probability distribution is:

[0114]

[0115] Where F is the fuzzy set of distributed photovoltaic power output probability distribution, ρ is the actual power output probability distribution matrix of distributed photovoltaic power of arbitrary capacity, and ρ t,n Let T be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, where t is time. Light Let N be the set of periods with sunlight, and N be the total number of discrete scenarios with distributed photovoltaic power output. Let θ1 and θ2 be the discrete probabilities corresponding to the nth initial output scenario of distributed photovoltaic power generation in time period t. ∞ These are the permissible deviations of the probability distribution under 1-norm and ∞-norm constraints, respectively.

[0116] Furthermore, the main problem in the planning phase is:

[0117]

[0118] Where y is the decision variable for the first-stage optimization problem, and C inv Let L be the daily value of the investment cost, η be the introduced auxiliary variable, and L be the daily value of the investment cost. B The lower bound of the constraint on the optimal value of the main problem, C ope For typical daily operating costs, x k The simulated operating power of each unit in the k-th iteration is... Let represent the probability distribution of distributed photovoltaic power output calculated from the subproblem at the k-th iteration, where * denotes a known quantity, k is the number of previous iterations, and j is the number of the current iteration.

[0119] Furthermore, the sub-problem of the operational phase is:

[0120]

[0121] Among them, C inv For the cost of investment on the same day, ρ t,n Let F be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, F be the fuzzy set of distributed photovoltaic power generation probability distribution, T be the scheduling period, N be the total number of discrete scenarios of distributed photovoltaic power generation, and C be the discrete probability of distributed photovoltaic power generation in time period t. ope The figure represents the typical daily operating cost, and * indicates a known quantity. * For, x n For the nth distributed photovoltaic power output scenario, the simulated operating power of each unit is given. This refers to the nth distributed photovoltaic power output scenario during time period t.

[0122] As one possible implementation, in the above embodiments, step S4 may specifically include the following steps:

[0123] Given a scenario probability distribution, the main problem of the planning stage is solved within the optimization gap to obtain the lower and upper bounds of the optimal value of the main problem;

[0124] Using the lower bound of the optimal value of the main problem and the constraint lower bound of the optimal value of the main problem, update the constraint lower bound of the optimal value of the main problem to be equal to the upper bound of the optimal value of the main problem;

[0125] The main problem of the planning phase is used to solve the sub-problems of the operation phase, the scenario probability distribution obtained from the solutions of the sub-problems of the operation phase is obtained, and the effective upper bound of the optimal value of the original problem is updated.

[0126] Use the effective upper and lower bounds of the optimal value of the original problem to determine whether the convergence criterion is met. If yes, obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit; otherwise, execute the first operation.

[0127] The first operation is as follows: using the effective upper bound of the optimal value of the original problem and the upper bound of the optimal value of the main problem to determine whether the imprecise criterion is satisfied; if so, then set the historical iteration count, the constraint lower bound of the optimal value of the main problem, and the optimization gap, and return the given scenario probability distribution; within the optimization gap, solve the main problem of the planning stage to obtain the lower bound and upper bound of the optimal value of the main problem; otherwise, return the scenario probability distribution obtained from the subproblems of the running stage to the given scenario probability distribution, solve the main problem of the planning stage within the optimization gap to obtain the lower bound and upper bound of the optimal value of the main problem, add variables and constraints, and update the iteration index.

[0128] Example 2

[0129] This embodiment analyzes and compares the optimal configuration method of photovoltaic-storage synergistic distributed photovoltaic (PV) rods in a rail transit green energy system that takes into account the uncertainty of distributed PV output. A typical structure of a rail transit green energy system is shown below. Figure 2 As shown, the specific steps include:

[0130] Step A: With the goal of minimizing the average daily total cost of the green energy system for rail transit, and with the safe operation of each unit of "grid-source-storage-vehicle" at 1-minute intervals as the boundary, establish a photovoltaic-storage collaborative optimization configuration model for the green energy system of rail transit.

[0131] Average daily total cost C total Investment cost C (based on daily value) inv and typical daily operating cost C ope It consists of two parts, and the specific expression is as follows:

[0132] min C total =C inv +C ope (1)

[0133] Among them, C inv This mainly includes the daily investment cost of distributed photovoltaic power generation and its supporting converter equipment. Equivalent daily investment cost of lithium-ion battery energy storage and its supporting converter equipment Equivalent daily investment cost of supercapacitor energy storage and its supporting converter equipment The specific expression is as follows:

[0134]

[0135] Where r is the investment discount rate calculated at an annual interest rate, and y DPV y LBES y SCES These refer to the investment periods for distributed photovoltaic power, lithium-ion battery energy storage, and supercapacitor energy storage, respectively. These are the investment cost coefficients for distributed photovoltaic power, lithium-ion battery energy storage, and supercapacitor energy storage, respectively. These are the configuration capacities for distributed photovoltaic, lithium-ion battery energy storage, and supercapacitor energy storage, respectively.

[0136] C ope This includes the cost of electricity (C). grid Demand electricity cost C dem And the operation and maintenance costs C of the photovoltaic and energy storage system (including each converter device) om The specific expression is as follows:

[0137]

[0138] Among them, c buy c fb These refer to the electricity purchase price from the grid and the billing price for electricity fed back to the grid for the green energy system of rail transit, respectively. dem The price is based on demand. These represent the power purchased from the grid and the power fed back to the grid by the green energy system of rail transit during time period t. The maximum power demand within the month is given by N, where Δt is the time interval (1 minute). day The system operates for 30 days per month; T is the scheduling period, taken as 1440 minutes; and t is time. These are the operation and maintenance cost coefficients for distributed photovoltaic power, lithium-ion battery energy storage, and supercapacitor energy storage, respectively, P. DPV,t The distributed photovoltaic power generation during time period t. These represent the charging and discharging power of the lithium-ion battery energy storage during time period t. These represent the charging and discharging power of the supercapacitor energy storage during time period t.

[0139] During system planning, the following constraints must be met: distributed photovoltaic planning and configuration constraints, hybrid energy storage system planning and configuration constraints, maximum demand power constraints, system power balance constraints, traction substation power constraints, back-to-back converter power constraints, hybrid energy storage system operation constraints, and grid-side three-phase voltage imbalance constraints, as detailed below:

[0140] To fully utilize the natural resources of the rail transit area, the capacity of distributed photovoltaic power generation must ensure that the penetration rate of new energy meets the standards. At the same time, due to limitations in installation site and grid-connected power, the following constraints must be met:

[0141]

[0142] in, These represent the upper and lower limits of the distributed photovoltaic configuration capacity, respectively. Configure power for distributed photovoltaic systems.

[0143] According to the policy requirements for energy storage allocation in newly built new energy projects, the configuration capacity and continuous energy storage duration of hybrid energy storage systems must meet the following constraints:

[0144]

[0145] in, These represent the configured power for lithium-ion battery energy storage and supercapacitor energy storage, respectively, with κ being the lower limit for the energy storage ratio in new energy projects. These represent the upper and lower limits of the configuration power for lithium-ion battery energy storage, respectively. These represent the upper and lower limits of the configuration power for supercapacitor energy storage, respectively, τ LBES τ SCES These refer to the continuous energy storage duration of lithium-ion battery energy storage and supercapacitor energy storage at rated power, respectively.

[0146] In actual operation, the maximum demand power is determined by the maximum value of the average traction load every 15 minutes within a month. Considering the daily repeatability of load data under a fixed train timetable, the time scale for calculating the maximum demand can be taken as 1 day, and it is obtained through the sliding method, as shown in the following expression:

[0147]

[0148] Because the converter operates with high efficiency, its power loss is relatively small and can be ignored. Based on the system power flow distribution, a system power balance constraint is established, expressed as follows:

[0149]

[0150] in, These represent the forward and reverse active power of the α-phase transformer during time period t. These represent the forward and reverse active power of the β-phase transformer during time period t. These represent the forward and reverse active power of the α-phase converter during time period t, respectively. These represent the forward and reverse active power of the β-phase converter during time period t, respectively. These represent the active load power of the α-phase and β-phase power supply arms during time period t. These represent the reactive power of the α-phase and β-phase transformers during time period t. These represent the reactive power of the α-phase and β-phase converters during time period t, respectively. These represent the reactive load power of the α-phase and β-phase power supply arms during time period t.

[0151] The power exchange between the traction substation and the power grid interconnection line, as well as the power of the V / V connected traction transformer, are both limited by the capacity of the traction transformer and must meet the following constraints:

[0152]

[0153] Among them, b grid,t Let t be the active power state variable of the tie line during time period t, where 1 indicates that the system purchases electricity from the grid and 0 indicates that the system feeds back energy to the grid. These are the rated capacities of the α-phase and β-phase transformers, respectively. These are the active power state variables of the α-phase and β-phase transformers during time period t, respectively, where 1 indicates positive and 0 indicates negative.

[0154] The active and reactive power compensation power of back-to-back converters is limited by their rated capacity and must meet the following constraints:

[0155]

[0156] in, These are the active power state variables of the α-phase and β-phase converters during time period t, respectively, where 1 indicates positive and 0 indicates negative. These represent the rated capacities of the α-phase and β-phase converters during time period t, respectively.

[0157] Both lithium-ion battery energy storage and supercapacitor energy storage must meet constraints on charge and discharge power, state of charge, and daily charge and discharge balance during operation. The constraint equations are as follows:

[0158]

[0159] Among them, b LBES,t b SCES,t S represents the charge / discharge state variables of lithium-ion battery energy storage and supercapacitor energy storage during time period t, where 1 represents charging and 0 represents discharging; LBES,t S SCES,t These represent the state of charge of lithium-ion battery energy storage and supercapacitor energy storage at time t, respectively. These are the initial state of charge (SOC) during the day for lithium-ion battery energy storage and supercapacitor energy storage, respectively. These represent the upper and lower limits of the state of charge (SOC) for lithium-ion battery energy storage, respectively. These represent the upper and lower limits of the state of charge (SOC) for supercapacitor energy storage, respectively. These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. These represent the charging and discharging power of the lithium-ion battery energy storage during time period t', respectively. These represent the charging and discharging power of the supercapacitor energy storage during time period t'. These represent the charging and discharging efficiencies of the supercapacitor, respectively.

[0160] Given that the cycle life of lithium-ion battery energy storage is much shorter than that of supercapacitor energy storage, it is necessary to focus on the lifespan loss of lithium-ion battery energy storage. Depth of discharge is considered a key influencing factor and quantified by the number of charge-discharge cycles. Therefore, the number of cycles N required for a lithium-ion battery to reach the end of its lifespan is determined. life Specifically, it is expressed as follows:

[0161]

[0162] Where N0 is the number of cycles required for a lithium-ion battery to reach the end of its lifespan at 100% depth of discharge, and d cyc k represents the depth of discharge. p is the Pukter lifecycle constant.

[0163] The charge-discharge cycle occurs during the period when the lithium-ion battery transitions from a discharged state to a charged state. The depth of discharge during this period is related to the state of charge of the previous period, as shown in the following expression:

[0164]

[0165] Where, d cyc,t Let S be the discharge depth during time period t. LBES,t-1 b represents the state of charge of the lithium-ion battery energy storage during time period t-1. LBES,t-1 S represents the charge / discharge state variable of the lithium-ion battery energy storage during time period t-1. e,t t represents the charge-discharge cycle state variable of the lithium-ion battery energy storage during time period t, where 1 indicates that a charge-discharge cycle has occurred and 0 indicates that no charge-discharge cycle has occurred.

[0166] The number of charge-discharge cycles N of the lithium-ion battery energy storage at different depths of discharge during time period t. life.t Converted to the equivalent number of cycles N at 100% discharge depth during time period t. eq.t The expression is as follows:

[0167]

[0168] To ensure that the actual lifespan of lithium-ion battery energy storage can reach the fixed service life, it is necessary to limit its equivalent cycle count per day. The constraint equation is as follows:

[0169]

[0170] According to GB / T 15543-2008 Power Quality Three-Phase Voltage Imbalance, the three-phase voltage imbalance at the point of common coupling of traction substations must meet the following constraints:

[0171]

[0172] Where, ξ UI2 is the three-phase voltage imbalance, and U is the negative sequence current. L S K These are the rated line voltage and short-circuit capacity of the primary power grid, respectively. This is the upper limit of the three-phase voltage imbalance.

[0173] When a traction transformer uses a V / V connection, the expression for the negative sequence current of the V / V connected traction transformer can be derived from the phasor relationship between the primary and secondary voltages and currents, as follows:

[0174]

[0175] Among them, I α I is the current of the α-phase power supply arm. β For the β-phase power supply arm current, The phase angle difference between the current of phase α supply arm and the voltage of phase A on the primary side. N represents the phase angle difference between the β-phase power supply arm current and the primary side B-phase voltage. T This refers to the voltage ratio of the traction transformer.

[0176] In summary, substituting equation (18) into equation (17), the transformed three-phase voltage imbalance constraint equation on the grid side is as follows:

[0177]

[0178] Step B: Taking into account the uncertainty of distributed photovoltaic power output, the original problem is reconstructed into a two-stage min-max-min Bruker optimization model;

[0179] Distributed photovoltaic (PV) output is highly dependent on changes in the natural environment, and prediction errors are unavoidable. Therefore, its uncertainties must be fully considered during the system planning phase. The original problem is reconstructed into a two-stage min-max-min bipolar optimization model. The first stage, the min problem, uses the configuration capacity of distributed PV, lithium-ion battery energy storage, and supercapacitor energy storage as decision variables, aiming to minimize investment costs. The second stage, the max-min problem, uses the operating power of each unit in the "grid-source-storage-vehicle" system as decision variables, aiming to minimize operating costs under the probability distribution of the worst-case distributed PV output scenario. The objective function expression is as follows:

[0180] minC inv +maxρminC ope (20)

[0181] Where ρ is the probability distribution matrix of the actual output of distributed photovoltaic power of any capacity.

[0182] To ensure that the probability of distributed photovoltaic (PV) power output scenarios fluctuates within a reasonable range, a comprehensive norm constraint based on the 1-norm and ∞-norm is used to restrict the probability distribution of uncertain scenarios, thereby constructing a fuzzy set F of the probability distribution of distributed PV power output of arbitrary capacity. The specific expression is as follows:

[0183]

[0184] Among them, T Light Let N be the set of periods with light, and ρ be the total number of discrete scenarios with distributed photovoltaic power output. t,n Let be the discrete probability corresponding to the nth actual power output scenario of distributed photovoltaic power generation in time period t. Let θ1 and θ2 be the discrete probabilities corresponding to the nth initial output scenario of distributed photovoltaic power generation in time period t. ∞ These are the allowable deviations of the probability distribution under 1-norm and ∞-norm constraints, respectively, representing the maximum deviation between the actual scenario probability and the initial scenario probability.

[0185] Step C: Decompose the min-max-min two-stage Bruker optimization model into a planning phase main problem and an operational phase subproblem, that is, decompose the original problem into a planning phase main problem (22) and an operational phase subproblem (23), as shown in the following expressions:

[0186]

[0187] Where * represents a known quantity, y is the decision variable for the first-stage optimization problem, represents the photovoltaic-storage configuration capacity, η is an introduced auxiliary variable, represents the optimal value of the objective function for the second stage, and L... B The constraint lower bound for the optimal value of the main problem is defined as follows: j and k are the current and historical iteration numbers, respectively; x is the decision variable for the second-stage optimization problem, representing the simulation power of each unit. k The simulated operating power of each unit in the k-th iteration is... Let y be the probability distribution of distributed photovoltaic power output calculated from the subproblem in the k-th iteration. * Let x be the photovoltaic-storage configuration capacity calculated from the main problem. n For the nth distributed photovoltaic power output scenario, the simulated operating power of each unit is given. This refers to the nth distributed photovoltaic power output scenario during time period t.

[0188] Step D: Solve the model using an imprecise column and constraint generation algorithm;

[0189] First, given the scenario probability distribution ρ of distributed photovoltaic power output. j In optimizing the gap ε MP,j Solve the primal problem internally to obtain a lower bound L for the optimal value of the primal problem. j and the upper realm U jIf L j >L B Let l = j, and denote L. l This is a valid lower bound for the optimal value of the original problem; update L. B =U j Where l is the index of the effective lower bound.

[0190] Secondly, the max-min two-level optimization problem in the subproblem is decoupled, as shown in the following expression:

[0191]

[0192] in, For the nth distributed photovoltaic power output scenario in time period t, h n D represents the minimum operating cost of the system corresponding to the nth distributed photovoltaic power output scenario. j This represents the maximum expected value of the operating cost obtained in the j-th iteration.

[0193] Based on the distributed photovoltaic configuration capacity passed from the main problem, formula (24) is used to obtain the minimum system operating cost corresponding to each output scenario of distributed photovoltaic; formula (25) uses fuzzy set constraints to solve the distributed photovoltaic probability distribution that maximizes the overall expected value of the operating cost, and updates the effective upper bound U of the optimal value of the original problem. B =min{U B C inv (y * )+D j}

[0194] Then, if the convergence criterion is met, the iteration ends and the optimal solution is returned, i.e., the optimized configuration result of photovoltaic and energy storage and the simulated operation result of a typical day are obtained as the optimized configuration result of photovoltaic and energy storage for the green energy system of rail transit; otherwise, the "utilization-exploration" step is entered. The convergence criterion expression is as follows:

[0195]

[0196] Where ε is the convergence gap.

[0197] The steps are as follows: If the imprecise criterion is met, set j = l, backtrack to solve the main problem, and simultaneously adjust the lower bound of the constraint on the optimal value of the main problem to L. B =L l And tighten the optimized gap ε MP,j =με MP,j μ∈(0,1), The inexact criterion expression is as follows:

[0198]

[0199] in, This refers to a non-precise relative gap.

[0200] Exploration steps: If the imprecise criterion is not met, then the expected value of the worst-case output scenario of distributed photovoltaic power obtained from the subproblem is transferred to the main problem, that is, the scenario probability distribution ρ obtained from the subproblem is transferred to the main problem. j+1 Return to the main problem, add new variables and constraints accordingly, and let j = j + 1 to continue the iterative calculation.

[0201] In summary, the algorithm flow for solving inexact columns and constraints is as follows: Figure 3 As shown.

[0202] To enable those skilled in the art to better understand the present invention and its advantages over the prior art, the applicant provides further explanation in conjunction with specific embodiments.

[0203] Finally, numerical examples demonstrate the rationality and effectiveness of the model established in this embodiment, and prove that the proposed method can reasonably optimize the configuration of photovoltaic and energy storage capacity, effectively guiding the construction of green energy systems for rail transit.

[0204] 1. Optimization method and model parameter settings for photovoltaic-storage synergistic distributed photovoltaic (PV) rod configuration in a green energy system for rail transit, considering the uncertainty of distributed PV output.

[0205] Using a traction substation of a high-speed railway in China as a test object, the rationality and effectiveness of the model and algorithm established in this invention are verified. A billing price c is set for the electrical energy fed back from the traction substation to the power grid. fb =0; Confidence level of uncertainty probability γ1 = 0.5, γ... ∞ =0.99; Convergence gap ε of inaccurate column and constraint generation algorithm = 2%; Inaccurate relative gap The initial optimization gap ε of the main problem MP =5%; using a reduction factor μ=0.8. The dynamic traction load power prediction curve is as follows: Figure 4 As shown; the discrete probability distribution of distributed photovoltaic power output and the typical daily expected power prediction curve are as follows. Figure 5 As shown in Table 1; the parameters of the green energy system model for rail transit are shown in Table 2; the results of the photovoltaic-storage synergistic optimization configuration are shown in Table 2.

[0206] Table 1

[0207]

[0208]

[0209] Table 2

[0210]

[0211] 2. Comparison of the impact of photovoltaic and energy storage integration

[0212] To further analyze the impact of the integration of distributed photovoltaic (PV) and hybrid energy storage systems on the optimization results, the existing traction power supply system without distributed PV and hybrid energy storage systems is considered as Scheme 1, and the rail transit green energy system integrating distributed PV and hybrid energy storage systems is considered as Scheme 2. Typical daily operation results of the two schemes are compared and analyzed. The comparison of the impact results of PV and energy storage integration is shown in Table 3.

[0213] Table 3

[0214]

[0215]

[0216] Analysis, as shown in Table 3, reveals that the integration of photovoltaic and energy storage systems reduces both the cost of electricity per unit and the cost of electricity based on demand. Even considering the operation and maintenance costs of photovoltaic and energy storage systems, the typical daily operating cost still decreases by 24.91%. By constructing a green energy system for rail transit, the amount of electricity purchased from the grid by traction substations can be reduced, and the utilization rate of regenerative braking energy can be improved, demonstrating a certain potential for energy self-sufficiency and energy conservation and efficiency improvement.

[0217] In summary, this invention proposes a method for optimizing the configuration of photovoltaic and energy storage synergistic distributed photovoltaic power generation in green energy systems for rail transit. Taking into account the impact of uncertainties in distributed photovoltaic output, a fuzzy set of probability distribution is constructed using comprehensive norm constraints. An inexact column and constraint generation algorithm is used to obtain the optimal configuration scheme of photovoltaic and energy storage capacity under the worst-case probability distribution, providing a useful reference for promoting the construction of green energy systems for rail transit.

[0218] 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.

[0219] 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 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0220] 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.

[0221] 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.

[0222] 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 coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit, characterized in that, include: S1. Establish a photovoltaic-storage synergistic optimization configuration model for green energy systems in rail transit; S2. Based on the photovoltaic-storage synergistic optimization configuration model of green energy system for rail transit, and utilizing the uncertainty of distributed photovoltaic output, a two-stage sub-Bruker optimization model of min-max-min is established. S3. Decompose the min-max-min two-stage sub-Bruker optimization model to obtain the main problem in the planning stage and the sub-problems in the running stage; S4. Based on the main problem of the planning stage and the sub-problems of the operation stage, the non-precise column and constraint generation algorithm is used to obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit.

2. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 1, characterized in that, S1. Establish a photovoltaic-storage synergistic optimization configuration model for green energy systems in rail transit, including: Minimize the average daily total cost of the green energy system for rail transit to obtain the minimum average daily total cost of the green energy system for rail transit. With the goal of minimizing the average daily total cost of the green energy system for rail transit and with safe operation as the boundary, a photovoltaic-storage collaborative optimization configuration model for the green energy system for rail transit is established.

3. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 2, characterized in that, The total daily cost of the green energy system for rail transit includes the equivalent daily investment cost and the typical daily operating cost. The equivalent daily investment cost includes the equivalent daily investment cost of distributed photovoltaic and its supporting converter equipment, the equivalent daily investment cost of lithium-ion battery energy storage and its supporting converter equipment, and the equivalent daily investment cost of supercapacitor energy storage and its supporting converter equipment. The typical daily operating cost includes electricity cost, demand cost, and operation and maintenance cost of the photovoltaic and energy storage system.

4. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 2, characterized in that, The safe operation includes distributed photovoltaic planning and configuration constraints, hybrid energy storage system planning and configuration constraints, maximum demand power constraints, system power balance constraints, traction substation power constraints, back-to-back converter power constraints, hybrid energy storage system operation constraints, and grid-side three-phase voltage imbalance constraints. The planning and configuration constraints for distributed photovoltaic systems are as follows: The planning and configuration constraints for the hybrid energy storage system are as follows: The maximum power demand constraint is: The system power balance constraint is: The power constraint of the traction substation is: The power constraint of the back-to-back converter is: The constraint on the three-phase voltage imbalance on the grid side is: in, This represents the upper limit of the capacity for distributed photovoltaic systems. This represents the lower limit for the capacity of distributed photovoltaic systems. Configure power for distributed photovoltaic systems. Configure power for lithium-ion battery energy storage. The power configuration for supercapacitor energy storage is given, where κ is the lower limit for the energy storage ratio in new energy projects. This represents the lower limit of the configuration power for lithium-ion battery energy storage. The upper limit of the configuration power for lithium-ion battery energy storage. This refers to the lower limit of the configuration power for supercapacitor energy storage. The upper limit of the configuration power for supercapacitor energy storage. Configure capacity for lithium-ion battery energy storage. Configure the capacity for supercapacitor energy storage, τ LBES τ is the continuous energy storage duration of a lithium-ion battery at its rated power. SCES This refers to the continuous energy storage time of a supercapacitor at its rated power. This represents the maximum power demand within the month. These represent the power purchased from the grid by the green energy system of rail transit during time period t. The power fed back to the grid by the green energy system of the rail transit system during time period t. These represent the forward and reverse active power of the α-phase transformer during time period t. These represent the forward and reverse active power of the β-phase transformer during time period t. These represent the forward and reverse active power of the α-phase converter during time period t, respectively. These represent the forward and reverse active power of the β-phase converter during time period t, respectively. These represent the active load power of the α-phase and β-phase power supply arms during time period t. These represent the reactive power of the α-phase and β-phase transformers during time period t. These represent the reactive power of the α-phase and β-phase converters during time period t, respectively. The reactive load power of phase α and phase β power supply arms during time period t are respectively, b grid,t Let t be the active power state variable of the tie line during time period t. These are the rated capacities of the α-phase and β-phase transformers, respectively. These are the active power state variables of the α-phase and β-phase transformers during time period t, respectively. These are the active power state variables of the α-phase and β-phase converters during time period t, respectively. These represent the rated capacities of the α-phase and β-phase converters respectively during time period t, where I2 is the negative sequence current, and I... α I is the current of the α-phase power supply arm. β For the β-phase power supply arm current, The phase angle difference between the current of phase α supply arm and the voltage of phase A on the primary side. N represents the phase angle difference between the β-phase power supply arm current and the primary side B-phase voltage. T This refers to the voltage ratio of the traction transformer.

5. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 4, characterized in that, The operational constraints of the hybrid energy storage system include charge and discharge power constraints, state of charge constraints, and daily charge and discharge balance constraints. The charging and discharging power constraint is: The charge state constraint is: The intraday charge / discharge balance constraint is: in, These represent the charging and discharging power of the lithium-ion battery energy storage during time period t. These represent the charging and discharging power of the supercapacitor stored during time period t, and b. LBES,t b SCES,t These represent the charge / discharge state variables of lithium-ion battery energy storage and supercapacitor energy storage during time period t, respectively. The power configurations for lithium-ion battery energy storage and supercapacitor energy storage are respectively, S LBES,t S SCES,t These represent the state of charge of lithium-ion battery energy storage and supercapacitor energy storage at time t, respectively. These are the initial state of charge (SOC) during the day for lithium-ion battery energy storage and supercapacitor energy storage, respectively. The respective capacities are configured for lithium-ion battery energy storage and supercapacitor energy storage. These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. These represent the charging and discharging power of the lithium-ion battery energy storage during time period t', respectively. These represent the charging and discharging efficiencies of supercapacitors, respectively. Δt represents the charging and discharging power of the supercapacitor during time period t', respectively, where Δt is the time interval. These represent the upper and lower limits of the state of charge (SOC) for lithium-ion battery energy storage, respectively. These represent the upper and lower limits of the state of charge (SOC) for supercapacitor energy storage, respectively.

6. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 1, characterized in that, S2. Based on the photovoltaic-storage synergistic optimization configuration model of the green energy system for rail transit, and utilizing the uncertainty of distributed photovoltaic output, a two-stage sub-Bruker optimization model (min-max-min) is established, including: Based on the photovoltaic-storage synergistic optimization configuration model of the green energy system for rail transit, the target configuration capacity and target operating power are obtained. Using the target configuration capacity as the decision variable for the min problem and the target operating power as the decision variable for the max-min problem, the predicted dynamic traction load power is obtained; Using comprehensive norm constraints, a fuzzy set of probability distribution for distributed photovoltaic power output is constructed; Based on the predicted dynamic traction load power and the fuzzy set of the probability distribution of distributed photovoltaic power output, a two-stage sub-Bruker optimization model of min-max-min is established.

7. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 6, characterized in that, The fuzzy set of the probability distribution of distributed photovoltaic power output is: Where F is the fuzzy set of distributed photovoltaic power output probability distribution, ρ is the actual power output probability distribution matrix of distributed photovoltaic power of arbitrary capacity, and ρ t,n Let T be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, where t is time. Light Let N be the set of periods with sunlight, and N be the total number of discrete scenarios with distributed photovoltaic power output. Let θ1 and θ2 be the discrete probabilities corresponding to the nth initial output scenario of distributed photovoltaic power generation in time period t. ∞ These are the permissible deviations of the probability distribution under 1-norm and ∞-norm constraints, respectively.

8. The method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 1, characterized in that, The main problem in the planning phase is: Where y is the decision variable for the first-stage optimization problem, and C inv Let L be the daily value of the investment cost, η be the introduced auxiliary variable, and L be the daily value of the investment cost. B The lower bound of the constraint on the optimal value of the main problem, C ope For typical daily operating costs, x k The simulated operating power of each unit in the k-th iteration is... Let represent the probability distribution of distributed photovoltaic power output calculated from the subproblem at the k-th iteration, where * denotes a known quantity, k is the number of previous iterations, and j is the number of the current iteration.

9. A method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 1, characterized in that, The sub-problems of the operational phase are: Among them, C inv For the cost of investment on the same day, ρ t,n Let F be the discrete probability corresponding to the nth actual output scenario of distributed photovoltaic power generation in time period t, F be the fuzzy set of distributed photovoltaic power generation probability distribution, T be the scheduling period, N be the total number of discrete scenarios of distributed photovoltaic power generation, and C be the discrete probability of distributed photovoltaic power generation in time period t. ope The figure represents the typical daily operating cost, and * indicates a known quantity. * For, x n For the nth distributed photovoltaic power output scenario, the simulated operating power of each unit is given. This refers to the nth distributed photovoltaic power output scenario during time period t.

10. A method for coordinated optimization of photovoltaic and energy storage configuration in a green energy system for rail transit according to claim 1, characterized in that, S4. Based on the main problem of the planning phase and the sub-problems of the operation phase, using an imprecise column and constraint generation algorithm, obtain the photovoltaic-storage synergistic optimization configuration results of the green energy system for rail transit, including: Given a scenario probability distribution, the main problem of the planning stage is solved within the optimization gap to obtain the lower and upper bounds of the optimal value of the main problem; Using the lower bound of the optimal value of the main problem and the constraint lower bound of the optimal value of the main problem, update the constraint lower bound of the optimal value of the main problem to be equal to the upper bound of the optimal value of the main problem; The main problem of the planning phase is used to solve the sub-problems of the operation phase, the scenario probability distribution obtained from the solutions of the sub-problems of the operation phase is obtained, and the effective upper bound of the optimal value of the original problem is updated. Use the effective upper and lower bounds of the optimal value of the original problem to determine whether the convergence criterion is met. If yes, obtain the photovoltaic-storage synergistic optimization configuration result of the green energy system for rail transit; otherwise, execute the first operation. The first operation is as follows: using the effective upper bound of the optimal value of the original problem and the upper bound of the optimal value of the main problem to determine whether the imprecise criterion is satisfied; if so, then set the historical iteration count, the constraint lower bound of the optimal value of the main problem, and the optimization gap, and return the given scenario probability distribution; within the optimization gap, solve the main problem of the planning stage to obtain the lower bound and upper bound of the optimal value of the main problem; otherwise, return the scenario probability distribution obtained from the subproblems of the running stage to the given scenario probability distribution, solve the main problem of the planning stage within the optimization gap to obtain the lower bound and upper bound of the optimal value of the main problem, add variables and constraints, and update the iteration index.

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