Urban rail transit light storage integrated project energy storage capacity configuration calculation method and system

Abstract

The application discloses a kind of urban rail transit light storage integrated project energy storage capacity configuration method and system, specifically includes the following steps: S100, and based on historical load data and photovoltaic output data, predict the photovoltaic output prediction value and load demand prediction value of future one year;S200, determine brake energy recovery efficiency, and fit energy storage annual attenuation coefficient curve;S300, to each period is carried out condition constraint, and optimization is carried out using objective function;S400, respectively calculate the photovoltaic consumption rate under different energy storage capacity configuration, annual average net income and static investment recovery period, obtain the economic optimal energy storage capacity;S500, according to the superposition of local peak-valley electricity price period, photovoltaic power generation period, different operating conditions are divided;S600, simulate the photovoltaic consumption rate in whole life cycle, annual average net income and investment recovery period, verify whether to achieve the expected goal.In pursuit of high photovoltaic consumption rate, the service life of energy storage equipment and the safety and stability of power grid are considered.

Description

Technical Field

[0001] This invention belongs to the field of energy storage configuration technology, and more specifically, relates to a method and system for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit. Background Technology

[0002] With the advancement of the integration of green transportation and renewable energy, the integration of large-capacity photovoltaic (PV) systems into urban rail transit is showing a clear trend. Utilizing the rooftops of buildings such as depots and stations to install PV systems can provide clean electricity for rail transit. However, the electricity load of urban rail transit is highly volatile, and braking energy may be fed back into the grid during train operation. This load characteristic, combined with the intermittent nature of PV power generation, results in a generally low PV absorption rate after large-capacity PV integration. Therefore, in practical engineering projects, energy storage devices are usually required for regulation.

[0003] In existing technologies, to address the aforementioned grid integration issues, energy storage systems are typically used to balance photovoltaic (PV) power generation with load demand. Current research on energy storage configurations is largely concentrated in academic fields, primarily focusing on calculating the charging and discharging behavior of energy storage through theoretical models. These conventional methods attempt to improve the system's self-consumption ratio by storing surplus PV power and releasing it during peak load periods, thereby alleviating the backfeed pressure from PV power to the grid.

[0004] However, existing energy storage configuration methods still have significant shortcomings in practical engineering applications. First, there is a lack of research on refined designs tailored to specific project types and closely integrated with local peak-valley electricity pricing policies. This makes it difficult for energy storage systems to fully utilize price differences to generate economic benefits while meeting grid connection requirements. Second, existing technologies often lack a systematic consideration of the entire lifecycle's economics. How to rationally design energy storage capacity in conjunction with peak-valley electricity pricing to maximize lifecycle returns has become a challenging issue. This makes current energy storage designs often fall short of economic optimization, lacking guiding design methods for practical integrated photovoltaic-energy storage projects in rail transit. Summary of the Invention

[0005] To address the aforementioned shortcomings or improvement needs of existing technologies, this invention provides a method and system for configuring energy storage capacity in integrated photovoltaic and energy storage projects for urban rail transit. By constructing a five-dimensional mathematical model incorporating photovoltaic characteristics, load fluctuations, battery aging losses, and grid impact limitations, this method pursues high photovoltaic absorption rates while simultaneously considering the lifespan of energy storage equipment and the safety and stability of the power grid. This method provides owners with an optimal configuration scheme that not only offers a high book return rate but also results in a low asset depreciation rate and strong risk resistance during actual operation, fundamentally enhancing the project's practical engineering value.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit is provided, specifically including the following steps: S100. Collect core basic data of the project, and based on historical load data and photovoltaic output data, predict the photovoltaic output and load demand for the next year. S200. Statistically analyze the temporal distribution characteristics of the train's braking energy recovery power, determine the braking energy recovery efficiency, and fit the annual energy storage attenuation coefficient curve. S300. Construct a mathematical model of the photovoltaic-storage system, impose conditions on each time period, and optimize it using an objective function to form a mathematical model system under multi-dimensional constraints. S400. Based on the established mathematical model, the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations are calculated respectively. The trend of indicator changes is analyzed to obtain the economically optimal energy storage capacity. S500: Formulate energy storage charging and discharging control strategies, and classify different operating conditions according to the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; S600. Based on actual project case data, substitute the energy storage capacity and control strategy determined in the above steps, simulate and calculate the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle, and verify whether the expected goals have been achieved.

[0007] Furthermore, in step S100, the core basic data of the project includes rail transit line parameters, photovoltaic system parameters, energy storage equipment parameters, economic parameters, and electricity price and policy parameters; The parameters for the rail transit line include length, power supply system, train formation, and peak / off-peak departure intervals; the parameters for the photovoltaic system include installed capacity, installation location, and local irradiance statistics; the parameters for the energy storage equipment include unit investment cost. Charging efficiency Discharge efficiency Depth of discharge Maximum charging and discharging power of energy storage devices Annual attenuation coefficient The economic parameters include the unit investment cost of photovoltaic power. Annual operating and maintenance costs Additional maintenance costs for ancillary services Additional investment in auxiliary service equipment Discount rate The electricity price and policy parameters include peak-valley time periods and corresponding unit electricity prices. Unit ancillary service revenue Power grid safety constraint threshold Carbon emission control targets .

[0008] Furthermore, when forecasting photovoltaic power output and load demand, the historical load data and photovoltaic power output data of the past three years are first cleaned to remove abnormal data such as extreme weather and equipment failures. The missing data is then supplemented by linear interpolation to ensure the integrity of the basic data. Using an LSTM neural network, a three-layer hidden layer structure was constructed. Input features included daily time period, seasonal attributes, historical average irradiance, and load temporal variation patterns. The output was a 15-minute forecast of photovoltaic power output for the next year. and load demand forecast During model training, the Adam optimizer is used to iteratively adjust parameters to keep the prediction error within 5%, which meets the accuracy requirements of engineering calculations.

[0009] Furthermore, in step S200, regarding train braking energy, by analyzing the temporal distribution characteristics of train braking frequency and braking intensity during different operating periods, and combining this with the actual operating efficiency test data of the braking energy recovery device, a default value for the braking energy recovery efficiency is determined. At the same time, an adjustment margin of ±0.05 is reserved to adapt to the differences in braking conditions of different lines. Then, based on the default value of the braking energy recovery efficiency... The braking energy recovery efficiency was adjusted to accommodate differences in track gradients and train formation sizes, specifically as follows: By dynamically adjusting the line parameters according to specific projects, the collaborative charging constraint formula is ensured. The accuracy of medium-energy co-calculation, among which This is the power feedback for train braking energy.

[0010] Furthermore, the annual energy storage degradation coefficient curve is obtained through quadratic polynomial fitting, specifically as follows: in, For energy storage Annual capacity decay coefficient, Based on energy storage Annual capacity decay coefficient You can also get the first Annual actual available energy storage capacity : and annual depreciation loss costs : .

[0011] Further, in step S400, the photovoltaic absorption rate is: According to the power of abandoned light The calculation results yield the corresponding The annual photovoltaic power consumption rate is required, of which the curtailed power must meet the following requirements. , Increase Simultaneous improvement Gradually reduce, It is showing an upward trend; The average annual net income is: First, calculate the revenue for the three operating states on an hourly basis. The total annual income is obtained by summing them up. After deducting the average annual operation and maintenance costs Additional maintenance costs for ancillary services and annual attenuation loss cost The net income in year t is obtained. The final calculation of the average annual net income after discounting over 25 years is as follows: , The increase is initially due to increased revenue from peak-valley arbitrage and ancillary services. Rapid growth, when After exceeding the optimal value, the growth rate of investment costs and attenuation losses exceeds the growth rate of returns. Gradually declining.

[0012] Furthermore, the static investment payback period for: in, , Initial follow Increase Faster growth than growth rate Continuously shortening; when After exceeding the optimal value, The growth rate has increased significantly. The trend is towards leveling off or even declining. It has begun to extend.

[0013] Furthermore, in step S500, based on the peak-valley electricity price period, photovoltaic power generation period, and braking energy distribution characteristics, five operating conditions are classified, and a dynamic control strategy is formulated, specifically as follows: Operating Condition 1: Photovoltaic + Braking Energy Co-charging Periods: 7:00-11:00, 14:00-17:00, Real-time Comparison and Calculate the charging power according to correction constraint 1; if the predicted value deviates from the actual value by more than 5%, trigger dynamic adjustment: , , This is the deviation correction coefficient; if the power grid fluctuation exceeds the threshold... Reduce charging power to within the threshold; Operating Condition 2: Valley charging period: 11:00~14:00. The total charging power is controlled according to constraint 2, and the grid charging power is adjusted in conjunction with the energy storage SOC. To avoid overcharging and accelerating degradation; and to reserve backup capacity for auxiliary services. Ensure that auxiliary service response capabilities are available; Operating Condition 3: Peak Price + Ancillary Service Discharge Period: 17:00~21:00. Calculate discharge power according to constraint 3, and supply load with coordinated braking energy; dynamically adjust upon receiving grid frequency regulation commands. Balance ancillary service revenue with power supply reliability; ensure discharge power does not exceed And satisfy ; Operating Condition 4: Period for smoothing fluctuations: 21:00 to 7:00 the next day. If there is temporary load due to train maintenance at night, i.e. Energy storage and low-power discharge power supply If the power grid experiences an extreme low in electricity prices and Initiate low-power charging Monitoring energy storage ,like This triggers emergency charging protection; Operating Condition 5: Period of Concentrated Braking Energy Recovery: During peak hours when trains brake intensively, if... Energy storage prioritizes absorbing braking energy, charging power The remaining capacity absorbs excess photovoltaic power, maximizing the recycling rate of braking energy.

[0014] Furthermore, in step S600, simulation verification is required first, followed by sensitivity analysis, then optimization and adjustment. Finally, after verification, a complete implementation plan is formed, including energy storage capacity, charging and discharging control logic, parameter thresholds, revenue calculation, and carbon emission assessment.

[0015] According to a second aspect of the present invention, a solar-energy storage capacity configuration system for an integrated solar-energy storage project in urban rail transit is provided, comprising: Data forecasting module: Used to collect core basic data of the project and, based on historical load data and photovoltaic output data, to forecast the photovoltaic output and load demand for the next year; Attenuation Fitting Module: Used to statistically analyze the temporal distribution characteristics of train braking energy recovery power, determine braking energy recovery efficiency, and fit the annual attenuation coefficient curve of energy storage. Condition Constraint Module: Used to construct a mathematical model of the photovoltaic-storage system, impose condition constraints on each time period, and optimize using an objective function to form a mathematical model system under multi-dimensional constraints; Energy storage calculation module: Based on the established mathematical model, it calculates the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations, analyzes the trend of indicator changes, and obtains the economically optimal energy storage capacity. Data prediction module: used to formulate energy storage charging and discharging control strategies, and to classify different operating conditions based on the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; Target verification module: Based on actual project case data, and using the energy storage capacity and control strategy determined in the above steps, it simulates and calculates the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle to verify whether the expected target has been achieved.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The energy storage capacity configuration method of this invention constructs a five-dimensional mathematical model that includes photovoltaic characteristics, load fluctuations, battery aging losses, and grid impact limitations. While pursuing high photovoltaic absorption rates, it also considers the lifespan of energy storage equipment and the safety and stability of the power grid. This method provides owners with an optimal configuration scheme that not only offers high book returns but also low asset depreciation rates and strong risk resistance in actual operation, fundamentally enhancing the project's engineering practical value.

[0017] 2. The energy storage capacity configuration method of this invention, by introducing a battery life-cycle loss cost correction term into the objective function, overcomes the defect in existing technologies that ignore battery health degradation in pursuit of short-term peak-valley arbitrage. The system can automatically identify and avoid deep charge-discharge behaviors that yield meager returns but severely damage lifespan, making the actual service life of the energy storage device closer to its design life, thereby ensuring the authenticity and reliability of the investment payback period calculation results.

[0018] 3. The energy storage capacity configuration method of the present invention subdivides the load into basic load and pulse traction load, and introduces a regenerative braking energy utilization coefficient, making the energy storage system a recycling station for train regenerative braking energy. Compared with traditional solutions, this significantly reduces energy waste caused by train braking, further taps the energy-saving potential in rail transit scenarios, and improves the overall energy efficiency ratio of the system.

[0019] 4. The energy storage capacity configuration method of the present invention can actively suppress the power fluctuations caused by the superposition of random photovoltaic output and the pulse nature of train traction load, improve power quality, and reduce mechanical damage and thermal shock to the upstream main substation and transformer. It is of great significance for ensuring the safety and stability of the urban rail transit power supply system as a primary load. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit, according to an embodiment of the present invention. Figure 2 This is a topology diagram of a main substation power supply zone system for an energy storage capacity configuration method in an integrated photovoltaic and energy storage project for urban rail transit, according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the relationship between energy storage capacity, photovoltaic consumption ratio, and annual revenue in an energy storage capacity configuration method for an integrated photovoltaic and energy storage project in urban rail transit, according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the energy storage SOC curve for an energy storage capacity configuration method in an urban rail transit photovoltaic-energy storage integrated project according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 like Figure 1-4 As shown in the figure, this embodiment of the invention provides a method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit, specifically including the following steps: S100. Collect core basic data of the project, and based on historical load data and photovoltaic output data, predict the photovoltaic output and load demand for the next year. S200. Statistically analyze the temporal distribution characteristics of the train's braking energy recovery power, determine the braking energy recovery efficiency, and fit the annual energy storage attenuation coefficient curve. S300. Construct a mathematical model of the photovoltaic-storage system, impose conditions on each time period, and optimize it using an objective function to form a mathematical model system under multi-dimensional constraints. S400. Based on the established mathematical model, the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations are calculated respectively. The trend of indicator changes is analyzed to obtain the economically optimal energy storage capacity. S500: Formulate energy storage charging and discharging control strategies, and classify different operating conditions according to the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; S600. Based on actual project case data, substitute the energy storage capacity and control strategy determined in the above steps, simulate and calculate the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle, and verify whether the expected goals have been achieved.

[0023] In step S100, the core basic data of the project includes rail transit line parameters, photovoltaic system parameters, energy storage equipment parameters, economic parameters, and electricity price and policy parameters.

[0024] The parameters for the rail transit line include length, power supply system, train formation, and peak / off-peak departure intervals; the parameters for the photovoltaic system include installed capacity, installation location, and local irradiance statistics; the parameters for the energy storage equipment include unit investment cost. Charging efficiency Discharge efficiency Depth of discharge Maximum charging and discharging power of energy storage devices Annual attenuation coefficient The economic parameters include the unit investment cost of photovoltaic power. Annual operating and maintenance costs Additional maintenance costs for ancillary services Additional investment in auxiliary service equipment Discount rate The electricity price and policy parameters include peak-valley time periods and corresponding unit electricity prices. Unit ancillary service revenue Power grid safety constraint threshold Carbon emission control targets .

[0025] When forecasting photovoltaic (PV) output and load demand, the historical load and PV output data from the past three years are first cleaned to remove abnormal data such as those caused by extreme weather or equipment failures. Missing data is then supplemented using linear interpolation to ensure the integrity of the basic data. An LSTM neural network is selected, with a three-layer hidden layer structure. Input features include daily time period, seasonal attributes, historical average irradiance, and load temporal variation patterns. The output is a 15-minute forecast of PV output for the next year. and load demand forecast During model training, the Adam optimizer is used to iteratively adjust parameters to keep the prediction error within 5%, which meets the accuracy requirements of engineering calculations.

[0026] In step S200, regarding train braking energy, by analyzing the temporal distribution characteristics of train braking frequency and braking intensity during different operating periods, and combining this with the actual operating efficiency test data of the braking energy recovery device, the default value of the braking energy recovery efficiency is determined. At the same time, an adjustment margin of ±0.05 is reserved to adapt to the differences in braking conditions of different lines. Then, based on the default value of the braking energy recovery efficiency... The braking energy recovery efficiency was adjusted to accommodate differences in track gradients and train formation sizes, specifically as follows: By dynamically adjusting the line parameters according to specific projects, the collaborative charging constraint formula is ensured. The accuracy of medium-energy co-calculation, among which This is the power feedback for train braking energy.

[0027] The annual energy storage degradation coefficient curve is obtained by fitting a quadratic polynomial, specifically: in, For energy storage Annual capacity decay coefficient, Based on energy storage Annual capacity decay coefficient You can also get the first Annual actual available energy storage capacity : and annual depreciation loss costs : .

[0028] In step S300, the condition constraints are formulated separately for each time period, and the constraint conditions are as follows: Constraint 1: During photovoltaic power generation periods, photovoltaic output and braking energy work together to supply the load, and excess electricity charges the energy storage; at this time, if ,but , , ,and , ;like ,but ;like ,but , , ; in, This represents the predicted value of photovoltaic power generation. For photovoltaic power absorption, This refers to unconsumed photovoltaic power. The charging power of the previous time step, For power grid safety constraint thresholds, This is the load demand forecast. For energy storage charging power, This refers to the maximum charging and discharging power of the energy storage device. This refers to the amount of solar power that has been curtailed.

[0029] Constraint 2: During off-peak hours, both photovoltaic power and the grid charge the energy storage simultaneously, and the total charging power meets the following requirements. ; in, To allow for a 5% fluctuation margin in the power supplied by the grid to the energy storage, adjustments will be made based on the current SOC of the energy storage. Then reduce .

[0030] Constraint 3: During peak price periods, energy storage discharge power and braking energy are supplied to the load in a coordinated manner. ,and If auxiliary services are required, adjustments will be made dynamically. .

[0031] Constraint 4: The total energy storage charging and discharging capacity must meet the following requirements. ,in Let t be the actual usable energy storage capacity in year t. ,and , This refers to the rated capacity of the energy storage.

[0032] Constraint 5: During periods of low or flat prices, while the charging status is maintained... During peak discharge periods or peak pricing periods, In the dedicated state of auxiliary services, ; in, For hourly income, For photovoltaic power absorption, This refers to the unit price of electricity. The power supplied by the grid to charge the energy storage. For the unit's ancillary service revenue, Power to provide ancillary services for energy storage, To predict the time step, For energy storage discharge power, For discharge efficiency, Additional maintenance costs for ancillary services; Thus, the annual net income is obtained: And the average annual net income after discounting over 25 years: .

[0033] in, For the first Annual net income The average annual net income after discounting over 25 years.

[0034] is the discount rate.

[0035] Constraint 6: When energy storage provides ancillary services, reserve capacity shall be reserved. The actual charging and discharging power meets the requirements. , ,and ; in, For energy storage backup capacity, For energy storage charging power, This refers to the actual charging power. This represents the actual discharge power. This refers to the energy storage discharge power.

[0036] Constraint 7: Total carbon emissions must meet the following requirements. , ; in, Total carbon emissions Carbon emission intensity is the weighting factor. For the first Annual power purchases by the power grid Carbon emission intensity per unit of electricity.

[0037] The objective function is: in, The objective function value, To minimize the objective function value, , , These are the weighting coefficients for economic benefits, photovoltaic consumption, and carbon emissions, respectively. , For the total initial investment, , For photovoltaic power absorption rate, .

[0038] In the aforementioned constraint 1, by incorporating photovoltaic output and braking energy recovery into the power supply logic, the rule of prioritizing load consumption of collaborative energy, storing excess energy, and discarding photovoltaic power when exceeding the energy storage capacity is clarified. At the same time, the grid fluctuation threshold limit is superimposed, which maximizes the recovery of braking energy and surplus photovoltaic power, improves energy utilization efficiency, avoids the impact of sudden changes in energy storage charging power on the urban power grid, and ensures the stability of grid access.

[0039] In constraint 2, the lowest electricity price occurs during off-peak hours, representing the optimal window for energy storage charging. However, the maximum power demand of rail transit directly impacts electricity costs. Blindly increasing grid charging power could lead to excessive power demand, thus increasing operating costs. By introducing dynamic power demand forecasts and reserving a 5% fluctuation margin, the total power limit for combined photovoltaic and grid charging is defined. Simultaneously, the grid charging power is adjusted based on the energy storage's SOC. This ensures that the energy storage is fully charged during off-peak hours to reduce charging costs, avoids exceeding power demand limits, and prevents overcharging from accelerating energy storage degradation.

[0040] In constraint 5, the revenue calculation logic is clearly defined by subdividing the three states of charging, discharging, and ancillary services, and the revenue of ancillary services is included in the accounting; the attenuation loss cost is deducted from the annual net revenue, and the average annual net revenue over 25 years is corrected by the discount rate.

[0041] In constraint 6, by setting a reserve capacity, it is clear that the actual charging and discharging power must be reduced by the reserve capacity, and the auxiliary service power must not exceed the upper limit of the reserve capacity. This ensures that the energy storage has a stable auxiliary service response capability without affecting its core charging and discharging power supply function.

[0042] In step S400, the photovoltaic absorption rate is: According to the power of abandoned light The calculation results yield the corresponding The annual photovoltaic power consumption rate is required, of which the curtailed power must meet the following requirements. , Increase Simultaneous improvement Gradually reduce, It is on the rise.

[0043] The average annual net income is: First, calculate the revenue for the three operating states on an hourly basis. The total annual income is obtained by summing them up. After deducting the average annual operation and maintenance costs Additional maintenance costs for ancillary services and annual attenuation loss cost The net income in year t is obtained. The final calculation of the average annual net income after discounting over 25 years is as follows: , The increase is initially due to increased revenue from peak-valley arbitrage and ancillary services. Rapid growth, when After exceeding the optimal value, the growth rate of investment costs and attenuation losses exceeds the growth rate of returns. Gradually declining.

[0044] The static investment payback period for: in, , Initial follow Increase Faster growth than growth rate Continuously shortening; when After exceeding the optimal value, The growth rate has increased significantly. The trend is towards leveling off or even declining. It has begun to extend.

[0045] When conducting trend analysis of indicator changes, it is necessary to perform trend fitting on indicator data from multiple operating conditions and plot the results. and , , , The relationship curve. Then filter out... Maximum and Minimum energy storage capacity, while verifying the performance at that capacity. , This ensures that economic benefits are balanced with core constraints.

[0046] In step S500, based on the peak-valley electricity price period, photovoltaic power generation period, and braking energy distribution characteristics, five operating conditions are classified, and a dynamic control strategy is formulated, specifically as follows: Operating Condition 1 (Photovoltaic + Braking Energy Co-charging Period: 7:00-11:00, 14:00-17:00): Real-time Comparison and Calculate the charging power according to correction constraint 1; if the predicted value deviates from the actual value by more than 5%, trigger dynamic adjustment: , , This is the deviation correction coefficient; if the power grid fluctuation exceeds the threshold... Reduce the charging power to within the threshold.

[0047] Operating Condition 2 (Deep Valley Joint Charging Period: 11:00~14:00): Control the total charging power according to constraint 2, and adjust the grid charging power in conjunction with the energy storage SOC. To avoid overcharging and accelerating degradation; and to reserve backup capacity for auxiliary services. Ensure that you have the capability to respond to auxiliary services.

[0048] Operating Condition 3 (Peak Price + Ancillary Service Discharge Period: 17:00~21:00): Calculate the discharge power according to constraint 3, and supply the load with coordinated braking energy; dynamically adjust when receiving grid frequency regulation commands. Balance ancillary service revenue with power supply reliability; ensure discharge power does not exceed And satisfy .

[0049] Operating Condition 4 (Smoothing Fluctuations Period: 21:00~7:00 the next day): If there is a temporary load for train maintenance at night, i.e. Energy storage and low-power discharge power supply If the power grid experiences an extreme low in electricity prices and Initiate low-power charging Monitoring energy storage ,like This triggers emergency charging protection.

[0050] Operating Condition 5 (Concentrated Braking Energy Recovery Period: Peak Hour Train Braking Period): If Energy storage prioritizes absorbing braking energy, charging power The remaining capacity absorbs excess photovoltaic power, maximizing the recycling rate of braking energy.

[0051] In step S600, simulation verification is required first, followed by sensitivity analysis, then optimization and adjustment. Finally, after verification, a complete implementation plan is formed, including energy storage capacity, charging and discharging control logic, parameter thresholds, revenue calculation, and carbon emission assessment.

[0052] The simulation verification involves: building a simulation model based on actual project data, incorporating the optimal energy storage capacity and multi-condition control strategies, simulating the entire 25-year lifecycle operation, and outputting annual photovoltaic absorption rate, average annual net income, investment payback period, total carbon emissions, and grid fluctuation data to verify whether the preset targets are met. Specifically: A MATLAB simulation model was built based on actual project data, and the optimal rated energy storage capacity determined in step three was substituted into it. The fourth step involves a multi-condition control strategy, simulating the entire 25-year lifecycle operation on an annual basis. The simulation outputs key annual indicators: photovoltaic grid integration rate. , No. Annual net income Dynamic investment payback period Total carbon emissions throughout the entire life cycle and power grid fluctuation amplitude Verify whether each indicator meets the preset target threshold: , Year, , This generates a simulation verification report.

[0053] The sensitivity analysis involves conducting sensitivity analysis on key parameters to assess the impact of parameter fluctuations on the effectiveness of the solution and determine the parameter tolerance range. Specifically: Three types of core sensitive parameters were selected, and fluctuation gradients of ±10% and ±20% were set. The single-factor variable method was used to quantify the impact of parameter fluctuations on the comprehensive objective function. The core calculation formula for the impact is the sensitivity coefficient: in, For sensitive parameters, The percentage of parameter fluctuation. For the corresponding Value fluctuation percentage; The selected sensitive parameters include: peak-valley price difference Benchmark value of annual energy storage degradation coefficient Unit ancillary service revenue By calculating each parameter Determine the sensitivity ranking and tolerance range: It is a low-sensitivity parameter with a fluctuation tolerance of ±20%. It is a moderately sensitive parameter with a fluctuation tolerance of ±10%. It is a highly sensitive parameter with a fluctuation tolerance of ±5%.

[0054] The optimization adjustment is as follows: if the simulation results do not meet the target, backtrack and adjust the accuracy of the dynamic prediction model, the combination of constraint parameters or weight coefficients, re-iterate the energy storage capacity and optimize the control strategy; if parameter fluctuations lead to a decrease in the feasibility of the scheme, supplement the plan with alternative capacity and emergency control plan. Specifically: If the simulation results do not meet the preset targets, adjustments will be made retrospectively according to the following logic: ① If Correcting the dynamic prediction error coefficient Adjusting the photovoltaic output forecast Recalculate the optimal energy storage capacity iteratively. ; ②If In [year], optimize weighting coefficients Correct the total initial investment , To optimize investment, the peak discharge duration in the charging and discharging strategy is adjusted synchronously; ③ If Reduce the grid fluctuation adaptation coefficient Constrained charging power adjustment rate If parameter fluctuations exceed the tolerance range, causing a decrease in the feasibility of the solution, supplement with 2-3 sets of alternative energy storage capacity. , Develop emergency control plans, which include suspending ancillary services and prioritizing power supply to loads during extreme electricity price fluctuations.

[0055] Example 2 This invention provides an energy storage capacity configuration system for an integrated photovoltaic and energy storage project in urban rail transit, comprising: Data forecasting module: Used to collect core basic data of the project and, based on historical load data and photovoltaic output data, to forecast the photovoltaic output and load demand for the next year; Attenuation Fitting Module: Used to statistically analyze the temporal distribution characteristics of train braking energy recovery power, determine braking energy recovery efficiency, and fit the annual attenuation coefficient curve of energy storage. Condition Constraint Module: Used to construct a mathematical model of the photovoltaic-storage system, impose condition constraints on each time period, and optimize using an objective function to form a mathematical model system under multi-dimensional constraints; Energy storage calculation module: Based on the established mathematical model, it calculates the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations, analyzes the trend of indicator changes, and obtains the economically optimal energy storage capacity. Data prediction module: used to formulate energy storage charging and discharging control strategies, and to classify different operating conditions based on the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; Target verification module: Based on actual project case data, and using the energy storage capacity and control strategy determined in the above steps, it simulates and calculates the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle to verify whether the expected target has been achieved.

[0056] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit, characterized in that, Specifically, the following steps are included: S100. Collect core basic data of the project, and based on historical load data and photovoltaic output data, predict the photovoltaic output and load demand for the next year. S200. Statistically analyze the temporal distribution characteristics of the train's braking energy recovery power, determine the braking energy recovery efficiency, and fit the annual energy storage attenuation coefficient curve. S300. Construct a mathematical model of the photovoltaic-storage system, impose conditions on each time period, and optimize it using an objective function to form a mathematical model system under multi-dimensional constraints. S400. Based on the established mathematical model, the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations are calculated respectively. The trend of indicator changes is analyzed to obtain the economically optimal energy storage capacity. S500: Formulate energy storage charging and discharging control strategies, and classify different operating conditions according to the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; S600. Based on actual project case data, substitute the energy storage capacity and control strategy determined in the above steps, simulate and calculate the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle, and verify whether the expected goals have been achieved.

2. The method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit according to claim 1, characterized in that, In step S100, the core basic data of the project includes rail transit line parameters, photovoltaic system parameters, energy storage equipment parameters, economic parameters, and electricity price and policy parameters; The parameters for the rail transit line include length, power supply system, train formation, and peak / off-peak departure intervals; the parameters for the photovoltaic system include installed capacity, installation location, and local irradiance statistics; the parameters for the energy storage equipment include unit investment cost. Charging efficiency Discharge efficiency Depth of discharge Maximum charging and discharging power of energy storage devices Annual attenuation coefficient The economic parameters include the unit investment cost of photovoltaic power. Annual operating and maintenance costs Additional maintenance costs for ancillary services Additional investment in auxiliary service equipment Discount rate The electricity price and policy parameters include peak-valley time periods and corresponding unit electricity prices. Unit ancillary service revenue Power grid safety constraint threshold Carbon emission control targets .

3. The method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit according to claim 2, characterized in that, When forecasting photovoltaic power output and load demand, the historical load data and photovoltaic power output data of the past three years are first cleaned to remove abnormal data such as extreme weather and equipment failures. The missing data is then supplemented by linear interpolation to ensure the integrity of the basic data. Using an LSTM neural network, a three-layer hidden layer structure was constructed. Input features included daily time period, seasonal attributes, historical average irradiance, and load temporal variation patterns. The output was a 15-minute forecast of photovoltaic power output for the next year. and load demand forecast During model training, the Adam optimizer is used to iteratively adjust parameters to keep the prediction error within 5%, which meets the accuracy requirements of engineering calculations.

4. The method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit according to claim 3, characterized in that, In step S200, regarding train braking energy, by analyzing the temporal distribution characteristics of train braking frequency and braking intensity during different operating periods, and combining this with the actual operating efficiency test data of the braking energy recovery device, the default value of the braking energy recovery efficiency is determined. Meanwhile, it reserves ±0.05 adjustment space to adapt to the differences in braking conditions of different lines; Then, based on the default value of the regenerative braking efficiency... The braking energy recovery efficiency was adjusted to accommodate differences in track gradients and train formation sizes, specifically as follows: ， By dynamically adjusting the line parameters according to specific projects, the collaborative charging constraint formula is ensured. The accuracy of medium-energy co-calculation, among which This is the power feedback for train braking energy.

5. The method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit according to claim 4, characterized in that, The annual energy storage degradation coefficient curve is obtained by fitting a quadratic polynomial, specifically: ， in, For energy storage Annual capacity decay coefficient, Based on energy storage Annual capacity decay coefficient You can also get the first Annual actual available energy storage capacity : ， and annual depreciation loss costs : 。 6. The method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit according to claim 5, characterized in that, In step S400, the photovoltaic absorption rate is: ， According to the power of abandoned light The calculation results yield the corresponding The annual photovoltaic power consumption rate is required, of which the curtailed power must meet the following requirements. , Increase Simultaneous improvement Gradually reduce, It is showing an upward trend; The average annual net income is: ， First, calculate the revenue for the three operating states on an hourly basis. The total annual income is obtained by summing them up. ; After deducting the average annual operation and maintenance costs Additional maintenance costs for ancillary services and annual attenuation loss cost The net income in year t is obtained. The final calculation of the average annual net income after discounting over 25 years is as follows: , The increase is initially due to increased revenue from peak-valley arbitrage and ancillary services. Rapid growth, when After exceeding the optimal value, the growth rate of investment costs and attenuation losses exceeds the growth rate of returns. Gradually declining.

7. The method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit according to claim 6, characterized in that, The static investment payback period for: ， in, , Initial follow Increase Faster growth than growth rate Continuously shortening; when After exceeding the optimal value, The growth rate has increased significantly. The trend is towards leveling off or even declining. It has begun to extend.

8. The method for configuring energy storage capacity in an integrated photovoltaic and energy storage project for urban rail transit according to claim 7, characterized in that, In step S500, based on the peak-valley electricity price period, photovoltaic power generation period, and braking energy distribution characteristics, five operating conditions are classified, and a dynamic control strategy is formulated, specifically as follows: Operating Condition 1: Photovoltaic + Braking Energy Co-charging Periods: 7:00-11:00, 14:00-17:00, Real-time Comparison and Calculate the charging power according to the modified constraint 1; If the predicted value deviates from the actual value by more than 5%, dynamic adjustment will be triggered. , , This is the deviation correction coefficient; if the power grid fluctuation exceeds the threshold... Reduce charging power to within the threshold; Operating Condition 2: Valley charging period: 11:00~14:

00. The total charging power is controlled according to constraint 2, and the grid charging power is adjusted in conjunction with the energy storage SOC. To avoid overcharging and accelerating degradation; and to reserve backup capacity for auxiliary services. Ensure that auxiliary service response capabilities are available; Operating Condition 3: Peak Price + Ancillary Service Discharge Period: 17:00~21:

00. Calculate discharge power according to constraint 3, and supply load with coordinated braking energy; dynamically adjust upon receiving grid frequency regulation commands. Balancing ancillary service revenue with power supply reliability; Ensure that the discharge power does not exceed And satisfy ; Operating Condition 4: Period for smoothing fluctuations: 21:00 to 7:00 the next day. If there is temporary load due to train maintenance at night, i.e. Energy storage and low-power discharge power supply ; If the power grid experiences an extreme low in electricity prices and Initiate low-power charging ; Monitoring energy storage ,like This triggers emergency charging protection; Operating Condition 5: Period of Concentrated Braking Energy Recovery: During peak hours when trains brake intensively, if... Energy storage prioritizes absorbing braking energy, charging power The remaining capacity absorbs excess photovoltaic power, maximizing the recycling rate of braking energy.

9. A method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit according to claim 8, characterized in that, In step S600, simulation verification is required first, followed by sensitivity analysis, then optimization and adjustment. Finally, after verification, a complete implementation plan is formed, including energy storage capacity, charging and discharging control logic, parameter thresholds, revenue calculation, and carbon emission assessment.

10. A system for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit, used to implement the method for configuring energy storage capacity in an integrated photovoltaic-energy storage project for urban rail transit as described in any one of claims 1-9, characterized in that, include: Data forecasting module: Used to collect core basic data of the project and, based on historical load data and photovoltaic output data, to forecast the photovoltaic output and load demand for the next year; Attenuation Fitting Module: Used to statistically analyze the temporal distribution characteristics of train braking energy recovery power, determine braking energy recovery efficiency, and fit the annual attenuation coefficient curve of energy storage. Condition Constraint Module: Used to construct a mathematical model of the photovoltaic-storage system, impose condition constraints on each time period, and optimize using an objective function to form a mathematical model system under multi-dimensional constraints; Energy storage calculation module: Based on the established mathematical model, it calculates the photovoltaic absorption rate, average annual net income and static investment payback period under different energy storage capacity configurations, analyzes the trend of indicator changes, and obtains the economically optimal energy storage capacity. Data prediction module: used to formulate energy storage charging and discharging control strategies, and to classify different operating conditions based on the overlap of local peak and valley electricity price periods and photovoltaic power generation periods; Target verification module: Based on actual project case data, and using the energy storage capacity and control strategy determined in the above steps, it simulates and calculates the photovoltaic absorption rate, average annual net income and investment payback period throughout the entire life cycle to verify whether the expected target has been achieved.

Images