A photovoltaic-energy storage-charging pile coordination system simulation method and device

Abstract

The application belongs to the technical field of energy system simulation, and specifically discloses a photovoltaic-energy storage-charging pile cooperative system simulation method and device, which comprises the following steps: generating historical data and calculating statistical characteristics; calculating a valley electricity dynamic charging target value according to the photovoltaic and load supply-demand gap in the morning photovoltaic climbing period; obtaining data of the day and performing strategy simulation, wherein the dynamic threshold strategy takes the dynamic value as the charging target; taking the simulation end state SOC as the next initial value to realize cross-period continuous simulation; and outputting and comparing the economic benefits and photovoltaic consumption rates of the two strategies. The application improves the simulation accuracy and practicability through data-driven dynamic charging target and cross-period SOC inheritance.

Description

Technical Field

[0001] This invention relates to the field of energy system simulation technology, and in particular to a simulation method and device for a photovoltaic-energy storage-charging pile collaborative system. Background Technology

[0002] With the rapid growth of new energy vehicle ownership and the widespread application of distributed photovoltaic (PV) power generation technology, the integrated and coordinated configuration of PV power generation systems, energy storage systems, and charging piles has become the mainstream energy supply model in scenarios such as industrial parks and residential communities. The PV-energy storage-charging pile coordinated system can effectively utilize PV power generation to meet the charging needs of electric vehicles, and through the energy storage system, shift energy over time, achieving time-of-use pricing arbitrage and improving the local consumption rate of PV power. However, this type of system involves the coupling of multiple complex factors, including the intermittency and uncertainty of PV output, the randomness and strong fluctuations of charging load, the multi-period differences in time-of-use pricing, and the timing constraints of energy storage charging and discharging. The system's operating characteristics are highly dynamic and difficult to solve analytically. Therefore, developing simulation tools that can accurately simulate system operating characteristics and verify the effects of different scheduling strategies is of great significance for system design, strategy optimization, and engineering demonstration.

[0003] In the prior art, there has been some research on simulation and scheduling methods for integrated photovoltaic-storage-charging systems. For example, Chinese patent application CN121886610A discloses a design and collaborative scheduling control method for a community-scale integrated photovoltaic-storage-charging system. This method obtains photovoltaic output data, charging load data, and time-of-use pricing policy data to form a predictive input dataset, performs photovoltaic output prediction and charging load prediction, and determines the energy storage charging and discharging strategy. As another example, Chinese patent application CN119171406A discloses a multi-objective optimization scheduling method and system for integrated photovoltaic-storage-charging systems. This method establishes a multi-objective optimization scheduling model with the objectives of minimum electricity purchase cost and minimum cycle power, and uses a multi-objective genetic algorithm to solve the problem.

[0004] However, the aforementioned prior art has at least the following shortcomings: First, existing simulation methods typically use a fixed initial state of charge (SOC) for energy storage systems, set independently for each simulation, which fails to reflect the system's state evolution over continuous operating cycles. In reality, a photovoltaic-storage-charging system is a continuously operating dynamic system; the energy storage SOC changes continuously with the daily charging and discharging process, and the final SOC value of the previous operating day becomes the initial SOC value for the next operating day. If each simulation starts from the same fixed initial SOC, the simulation results will deviate from the continuous state characteristics of the actual system's long-term operation, reducing the realism and practicality of the simulation results.

[0005] Second, existing technologies for energy storage systems typically use fixed thresholds (such as charging to 80% SOC) for charging during off-peak hours. This approach lacks analysis and utilization of historical operating data. Since photovoltaic output is significantly uncertain due to weather conditions, and the matching characteristics between photovoltaic power and load vary at different times, using fixed charging targets cannot adaptively match the supply-demand gap between photovoltaic power and load during actual operation. This can easily lead to charging redundancy (overcharging results in insufficient space for energy storage to absorb photovoltaic power during the remaining periods) or undercharging (undercharging results in insufficient energy storage discharge capacity during peak load periods), thereby reducing photovoltaic absorption rate and system economic benefits.

[0006] Third, most existing simulation methods focus on single-day or single-cycle simulation operations, lacking simulation of the long-term continuous operation characteristics of the system. In actual systems, photovoltaic output and charging load exhibit fluctuations and uncertainties both during and within a day. The results of a single simulation are insufficient to fully reflect the comprehensive performance of the system under different operating conditions, reducing the reference value of simulation results for practical engineering decisions. Summary of the Invention

[0007] To address the problems in existing simulation methods, such as the fixed initial SOC of energy storage leading to simulation results deviating from actual continuous operating conditions, the inability to adaptively match photovoltaic and load matching characteristics when using a fixed threshold for off-peak charging targets, and the lack of long-term continuous simulation capabilities, this invention provides a simulation method and device for a photovoltaic-energy storage-charging pile collaborative system, achieving more accurate and practical system operation simulation.

[0008] To achieve the above objectives, the present invention is implemented according to the following technical solution: The first technical solution provided by this invention is a simulation method for a photovoltaic-energy storage-charging pile collaborative system, comprising the following steps: S1. Based on preset photovoltaic parameters, charging pile load parameters and time parameters, generate historical operating data containing multiple complete operating cycles. The historical operating data includes historical photovoltaic output data and historical charging pile load data. S2. Extract statistical features from the historical operating data to obtain historical photovoltaic power output statistics and historical load statistics under specified statistical indicators; S3. Based on the historical photovoltaic output statistics and historical load statistics, calculate the dynamic charging target value of the off-peak electricity storage system. The dynamic charging target value is determined according to the supply and demand gap between photovoltaic output and load demand during a preset period in historical operation. S4. Obtain the daily operating data, which includes the daily photovoltaic power output curve, the daily charging pile load curve, and the time-of-use electricity price curve. S5. Obtain the current initial state of charge value of the energy storage system; S6. Based on the daily operating data and the current initial state of charge value, perform simulations of fixed threshold energy storage scheduling strategy and dynamic threshold energy storage scheduling strategy respectively; wherein, the fixed threshold strategy uses a preset fixed charging target value to control energy storage charging during off-peak hours, and the dynamic threshold strategy uses the dynamic charging target value calculated in step S3 to control energy storage charging during off-peak hours. S7. Store the final value of the state of charge of the energy storage system at the end of this simulation, and use it as the current initial state of charge value for the next simulation to achieve continuous simulation across cycles. S8. Output the simulation results under the two strategies. The simulation results shall include at least the economic benefit index and the photovoltaic grid connection rate index.

[0009] Further, the calculation of the dynamic charging target value in step S3 specifically includes: based on the historical photovoltaic output statistics and historical load statistics, traversing the preset morning photovoltaic ramp-up periods, calculating the gap between load demand and photovoltaic output in each period; determining the gap that the energy storage system can compensate for in each period based on the upper limit of the charging and discharging power of the energy storage system, and summing them to obtain the total gap; calculating the theoretical charging target state of charge value based on the total gap and the rated capacity of the energy storage system; applying a preset safety margin coefficient to the theoretical charging target state of charge value, and limiting the result between the preset upper limit and the lower limit of the state of charge, to obtain the dynamic charging target value.

[0010] Furthermore, the number of days for the historical operating data mentioned in step S1 is not less than 7 days, preferably 30 days. The historical photovoltaic output statistics value is the upper quantile of photovoltaic output, preferably the 75th quantile; the historical load statistics value is the upper quantile of load, preferably the 75th quantile. Using the upper quantile instead of the mean for statistics can effectively avoid the interference of extreme values ​​on the statistical results, better reflect the upper-leaning level of photovoltaic output and load demand in historical operation, and provide a more reliable basis for determining dynamic charging targets.

[0011] Furthermore, the specific implementation of cross-cycle SOC inheritance in step S7 is as follows: The final state of charge (SOC) values ​​of the energy storage system corresponding to the fixed threshold strategy and the dynamic threshold strategy at the end of the current simulation are saved to data files respectively; when the next simulation starts, the existence of the data files is checked; if they exist, the final SOC value saved in the data files is read as the current initial SOC value for the corresponding strategy; if they do not exist, a preset default initial SOC value is used. This mechanism ensures that each simulation starts from the end state of the previous simulation, forming a continuous state evolution chain, realistically simulating the dynamic changes of the energy storage SOC during long-term continuous operation of the actual system.

[0012] Furthermore, the simulation of the fixed threshold energy storage scheduling strategy and the simulation of the dynamic threshold energy storage scheduling strategy in step S6 both include the following sub-steps: traversing each time period of the day, calculating the net power difference between photovoltaic output and charging pile load; when the net power difference is negative, i.e., there is a load gap, the energy storage system is preferentially used to discharge and compensate for the load gap, and the discharge capacity of the energy storage system is constrained by a preset state of charge limit; if there is still a gap after the energy storage discharges, electricity is purchased from the grid to compensate; when the net power difference is positive, i.e., there is surplus photovoltaic power, the surplus photovoltaic power is preferentially used to charge the energy storage system, and the charging capacity of the energy storage system is constrained by a preset state of charge limit; if there is still surplus after the energy storage is charged, the surplus photovoltaic power is connected to the grid to obtain grid-connected revenue; the grid purchase cost and photovoltaic grid-connected revenue for each time period are calculated according to the time-of-use price, and the net revenue for the day is accumulated. The above charging and discharging logic reflects the energy scheduling principle of "photovoltaic priority for self-use, energy storage for peak shaving and valley filling, and the grid as a backup," and can truly reflect the operating characteristics of the actual photovoltaic-energy storage-charging collaborative system.

[0013] Furthermore, the morning photovoltaic (PV) power ramp-up period is from 6:00 to 12:00, during which PV output gradually increases from zero to its peak value, representing a critical window for determining whether PV output can meet daytime charging load demands. The safety margin coefficient ranges from 1.0 to 1.5, preferably 1.2 (i.e., a 20% safety margin), to address uncertainties caused by load fluctuations. The minimum state of charge (SOC) is 20% to prevent deep discharge of the energy storage system from damaging equipment lifespan; the maximum SOC is 80% to prevent overcharging of the energy storage system.

[0014] Furthermore, the generation of the charging pile load data includes: randomly initializing the initial state of charge and charging duration of the electric vehicles corresponding to each charging pile; setting a range for the number of vehicles charging simultaneously according to the time period; and determining the corresponding charging power level based on the threshold range of the electric vehicle's state of charge. Specifically, when the electric vehicle's state of charge is below a first preset threshold (e.g., 80%), the maximum charging power (e.g., 7kW) is provided; when the electric vehicle's state of charge is between the first and second preset thresholds (e.g., 95%), a linearly decreasing medium charging power (e.g., 2kW) is provided; and when the electric vehicle's state of charge is above the second preset threshold, the minimum charging power (e.g., 1kW) is provided. This three-stage power model accurately reproduces the battery management system's adjustment characteristics of charging power during the actual charging process of electric vehicles, effectively improving the realism of the charging load simulation.

[0015] Furthermore, the simulation results in step S8 also include: photovoltaic grid-connected electricity, grid-purchased electricity, revenue from direct photovoltaic power supply, revenue from power supply after photovoltaic energy storage, revenue from photovoltaic grid connection, grid-purchased electricity cost, and the improvement in revenue and photovoltaic grid integration rate of the dynamic threshold strategy compared to the fixed threshold strategy. By comparing the simulation results of the two strategies, the optimization effect of the dynamic threshold strategy in terms of both economic benefits and photovoltaic grid integration can be quantitatively evaluated.

[0016] The second technical solution provided by this invention is a photovoltaic-energy storage-charging pile collaborative system simulation device, comprising: a historical data generation module for generating historical operating data; a statistical feature calculation module for extracting statistical features from historical data; a dynamic target calculation module for calculating the dynamic charging target value of the energy storage system during off-peak hours; a daily data acquisition module for acquiring daily operating data; an initial state acquisition module for acquiring the current initial state of charge value of the energy storage system; a simulation execution module for executing simulations of fixed threshold and dynamic threshold energy storage scheduling strategies respectively; a state inheritance module for realizing cross-cycle SOC inheritance; and a result output module for outputting simulation results under the two strategies.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention overcomes the shortcomings of existing technologies that use fixed charging thresholds and cannot adaptively match the actual photovoltaic-load supply and demand characteristics by introducing a dynamic charging target calculation method based on historical data statistical characteristics. This method utilizes the upper quantile statistical values ​​of multi-day historical operating data to quantitatively analyze the photovoltaic and load supply and demand gap during the morning photovoltaic ramp-up period, and calculates the dynamic charging target for off-peak hours accordingly. This allows the energy storage charging amount to be adaptively adjusted based on historical operating characteristics, effectively improving the rationality and targeting of off-peak charging.

[0018] 2. This invention solves the problem that existing simulation methods start from a fixed initial SOC in each simulation and cannot reflect the continuous operating state of the system by constructing a cross-cycle SOC inheritance mechanism. This mechanism saves the final value of the energy storage SOC of each simulation and uses it as the initial value of the SOC for the next simulation, forming a continuous state evolution chain. It realistically simulates the working conditions of the actual system operating continuously for many days, significantly improving the realism and engineering applicability of the simulation results.

[0019] 3. This invention executes simulations of fixed threshold and dynamic threshold strategies in parallel within the same simulation framework, and directly outputs the comparison results of the two strategies in terms of economic benefits and photovoltaic grid integration rate. This makes the quantitative evaluation of the advantages and disadvantages of the strategies intuitive and efficient, and provides clear data support for the selection of scheduling strategies in practical engineering.

[0020] 4. Simulation results show that, compared with the fixed threshold strategy, the dynamic threshold scheduling strategy significantly improves the photovoltaic absorption rate and overall economic benefits, demonstrating a remarkable optimization effect. Attached Figure Description

[0021] Figure 1 This is the curve showing the photovoltaic power output and charging load for the day.

[0022] Figure 2 The curves show the changes in SOC for the two energy storage strategies.

[0023] Figure 3 A bar chart comparing the photovoltaic grid integration rates of the two strategies.

[0024] Figure 4 This is a bar chart comparing the daily net returns of the two strategies.

[0025] Figure 5 This is an example simulation result. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0027] This embodiment first provides a simulation device for a photovoltaic-energy storage-charging pile collaborative system, including: a historical data generation module for generating historical operating data; a statistical feature calculation module for extracting statistical features from historical data; a dynamic target calculation module for calculating the dynamic charging target value of the energy storage system during off-peak hours; a daily data acquisition module for acquiring daily operating data; an initial state acquisition module for acquiring the current initial state of charge value of the energy storage system; a simulation execution module for executing simulations of fixed threshold and dynamic threshold energy storage scheduling strategies respectively; a state inheritance module for realizing cross-cycle SOC inheritance; and a result output module for outputting simulation results under the two strategies.

[0028] Based on the above system, this embodiment provides a simulation method for a photovoltaic-energy storage-charging pile collaborative system, implemented on the MATLAB platform, including the following steps: I. Parameter Configuration The simulation system requires five main categories of parameters: photovoltaic parameters, charging pile load parameters, electricity price parameters, energy storage parameters, and time parameters. The specific settings for each parameter are shown below: (1) Photovoltaic parameter configuration The photovoltaic parameters are mainly used to simulate the output characteristics of distributed photovoltaic arrays, closely reflecting the actual changes in sunlight. Three core parameters are set, as follows: 1) The upper limit of photovoltaic peak power is set at 20kW. This parameter defines the maximum output capacity of the distributed photovoltaic array and is suitable for the energy supply needs of small and medium-sized scenarios. 2) The photovoltaic power fluctuation range is set to 0.1, which corresponds to ±10% output fluctuation in actual operation, and is used to simulate the uncertainty of photovoltaic output caused by changes in light intensity; 3) The historical data period is set to 30 days to generate a historical data sequence of photovoltaic power output, providing a reliable statistical basis for the calculation of charging targets for subsequent dynamic scheduling strategies.

[0029] (2) Configuration of charging pile load parameters The charging pile load parameters are set according to the actual charging conditions of electric vehicles, focusing on reproducing the rated charging power of 7kW during low SOC periods. A total of 7 core parameters are set to comprehensively simulate the charging behavior and load characteristics of the charging pile, as detailed below: 1) The total number of charging piles is set at 5 to meet the charging needs of users in small and medium-sized scenarios and to provide a basic basis for load calculation; 2) The maximum power limit for the load is set to 21kW, which corresponds to the extreme working condition of three electric vehicles charging at full power of 7kW at the same time. This can effectively avoid overload and ensure the stable operation of the system. 3) The maximum charging power is set at 7kW, which specifically refers to the rated charging power when the electric vehicle's SOC is below 80%. In actual operation, the power will fluctuate between 6.3 and 7kW, accurately reflecting the characteristics of real charging power. 4) The medium charging power is set at 2kW as the power adjustment benchmark when the electric vehicle's SOC is between 80% and 95%. A linear power reduction mode is adopted for transition to simulate the power decay characteristics in the later stage of battery charging. 5) The minimum charging power is set to 1kW, which is used for trickle charging when the SOC of the electric vehicle is higher than 95%. The actual fluctuation range is 1~1.5kW. Its core function is to protect the battery, avoid overcharging damage, and extend the battery life. 6) The high SOC threshold is set to 0.8 (i.e. 80%). When the SOC of an electric vehicle reaches this threshold, the charging power will linearly decrease from the maximum charging power to the medium charging power, achieving a smooth power transition. 7) The full SOC threshold is set to 0.95 (i.e., 95%). When the electric vehicle's SOC reaches this threshold, it will switch to trickle charging mode to further ensure battery safety and avoid overcharging. (3) Time-of-use electricity price parameter configuration The electricity price parameters are set using my country's mainstream time-of-use pricing mechanism, distinguishing between valley, flat, and peak periods, clearly defining the scope of each period and its corresponding electricity price, and also setting the photovoltaic feed-in tariff. A total of 7 core parameters are included to provide a basis for calculating economic benefits, as detailed below: 1) The off-peak electricity period is set from 0:00 to 6:00 (corresponding to 0 to 6 hours in the program). The electricity price is the lowest during this period, which is the best time for the energy storage system to charge and store energy and reduce the cost of purchasing electricity. 2) The flat power supply period is set to three periods: 7:00-9:00, 15:00-17:00, and 22:00-23:00 (corresponding to 7 to 9 hours, 15 to 17 hours, and 22 to 23 hours in the program). During these periods, the electricity price is at a moderate level, the photovoltaic output is basically matched with the charging load, and the energy storage system makes minor adjustments as needed. 3) Peak power periods are set as two periods, namely 10:00-14:00 and 18:00-21:00 (corresponding to 10 to 14 hours and 18 to 21 hours in the program). The electricity price is the highest during these periods, and it also includes the evening peak charging period of charging piles. The energy storage system is given priority to discharge and supply power, thereby reducing the grid purchase cost during peak periods. 4) The off-peak electricity price is set at 0.2495 yuan / kWh, which is the grid purchase price during off-peak hours, and is used to calculate the charging cost of energy storage during off-peak hours; 5) The flat electricity price is set at 0.5530 yuan / kWh, which is the grid purchase price during the flat electricity period, and is used to calculate the grid purchase cost during the regular flat electricity period; 6) The peak electricity price is set at 0.9242 yuan / kWh, which is the grid purchase price during peak hours. It is used to calculate the cost of purchasing electricity during peak hours and the revenue from energy storage discharge to replace electricity purchase. 7) The photovoltaic feed-in tariff is set at 0.3749 yuan / kWh, which is the revenue price for surplus photovoltaic power fed into the grid. This price is used to calculate the revenue from photovoltaic feed-in and improve the economic benefit accounting.

[0030] (4) Energy storage parameter configuration The core energy storage parameters are used to simulate the charging and discharging characteristics, capacity limitations, and dispatch thresholds of energy storage systems, ensuring the safe and efficient operation of the energy storage system. A total of 10 core parameters are set, as follows: 1) The energy storage capacity is set at 40kWh to adapt to the adjustment needs of photovoltaic power output and charging load, providing a basis for energy storage charging and discharging calculation; 2) The upper limit of energy storage charging and discharging power is set at 18kW to limit the charging and discharging rate of the energy storage system, avoid damage to the energy storage equipment due to excessive power, and ensure the safe operation of the equipment; 3) The energy storage charging and discharging efficiency is set to 0.9, which means that there is a 10% energy loss during the charging and discharging process, which is consistent with the efficiency level of actual energy storage devices; 4) The initial SOC is set to 0.5 (i.e. 50%), which serves as the initial remaining charge percentage of the energy storage system when the simulation starts, providing a baseline for the initial state of the simulation. 5) The target SOC for off-peak charging under the fixed strategy is set to 0.8 (i.e. 80%), which is used in the fixed threshold scheduling strategy to specify the target remaining capacity for energy storage charging during off-peak hours; 6) The fixed strategy sets the daytime SOC upper limit to 0.8 (i.e. 80%), which is used in the fixed threshold scheduling strategy to limit the maximum value of daytime energy storage SOC and avoid overcharging of energy storage; 7) The fixed strategy sets the daytime SOC lower limit to 0.2 (i.e. 20%), which is used in the fixed threshold scheduling strategy to limit the minimum value of daytime energy storage SOC and avoid deep discharge of energy storage. 8) The fixed strategy SOC protection lower limit is set to 0.2 (i.e. 20%), which serves as the overall SOC protection baseline for the energy storage system in the fixed threshold strategy, comprehensively avoiding deep discharge and extending the service life of energy storage equipment; 9) The off-peak charging buffer ratio is set to 1.2 (i.e., a 20% safety margin) to be used in the dynamic scheduling strategy to adjust the off-peak charging target and cope with the uncertainty brought about by load fluctuations; 10) The morning photovoltaic ramp-up period is set from 6:00 to 12:00 (corresponding to 6 to 12 hours in the program). During this period, the photovoltaic output gradually increases and is used to calculate the dynamic charging target, ensuring that the energy storage scheduling matches the photovoltaic output characteristics.

[0031] (5) Time parameter configuration The time parameters are used to define the simulation time range and time step size to ensure the rationality of the simulation timing logic. Two core parameters are set, as follows: 1) The time axis is set to 0:00-23:00 (corresponding to 0 to 23 hours in the program), covering a complete 24 hours, which can fully simulate the dynamic operation of the system throughout the day; 2) The time step is set to 1 hour, that is, the system operating status (photovoltaic output, charging load, energy storage SOC, etc.) is updated every 1 hour, taking into account both simulation accuracy and operating efficiency.

[0032] II. Historical Data Generation Call the historical data generation function gen_history_data to generate 30 days of historical photovoltaic power output data (history_pv, a 30-row, 24-column matrix) and historical charging pile load data (history_load, a 30-row, 24-column matrix) based on the above parameter configuration.

[0033] The daily photovoltaic output generation logic is as follows: during the sunshine period from 6:00 to 18:00, a sine function is used to simulate the basic photovoltaic output, and a random fluctuation of ±10% is added in combination with the upper limit and fluctuation range of photovoltaic peak power; the photovoltaic output is set to 0 during other periods.

[0034] The logic for generating the daily charging pile load is as follows: Initialize the initial SOC and charging duration of 5 electric vehicles randomly; set the number of vehicles charging simultaneously according to different time periods (0:00-6:00, 10:00-16:00, 21:00-23:00: 0-1 vehicles; 7:00-9:00: 1-2 vehicles; 17:00-20:00: 2-3 vehicles); determine the power range of each electric vehicle based on its current SOC (7kW for SOC<80%, 2kW for 80%≤SOC<95%, 1kW for SOC≥95%), sum up to obtain the hourly load, and limit it to within 21kW. III. Calculation of Statistical Characteristics of Historical Data The statistical feature calculation function `calc_history_stats` is called to perform statistical analysis on 30 days of historical data. It iterates through 24 hours, calculating the 75th quantile for the photovoltaic output and load data for the same hour each day, obtaining `hourly_pv_p75` (75th quantile of hourly photovoltaic output) and `hourly_load_p75` (75th quantile of hourly load). Simultaneously, the difference between load and photovoltaic output for the same hour each day (taking non-negative values) is calculated, and then the average is taken to obtain `hourly_load_gap` (mean of the difference between hourly load and photovoltaic output).

[0035] IV. Calculation of Dynamic Charging Target The dynamic charging target calculation function `calc_valley_charge_target` is called to calculate the dynamic charging target `valley_charge_target` of the energy storage system during off-peak hours. The specific steps are as follows: (1) Iterate through each hour of the morning photovoltaic ramp-up period from 6:00 to 12:00, calculate the difference between the 75th percentile of the load and the 75th percentile of the photovoltaic output (take non-negative values) for that hour, and obtain the load gap for that hour; (2) Based on the upper limit of the energy storage charging and discharging power of 18kW, determine the actual amount of the energy storage system can compensate for the gap in that hour (take the smaller value between the gap and the upper limit of the charging and discharging power). (3) Summarize the energy storage compensation gap for all hours during the morning photovoltaic ramp-up period to obtain the total gap; (4) Based on the total deficit and the energy storage capacity of 40kWh, calculate the theoretical charging target SOC: Theoretical charging target SOC = lower limit of basic SOC (0.2) + total deficit / energy storage capacity; (5) Multiply the theoretical target SOC by the off-peak charging cache ratio of 1.2, add a 20% safety margin, and limit the result to between 0.2 and 0.8 to obtain the final dynamic charging target value.

[0036] V. Generation of Daily Operational Data The following three functions are called respectively to generate the 24-hour runtime data for the current day: (1) The gen_pv_curve function generates the photovoltaic output curve P_pv (a vector with 1 row and 24 columns) for the day, and the generation logic is consistent with the historical photovoltaic output. (2) The gen_load_curve function generates the daily charging pile load curve P_load (a vector with 1 row and 24 columns), and the generation logic is consistent with the historical load. (3) The gen_price_curve function generates the daily time-of-use electricity price curve price (1 row and 24 columns vector). According to the time period division in Table 1, the values ​​are assigned as valley price of 0.2495 yuan / kWh, flat price of 0.5530 yuan / kWh, and peak price of 0.9242 yuan / kWh respectively.

[0037] VI. Obtaining the Initial SOC At simulation start, check if the data file soc_inherit.mat exists. If the file exists, read the last_SOC_fixed (final SOC value of the last simulation for the fixed threshold strategy) and last_SOC_dynamic (final SOC value of the last simulation for the dynamic threshold strategy) stored in the file, and set them as the initial SOC for the two strategies in this simulation, respectively. If the file does not exist, set them uniformly to the default initial SOC defined in the parameter configuration: 0.5 (50%).

[0038] VII. Simulation of Execution of Energy Storage Dispatch Strategy The simulation functions for the fixed threshold strategy (fixed_strategy_simulation) and the dynamic threshold strategy (dynamic_strategy_simulation) are called respectively to perform simulations for the two strategies.

[0039] The charging and discharging logic of the two strategies is the same. The only difference is the charging target during off-peak hours (0:00-6:00): the fixed threshold strategy uses a preset fixed value SOC=0.8 as the charging target; the dynamic threshold strategy uses the dynamic charging target value calculated in step (four) as the charging target.

[0040] The core logic of charging and discharging is as follows: (1) Initialize SOC to an initial value, and initialize revenue and cost variables; (2) Iterate through the 24 hours of the day and calculate the net power difference = photovoltaic output − charging pile load; (3) If the net power difference is negative (there is a load gap), the energy storage discharge should be used first to compensate for the gap (discharge condition: SOC>SOC lower limit 0.2); if there is still a gap after the energy storage discharge, the power should be purchased from the grid to compensate. (4) If the net power difference is positive (there is remaining photovoltaic power), the remaining photovoltaic power is preferentially used for energy storage charging (charging condition: SOC < SOC upper limit 0.8); if there is still remaining power after energy storage charging, the remaining photovoltaic power is integrated into the power grid, and revenue is obtained at the photovoltaic on-grid electricity price of 0.3749 yuan / kWh; (5) Calculate the grid power purchase cost for each time period according to the time-of-use electricity price (valley electricity charging is charged at the valley electricity price, and daytime power purchase is charged at the electricity price of the corresponding time period) and the photovoltaic on-grid revenue, and update the SOC; (6) Accumulate to obtain the daily net revenue = photovoltaic on-grid revenue − grid power purchase cost + power purchase revenue saved by direct photovoltaic power supply and power supply after energy storage, and calculate the photovoltaic accommodation rate = (direct photovoltaic power supply + photovoltaic power supplied after energy storage) / total photovoltaic power generation × 100%.

[0041] 8. Saving the Final SOC Value The final energy storage SOC values (soc_fixed(end) and soc_dynamic(end)) of the two strategies obtained at the end of this simulation are saved to the data file soc_inherit.mat, overwriting the original final value data, to provide the initial SOC for the next simulation and realize cross-cycle continuous simulation.

[0042] 9. Output and Visualization of Simulation Results After the simulation is completed, the system outputs the core simulation results through the command line window, which are presented in the order of "basic output data - economic revenue comparison - photovoltaic accommodation rate comparison". The data is accurate and well-organized, facilitating quick reading of core information. The specific output content is as follows: 1) Basic output data: includes the daily photovoltaic power generation of the current day (sum(P_pv)*params.dt), photovoltaic output peak (max(P_pv)), daily charging pile load consumption of the current day (sum(P_load)*params.dt), load peak (max(P_load)) and dynamic valley charging target (valley_charge_target*100), which intuitively presents the operation scale of photovoltaic and load of the system on the current day; 2) Economic revenue comparison: outputs the daily net revenue of the fixed threshold strategy and the dynamic threshold strategy respectively, as well as the revenue increase range of the dynamic threshold strategy relative to the fixed threshold strategy, to quantify the economic advantages of the two strategies; 3) Photovoltaic accommodation rate comparison: outputs the photovoltaic accommodation rate of the two strategies respectively, as well as the accommodation rate increase range of the dynamic threshold strategy relative to the fixed threshold strategy, which intuitively reflects the promotion effect of the two strategies on clean energy accommodation.

[0043] 4) All output data retains a reasonable number of decimal places to ensure the accuracy and readability of the data. Power generation and consumption are retained to 1 decimal place, revenue is retained to 2 decimal places, and the increase is retained to 1 decimal place, which conforms to the engineering data presentation specifications.

[0044] In this embodiment, each core function is implemented through a custom MATLAB function. The definition and function of each function are shown in Table 1.

[0045] Table 1 gen_history_data params history_pv, history_load Generate 30-day historical photovoltaic and load data calc_history_stats history_pv, history_load hourly_pv_p75, hourly_load_p75, hourly_load_gap Calculate the statistical characteristics of historical data calc_valley_charge_target params, hourly_pv_p75, hourly_load_p75, hourly_load_gap valley_charge_target Calculate dynamic charging target gen_pv_curve params P_pv Generate the photovoltaic power output curve for the day gen_load_curve params P_load Generate the daily charging load curve gen_price_curve params price Generate time-of-use electricity price curve fixed_strategy_simulation params, P_pv, P_load, price soc_fixed, profit_fixed, pv_usage_rate_fixed, etc. Simulation of a fixed threshold strategy dynamic_strategy_simulation params, P_pv, P_load, price, hourly_pv_p75, hourly_load_gap, valley_charge_target soc_dynamic, profit_dynamic, pv_usage_rate_dynamic, etc. Simulation of dynamic threshold strategy execution To present the simulation results more intuitively, the system uses MATLAB's plotting function to generate a graphics window containing four subplots, which are displayed separately: (1) Daily photovoltaic power output and charging load curves ( Figure 1 ): This visually presents the sinusoidal trend of photovoltaic output and the time-period distribution characteristics of charging load over 24 hours, facilitating the analysis of the matching degree between photovoltaic power and load.

[0046] (2) Comparison curves of energy storage SOC changes for the two strategies ( Figure 2 ): This section shows the evolution trajectory of energy storage SOC over 24 hours under fixed threshold and dynamic threshold strategies, and compares the differences between the two strategies in terms of charging and discharging timing and depth.

[0047] (3) A bar chart comparing the photovoltaic absorption rates of the two strategies ( Figure 3 ): Quantitatively presenting the improvement effect of dynamic threshold strategy on local photovoltaic consumption.

[0048] (4) Bar chart comparing the daily net returns of the two strategies ( Figure 4 ): This visually demonstrates the economic advantages of the dynamic threshold strategy.

[0049] To verify the effectiveness of the technical solution of this invention, a simulation experiment was conducted based on the above parameter configuration. The experimental results are as follows: Figure 5 As shown.

[0050] Depend on Figure 5 It can be seen that the program will output the total daily photovoltaic power generation and total load consumption in the command line window, automatically calculate the charging during off-peak hours and output the target value, and compare the economic benefits and photovoltaic absorption rate of the two strategies. Figure 5 The results show that the dynamic threshold strategy used in this operation increased economic benefits by 40.1% and photovoltaic grid integration rate by 27.3% compared to the fixed threshold strategy.

[0051] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A simulation method for a photovoltaic-energy storage-charging pile collaborative system, characterized in that, Includes the following steps: S1. Based on preset photovoltaic parameters, charging pile load parameters and time parameters, generate historical operating data containing multiple complete operating cycles. The historical operating data includes historical photovoltaic output data and historical charging pile load data. S2. Extract statistical features from the historical operating data to obtain historical photovoltaic power output statistics and historical load statistics under specified statistical indicators; S3. Based on the historical photovoltaic output statistics and historical load statistics, calculate the dynamic charging target value of the off-peak electricity storage system. The dynamic charging target value is determined according to the supply and demand gap between photovoltaic output and load demand during a preset period in historical operation. S4. Obtain the daily operating data, which includes the daily photovoltaic power output curve, the daily charging pile load curve, and the time-of-use electricity price curve. S5. Obtain the current initial state of charge value of the energy storage system; S6. Based on the daily operating data and the current initial state of charge value, perform simulations of fixed threshold energy storage scheduling strategy and dynamic threshold energy storage scheduling strategy respectively; wherein, the fixed threshold strategy uses a preset fixed charging target value to control energy storage charging during off-peak hours, and the dynamic threshold strategy uses the dynamic charging target value calculated in step S3 to control energy storage charging during off-peak hours. S7. Store the final value of the state of charge of the energy storage system at the end of this simulation, and use it as the current initial state of charge value for the next simulation to achieve continuous simulation across cycles. S8. Output the simulation results under the two strategies. The simulation results shall include at least the economic benefit index and the photovoltaic grid connection rate index.

2. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, The calculation of the dynamic charging target value in step S3 specifically includes: Based on the historical photovoltaic output statistics and historical load statistics, the preset morning photovoltaic ramp-up periods are iterated to calculate the gap between load demand and photovoltaic output in each period. Based on the upper limit of the charging and discharging power of the energy storage system, determine the amount of gap that the energy storage system can compensate for in each time period, and sum them up to obtain the total gap. Based on the total shortfall and the rated capacity of the energy storage system, calculate the theoretical target state of charge value for charging. A preset safety margin coefficient is applied to the theoretical target state of charge value, and the result is restricted between the preset upper and lower limits of the state of charge value to obtain the dynamic target charging value.

3. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, The number of days for the historical operating data mentioned in step S1 is not less than 7 days, the historical photovoltaic output statistics value is the upper quantile of photovoltaic output, and the historical load statistics value is the upper quantile of load.

4. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, Step S7 specifically includes: saving the final state of charge (SOC) values ​​of the energy storage system corresponding to the fixed threshold strategy and the dynamic threshold strategy at the end of this simulation to data files respectively; when the next simulation starts, checking whether the data files exist; if they exist, reading the final SOC values ​​saved in the data files as the current initial SOC value of the corresponding strategy; if they do not exist, using the preset default initial SOC value.

5. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, The simulation of the fixed threshold energy storage scheduling strategy and the simulation of the dynamic threshold energy storage scheduling strategy in step S6 both include the following sub-steps: Iterate through each time period of the day and calculate the net power difference between photovoltaic output and charging pile load; When the net power difference is negative, i.e. there is a load gap, the energy storage system is preferentially used to discharge and compensate for the load gap. The discharge capacity of the energy storage system is constrained by a preset state of charge limit. If there is still a gap after the energy storage system discharges, electricity is purchased from the grid to compensate. When the net power difference is positive, meaning there is surplus photovoltaic power, the surplus photovoltaic power is preferentially used to charge the energy storage system. The charging capacity of the energy storage system is constrained by a preset upper limit of state of charge. If there is still surplus after the energy storage system is charged, the surplus photovoltaic power is connected to the grid to obtain grid-connected revenue. The grid purchase cost and photovoltaic grid connection revenue for each time period are calculated based on the time-of-use electricity price, and the net revenue for the day is accumulated.

6. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 2, characterized in that, The morning photovoltaic ramp-up period is from 6:00 to 12:00, the safety margin coefficient ranges from 1.0 to 1.5, the lower limit of the state of charge is 20%, and the upper limit of the state of charge is 80%.

7. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, The generation of the charging pile load data includes: randomly initializing the initial state of charge and charging duration of the electric vehicles corresponding to each charging pile; setting the range of the number of vehicles charging simultaneously according to the time period; determining the corresponding charging power level according to the threshold range of the state of charge of the electric vehicle, wherein the charging power level includes at least: the maximum charging power when the state of charge is lower than a first preset threshold, the medium charging power that decreases linearly when the state of charge is between the first preset threshold and the second preset threshold, and the minimum charging power when the state of charge is higher than the second preset threshold.

8. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 7, characterized in that, The first preset threshold is 80%, the second preset threshold is 95%, the maximum charging power is 7kW, the medium charging power is 2kW, and the minimum charging power is 1kW.

9. The simulation method for a photovoltaic-energy storage-charging pile collaborative system according to claim 1, characterized in that, The simulation results in step S8 also include: photovoltaic grid-connected electricity, grid-purchased electricity, photovoltaic direct power supply revenue, photovoltaic energy storage power supply revenue, photovoltaic grid-connected revenue, grid-purchased electricity cost, and the revenue improvement and photovoltaic absorption rate improvement of the dynamic threshold strategy relative to the fixed threshold strategy.

10. A simulation device for a photovoltaic-energy storage-charging pile collaborative system, characterized in that, include: The historical data generation module is used to generate historical operating data containing multiple complete operating cycles based on preset photovoltaic parameters, charging pile load parameters, and time parameters. The statistical feature calculation module is used to extract statistical features from the historical operating data to obtain historical photovoltaic power output statistics and historical load statistics. The dynamic target calculation module is used to calculate the dynamic charging target value of the energy storage system during off-peak hours based on the historical photovoltaic output statistics and historical load statistics. The daily data acquisition module is used to acquire daily operating data, which includes the daily photovoltaic power output curve, the daily charging pile load curve, and the time-of-use electricity price curve. The initial state acquisition module is used to acquire the current initial state of charge value of the energy storage system; The simulation execution module is used to execute a fixed threshold energy storage scheduling strategy simulation and a dynamic threshold energy storage scheduling strategy simulation based on the daily operating data and the current initial state of charge value. The dynamic threshold strategy controls the energy storage charging during off-peak hours with the dynamic charging target value. The state inheritance module is used to store the final value of the energy storage system's state of charge at the end of this simulation and use it as the current initial state of charge value for the next simulation. The results output module is used to output the simulation results under the two strategies. The simulation results include at least the economic benefit index and the photovoltaic absorption rate index.

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