Optical storage and charging scheduling control system and method

Abstract

This application relates to a photovoltaic-storage-charging scheduling control system and method. The system includes: photovoltaic modules, energy storage modules, charging equipment, measurement modules, a scheduling controller, and a cloud platform. The scheduling controller executes the following steps: based on charging load prediction and photovoltaic power generation prediction algorithms, it processes a historical data feature database and outputs prediction results for a future period; the historical data feature database is generated by processing historical data, time period information, and meteorological data; based on the prediction results, it obtains the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods, and schedules the photovoltaic modules, energy storage modules, and charging equipment based on the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods. This achieves the linkage between energy storage capacity planning and weather conditions, fully utilizes historical data, time period information, and meteorological data to predict energy storage, enabling the full absorption of photovoltaic surplus on sunny days and effectively smoothing load fluctuations on rainy days, avoiding energy waste and grid impact.

Description

Technical Field

[0001] This application relates to the field of new energy photovoltaic energy storage and charging technology, and in particular to a photovoltaic energy storage and charging scheduling control system and method. Background Technology

[0002] A photovoltaic-energy storage-charging integrated station (PV-ESS-PCS Station) is a new type of energy service facility that integrates three core functions: photovoltaic power generation (solar), energy storage system (storage), and electric vehicle charging (charging). Its core logic is to convert solar energy into electricity through photovoltaic modules. A portion of this electricity is directly supplied to electric vehicles for charging, while excess electricity is stored in the energy storage system. During peak electricity demand or when photovoltaic power supply is insufficient, the energy storage system releases electricity to supplement the power supply. Simultaneously, it can intelligently interact with the power grid to achieve "self-generation and self-consumption, surplus electricity fed into the grid, and peak shaving and valley filling," solving the pain points of traditional charging stations such as "reliance on the grid, large load impacts, and low clean energy utilization." With the popularization of new energy vehicles and the widespread application of distributed photovoltaics, PV-ESS-PCS stations have become a core carrier for vehicle-to-grid (V2G) interaction. However, existing PV-ESS-PCS stations cannot fully absorb photovoltaic surplus on sunny days and cannot effectively smooth load fluctuations on rainy days, leading to energy waste and grid impact, resulting in low energy utilization efficiency. Summary of the Invention

[0003] Therefore, it is necessary to provide a photovoltaic-storage-charging scheduling control system and method that can improve energy utilization and avoid grid impact, in order to address the above-mentioned technical problems.

[0004] A photovoltaic-storage-charging scheduling and control system includes: a photovoltaic module, an energy storage module, a charging device, a measurement module, a scheduling controller, and a cloud platform;

[0005] The photovoltaic module is electrically connected to the energy storage module and the charging equipment respectively, and the photovoltaic module, energy storage module and charging equipment are all connected to the power grid; the measurement module is used to detect the power grid status;

[0006] The dispatch controller, photovoltaic modules, energy storage modules, and charging equipment are interconnected. The dispatch controller is also interconnected with the cloud platform and measurement module. The cloud platform is used to connect to the meteorological service interface.

[0007] The scheduling controller performs the following steps:

[0008] Based on charging load forecasting and photovoltaic power generation forecasting algorithms, the system processes historical data feature databases and outputs forecast results for a future period. The historical data feature database is generated by processing historical data, time period information, and meteorological data.

[0009] Based on the prediction results, the required energy storage capacity during off-peak periods and during parity periods is obtained, and photovoltaic modules, energy storage modules and charging equipment are dispatched based on the required energy storage capacity during off-peak periods and during parity periods.

[0010] In one embodiment, in the step of processing historical data feature databases and outputting prediction results for a future period based on charging load prediction and photovoltaic power generation prediction algorithms, the scheduling controller performs the following steps:

[0011] Based on charging load prediction and photovoltaic power generation prediction algorithms, the historical average electricity consumption for the same period is selected from the historical data feature database, and the historical average electricity consumption is corrected by the first correction coefficient to obtain the predicted electricity consumption.

[0012] Based on charging load forecasting and photovoltaic power generation forecasting algorithms, the historical average power generation under the same weather conditions is selected from the historical data feature database. The historical average power generation is then corrected using a second correction coefficient to obtain the predicted power generation.

[0013] In one embodiment, in the step of obtaining the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the prediction results, the scheduling controller performs the following steps:

[0014] Based on the predicted electricity consumption and predicted power generation, obtain the amount of energy storage required during off-peak periods and the amount of energy storage required during the parity period.

[0015] In one embodiment, in the step of obtaining the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods based on predicted electricity consumption and predicted power generation, the dispatch controller performs the following steps:

[0016] The predicted electricity consumption and predicted power generation are divided into predicted unit electricity consumption and predicted unit power generation for each unit time period, and the photovoltaic absorption correction coefficient and load power supply correction coefficient are obtained; the unit time period is obtained by dividing a future period into several parts.

[0017] Based on the predicted unit electricity consumption and predicted unit power generation before the peak electricity consumption period, calculate the first net photovoltaic surplus power generation per unit time, and use the photovoltaic absorption correction coefficient to correct the first net photovoltaic surplus power generation to obtain the first photovoltaic power generation before the peak electricity consumption period.

[0018] Based on the predicted unit electricity consumption and predicted unit power generation during peak electricity consumption periods, calculate the net electricity demand for charging load per unit time, and use the load power supply correction coefficient to correct the net electricity demand for charging load to obtain the total power supply gap during peak electricity consumption periods.

[0019] Based on the predicted unit electricity consumption and predicted unit power generation during peak electricity consumption periods, calculate the second net photovoltaic surplus power generation per unit time, and use the photovoltaic absorption correction coefficient to correct the second net photovoltaic surplus power generation to obtain the second photovoltaic power generation during peak electricity consumption periods.

[0020] Based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, calculate the amount of energy storage required during off-peak periods and the amount of energy storage required during parity periods.

[0021] In one embodiment, in the step of calculating the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, the dispatch controller performs the following steps:

[0022] If the difference between the total power supply gap and the first photovoltaic power generation is positive and less than the difference between the rated capacity of the energy storage module and the first photovoltaic power generation, then the amount of electricity to be stored during the off-peak period is determined to be the difference between the total power supply gap and the first photovoltaic power generation.

[0023] If the difference between the total power supply gap and the first photovoltaic power generation is positive and greater than the difference between the rated capacity of the energy storage module and the first photovoltaic power generation, then the amount of electricity to be stored during the off-peak period is determined to be the difference between the rated capacity of the energy storage module and the first photovoltaic power generation.

[0024] In one embodiment, in the step of calculating the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, the dispatch controller performs the following steps:

[0025] If it is determined that the second photovoltaic power generation is greater than the total power supply gap, then the amount of electricity that needs to be stored during the grid parity period is zero.

[0026] If it is determined that the second photovoltaic power generation is less than the total power supply gap, then the amount of electricity that needs to be stored during the grid parity period is not zero.

[0027] In one embodiment, before the step of processing the historical data feature library based on the charging load prediction and photovoltaic power generation prediction algorithms and outputting the prediction results for a future period of time, the scheduling controller also performs the following steps:

[0028] Send an authentication request to the cloud platform to request the cloud platform to verify your identity;

[0029] Accept historical data collection instructions sent upon successful authentication on the cloud platform;

[0030] In response to the historical data collection command, the system acquires the historical data corresponding to the photovoltaic module and charging equipment, and uploads the historical data to the cloud platform. This instructs the cloud platform to process the historical data, time period information, and weather data based on the time period determination rules and weather classification standards to obtain a historical data feature library.

[0031] Receive historical data feature database sent by the cloud platform.

[0032] In one embodiment, before the step of processing the historical data feature library based on the charging load prediction and photovoltaic power generation prediction algorithms and outputting the prediction results for a future period of time, the scheduling controller also performs the following steps:

[0033] Send an authentication request to the cloud platform to request the cloud platform to verify your identity;

[0034] Accept historical data collection instructions, weather data, time period determination rules, and weather classification standards sent when identity verification is successful on the cloud platform;

[0035] Responding to historical data collection commands, it acquires historical data corresponding to photovoltaic modules and charging equipment;

[0036] Based on time period determination rules and weather classification standards, historical data, time period information, and weather data are processed to obtain a historical data feature library.

[0037] In one embodiment, the scheduling controller includes a core control module, a parsing module, a status monitoring module, a prediction calculation module, a parameter configuration module, a collaborative scheduling module, a first communication module, and a second communication module;

[0038] The core control module is connected to the parsing module, the status monitoring module, the prediction calculation module, the parameter configuration module, the collaborative scheduling module, and the first communication module, respectively; the first communication module is connected to the cloud platform; the status monitoring module is used to detect the power grid status.

[0039] The second communication module is electrically connected to the status monitoring module and the collaborative scheduling module; the second communication module is also communicatively connected to the photovoltaic module, the energy storage module, and the charging equipment.

[0040] The prediction calculation module is based on charging load prediction and photovoltaic power generation prediction algorithms, processes historical data feature library, and outputs prediction results for a future period of time; the historical data feature library is generated by processing historical data and meteorological data;

[0041] The collaborative scheduling module obtains the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the prediction results, and schedules photovoltaic modules, energy storage modules and charging equipment based on the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods.

[0042] A method for scheduling and controlling photovoltaic energy storage and charging includes the following steps:

[0043] Based on charging load forecasting and photovoltaic power generation forecasting algorithms, the system processes historical data feature databases and outputs forecast results for a future period. The historical data feature database is generated by processing historical data, time period information, and meteorological data.

[0044] Based on the prediction results, the required energy storage capacity during off-peak periods and during parity periods is obtained, and photovoltaic modules, energy storage modules and charging equipment are dispatched based on the required energy storage capacity during off-peak periods and during parity periods.

[0045] One of the above technical solutions has the following advantages and beneficial effects:

[0046] This application's photovoltaic-storage-charging scheduling control system includes photovoltaic modules, energy storage modules, charging equipment, measurement modules, a scheduling controller, and a cloud platform. The scheduling controller executes the following steps: based on charging load forecasting and photovoltaic power generation forecasting algorithms, it processes a historical data feature database and outputs forecast results for a future period. The historical data feature database is generated by processing historical data, time period information, and meteorological data. Based on the forecast results, it obtains the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods, and schedules photovoltaic modules, energy storage modules, and charging equipment based on these requirements. This achieves the linkage between energy storage capacity planning and weather conditions, fully utilizing historical data, time period information, and meteorological data to predict energy storage, ensuring that photovoltaic surplus is fully absorbed on sunny days and load fluctuations are effectively mitigated on rainy days, avoiding energy waste and grid impact. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structure of the photovoltaic-storage-charging scheduling control system in the embodiments of this application.

[0048] Figure 2 This is a flowchart illustrating the execution steps of the scheduling controller in an embodiment of this application.

[0049] Figure 3 This is a flowchart illustrating the prediction steps performed by the scheduling controller in an embodiment of this application.

[0050] Figure 4 This is a flowchart illustrating the process of the scheduling controller performing the calculation of stored power in an embodiment of this application.

[0051] Figure 5 This is a flowchart illustrating one step of the scheduling controller in this application to obtain the historical data feature library.

[0052] Figure 6 This is another flowchart illustrating the step of the scheduling controller in obtaining the historical data feature library in an embodiment of this application.

[0053] Figure 7 This is a schematic diagram of the scheduling controller in an embodiment of this application. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] Currently, with the popularization of new energy vehicles and the widespread application of distributed photovoltaic power, integrated photovoltaic-storage-charging stations have become the core carrier of vehicle-grid interaction, placing higher demands on prediction accuracy, scheduling coordination, system compatibility, and operational safety. Traditional photovoltaic-storage-charging scheduling and control methods have the following problems:

[0056] The problem of low forecast accuracy: the charging load forecast does not distinguish between time periods and the photovoltaic power forecast does not take into account weather classifications, resulting in a large deviation between the forecast results and actual demand. The dispatch and control lacks a scientific basis and cannot achieve precise matching of energy supply and demand.

[0057] The problem of insufficient coordinated scheduling: charging equipment, energy storage system and photovoltaic module operate independently without unified scheduling logic. Energy storage charging and discharging does not respond to photovoltaic fluctuations and load changes, resulting in drastic fluctuations in the power curve and affecting grid stability.

[0058] Poor operational safety issues: There is no coordinated mechanism between the charging equipment stopping charging and the energy storage discharging switch, which can easily lead to sudden voltage drops and equipment restarts. When photovoltaic power changes suddenly, the energy storage control needs to be adjusted frequently, which reduces the service life of the equipment and poses safety hazards.

[0059] The problem of poor system scalability: When adding new charging equipment or photovoltaic modules, the scheduling and control logic needs to be redesigned, and the original system needs to be modified on a large scale, which is costly and time-consuming, and it is difficult to meet the dynamic expansion needs of the site.

[0060] The problem of low energy efficiency: Energy storage capacity planning is not linked to weather conditions. On sunny days, the surplus of photovoltaic power cannot be fully absorbed, and on rainy days, load fluctuations cannot be effectively smoothed out, resulting in both energy waste and grid impact.

[0061] To address the above issues, in one embodiment, such as Figure 1As shown, a photovoltaic-storage-charging scheduling and control system is provided, including: a photovoltaic module 11, an energy storage module 13, a charging device 15, a measurement module 17, a scheduling controller 19, and a cloud platform 21. The photovoltaic module 11 converts solar energy into electrical energy and then transmits the converted photovoltaic electrical energy to the energy storage module 13 or the charging device 15. The energy storage module 13 is used to regulate the power balance of the charging device 15 through charging and discharging. The charging device 15 is used to charge electrical equipment (e.g., new energy vehicles). The measurement module 17 is used to collect the photovoltaic output power of the photovoltaic module 11, the energy storage SOC (State of Charge) of the energy storage module 13, the power data of the charging device 15, and the power information of the power grid (including voltage, current, etc.). The scheduling controller 19 serves as the control center and scheduling center of the photovoltaic-storage-charging scheduling and control system. The cloud platform 21 can be used to send data and / or instructions to the scheduling controller 19, and can also process data and send the processed results.

[0062] Specifically, the photovoltaic module 11 is electrically connected to the energy storage module 13 and the charging device 15, respectively. All three are connected to the power grid. The energy storage module 13 and the charging device 15 can draw power from the grid or the photovoltaic module 11. The energy storage system can supply power to the charging device 15 and can also feed excess power back to the grid. The photovoltaic module 11 can also feed excess power back to the grid. The measurement module 17 is directly or indirectly connected to the power grid and is used to detect the grid status; specifically, it is used to collect power data such as voltage and current from the grid.

[0063] The dispatch controller 19, photovoltaic module 11, energy storage module 13, and charging equipment 15 are communicatively connected. The dispatch controller 19 is also communicatively connected to the cloud platform 21 and the measurement module 17. Specifically, the communication methods include, but are not limited to, Ethernet, WiFi (Wireless Fidelity), and Modbus-TCP (Modbus Transmission Control Protocol). The cloud platform 21 is used to communicate with the meteorological service interface 23, through which the cloud platform 21 obtains real-time and future weather forecast data (e.g., for the next 24 hours).

[0064] During the operation of the photovoltaic-storage-charging scheduling and control system, such as Figure 2 As shown, the scheduling controller 19 performs the following steps (the scheduling controller 19 performs the following steps once every preset time interval, for example, the preset time is 5 minutes):

[0065] Step S210: Based on the charging load prediction and photovoltaic power generation prediction algorithms, process the historical data feature library and output the prediction results for a future period. The charging load prediction and photovoltaic power generation prediction algorithms include charging load prediction and photovoltaic power generation prediction. Charging load prediction targets charging equipment 15 such as electric vehicles and electric bicycles, predicting the total charging power or electricity demand for a future period. Photovoltaic power generation prediction targets photovoltaic power generation systems, predicting the output power for a future period. The charging load prediction and photovoltaic power generation prediction algorithms include, but are not limited to: LSTM / GRU model algorithms, CNN-LSTM / ConvLSTM model algorithms, and generative adversarial network model algorithms.

[0066] The historical data feature library is a structured collection of prediction-related feature data for storage, management, and reuse. Its core function is to extract valuable information from raw historical data, providing high-quality input for prediction algorithms, thereby improving model accuracy and generalization ability. Its generation process involves cleaning, transforming, fusing, and deriving from the raw data to form a structured feature set. The historical data feature library includes average load during weekday morning peak hours, solar power curves on sunny days, etc. In this embodiment, the historical data feature library is generated by processing historical data, time period information, and meteorological data. The historical data includes historical charging data and historical solar power generation data for a certain period (e.g., three months). This data can be collected by the dispatch controller 19 from the charging device 15 and solar module 11 via the local data bus, or by the measurement module 17 from the charging device 15 and solar module 11 and then sent to the dispatch controller 19. The time period information includes the date and its corresponding nature (e.g., weekday, rest day, etc.). The meteorological data is weather forecast data for a future period (e.g., the next 24 hours) collected through the meteorological service interface 23 and distributed by the cloud platform 21. In one example, historical data, time period information, and meteorological data are preprocessed (preprocessing may include steps such as data loading, data exploration and understanding, data cleaning, data integration, and data transformation). Based on time period determination rules and weather classification standards, the preprocessed historical data, time period information, and meteorological data are classified and statistically analyzed to generate a historical data feature library.

[0067] To accurately predict electricity consumption and power generation, in one embodiment, such as Figure 3 As shown, in the steps of processing historical data feature databases and outputting prediction results for a future period based on charging load prediction and photovoltaic power generation prediction algorithms, the scheduling controller 19 performs the following steps:

[0068] Step S310: Based on the charging load prediction and photovoltaic power generation prediction algorithms, the historical average electricity consumption for the same type of time period in the future is selected from the historical data feature database. The historical average electricity consumption is then corrected using a first correction coefficient to obtain the predicted electricity consumption. Here, the same type of time period refers to a historical time period that is identical to the future period in terms of date and weather conditions. For example, if the future period consists of weekdays with sunny weather, then the same type of time period also consists of weekdays with sunny weather. The historical average electricity consumption for the same type of time period is calculated. In one example, the first correction coefficient is the current charged amount / the historical charged amount for the same period.

[0069] Step S320: Based on the charging load forecasting and photovoltaic power generation forecasting algorithms, the historical average power generation for the same weather conditions over a future period is selected from the historical data feature database. The historical average power generation is then corrected using a second correction coefficient to obtain the predicted power generation. "Same weather condition" refers to a historical period with the same weather conditions as the future period. For example, if the future period is characterized by light rain, then the historical period with the same weather condition is also characterized by light rain, and the historical average power generation for that weather condition is calculated. In one example, the second correction coefficient is the current power generation divided by the historical power generation for the same period. If the future period is from before sunset to sunset, the historical average power generation is corrected linearly, with the correction coefficient increasing closer to sunset, resulting in a greater reduction in the historical average power generation.

[0070] This application distinguishes time periods by electricity consumption forecasting and combines photovoltaic power generation forecasting with weather classification, so that the forecast results match the actual demand, the dispatch and control have a scientific basis, and the energy supply and demand are accurately matched.

[0071] Step S220: Based on the prediction results, obtain the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods, and schedule the photovoltaic module 11, energy storage module 13 and charging equipment 15 based on the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods.

[0072] To ensure that energy storage module 13 can provide power support during peak electricity demand periods, the required energy storage capacity during off-peak and parity periods is calculated based on forecast results. Energy storage module 13 is then charged using electricity from the grid during off-peak and parity periods for use during peak demand periods. Dispatch controller 19 generates energy storage charging and discharging control commands, photovoltaic power adjustment commands, and charging equipment 15 coordination control commands based on the required energy storage capacity during off-peak and parity periods. This coordinates the energy storage module 13, photovoltaic module 11, and charging equipment 15 to ensure overall coordination. This addresses the problem of existing systems where charging equipment 15, energy storage system, and photovoltaic module 11 operate independently without unified scheduling logic, and energy storage charging and discharging fail to respond to photovoltaic fluctuations and load changes, leading to drastic power curve fluctuations and affecting grid stability. Simultaneously, by unifying the scheduling of energy storage module 13, photovoltaic module 11, and charging equipment 15, the lack of a coordinated mechanism between the charging equipment 15 stopping charging and the energy storage module 13 discharging is avoided. This prevents sudden voltage drops, equipment restarts, and frequent control adjustments of energy storage module 13 during photovoltaic power surges, which can reduce the lifespan of other components and pose safety hazards. Since energy storage module 13, photovoltaic module 11, and charging equipment 15 are subject to unified scheduling, the same set of control logic can be used to schedule the newly added energy storage module 13, photovoltaic module 11, and charging equipment 15 without adjusting the control logic or undertaking large-scale system modifications. This reduces expansion costs and engineering time, thus meeting the dynamic expansion needs of the site.

[0073] In the example of obtaining forecast results including forecasted electricity consumption and forecasted power generation, in the step of obtaining the energy storage capacity required during off-peak periods and the energy storage capacity required during the parity period based on the forecast results, the dispatch controller 19 performs the following steps: obtaining the energy storage capacity required during off-peak periods and the energy storage capacity required during the parity period based on the forecasted electricity consumption and forecasted power generation.

[0074] In a specific embodiment, such as Figure 4 As shown, in the step of obtaining the required energy storage capacity during off-peak periods and the required energy storage capacity during the parity period based on the predicted electricity consumption and predicted power generation, the dispatch controller 19 performs the following steps:

[0075] Step S410 involves dividing the predicted electricity consumption and predicted power generation into predicted unit electricity consumption and predicted unit power generation within each unit time period, and obtaining the photovoltaic absorption correction coefficient and load power supply correction coefficient. The unit time is obtained by dividing a future period into several parts. Further, the unit time is obtained by evenly dividing a future period into several parts. In one example, the unit time is a value between 15 minutes and 60 minutes, for example, 15 minutes, 20 minutes, 30 minutes, 40 minutes, or 60 minutes. Corresponding to the unit time, the predicted electricity consumption is divided into predicted unit electricity consumption, and the predicted power generation is divided into predicted unit power generation. Predicted unit electricity consumption is the electricity consumption within a unit time period, and predicted unit power generation is the power generation within a unit time period. The photovoltaic absorption correction coefficient is a correction parameter used to quantify the difference between the actual absorbable photovoltaic power generation and the theoretical power generation. Its core function is to adjust the theoretical power generation capacity of the photovoltaic power station to reflect the impact of actual factors such as grid absorption capacity, load characteristics, and grid connection constraints on photovoltaic output, ultimately obtaining the actual grid-connectable power generation. The load power supply correction factor is used to correct the predicted output load of the charging pile. In one example, the photovoltaic absorption correction factor and the load power supply correction factor can be issued by the cloud platform 21 or updated locally.

[0076] Step S420: Based on the predicted unit electricity consumption and predicted unit power generation before the peak electricity consumption period, calculate the first net photovoltaic surplus power generation per unit time. Correct the first net photovoltaic surplus power generation using a photovoltaic absorption correction coefficient to obtain the first photovoltaic power generation before the peak electricity consumption period. Net photovoltaic surplus power generation refers to the surplus power generation that a photovoltaic power station can transmit to or store on the grid after deducting its own electricity consumption and local load absorption.

[0077] Step S430: Based on the predicted unit electricity consumption and predicted unit power generation during peak electricity consumption periods, calculate the net electricity demand for charging load per unit time. Correct the net electricity demand for charging load using a load power supply correction coefficient to obtain the total power supply gap during peak electricity consumption periods. Net electricity demand for charging load refers to the total electricity that a charging pile cluster or charging station needs to purchase from the grid after deducting the self-generated and self-consumed electricity from local renewable energy sources such as photovoltaics and the electricity supplemented by energy storage discharge. It is a core indicator for measuring the grid dependence and energy self-sufficiency rate of charging stations.

[0078] Step S440: Based on the predicted unit electricity consumption and predicted unit power generation during the peak electricity consumption period, calculate the second net photovoltaic surplus power generation per unit time, and use the photovoltaic absorption correction coefficient to correct the second net photovoltaic surplus power generation to obtain the second photovoltaic power generation during the peak electricity consumption period.

[0079] Step S450: Calculate the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation.

[0080] In one example, a feasible method for calculating the required energy storage capacity during off-peak periods is provided. Specifically, in the step of calculating the required energy storage capacity during off-peak periods and the required energy storage capacity during the parity period based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, the dispatch controller 19 performs the following steps:

[0081] If the difference between the total power supply gap and the first photovoltaic power generation is positive and less than the difference between the rated capacity of the energy storage module 13 and the first photovoltaic power generation, then the amount of electricity to be stored during the off-peak period is determined to be the difference between the total power supply gap and the first photovoltaic power generation.

[0082] If the difference between the total power supply gap and the first photovoltaic power generation is positive and greater than the difference between the rated capacity of the energy storage module 13 and the first photovoltaic power generation, then the amount of energy to be stored during the off-peak period is determined to be the difference between the rated capacity of the energy storage module 13 and the first photovoltaic power generation.

[0083] In one example, a feasible method for calculating the required energy storage capacity during the parity period is provided. Specifically, in the step of calculating the required energy storage capacity during the off-peak period and the required energy storage capacity during the parity period based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, the dispatch controller 19 performs the following steps:

[0084] If it is determined that the second photovoltaic power generation exceeds the total power supply gap, then the required energy storage capacity during the grid parity period is zero. The required energy storage capacity during the grid parity period is the electricity sourced from the power grid.

[0085] If it is determined that the second photovoltaic power generation is less than the total power supply gap, then the amount of electricity that needs to be stored during the grid parity period is not zero.

[0086] Let's take the actual use of charging twice and discharging twice, with the future period being the next 24 hours, as an example for explanation:

[0087] The photovoltaic power generation before the first grid peak period is predicted after deducting the real-time use of charging vehicles. The result is multiplied by a correction factor to obtain the photovoltaic power generation that needs to be absorbed before the first peak period.

[0088] The predicted power supply gap during the first peak period is the total power supply shortfall after deducting the photovoltaic power generation from the charging equipment 15.

[0089] Predict the amount of photovoltaic power generated during the first peak period, excluding the photovoltaic power generated by charging and consumption.

[0090] The forecast for photovoltaic power generation before the second peak period is calculated by deducting the photovoltaic power generation consumed by charging.

[0091] The predicted power supply gap during the second peak period is the total power supply shortfall after deducting the photovoltaic power generation from the charging equipment.

[0092] The forecast for photovoltaic power generation during the second peak period is based on the amount of photovoltaic power generated after deducting the photovoltaic power generated by charging.

[0093] The comprehensive calculation shows that the energy storage module 13 needs to meet the power requirements of photovoltaic absorption and charging equipment 15.

[0094] By multiplying the calculated result by a correction factor, we can obtain the amount of energy storage required during off-peak periods and the amount required during periods of parity.

[0095] The specific process is as follows:

[0096] Forecast data and time period standards: Obtain the predicted unit electricity consumption and predicted unit power generation for each unit time period; clarify the division standards for the grid's off-peak, flat, and peak periods, and determine the specific time range of the two peak periods.

[0097] Obtain the photovoltaic absorption correction coefficient and the load power supply correction coefficient. These two coefficients can be updated based on historical operating data or system requirements, either by being distributed through the cloud platform 21 or by local configuration.

[0098] Calculation of photovoltaic surplus absorption before the first peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time in the period before the first peak period, calculate the first net photovoltaic surplus power generation for each unit time in this period, sum them up and multiply by the photovoltaic absorption correction coefficient to obtain the first photovoltaic power generation that needs to be absorbed by energy storage before the first peak period.

[0099] Calculation of power supply gap for charging load during the first peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time during the first peak period, calculate the net electricity demand of charging load for each unit time during this period, sum them up and multiply by the load power supply correction coefficient to obtain the total power supply gap for charging load during the first peak period.

[0100] Calculation of photovoltaic surplus absorption during the first peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time during the first peak period, calculate the second photovoltaic surplus power generation for each unit time during this period, sum them up and multiply by the photovoltaic absorption correction coefficient to obtain the second photovoltaic power generation that needs to be absorbed by energy storage during the first peak period.

[0101] Calculation of photovoltaic surplus absorption before the second peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time in the period before the second peak period, calculate the first net photovoltaic surplus power generation for each unit time in this period, sum them up and multiply by the photovoltaic absorption correction coefficient to obtain the first photovoltaic power generation that needs to be absorbed by energy storage before the second peak period.

[0102] Calculation of power supply gap for charging load during the second peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time during the second peak period, calculate the net electricity demand of charging load for each unit time during this period, sum them up and multiply by the load power supply correction coefficient to obtain the total power supply gap for charging load during the second peak period.

[0103] Calculation of photovoltaic surplus absorption during the second peak period: Extract the predicted unit electricity consumption and predicted unit power generation for each unit time during the second peak period, calculate the second photovoltaic surplus power generation for each unit time during this period, sum them up and multiply by the photovoltaic absorption correction coefficient to obtain the second photovoltaic power generation that needs to be absorbed by energy storage during the second peak period.

[0104] Based on the photovoltaic power generation and total power supply gap calculated above, the required energy storage capacity during off-peak periods and during parity periods are calculated. The dispatch controller 19 then dispatches the photovoltaic module 11, energy storage module 13, and charging equipment 15 to charge them using power from the grid, according to the required energy storage capacity during off-peak and parity periods.

[0105] The process for calculating the required energy storage capacity during off-peak periods and the required energy storage capacity during periods of price parity is as follows:

[0106] If the total power supply gap of the charging load during the first peak period minus the first photovoltaic power generation before the first peak period is a positive number and less than the rated capacity of the energy storage module 13 minus the first photovoltaic power generation before the first peak period, then the energy storage capacity required during the off-peak period is the total power supply gap of the charging load during the first peak period minus the first photovoltaic power generation before the first peak period. If the total power supply gap of the charging load during the first peak period minus the first photovoltaic power generation before the first peak period is a positive number and greater than the rated capacity of the energy storage module 13 minus the first photovoltaic power generation before the first peak period, then the energy storage capacity required during the off-peak period is the rated capacity of the energy storage module 13 minus the first photovoltaic power generation before the first peak period.

[0107] By calculating the value of the second photovoltaic power generation during the first peak period plus the first photovoltaic power generation before the second peak period, and determining whether this value is greater than the total power supply gap of the charging load during the second peak period, if it is greater, the energy storage module 13 will not need to use the power supply from the grid for charging during the parity period; if it is less, the total power supply gap of the charging load during the second peak period will be calculated minus the value of the second photovoltaic power generation during the first peak period plus the first photovoltaic power generation before the second peak period, and then the energy storage module 13 will use the power supply from the grid for charging during the parity period.

[0108] The following provides two ways to perform the pre-processing steps:

[0109] In one example, such as Figure 5As shown, before the step of processing the historical data feature library based on the charging load prediction and photovoltaic power generation prediction algorithms and outputting the prediction results for a future period, the scheduling controller 19 also performs the following steps:

[0110] In step S510, an authentication request is sent to the cloud platform 21 to request the cloud platform 21 to verify the identity. Prior to this step, the cloud platform 21 also creates a data group for the photovoltaic module 11, energy storage module 13, charging device 15, measurement module 17, and dispatch controller 19 to determine data interaction permissions and formats. The dispatch controller 19 actively sends an authentication request to the cloud platform 21. The core of the identity verification revolves around the unique identifier of the dispatch controller 19, key / credential security, and adaptation to the computing power of the dispatch controller 19. The verification logic mainly relies on the device actively submitting identity credentials, and the cloud platform 21 verifying the legality, such as verification based on a pre-shared key, verification based on a digital certificate, etc., thereby establishing a trusted communication link between the dispatch controller 19 and the cloud platform 21, ensuring the security, compliance, and data integrity of the IoT system.

[0111] Step S520: Accept the historical data acquisition command sent when authentication is successful on cloud platform 21. The historical data acquisition command instructs the scheduling controller 19 to read memory and acquire historical data. The historical data may be data collected by the measurement module 17 from other devices (e.g., photovoltaic module 11 and charging device 15) and sent to the scheduling controller 19 for storage, or data reported by other devices to the scheduling controller and stored.

[0112] Step S530: Responding to the historical data acquisition command, obtain the historical data corresponding to the photovoltaic module 11 and the charging device 15, and upload the historical data to the cloud platform 21. This instructs the cloud platform 21 to process the historical data, time period information, and weather data based on the time period determination rules and weather classification standards to obtain a historical data feature library. The historical data of the photovoltaic module 11 is historical photovoltaic power generation data. The historical data of the charging device 15 is historical charging data. The steps for generating the historical data feature library are as described above and will not be repeated here. The time period determination rules are the list of national statutory holidays plus custom rest day rules. The weather classification standards are sunny, cloudy, overcast, light rain, moderate rain, heavy rain, etc.

[0113] Step S540: Receive the historical data feature database sent by the cloud platform 21.

[0114] In one example, such as Figure 6 As shown, before processing the historical data feature library based on the charging load prediction and photovoltaic power generation prediction algorithms and outputting the prediction results for a future period, the scheduling controller 19 also performs the following steps (this example differs from the previous example in that the execution subject is changed, but the technology used is the same):

[0115] In step S610, an authentication request is sent to the cloud platform 21 to request the cloud platform 21 to verify the identity. Prior to this step, the cloud platform 21 also creates data groups for the photovoltaic module 11, energy storage module 13, charging equipment 15, measurement module 17, and scheduling controller 19 to determine data interaction permissions and formats.

[0116] Step S620: Accept the historical data collection instructions, weather data, time period determination rules, and weather classification standards sent when the identity verification of the cloud platform 21 is successful.

[0117] Step S630: Respond to the historical data acquisition command and obtain the historical data corresponding to the photovoltaic module 11 and the charging device 15.

[0118] Step S640: Based on the time period determination rules and weather classification standards, process historical data, time period information and weather data to obtain a historical data feature library.

[0119] In one embodiment, such as Figure 7 As shown, the dispatch controller 19 includes a core control module 191, an analysis module 192, a status monitoring module 193, a prediction calculation module 194, a parameter configuration module 195, a collaborative dispatch module 196, a first communication module 197, and a second communication module 198. The core control module 191, as the core hub of the intelligent photovoltaic-storage-charging dispatch controller 19, is responsible for coordinating the work of each module, processing prediction data, executing dispatch commands, and providing feedback on operating status. The prediction calculation module 194 receives historical data, time period information, and meteorological data, executes charging load prediction and photovoltaic power prediction algorithms, and outputs prediction results. The status monitoring module 193 continuously monitors the operating parameters (power, voltage, SOC) and fault information of the photovoltaic module 11, energy storage module 13, and charging equipment 15, as well as the actual operating electrical parameters of the power grid (obtained through communication with the measurement module 17), and provides real-time feedback to the core control module 191. The collaborative dispatch module 196 generates energy storage charging and discharging control commands, photovoltaic power adjustment commands, and collaborative control commands for the charging equipment 15 based on the prediction results and equipment status. The parameter configuration module 195 is used to set and adjust parameters for charging load prediction and photovoltaic power generation prediction algorithms (historical data period, correction coefficient threshold), scheduling control parameters (power fluctuation threshold, energy storage target SOC), communication parameters, etc. The first communication module 197 is responsible for data communication with the cloud platform 21, uploading prediction results, equipment status, scheduling logs, etc., and receiving strategy parameters and control commands from the cloud platform 21 and transmitting them to the core control module 191. The second communication module is used to communicate with the energy storage module 13, photovoltaic module 11, and charging equipment 15. The parsing module 192 parses the time period type (weekday / rest day / holiday) and meteorological data obtained by the cloud platform 21, and performs time period determination and weather classification according to preset rules.

[0120] In the specific connection relationship, the core control module 191 is connected to the parsing module 192, the status monitoring module 193, the prediction calculation module 194, the parameter configuration module 195, the collaborative scheduling module 196, and the first communication module 197 respectively; the first communication module 197 is connected to the cloud platform 21; the status monitoring module 193 is used to detect the power grid status; the second communication module 198 is electrically connected to the status monitoring module 193 and the collaborative scheduling module 196 respectively; the second communication module 198 is connected to the photovoltaic module 11, the energy storage module 13, and the charging equipment 15 respectively.

[0121] The prediction and calculation module 194, based on charging load prediction and photovoltaic power generation prediction algorithms, processes historical data feature databases and outputs prediction results for a future period. The historical data feature database is generated by processing historical data and meteorological data. The collaborative scheduling module 196, based on the prediction results, obtains the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods, and schedules the photovoltaic module 11, energy storage module 13, and charging equipment 15 based on the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods. The specific steps are as described in the aforementioned embodiments and will not be repeated here.

[0122] This application's photovoltaic-storage-charging scheduling control system includes a photovoltaic module 11, an energy storage module 13, a charging device 15, a measurement module 17, a scheduling controller 19, and a cloud platform 21. The scheduling controller 19 executes the following steps: based on charging load prediction and photovoltaic power generation prediction algorithms, it processes a historical data feature library and outputs prediction results for a future period; the historical data feature library is generated by processing historical data, time period information, and meteorological data; based on the prediction results, it obtains the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods, and schedules the photovoltaic module 11, energy storage module 13, and charging device 15 based on the energy storage capacity required during off-peak periods and the energy storage capacity required during parity periods. This achieves the linkage between energy storage capacity planning and weather conditions, fully utilizing historical data, time period information, and meteorological data to predict energy storage, enabling the full absorption of photovoltaic surplus on sunny days and effectively smoothing load fluctuations on rainy days, avoiding energy waste and grid impact.

[0123] In one embodiment, a method for scheduling and controlling optical storage and charging is provided, comprising the following steps:

[0124] Based on charging load forecasting and photovoltaic power generation forecasting algorithms, the system processes historical data feature databases and outputs forecast results for a future period. The historical data feature database is generated by processing historical data, time period information, and meteorological data.

[0125] Based on the prediction results, the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods are obtained, and the photovoltaic module 11, energy storage module 13 and charging equipment 15 are scheduled based on the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods.

[0126] It should be noted that the specific steps of this embodiment are as described in the various embodiments of the photovoltaic storage and charging scheduling control system, and will not be repeated here.

[0127] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A photovoltaic-storage-charging scheduling and control system, characterized in that, include: Photovoltaic modules, energy storage modules, charging equipment, measurement modules, dispatch controllers, and cloud platforms; The photovoltaic module is electrically connected to the energy storage module and the charging device respectively, and the photovoltaic module, the energy storage module and the charging device are all connected to the power grid; the measurement module is used to detect the power grid status; The dispatch controller, the photovoltaic module, the energy storage module, and the charging equipment are communicatively connected. The dispatch controller is also communicatively connected to the cloud platform and the measurement module. The cloud platform is used to communicate with the meteorological service interface. The scheduling controller performs the following steps: Based on charging load forecasting and photovoltaic power generation forecasting algorithms, a historical data feature library is processed to output forecast results for a future period of time; the historical data feature library is generated by processing historical data, time period information, and meteorological data; the forecast results include forecasted electricity consumption and forecasted power generation. Based on the prediction results, the required energy storage capacity during off-peak periods and during the parity period is obtained. The specific steps include: dividing the predicted electricity consumption and predicted power generation into predicted unit electricity consumption and predicted unit power generation for each unit time period, and obtaining the photovoltaic absorption correction coefficient and load power supply correction coefficient; the unit time period is obtained by dividing the future period into several parts; based on the predicted unit electricity consumption and predicted unit power generation before the peak electricity consumption period, the first net photovoltaic surplus power generation corresponding to the unit time period is calculated, and the first net photovoltaic surplus power generation is corrected using the photovoltaic absorption correction coefficient. The system first obtains the first photovoltaic power generation before the peak electricity consumption period; based on the predicted unit electricity consumption and the predicted unit power generation during the peak electricity consumption period, it calculates the net charging load demand for the unit time, corrects the net charging load demand for the unit time using the load power supply correction coefficient, and obtains the total power supply gap during the peak electricity consumption period; based on the predicted unit electricity consumption and the predicted unit power generation during the peak electricity consumption period, it calculates the second net photovoltaic surplus power generation for the unit time, corrects the second net photovoltaic surplus power generation using the photovoltaic absorption correction coefficient, and obtains the second photovoltaic power generation during the peak electricity consumption period. Based on the first photovoltaic power generation, the total power supply gap, and the second photovoltaic power generation, the required energy storage capacity during the off-peak period and the required energy storage capacity during the grid parity period are calculated. Specific steps include: if the difference between the total power supply gap and the first photovoltaic power generation is positive and less than the difference between the rated capacity of the energy storage module and the first photovoltaic power generation, then the required energy storage capacity during the off-peak period is determined to be the difference between the total power supply gap and the first photovoltaic power generation; if the difference between the total power supply gap and the first photovoltaic power generation is positive and greater than the difference between the rated capacity of the energy storage module and the first photovoltaic power generation, then the required energy storage capacity during the off-peak period is determined to be the difference between the rated capacity of the energy storage module and the first photovoltaic power generation; if the second photovoltaic power generation is greater than the total power supply gap, then the required energy storage capacity during the grid parity period is determined to be zero; if the second photovoltaic power generation is less than the total power supply gap, then the required energy storage capacity during the grid parity period is determined to be non-zero. The photovoltaic module, the energy storage module, and the charging equipment are scheduled based on the energy storage capacity required during off-peak periods and the energy storage capacity required during grid parity periods.

2. The photovoltaic-storage-charging scheduling control system according to claim 1, characterized in that, In the step of processing historical data feature databases and outputting prediction results for a future period based on charging load prediction and photovoltaic power generation prediction algorithms, the scheduling controller performs the following steps: Based on the charging load prediction and photovoltaic power generation prediction algorithms, the historical average electricity consumption of the same type of period in the future is selected from the historical data feature library, and the historical average electricity consumption is corrected by the first correction coefficient to obtain the predicted electricity consumption. Based on the charging load prediction and photovoltaic power generation prediction algorithms, the historical average power generation under the same weather conditions for a future period is selected from the historical data feature library. The historical average power generation is then corrected using a second correction coefficient to obtain the predicted power generation.

3. The photovoltaic-storage-charging scheduling control system according to claim 1 or 2, characterized in that, Before the step of processing historical data feature databases and outputting prediction results for a future period based on charging load prediction and photovoltaic power generation prediction algorithms, the scheduling controller also performs the following steps: Send an authentication request to the cloud platform to request the cloud platform to verify your identity; Receive historical data collection instructions sent when the authentication is successful on the cloud platform; In response to the historical data acquisition instruction, the system acquires the historical data corresponding to the photovoltaic module and the charging device, and uploads the historical data to the cloud platform to instruct the cloud platform to process the historical data, the time period information and the weather data based on the time period determination rules and weather classification standards to obtain the historical data feature library; Receive the historical data feature database sent by the cloud platform.

4. The photovoltaic-storage-charging scheduling control system according to claim 1 or 2, characterized in that, Before the step of processing historical data feature databases and outputting prediction results for a future period based on charging load prediction and photovoltaic power generation prediction algorithms, the scheduling controller also performs the following steps: Send an authentication request to the cloud platform to request the cloud platform to verify your identity; Accept historical data collection instructions, weather data, time period determination rules, and weather classification standards sent when identity verification is successful on the cloud platform; In response to the historical data acquisition command, acquire the historical data corresponding to the photovoltaic module and the charging device; Based on the time period determination rules and the weather classification standards, the historical data, the time period information, and the weather data are processed to obtain the historical data feature library.

5. The photovoltaic-storage-charging scheduling control system according to claim 1 or 2, characterized in that, The scheduling controller includes a core control module, a parsing module, a status monitoring module, a prediction calculation module, a parameter configuration module, a collaborative scheduling module, a first communication module, and a second communication module. The core control module is connected to the parsing module, the status monitoring module, the prediction calculation module, the parameter configuration module, the collaborative scheduling module, and the first communication module, respectively; the first communication module is communicatively connected to the cloud platform; the status monitoring module is used to detect the power grid status; The second communication module is electrically connected to the status monitoring module and the collaborative scheduling module respectively; the second communication module is also communicatively connected to the photovoltaic module, the energy storage module, and the charging device respectively. The prediction calculation module is based on charging load prediction and photovoltaic power generation prediction algorithms, processes historical data feature library, and outputs prediction results for a future period of time; the historical data feature library is generated by processing historical data and meteorological data. The collaborative scheduling module obtains the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods based on the prediction results, and schedules the photovoltaic module, the energy storage module, and the charging equipment based on the required energy storage capacity during off-peak periods and the required energy storage capacity during parity periods.

6. A photovoltaic-storage-charging scheduling control method, applied to the photovoltaic-storage-charging scheduling control system according to any one of claims 1 to 5, characterized in that, Includes the following steps: Based on charging load forecasting and photovoltaic power generation forecasting algorithms, a historical data feature library is processed to output forecast results for a future period of time; the historical data feature library is generated by processing historical data, time period information and meteorological data; Based on the prediction results, the required energy storage capacity during off-peak periods and during the parity period are obtained, and the photovoltaic module, the energy storage module, and the charging equipment are scheduled based on the required energy storage capacity during off-peak periods and the required energy storage capacity during the parity period.

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