Light storage and charging integrated energy management system and method and electronic equipment

Abstract

The invention relates to the field of energy management, and discloses a light storage and charging integrated energy management system, which comprises a central energy management platform used for monitoring and scheduling energy flow among photovoltaic power generation, energy storage equipment and a charging pile in real time, and dynamically adjusting the energy flow according to the change of the power grid load, the state of the energy storage equipment and the photovoltaic power generation amount; the photovoltaic power generation monitoring and scheduling module communicates with the central platform and is used for acquiring the power generation amount data of the photovoltaic module and adjusting the use priority of photovoltaic power generation according to the load condition of the power grid; and an energy storage management and scheduling module. Through cooperative work of the central energy management platform, photovoltaic power generation is preferentially supplied to the energy storage equipment and the charging pile, electric power waste is avoided, maximum utilization is achieved through combined action of the photovoltaic power generation and the energy storage equipment when a power grid is in a low ebb, the energy storage equipment discharges to relieve the pressure of the power grid when the power grid is in a peak period, and therefore the energy utilization efficiency of the system is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of energy management, and specifically to an integrated photovoltaic energy storage and charging energy management system, method, and electronic device. Background Art

[0002] With the growing global demand for sustainable energy, photovoltaic power generation, as a green and clean energy source, has become an important part of modern energy systems. At the same time, the popularization of energy storage technologies and electric vehicles has also provided new development opportunities for the optimization of photovoltaic power generation systems. The integrated photovoltaic energy storage and charging system integrates photovoltaic power generation, energy storage devices, and charging piles, and through coordinated scheduling, realizes the efficient utilization and intelligent management of energy. Such systems can not only improve energy utilization efficiency but also effectively reduce the load on the power grid, promoting the realization of a green and low-carbon society.

[0003] Currently, integrated photovoltaic energy storage and charging energy management systems have gradually been applied in commercial and household energy management. Existing technologies mainly rely on a central energy management platform for scheduling and management between photovoltaic power generation, energy storage devices, and charging piles. Through real-time monitoring and data collection, traditional systems can conduct energy flow scheduling based on factors such as photovoltaic power generation, energy storage device status, and power grid load. However, in actual applications, these systems generally rely on rule-driven scheduling methods and have not fully achieved intelligent and automated efficient scheduling.

[0004] However, there is still a significant problem in existing technologies: in the case of large fluctuations in power grid load or unstable photovoltaic power generation, the scheduling control strategies of existing systems lack real-time performance and accuracy, and cannot dynamically adjust the charge and discharge strategies of energy storage devices and the charging power of charging piles, resulting in low energy utilization efficiency and even potentially causing excessive pressure on the power grid. Especially during peak power grid loads, the system fails to optimize the scheduling of energy storage devices and charging piles in a timely manner according to real-time load and photovoltaic power generation changes, thus failing to effectively reduce the power grid pressure. Summary of the Invention

[0005] Aiming at the deficiencies of existing technologies, the present invention provides an integrated photovoltaic energy storage and charging energy management system, method, and electronic device, which solve the problem of the lack of intelligent and precise scheduling in existing integrated photovoltaic energy storage and charging energy management systems when the power grid load fluctuates and photovoltaic power generation is unstable.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An integrated photovoltaic energy storage and charging energy management system, including: A central energy management platform for real-time monitoring and scheduling of the energy flow between photovoltaic power generation, energy storage devices, and charging piles, and the platform dynamically adjusts the energy flow according to changes in the power grid load, energy storage device status, and photovoltaic power generation; Photovoltaic power generation monitoring and scheduling module, communicating with the central platform, used to obtain the power generation data of photovoltaic modules, and adjust the usage priority of photovoltaic power generation according to the grid load situation; Energy storage management and scheduling module, connecting the central platform and energy storage devices, used to adjust the charging and discharging strategies of energy storage devices according to the grid load fluctuations and changes in photovoltaic power generation; Charging pile management and scheduling module, connected to the central platform and charging piles, used to adjust the charging power and charging time period of charging piles according to the grid load, energy storage device status and photovoltaic power generation situation; Grid load monitoring module, used to monitor the real-time load situation of the grid and feed back the data to the central platform to assist in dispatching decisions; Deep learning prediction module, connected to the central platform, used to predict the photovoltaic power generation and energy demand in the future for a period of time and provide prediction data support; Edge computing node, connecting each module and communicating with the central platform, used to perform local data processing and dispatching decisions.

[0007] Preferably, the platform includes: Data acquisition unit, used to collect real-time data from photovoltaic modules, energy storage devices, charging piles and the grid; Dispatch control unit, used to adjust the power of photovoltaic power generation, energy storage device charging and discharging, and charging time period of charging piles according to real-time data and prediction results; Optimization algorithm unit, through intelligent algorithms, optimizes the charging and discharging strategies of energy storage devices to reduce grid load fluctuations; Communication module, used to perform real-time data interaction with edge computing nodes and other modules.

[0008] Preferably, the module includes: Data acquisition unit, used to obtain the power generation data of photovoltaic modules in real time; Dispatch control unit, adjusts the priority usage strategy of photovoltaic power generation according to the grid load and energy storage device status; Power generation optimization unit, adjusts the coordinated use of photovoltaic power generation and energy storage devices according to the grid load prediction results.

[0009] Preferably, the module includes: Data acquisition unit, used to obtain the charging and discharging data of energy storage devices; Charging and discharging scheduling unit, controls the charging and discharging timing of energy storage devices according to the peak and valley periods of the grid load; Optimization control unit, automatically adjusts the charging and discharging strategies of energy storage devices according to the grid load prediction results.

[0010] Preferably, the module includes: A charging demand prediction unit predicts the charging time period according to the charging demand of the electric vehicle; A charging power control unit adjusts the charging power of the charging pile according to the grid load condition; A scheduling strategy unit adjusts the charging schedule of the charging pile according to the state of the energy storage device and the grid load condition.

[0011] Preferably, the module includes: A load data acquisition unit is used to acquire real-time data of the grid load; A load analysis and prediction unit is used to predict the future load trend according to historical load data; A data feedback unit feeds back the predicted load change to the central energy management platform to assist in energy scheduling decisions.

[0012] Preferably, the module includes: A data input unit is used to input historical photovoltaic power generation, grid load, and weather data; A deep learning model unit is trained and predicted through deep learning models such as long short-term memory neural networks; An output unit is used to output the prediction result.

[0013] Preferably, the edge computing node includes: A local data acquisition unit is used to collect operation data of the energy storage device, the charging pile, and the grid in real time; A local scheduling control unit makes rapid scheduling decisions according to local load fluctuations and device states; A local optimization unit performs charge and discharge control of the energy storage device by using local optimization algorithms such as the greedy algorithm or linear programming.

[0014] An integrated energy management method for photovoltaic energy storage and charging, the method includes: Step 1, data acquisition, collecting real-time data of photovoltaic power generation, energy storage devices, charging piles, and grid loads; Step 2, data preprocessing, performing denoising, missing value filling, and normalization processing on the collected data; Step 3, optimized scheduling, based on real-time data and prediction results, dynamically adjusting the charge and discharge states of the energy storage device, the charging power of the charging pile, and the usage priority of photovoltaic power generation through an optimization algorithm; Step 4, execute scheduling, implement charge and discharge scheduling of the energy storage device and the charging pile according to the optimized scheduling result.

[0015] An integrated energy management electronic device for photovoltaic energy storage and charging, the device includes: A processor is used to execute energy scheduling, data processing, and prediction calculations; A memory for storing a scheduling algorithm, a prediction model, historical data, and a control program; A sensor for real-time monitoring of the states of energy storage devices, charging piles, and grid loads; An actuator for adjusting the charge and discharge states of energy storage devices and the charging power of charging piles according to the scheduling control results; A communication module for data interaction with other devices.

[0016] The present invention provides an integrated energy management system, method, and electronic device for photovoltaic energy storage and charging. It has the following beneficial effects: 1. Through the collaborative work between the central energy management platform and each module, the present invention realizes the efficient integration of photovoltaic power generation, energy storage devices, and charging piles. Photovoltaic power generation is preferentially supplied to energy storage devices and charging piles, avoiding waste of electric power resources. During the low grid load period, photovoltaic power generation and energy storage devices work together to maximize energy utilization. During the peak load period, the energy storage device effectively reduces the grid pressure by discharging. Through this intelligent scheduling method, the overall energy utilization efficiency of the system is significantly improved.

[0017] 2. The present invention adopts a charging pile management and scheduling module, which dynamically adjusts the charging power and charging time period of charging piles based on real-time grid load, the state of energy storage devices, and photovoltaic power generation. Especially during the peak grid load period, the system avoids centralized charging during peak hours through intelligent scheduling, reduces the power demand of charging piles, and thus effectively reduces the charging cost. In addition, the energy storage device charges during the low period and supplies power during the peak period, reducing dependence on the grid and further optimizing the cost structure.

[0018] 3. By introducing edge computing nodes, the present invention reduces the burden on the central platform and improves the system's response speed to load fluctuations and emergencies. Edge computing nodes can process local data in real time and make quick adjustments and decisions. For example, when the grid load changes or a device malfunctions, the edge node can immediately adjust the charging power of the charging pile or the charge and discharge state of the energy storage device, thereby improving the system's response speed and stability. This design greatly enhances the flexibility of the system and ensures the continuous and stable operation of energy management.

[0019] 4. The deep learning prediction module of the present invention accurately predicts future energy demand and photovoltaic power generation using historical data and external environment information through a long short-term memory neural network model, thereby providing accurate scheduling basis for the system. Through the prediction of photovoltaic power generation and load demand, the central management platform can optimize the charge and discharge strategy of the energy storage device according to the prediction results to ensure the balance and efficient utilization of the energy flow at any time. Specific implementation manners

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1: The embodiment of the present invention provides an integrated energy management system for photovoltaic energy storage and charging, including: A central energy management platform for real-time monitoring and scheduling of the energy flow between photovoltaic power generation, energy storage devices, and charging piles. The platform dynamically adjusts the energy flow according to the changes in grid load, energy storage device status, and photovoltaic power generation. A photovoltaic power generation monitoring and scheduling module for real-time obtaining the power generation data of photovoltaic modules and adjusting the usage priority of photovoltaic power generation according to the grid load situation. An energy storage management and scheduling module for real-time adjusting the charge and discharge strategies of energy storage devices according to the grid load fluctuations and changes in photovoltaic power generation. A charging pile management and scheduling module for dynamically adjusting the charging power and charging time period of charging piles according to the grid load, energy storage device status, and photovoltaic power generation situation. A grid load monitoring module for monitoring the real-time load situation of the grid and feeding back the data to the central platform to assist in scheduling decisions. A deep learning prediction module for predicting the photovoltaic power generation and energy demand in the next period of time, providing prediction data support to optimize the scheduling strategy. An edge computing node for performing local data processing and scheduling decisions, reducing the burden on the central platform and improving the response speed.

[0022] The platform includes: A data acquisition unit for collecting real-time data from photovoltaic modules, energy storage devices, charging piles, and the grid. A scheduling control unit for adjusting the power of photovoltaic power generation, energy storage device charge and discharge, and charging time period of charging piles according to the real-time data and prediction results. An optimization algorithm unit for optimizing the charge and discharge strategies of energy storage devices through intelligent algorithms to reduce grid load fluctuations. A communication module for real-time data interaction with edge computing nodes and other modules.

[0023] The module includes: A data acquisition unit for real-time obtaining the power generation data of photovoltaic modules. A scheduling control unit for adjusting the priority usage strategy of photovoltaic power generation according to the grid load and energy storage device status. The power generation optimization unit adjusts the coordinated use of photovoltaic power generation and energy storage devices according to the grid load prediction results.

[0024] The module includes: The data acquisition unit is used to obtain the charge and discharge data of the energy storage device; The charge and discharge scheduling unit controls the charge and discharge timing of the energy storage device according to the peak and valley periods of the grid load; The optimization control unit automatically adjusts the charge and discharge strategy of the energy storage device according to the grid load prediction results to achieve peak shaving and valley filling.

[0025] The module includes: The charging demand prediction unit predicts the charging period according to the charging demand of electric vehicles; The charging power control unit adjusts the charging power of the charging pile according to the grid load condition; The scheduling strategy unit adjusts the charging schedule of the charging pile according to the energy storage device status and the grid load condition.

[0026] The module includes: The load data acquisition unit is used to obtain the real-time data of the grid load; The load analysis and prediction unit is used to predict the future load trend according to the historical load data; The data feedback unit feeds back the predicted load change to the central energy management platform to assist in energy scheduling decisions.

[0027] The module includes: The data input unit is used to input historical photovoltaic power generation, grid load, and weather data; The deep learning model unit is trained and predicted through deep learning models such as long short-term memory neural networks; The output unit is used to output the prediction results to provide a decision-making basis for the scheduling control unit.

[0028] The edge computing node includes: The local data acquisition unit is used to collect the operation data of the energy storage device, charging pile, and grid in real time; The local scheduling control unit makes rapid scheduling decisions according to local load fluctuations and device status; The local optimization unit uses local optimization algorithms such as greedy algorithms or linear programming for charge and discharge control of the energy storage device.

[0029] In one embodiment, data acquisition and preprocessing In the initial stage of system operation, data of all photovoltaic modules, energy storage devices, charging piles, and grid loads are collected in real time through sensors. The data acquisition unit transmits the data to the central energy management platform. After preprocessing, the data, including denoising, missing value filling, and normalization, is processed to ensure data quality and accuracy.

[0030] Photovoltaic power generation and energy storage scheduling The system dynamically adjusts the priority use of photovoltaic power generation according to grid load prediction and photovoltaic power generation. During low grid load periods, the system preferentially uses photovoltaic power generation to charge energy storage devices; during peak load periods, the energy storage devices discharge to the grid to reduce the grid load. The energy storage management module uses optimization algorithms to ensure that the charging and discharging of energy storage devices play a role in peak shaving and valley filling during grid load fluctuations.

[0031] Intelligent scheduling of charging piles According to the grid load and the status of energy storage devices, the charging power and time of charging piles will be dynamically adjusted. The system restricts the charging power of charging piles during peak grid load periods to avoid excessive grid load; during low grid load periods, the charging piles will preferentially use energy storage devices for charging to reduce dependence on the grid.

[0032] Deep learning prediction and optimal scheduling The system uses a deep learning prediction module to predict factors such as photovoltaic power generation, load demand, and weather forecast, and makes scheduling decisions in advance. For example, if it is predicted that there will be a peak grid load period in the next 12 hours, the system will adjust the charging and discharging strategies of energy storage devices in advance to prepare for emergency power supply during the peak period.

[0033] Edge computing node response When the grid load in a certain area suddenly increases, the edge computing node can quickly respond, adjust the charging power of charging piles or the charging and discharging status of energy storage devices to ensure local grid load balance. The introduction of edge computing nodes reduces the dependence on the central platform and improves the response speed and flexibility of the system.

[0034] Data acquisition and preprocessing The system collects real-time data through sensors of each module, mainly including photovoltaic power generation, charging and discharging status of energy storage devices, charging pile power, and grid load, etc. After data acquisition, denoising, missing value filling, and normalization are performed to ensure data quality and consistency.

[0035] Denoising: A low-pass filter is used to smooth the data and remove high-frequency noise.

[0036] Missing value filling: The mean interpolation method is used to fill in missing data.

[0037] Normalization: Standardize each piece of data to keep the data on a consistent scale for subsequent processing.

[0038] Photovoltaic power generation and energy storage scheduling Based on the predicted grid load and photovoltaic power generation, the system intelligently schedules through the photovoltaic power generation monitoring and scheduling module and the energy storage management and scheduling module: During the low grid load period, the photovoltaic power generation is preferentially used for charging the energy storage device.

[0039] During the peak load period, the energy storage device discharges to the grid to relieve the grid pressure and balance the grid load.

[0040] The scheduling priority order of photovoltaic power generation: First, supply power to the energy storage device, second, to the charging pile, and finally supply to the grid.

[0041] Intelligent scheduling of charging piles During the charging pile scheduling process, the system dynamically adjusts the charging power and charging time of the charging pile according to the real-time grid load and the charge and discharge status of the energy storage device: During the peak grid load period, the system automatically limits the charging power of the charging pile to avoid grid overload.

[0042] During the low grid load period, the charging pile preferentially obtains power from the energy storage device for charging, reducing dependence on the grid.

[0043] Deep learning prediction and optimized scheduling Through the deep learning prediction module, the system predicts the photovoltaic power generation, grid load, and charging demand for the next 24 hours and optimizes the scheduling strategy: Prediction input: Historical load data, weather information, photovoltaic power generation data, charging pile usage records, etc.

[0044] Output: Prediction of grid load and photovoltaic power generation for a future period of time.

[0045] Based on the prediction results, the system can schedule the charge and discharge timing of the energy storage device in advance to ensure that load fluctuations are effectively balanced.

[0046] Edge computing node response The edge computing node quickly responds and makes scheduling decisions according to the real-time data changes in the local area. Suppose the charging pile demand in a certain area of the park surges, and the edge computing node will immediately adjust the charging power of the charging piles in that area to avoid grid overload in that area.

[0047] Local scheduling: The edge computing node adjusts the charge and discharge strategy of the energy storage device through rapid calculation to ensure the load balance of the local grid.

[0048] Example two: This embodiment provides an integrated energy management method for photovoltaic energy storage and charging on the basis of the above embodiment. The method includes: A data collection step of collecting photovoltaic power generation, energy storage device, charging pile, and grid load data in real time; A data preprocessing step of denoising, filling missing values, and normalizing the collected data; An optimization scheduling step of dynamically adjusting the charge and discharge states of the energy storage device, the charging power of the charging pile, and the usage priority of photovoltaic power generation through an optimization algorithm based on real-time data and prediction results; An execution scheduling step of implementing the charge and discharge scheduling of the energy storage device and the charging pile according to the optimization scheduling result to ensure grid load balance and the lowest charging cost.

[0049] Embodiment 3: This embodiment provides an integrated energy management electronic device for photovoltaic energy storage and charging on the basis of the above embodiment. The device includes: A processor for performing energy scheduling, data processing, and prediction calculation; A memory for storing scheduling algorithms, prediction models, historical data, and control programs; A sensor for real-time monitoring of the states of the energy storage device, the charging pile, and the grid load; An actuator for adjusting the charge and discharge states of the energy storage device and the charging power of the charging pile according to the scheduling control result; A communication module for data interaction with other devices.

[0050] In one embodiment, data collection and real-time monitoring The system collects data in real time through the sensors of each module, namely the photovoltaic power generation module, the energy storage device, the charging pile, and the grid load. The data includes photovoltaic power generation, the charge and discharge states of the energy storage device, the charging power of the charging pile, the real-time grid load, etc. Data collection is the basis for implementing intelligent scheduling and ensures that the operation state of the system is reflected in real time.

[0051] The collected data is cleaned and standardized through the data preprocessing step. First, the possible noise signals are removed through a denoising method to ensure the accuracy of the data; secondly, the missing values in the data are filled (such as using the mean imputation method or the interpolation method); finally, normalization processing is performed to convert different types of data into a standard format with the same dimension for subsequent processing.

[0052] Based on historical data and real-time inputs (such as weather forecasts, photovoltaic power generation conditions, grid loads, etc.), the system predicts the photovoltaic power generation and grid load demand for the next 24 hours using deep learning (such as the LSTM neural network). This prediction result provides an important basis for subsequent scheduling decisions, making the scheduling more accurate and efficient.

[0053] Based on the deep learning prediction results and real-time data, the optimization scheduling step dynamically adjusts the charging and discharging states of energy storage devices, the charging power of charging piles, and the usage priority of photovoltaic power generation through intelligent algorithms (such as dynamic programming, reinforcement learning, etc.). The goals of the optimization scheduling are as follows: Maximize the utilization efficiency of photovoltaic power generation and energy storage devices; Reduce the risk of grid overload by balancing the grid load; Optimize the charging strategy of charging piles to reduce the charging cost.

[0054] The core of this step is to calculate the scheduling plan to minimize the system cost and optimize the overall system benefit.

[0055] Once the optimization scheduling plan is generated, the execution scheduling step will implement the charging and discharging scheduling of energy storage devices and charging piles according to the scheduling results. Specifically: During the low grid load period, the system preferentially obtains power from photovoltaic power generation and energy storage devices to supply power to the charging piles for charging; During the peak load period, the energy storage device discharges to the grid to relieve the grid load, and at the same time adjusts the charging power of the charging piles according to the grid load condition to ensure that the grid load does not exceed the safe load.

[0056] The execution scheduling process is jointly completed by the central platform and the edge computing nodes. The edge computing nodes can quickly respond and adjust the states of the charging piles or energy storage devices when the local grid load fluctuates greatly.

[0057] The system state and grid load data after the execution of the scheduling will be fed back to the central energy management platform in real time. The platform monitors the system state according to the feedback information and continues to adjust and optimize the scheduling plan to ensure that the system is always in the best operating state.

[0058] Comparative Example 1: Experimental Method The experiment is carried out in two parts: Benchmark experiment (existing technology): A traditional decentralized control energy management system is adopted without combining deep learning prediction and edge computing.

[0059] Experiment of the present invention: The integrated photovoltaic-storage-charging energy management system of the present invention is adopted, and intelligent scheduling is carried out by combining deep learning prediction, optimization scheduling, and edge computing nodes.

[0060] Experimental Environment: Photovoltaic power generation system: 3 sets of 100kW photovoltaic modules.

[0061] Energy storage device: 2 sets of 100kWh energy storage devices.

[0062] Charging piles: 10 electric vehicle charging piles.

[0063] Power grid: Directly connected to the distribution network of the park, with large load fluctuations.

[0064] Experimental indicators Energy utilization efficiency: Calculate the utilization rates of photovoltaic power generation, energy storage device stored power, and charging pile charging power within a certain period of time.

[0065] Charging cost: Measure the charging cost of electric vehicles, especially the charging fees during peak and off-peak hours.

[0066] Power grid load fluctuation range: By monitoring the fluctuation range of the power grid load, calculate the maximum and minimum values of the load fluctuation, as well as the load stability.

[0067] System stability and response time: Measure the time and stability of the system to respond to sudden load fluctuations (such as a sudden increase in charging pile demand).

[0068] Table 1 Experimental analysis Comparison of energy utilization efficiency The energy utilization efficiency in the benchmark experiment is 75%, which means that about 25% of the photovoltaic power generation is not effectively utilized, and some power is wasted when the power grid is overloaded or the energy storage device is not fully charged.

[0069] In the experiment of the present invention, efficient cooperation is carried out between photovoltaic power generation and energy storage devices through intelligent scheduling, and the energy utilization efficiency is increased to 89%. The photovoltaic power generation is preferentially supplied to the energy storage device and dynamically scheduled according to the power grid load prediction, avoiding the waste of photovoltaic power when the power grid load is too high.

[0070] Beneficial effect: The present invention improves the energy utilization efficiency by 14% and reduces the waste of photovoltaic power generation resources.

[0071] Comparison of charging costs The charging cost in the benchmark experiment is 0.12 yuan / kWh, which is mainly affected by the power grid load fluctuation and the price increase during the peak hours of the power grid.

[0072] In the experiment of the present invention, by intelligently scheduling the charging piles and energy storage devices, the energy storage power is preferentially used to supply power to the charging piles during off-peak hours, effectively reducing the charging cost, and the charging cost is reduced to 0.08 yuan / kWh.

[0073] Beneficial effect: The present invention reduces the charging cost by about 33%, especially during peak hours, which can effectively reduce the power grid load and thus reduce the charging cost.

[0074] Comparison of power grid load fluctuation ranges In the benchmark experiment, due to the lack of intelligent scheduling, the power grid load fluctuates greatly, and the load fluctuation range reaches 150 kW, resulting in the instability of the power grid and possibly causing the power grid to be overloaded.

[0075] Through real-time monitoring and dynamic regulation of energy storage devices in the experiment of the present invention, the power grid load fluctuation range is reduced to 80 kW. The energy storage device provides power during peak loads to balance the power grid load.

[0076] Beneficial effects: The present invention reduces the power grid load fluctuation by about 47%, effectively improving the stability and reliability of the power grid.

[0077] Comparison of system response time In the benchmark experiment, when the demand for charging piles suddenly increases or the power grid load fluctuates sharply, the system response time is relatively long, about 15 seconds, resulting in the failure to balance the power grid load in a timely manner and affecting the system stability.

[0078] Through edge computing nodes and fast scheduling algorithms in the experiment of the present invention, the system can respond to power grid load changes within 5 seconds, quickly adjust the power of charging piles and the state of energy storage devices, and ensure the stable operation of the power grid.

[0079] Beneficial effects: The present invention significantly improves the response speed, reduces the response time by about 66%, and enhances the flexibility and stability of the system.

[0080] Comparison of the power regulation accuracy of charging piles In the benchmark experiment, the power regulation accuracy of the charging pile is 15%. In the case of large load changes, the charging power is prone to fluctuate, resulting in unnecessary waste of electric energy.

[0081] In the experiment of the present invention, the power regulation accuracy of the charging pile is increased to 5%. The system can more precisely regulate the power of the charging pile to ensure that the power supply during the charging process matches the power grid load.

[0082] Beneficial effects: The power regulation accuracy of the charging pile is increased by about 67%, reducing the waste of power resources.

[0083] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The integrated energy management system of photovoltaic storage and charging is characterized by: The system comprises: A central energy management platform for real-time monitoring and dispatching of energy flows between photovoltaic power generation, energy storage equipment and charging piles. The platform dynamically adjusts energy flows according to changes in grid load, energy storage equipment status and photovoltaic power generation; The photovoltaic power generation monitoring and scheduling module communicates with the central platform to obtain the power generation data of photovoltaic modules and adjust the use priority of photovoltaic power generation according to the grid load conditions; The energy storage management and dispatching module connects the central platform with the energy storage equipment and is used to adjust the charging and discharging strategies of the energy storage equipment according to the fluctuations in grid load and changes in photovoltaic power generation; The charging pile management and dispatching module is connected to the central platform and charging piles to adjust the charging power and charging period of the charging piles according to the grid load, energy storage equipment status and photovoltaic power generation; The power grid load monitoring module is used to monitor the real-time load of the power grid and feed the data back to the central platform to assist in scheduling decisions; The deep learning prediction module is connected to the central platform to predict photovoltaic power generation and energy demand in the future and provide prediction data support; Edge computing nodes connect various modules and communicate with the central platform to perform data processing and scheduling decisions locally.

2. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The platform includes: Data acquisition unit, used to collect real-time data from photovoltaic modules, energy storage equipment, charging piles and power grid; A dispatching control unit is used to adjust the power of photovoltaic power generation, energy storage equipment charging and discharging, and charging pile charging periods according to real-time data and prediction results; The optimization algorithm unit optimizes the charging and discharging strategies of energy storage equipment through intelligent algorithms to reduce grid load fluctuations; Communication module, used for real-time data interaction with edge computing nodes and other modules.

3. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The modules include: A data acquisition unit, used to obtain power generation data of photovoltaic modules in real time; The dispatch control unit adjusts the priority use strategy of photovoltaic power generation according to the grid load and the status of energy storage equipment; The power generation optimization unit adjusts the coordinated use of photovoltaic power generation and energy storage equipment according to the grid load forecast results.

4. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The modules include: A data acquisition unit, used to obtain charging and discharging data of the energy storage device; The charging and discharging scheduling unit controls the charging and discharging timing of the energy storage equipment according to the peak and off-peak periods of the grid load; The optimization control unit automatically adjusts the charging and discharging strategies of the energy storage equipment according to the grid load forecast results.

5. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The modules include: A charging demand prediction unit, which predicts the charging period according to the charging demand of the electric vehicle; Charging power control unit, which adjusts the charging power of the charging pile according to the grid load; The scheduling strategy unit adjusts the charging schedule of the charging pile according to the status of the energy storage equipment and the grid load.

6. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The modules include: Load data acquisition unit, used to obtain real-time data of power grid load; Load analysis and forecasting unit, used to forecast future load trends based on historical load data; The data feedback unit feeds back the predicted load changes to the central energy management platform to assist energy scheduling decisions.

7. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The modules include: A data input unit for inputting historical photovoltaic power generation, grid load and weather data; Deep learning model unit, which performs training and prediction through deep learning models such as long short-term memory neural network; Output unit, used to output prediction results.

8. The integrated photovoltaic storage and charging energy management system according to claim 1 is characterized in that: The edge computing node includes: Local data acquisition unit, used to collect real-time operating data of energy storage equipment, charging piles and power grid; The local dispatch control unit makes quick dispatch decisions based on local load fluctuations and equipment status; The local optimization unit uses a local optimization algorithm, a greedy algorithm, or linear programming to control the charge and discharge of the energy storage device.

9. A photovoltaic, storage and charging integrated energy management method, according to the photovoltaic, storage and charging integrated energy management system according to any one of claims 1 to 8, characterized in that: The method comprises: Step 1: Data collection: real-time collection of photovoltaic power generation, energy storage equipment, charging piles and grid load data; Step 2: Data preprocessing: denoising, missing value filling and normalization of the collected data; Step 3: Optimize scheduling. Based on real-time data and prediction results, the optimization algorithm dynamically adjusts the charging and discharging status of energy storage equipment, the charging power of charging piles, and the priority of photovoltaic power generation. Step 4: Execute scheduling. According to the optimized scheduling results, implement charging and discharging scheduling for energy storage equipment and charging piles.

10. An integrated photovoltaic, storage and charging energy management electronic device, according to the integrated photovoltaic, storage and charging energy management system according to any one of claims 1 to 8, characterized in that: The device comprises: processors to perform energy scheduling, data processing, and predictive calculations; Memory, used to store scheduling algorithms, prediction models, historical data and control programs; Sensors for real-time monitoring of the status of energy storage devices, charging piles, and grid loads; An actuator is used to adjust the charging and discharging state of the energy storage device and the charging power of the charging pile according to the scheduling control results; Communication module, used for data exchange with other devices.

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