Off-grid operation control method of optical storage and charging micro-grid and computer program product

Abstract

The invention provides an off-grid operation control method of an optical storage and charging micro-grid and a computer program product, and relates to the field of data processing. The off-grid operation control method of the optical storage and charging micro-grid comprises the following steps: acquiring operation state data of the optical storage and charging micro-grid; simulating and predicting a power balance state after the optical storage and charging micro-grid is switched to off-grid operation based on the operation state data by using micro-grid digital twin bodies pre-configured in the edge side controller; and formulating a load regulation and control strategy according to the power balance state, and executing the load regulation and control strategy after obtaining an off-grid instruction. By using the scheme of the invention, the optical storage and charging micro-grid dynamically adjusts the load adjustment strategy after the off-grid according to the operation state data, the pertinence is stronger, the equipment impact caused by directly cutting off the load is avoided, and the satisfaction degree of the electricity user is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to an off-grid operation control method and computer program product for a photovoltaic, energy storage and charging microgrid. Background Technology

[0002] A photovoltaic-storage-charging microgrid combines solar photovoltaic power generation, energy storage systems, and charging facilities into a small-scale power system that achieves self-operation and energy balance through a control and management system. When sunlight is abundant, the electricity generated by the photovoltaic power generation system not only meets the current load demand but also stores excess energy in the energy storage system. When sunlight is insufficient or the load demand is high, the energy storage system releases the electricity, achieving energy self-sufficiency and reducing dependence on the external power grid.

[0003] Photovoltaic-storage-charging microgrids can be widely used in industrial parks, commercial areas, residential communities, parking lots, and other places. For example, they can be used in conjunction with electric vehicle charging systems, building HVAC systems, lighting systems, and other electrical loads to improve energy efficiency and reduce electricity costs.

[0004] Photovoltaic-storage-charging microgrids can generally exchange energy with the external public power grid. When generating more electricity, they can transmit it to the public grid for energy consumption; when the load is high, the public grid can supply power to the photovoltaic-storage-charging microgrid to make up for the power difference. However, photovoltaic power generation systems are highly volatile, and some public power grids have inadequate infrastructure. Therefore, it is necessary to restrict the energy exchange between photovoltaic-storage-charging microgrids and the grid. For example, reverse transmission from the photovoltaic-storage-charging microgrid to the grid should be prohibited (i.e., anti-reverse current function). Alternatively, the public grid can disconnect from the photovoltaic-storage-charging microgrid based on its own operating status, allowing the microgrid to operate off-grid and achieve its own power balance.

[0005] Off-grid operation of a photovoltaic-storage-charging microgrid refers to the effective coordination and management of a microgrid composed of photovoltaic power generation systems, energy storage systems, and charging systems without relying on an external power grid, to ensure the stable and reliable operation of the microgrid. During off-grid operation of a photovoltaic-storage-charging microgrid, where photovoltaic power generation fluctuates greatly and energy storage capacity is limited, a sudden increase in electricity demand can easily lead to power supply-demand imbalance. This may result in problems such as system voltage / frequency exceeding limits and over-discharge of energy storage, threatening the stable operation of the microgrid.

[0006] There are two existing solutions to the technical problems of off-grid operation: 1. Physical expansion, increasing the capacity of photovoltaic panels or energy storage batteries; 2. Predictive regulation, predicting load and power generation based on historical data and directly reducing the load. The former requires high costs, may result in equipment capacity redundancy, poor economic efficiency, and low feasibility; the latter has poor responsiveness to load fluctuations, and directly cutting off the load may lead to equipment damage, decreased user satisfaction, and shortened equipment lifespan. Summary of the Invention

[0007] One objective of this invention is to provide an off-grid operation control method for photovoltaic-storage-charging microgrids with better dynamic response.

[0008] A further objective of this invention is to reduce or even avoid directly cutting off electrical loads, thereby reducing the impact on electrical equipment and improving the user experience.

[0009] A further objective of this invention is to employ various adjustment methods to specifically adjust electrical loads of different load levels, thereby ensuring stable and reliable operation of the equipment.

[0010] Specifically, this invention provides an off-grid operation control method for a photovoltaic-storage-charging microgrid. This off-grid operation control method for a photovoltaic-storage-charging microgrid includes:

[0011] Collect operational status data of the photovoltaic-storage-charging microgrid;

[0012] The power balance state of the photovoltaic-storage-charging microgrid after switching to off-grid operation is simulated and predicted based on the operating status data using a microgrid digital twin pre-configured in the edge controller.

[0013] The load control strategy is formulated based on the power balance status and executed after receiving the off-grid instruction.

[0014] Optionally, the microgrid digital twin is pre-configured with a photovoltaic power generation model, an energy storage model, and a load management model. The steps for simulating and predicting the power balance state of the photovoltaic-storage-charging microgrid after switching to off-grid operation based on operational status data using the pre-configured microgrid digital twin within the edge controller include:

[0015] The photovoltaic power generation model simulates and predicts the power generation within a set period after grid disconnection based on the operating status data, thus obtaining the power generation prediction data;

[0016] The load management model simulates and predicts the power consumption within a set period after disconnection from the grid based on the operating status data, and obtains the load prediction data.

[0017] Energy storage prediction data is obtained by simulating and predicting the power adjustment range of energy storage devices within a set period after disconnection from the grid using the energy storage model based on the operating status data.

[0018] The power balance status is determined based on power generation forecast data, load forecast data, and energy storage forecast data.

[0019] Optionally, the steps for developing a load control strategy based on the power balance state include:

[0020] Calculate the power deficit of a photovoltaic-storage-charging microgrid under power balance conditions;

[0021] The power load of each load level in the photovoltaic-storage-charging microgrid is determined based on load forecast data analysis.

[0022] Based on the power deficit, adjustment methods for electrical equipment at each load level are formulated to obtain load control strategies. The adjustment methods for electrical equipment include: reducing load before disconnection from the grid, disconnecting load before disconnection from the grid, reducing load all at once after disconnection from the grid, gradually reducing load after disconnection from the grid, limiting load growth, and allowing free load adjustment.

[0023] Optionally, the steps for analyzing load forecast data to determine the electrical load at each load level in the photovoltaic-storage-charging microgrid include:

[0024] Clustering of electrical loads in photovoltaic-storage-charging microgrids;

[0025] The load level of each type of electrical load is determined based on the operating status data;

[0026] The size of the electrical load at each load level is determined by analyzing the load forecast data.

[0027] Optionally, the steps for clustering the electrical loads in a photovoltaic-storage-charging microgrid include:

[0028] Obtain historical operating data of electricity load and extract operating pattern characteristics;

[0029] Based on the characteristics of their operating patterns, the electrical loads are clustered to determine the category to which each electrical load belongs.

[0030] Optionally, the step of determining the load level of each type of electrical load based on operating status data includes:

[0031] Determine the adjustment range and / or limiting conditions for each type of electrical load based on the operating status data;

[0032] The load level is determined according to the adjustment range and / or the defined limiting conditions.

[0033] Optionally, after formulating a load control strategy based on the power balance state, the method further includes:

[0034] The energy storage model is used to simulate the state of the energy storage equipment after the load regulation strategy is implemented, so as to obtain the expected operating state of the energy storage equipment.

[0035] Output a report interface corresponding to the expected operating status of the energy storage device.

[0036] Optionally, after implementing the load regulation strategy, the following may also be included:

[0037] Obtain the actual operating status of energy storage devices;

[0038] Compare the actual operating status with the expected operating status;

[0039] When the deviation between the actual operating state and the expected operating state exceeds a preset threshold, the load control strategy is adjusted according to the actual operating state.

[0040] Optionally, after the deviation between the actual operating state and the expected operating state exceeds a preset threshold, the following steps are also included:

[0041] Collect off-grid operation data of photovoltaic-storage-charging microgrids;

[0042] Off-grid operation data is used to dynamically update power generation forecast data and load forecast data.

[0043] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of the off-grid operation control method for any of the above-described photovoltaic-storage-charging microgrids.

[0044] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the off-grid operation control method for any of the above-described photovoltaic-storage-charging microgrids are implemented.

[0045] According to another aspect of the present invention, a computer device is also provided, which includes a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the off-grid operation control method for any of the above-described photovoltaic-storage-charging microgrids.

[0046] The off-grid operation control method for photovoltaic-storage-charging microgrids of the present invention utilizes a microgrid digital twin pre-configured within the edge controller to simulate and predict the power balance state of the microgrid after switching to off-grid operation based on operational status data. It dynamically predicts the power balance state of the microgrid after switching to off-grid operation using real-time collected operational status data, and formulates a load regulation strategy based on the power balance state for overall control upon receiving an off-grid command. This method enables the photovoltaic-storage-charging microgrid to dynamically adjust its load regulation strategy after off-grid operation based on operational status data, resulting in greater targeting, avoiding equipment impact caused by directly cutting off loads, and improving user satisfaction.

[0047] Furthermore, in the off-grid operation control method of the photovoltaic-storage-charging microgrid of the present invention, the microgrid digital twin is pre-configured with a photovoltaic power generation model, an energy storage model, and a load management model. These models are used to accurately determine the power balance state, providing an accurate basis for determining the power balance state.

[0048] Furthermore, the off-grid operation control method for the photovoltaic-storage-charging microgrid of the present invention includes the following adjustment methods for electrical equipment: reducing load before off-grid operation, disconnecting load before off-grid operation, reducing load all at once after off-grid operation, gradually reducing load after off-grid operation, limiting load growth, and allowing free load adjustment. These adjustment methods are flexible and diverse, ensuring both stable operation of the photovoltaic-storage-charging microgrid and meeting the requirements for adjusting electrical load.

[0049] Furthermore, the off-grid operation control method of the photovoltaic-storage-charging microgrid of the present invention determines the load level by clustering the power load in the photovoltaic-storage-charging microgrid. Compared with the predefined load level method in the prior art, it is more flexible, applicable to various power consumption scenarios, has a wider control range, and is more targeted.

[0050] Furthermore, the off-grid operation control method for photovoltaic-storage-charging microgrids of the present invention, after the deviation of the actual operating state from the expected operating state exceeds a preset threshold, corrects the load regulation strategy according to the actual operating state, and uses off-grid operation data to dynamically update the power generation forecast data and load forecast data, thereby realizing the dynamic adjustment of the operation control strategy.

[0051] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0052] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0053] Figure 1 This is a schematic diagram of the architecture of a photovoltaic-storage-charging microgrid according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram illustrating the simulated and predicted power balance state in an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram illustrating the load regulation strategy based on the power balance state in the off-grid operation control method of a photovoltaic-storage-charging microgrid according to an embodiment of the present invention.

[0057] Figure 5This is a schematic diagram illustrating the determination of electrical load in an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention;

[0058] Figure 6 This is a flowchart illustrating the dynamic adjustment strategy of the off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention after off-grid operation.

[0059] Figure 7 This is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0060] Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention;

[0061] Figure 9 This is a schematic block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0062] Those skilled in the art should understand that the embodiments described below are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. These partial embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present invention.

[0063] Figure 1 This is a schematic diagram of the architecture of a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. The photovoltaic-storage-charging microgrid generally includes: a cloud management device 102, an edge controller 101, a photovoltaic power generation system 11, an energy storage subsystem 12, an electrical equipment subsystem 13, a grid interaction subsystem 14, and an external public power grid 21.

[0064] The photovoltaic-storage-charging microgrid can adopt a layered control architecture of "cloud-edge-device". End-side equipment includes the grid-connected inverter 111 and photovoltaic modules 112 of the photovoltaic power generation subsystem 11, the energy storage converter 121 and energy storage batteries 122 of the energy storage subsystem 12, various electrical devices (such as DC charging piles, AC charging piles, lighting equipment, control power, etc.) of the electrical equipment subsystem 13, and transformers and distribution boxes of the grid interaction subsystem 14. In addition, end-side equipment may also include various electrical signal detection devices, electricity meters, various sensors, security equipment, etc. Edge-side equipment may include: an edge controller 101 and various environmental monitoring devices (such as solar irradiance monitoring devices, wind power monitoring devices, etc.). Cloud-side equipment includes a micro-cloud management device 102 and a visualization display system (IOC display system).

[0065] The edge controller 101 acts as an edge computing node, executes localized control strategies, processes data locally, and is responsible for data interaction with the cloud management device 102, uploading data to the cloud management device 102, and receiving and executing scheduling instructions from the cloud management device 102.

[0066] The cloud-based management device 102 integrates end-side and edge-side data to construct a digital twin model, supporting cross-system collaborative scheduling. It sends various control strategies to the edge controller 101, such as load control measurement, energy storage control strategies, and photovoltaic operation control strategies. The visualization system uses digital twin technology to display the microgrid topology, equipment status, and energy flow, dynamically displaying key indicators (such as load curves, grid interaction power, and grid-connected or off-grid modes) and triggering anomaly alarms. The digital twin model can also be deployed on the edge controller 101 and includes multiple predictive models to achieve data prediction simulation, providing a basis for edge-side control.

[0067] The photovoltaic power generation system 11 uses photovoltaic panels to convert solar energy into electrical energy and performs preliminary processing on the direct current generated by the photovoltaic panels. For example, the grid-connected inverter 111 uses maximum power point tracking (MPPT) technology to ensure that the photovoltaic module 112 always outputs electrical energy at maximum power, thereby improving the utilization efficiency of solar energy.

[0068] The energy storage subsystem 12 utilizes energy storage batteries 122 to store and release electrical energy, addressing the mismatch between electricity demand and the power generation periods of the photovoltaic power generation system 11. The power conversion system 121 (PCS) is used to convert the stored energy in the energy storage batteries 122. The energy storage subsystem 12 also works in conjunction with the grid interaction subsystem 14 to release stored energy during peak electricity demand periods, reducing grid supply pressure; and to absorb and store electrical energy from the grid during off-peak periods, reducing electricity costs and achieving optimal allocation of power resources.

[0069] The grid interaction subsystem 14 is used to realize the power interaction between the photovoltaic-storage-charging microgrid and the external public grid 21. The grid interaction subsystem 14 can control the interaction mode between the photovoltaic-storage-charging microgrid and the public grid 21 according to the dispatch instructions and operating constraints of the public grid 21. When the photovoltaic-storage-charging microgrid is operating in grid-connected mode, it can interact with the public grid. For example, the grid interaction subsystem 14 can enable bidirectional power flow between the photovoltaic-storage-charging microgrid and the external public grid 21, that is, when the photovoltaic-storage-charging microgrid has excess power, it supplies power to the external public grid 21, and when there is a power shortage, it obtains power from the external public grid 21. Alternatively, the grid interaction subsystem 14 can allow the external public grid 21 to only supply power to the photovoltaic-storage-charging microgrid in one direction, and not allow the photovoltaic-storage-charging microgrid to supply power in the opposite direction; that is, the grid interaction subsystem 14 is configured with anti-reverse current function. In some embodiments, when the photovoltaic-storage-charging microgrid is off-grid, the grid interaction subsystem 14 disconnects the power connection between the photovoltaic-storage-charging microgrid and the external public grid 21.

[0070] The electrical equipment subsystem 13 adjusts the power consumption of each electrical device, such as reducing load power, increasing load power, or shutting down loads. The electrical equipment subsystem 13 can also record and manage the power consumption data of the electrical devices, facilitating load statistics. Through load management, the electrical equipment subsystem 13 can achieve power balance in the photovoltaic-storage-charging microgrid.

[0071] This embodiment provides an off-grid operation control method for a photovoltaic-storage-charging microgrid. When the photovoltaic-storage-charging microgrid switches from grid-connected operation to off-grid operation, a load regulation strategy is formulated based on the power balance state for overall regulation after receiving the off-grid command.

[0072] Figure 2 This is a schematic diagram of an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. The off-grid operation control method for the photovoltaic-storage-charging microgrid generally includes:

[0073] Step S201: Collect operational status data of the photovoltaic-storage-charging microgrid. This operational status data may include: DC-side voltage, current, and power of the photovoltaic power generation system; AC-side voltage, current, and power; grid-connected inverter operating status; photovoltaic module 112 operating status; irradiance; ambient temperature and humidity; wind speed, and other meteorological data related to photovoltaic power generation; battery voltage, battery temperature, battery SOC (state of charge), charging and discharging power, charging and discharging current, and energy storage converter operating mode in the energy storage subsystem; and power, load current, and load operating mode of the electrical load. Those skilled in the art can select the required operational status data according to control needs.

[0074] Step S202 involves using a pre-configured microgrid digital twin within the edge controller to simulate and predict the power balance state of the photovoltaic-storage-charging microgrid after it switches to off-grid operation based on operational status data. The power balance state is the operational state that maintains a balance between power input and output after the microgrid switches to off-grid operation. The microgrid digital twin is pre-configured with a photovoltaic power generation model, an energy storage model, and a load management model.

[0075] Photovoltaic power generation models are used to simulate the real-time power generation of photovoltaic modules based on environmental parameters such as irradiance and temperature, and to predict the power generation in the future. For example, photovoltaic output can be predicted by using a network LSTM (Long Short-Term Memory) and combined with simulations of shading, fault scenarios, etc., to evaluate system efficiency degradation and power loss.

[0076] The energy storage model is used to dynamically track the battery's SOC and SOH (health status), and, in conjunction with the charging and discharging conditions, determine the battery's discharge duration, charging duration, and charging and discharging power, thereby predicting the energy storage's charging and discharging capabilities.

[0077] The load management model predicts future load data based on the characteristics of electrical load and the status of load equipment.

[0078] Step S203: Formulate a load control strategy based on the power balance status.

[0079] The load control strategy in this embodiment adopts a variety of flexible adjustment methods, which is more flexible than the simple load cut-off or reduction in the prior art. It reduces load fluctuations, ensures the reliable and stable operation of electrical equipment, and improves the satisfaction of electricity users.

[0080] Adjustment methods for electrical equipment include: reducing load before disconnecting from the grid, disconnecting load before disconnecting from the grid, reducing load all at once after disconnecting from the grid, gradually reducing load after disconnecting from the grid, limiting load growth, and allowing free adjustment of load. Pre-off-grid load reduction refers to reducing the power supply to electrical loads before disconnecting from the public power grid, thereby ensuring that these loads maintain operation with a certain performance reduction. Pre-off-grid load disconnection refers to shutting off electrical loads before disconnecting from the public power grid, mainly for non-essential electrical equipment and equipment for which energy has been pre-stored. Post-off-grid one-time load reduction refers to reducing the power supply to electrical loads all at once after disconnecting from the public power grid when set conditions are met (e.g., off-grid operation time exceeds a set time, or the increase in electrical load exceeds a preset range). Post-off-grid gradual load reduction refers to gradually reducing the power supply to electrical loads multiple times after disconnecting from the public power grid when set conditions are met (e.g., off-grid operation time exceeds a set time, or the increase in electrical load exceeds a preset range), so that electrical equipment gradually reduces its power consumption. Restricting load growth refers to controlling the load power to remain unchanged or automatically reducing it after disconnecting from the public power grid, without allowing an increase in load power. Allowing free load adjustment refers to allowing particularly important electrical equipment to freely adjust its load size according to its own needs.

[0081] Step S204: After receiving the off-grid instruction, execute the load control strategy.

[0082] The method described in the above embodiment dynamically predicts the power balance state of the photovoltaic-storage-charging microgrid after switching to off-grid operation using real-time collected operating status data. Based on the power balance state, a load control strategy is formulated for overall control after receiving the off-grid command. This enables the photovoltaic-storage-charging microgrid to dynamically adjust the load adjustment strategy after off-grid operation based on operating status data, making it more targeted, avoiding equipment impact caused by directly cutting off the load, and improving the satisfaction of electricity users.

[0083] Figure 3 This is a schematic diagram illustrating the simulation and prediction of power balance state in an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. Step S202, the step of predicting the power balance state, may include:

[0084] Step S301: The photovoltaic power generation model simulates and predicts the power generation within a set time after off-grid operation based on the operating status data, and obtains the power generation prediction data.

[0085] Step S302: The load management model simulates and predicts the power consumption within a set time after disconnection from the grid based on the operating status data, and obtains the load prediction data.

[0086] Step S303: The energy storage model simulates and predicts the power adjustment range of the energy storage device within a set time after disconnection from the grid based on the operating status data, and obtains the energy storage prediction data.

[0087] Step S304: Determine the power balance state based on power generation forecast data, load forecast data, and energy storage forecast data.

[0088] The microgrid digital twin is pre-configured with photovoltaic power generation models, energy storage models, and load management models. These models are used to accurately determine the power balance state, providing an accurate basis for determining the power balance state.

[0089] Figure 4 This is a schematic diagram illustrating the formulation of a load regulation strategy based on the power balance state in an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. Step S204 above, which involves formulating the load regulation strategy, may include:

[0090] Step S401: Calculate the power deficit of the photovoltaic-storage-charging microgrid under power balance conditions. The power deficit can be the difference between the power generated and the power consumed by the photovoltaic-storage-charging microgrid, which reflects the amount of energy required for the microgrid to maintain power balance.

[0091] Step S402: Analyze the load forecast data to obtain the power load of each load level in the photovoltaic-storage-charging microgrid;

[0092] Step S403: Based on the power deficit, formulate adjustment methods for electrical equipment at each load level to obtain a load control strategy. The adjustment methods for electrical equipment include: reducing load before disconnection from the grid, disconnecting load before disconnection, reducing load all at once after disconnection, gradually reducing load after disconnection, limiting load growth, and allowing free load adjustment. The load control strategy can set multiple different adjustment methods for different electrical equipment, that is, use one or more of the above adjustment methods simultaneously.

[0093] In existing technologies, load levels are generally pre-configured and fixed by management personnel based on load importance and related power requirements. They are typically divided into Level 1, Level 2, and Level 3 loads. This load classification standard is based on the national standard "Design Code for Power Supply and Distribution Systems" and addresses the power load's requirements for power supply reliability. However, this classification method is not suitable for photovoltaic-storage-charging microgrids, where load variations are significant, the load adjustment range is large, and load importance changes in different scenarios. Therefore, the method in this embodiment provides another way to determine load levels.

[0094] Figure 5This is a schematic diagram illustrating the determination of electrical load in an off-grid operation control method for a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. Step S402, which involves analyzing load forecast data to derive the electrical load at each load level in the photovoltaic-storage-charging microgrid, may include:

[0095] Step S501 involves clustering the electricity loads in the photovoltaic-storage-charging microgrid. The clustering process may include: acquiring historical operating data of the electricity loads and extracting operational pattern characteristics; clustering the electricity loads according to these characteristics to determine the category of each load. Operational pattern characteristics may include: time characteristics, statistical characteristics, and inter-load correlations. Time characteristics include the shape of the daily load curve (peak time, valley time, peak-valley difference rate, load factor, etc.), periodicity characteristics (daily periodicity, weekly periodicity, seasonal periodic parameters), and time-series characteristics (autocorrelation coefficient, partial autocorrelation coefficient, power change rate, etc.). Statistical characteristics may include: basic statistics (e.g., mean, variance, standard deviation, median, quartiles, etc.); fluctuation characteristics (e.g., power fluctuation rate, extreme value ratio, coefficient of variation, etc.). Correlation characteristics may include: complementarity with renewable energy sources (e.g., time-series matching degree between photovoltaic power generation and load); and inter-load correlations (e.g., synergy of equipment start-up and shutdown, correlation coefficient of power changes). Clustering can be performed using the K-means clustering algorithm to cluster the extracted multidimensional feature vectors. First, the aforementioned operational characteristics are standardized to eliminate the influence of dimensions. Through iterative clustering, the category to which each electrical load belongs is determined. By clustering, loads with similar operating cycles and high correlation can be grouped into one category.

[0096] Step S502: Determine the load level for each type of electrical load based on the operating status data. Specific steps may include: determining the adjustment range and / or limiting conditions for each type of electrical load based on the operating status data; and determining the load level according to the adjustment range and / or the limiting conditions. For example, loads with a large adjustment range and low correlation are prioritized for adjustment, while loads with a small adjustment range and high correlation are considered loads that should operate as stably as possible. The adjustment method for the above load levels must meet the limiting conditions to ensure that the loads perform their basic functions.

[0097] Step S503: Analyze the load forecast data to determine the size of the electrical load for each load level. That is, statistically distinguish the electrical load for each load level from the load forecast data.

[0098] After determining the electrical load for each load level, adjustment methods for electrical equipment at each load level are formulated based on the power deficit, resulting in a load control strategy. For loads with high flexibility and priority adjustment, methods such as disconnection or power reduction before and after grid disconnection can be used. For loads maintaining stable operation, methods to limit load growth can be used. For particularly important loads, methods to allow free power adjustment can be used. The correspondence between the above adjustment methods and load levels can be adjusted according to the power deficit to improve control flexibility. For example, when the power deficit is large, loads that were originally allowed free power adjustment can be changed to load growth restriction methods, and loads that had their power reduced before grid disconnection can be directly disconnected. Conversely, when the power deficit is large, the adjustment range of the load before and after grid disconnection should be maintained as much as possible.

[0099] After formulating a load control strategy based on the power balance state, the method in this embodiment may further include: simulating the state of the energy storage device after implementing the load control strategy using an energy storage model to obtain the expected operating state of the energy storage device; and outputting a report interface corresponding to the expected operating state of the energy storage device. The report interface can display the expected operating state of the energy storage device, thereby reporting the expected operating state of the photovoltaic-storage-charging microgrid after it is disconnected from the grid to the management personnel.

[0100] In some embodiments, the method of this embodiment can continue to be dynamically adjusted after being disconnected from the grid. For example, after executing the load control strategy, it can also include: obtaining the actual operating status of the energy storage device; comparing the actual operating status with the expected operating status; and correcting the load control strategy according to the actual operating status after the deviation between the actual operating status and the expected operating status exceeds a preset threshold.

[0101] If the deviation between the actual operating state and the expected operating state exceeds a preset threshold, the method in this embodiment can also collect off-grid operation data of the photovoltaic-storage-charging microgrid; and use the off-grid operation data to dynamically update the power generation forecast data and load forecast data. The dynamically updated forecast data can be used to readjust the load regulation strategy. The above adjustment process can be consistent with the method of formulating the load adjustment strategy before off-grid operation, and will not be elaborated here.

[0102] The modeling process of the microgrid digital twin used in the methods of the above embodiments may include steps such as data acquisition, model training, off-grid simulation, and model deployment and application. The data acquisition process includes: determining the modeling object (the object of the photovoltaic-storage-charging microgrid), acquiring sensor data, acquiring operational data, and acquiring historical data. The above data undergoes data preprocessing (including data cleaning, feature extraction, and data standardization). The model training process includes: model building (including modeling method selection, mathematical model construction, and model training), and model verification and optimization (including accuracy verification and parameter optimization). The off-grid simulation process may include: off-grid simulation testing and scenario simulation analysis. The model deployment and application process may include: deployment in the actual operating environment, integration with the monitoring system, data input, and model updates.

[0103] Through the modeling of the aforementioned microgrid digital twin, the deployment and application of photovoltaic power generation models, energy storage models, and load management models can be realized.

[0104] Figure 6 This is a flowchart illustrating the dynamic adjustment strategy after off-grid operation control of a photovoltaic-storage-charging microgrid according to an embodiment of the present invention. The adjustment steps may include:

[0105] Step S601: Monitor the power shortage;

[0106] Step S602: Determine if a power shortage occurs;

[0107] Step S603: After a power shortage occurs, select a partial load level and reduce its load power;

[0108] Step S604: Determine whether the power deficit continues to increase;

[0109] Step S605: Increase the magnitude of load power reduction, and select some loads to start reducing their load power;

[0110] Step S606: Determine whether a SOC protection threshold alarm for the energy storage battery has occurred;

[0111] Step S607: If an alarm is detected, force the shutdown of non-critical loads and trigger an alarm.

[0112] Taking a microgrid application of photovoltaic-storage-charging in a certain industrial park as an example, the edge controller can collect operating data of the grid-connected inverter, energy storage PCS, and charging piles every 5 seconds (adjustable period) when the microgrid is connected to the grid. It uses an LSTM neural network to predict the photovoltaic output for the next 15 minutes (adjustable duration) and constructs a power balance simulation model based on the energy storage charging and discharging efficiency curve. When an off-grid signal is detected, the edge controller limits the power of some load levels to 80% of the digital twin prediction value and adjusts the limit minute by minute until the system returns to steady state. Load levels can differentiate between charging piles for different types of vehicles; for example, the power of high-priority emergency vehicle charging piles is not adjustable or is limited to a higher limit; the power of medium-priority bus fast charging piles can be reduced to 70%; and the power of low-priority private car slow charging piles can be reduced to 30% or suspended.

[0113] After testing, the application scenarios of the above embodiments achieved the following effects: improved off-grid operation stability: voltage fluctuation range reduced from ±10% to within ±5%; extended energy storage life: avoiding sudden drops in SOC and increasing the number of charge-discharge cycles; reduced the demand for photovoltaic-energy storage expansion by 30% and lowered construction costs.

[0114] This embodiment also provides a computer program product 80, a computer-readable storage medium 820, and a computer device 830. Figure 7 This is a schematic diagram of a computer program product 80 according to an embodiment of the present invention. Figure 8 This is a schematic diagram of a computer-readable storage medium 820 according to an embodiment of the present invention. Figure 9 This is a schematic block diagram of a computer device 830 according to an embodiment of the present invention.

[0115] Computer program product 80 includes computer program 811, which, when executed by processor 831, implements the steps of the off-grid operation control method for any of the above-described photovoltaic-storage-charging microgrids. Computer-readable storage medium 820 stores the aforementioned computer program 811, which, when executed by processor 831, implements the steps of the off-grid operation control method for any of the above-described embodiments of the photovoltaic-storage-charging microgrid. Computer device 830 may include memory 832, processor 831, and computer program 811 stored in memory 832 and running on processor 831.

[0116] The computer program 811 used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages.

[0117] Computer program 811 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of the invention, electronic circuits including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer-readable program instructions to personalize the electronic circuits by utilizing state information of computer-readable program instructions.

[0118] For the purposes of this embodiment, computer program product 80 is a related product that includes computer program 811.

[0119] For the purposes of this embodiment, a computer-readable storage medium 820 is a tangible device capable of holding and storing a computer program 811. It can be any device capable of containing, storing, communicating, propagating, or transmitting the program 811 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of a computer-readable storage medium 820 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0120] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for off-grid operation control of a light storage and charging microgrid, characterized in that The method comprises the following steps: Collecting operation state data of the light storage and charging micro-grid; Simulating and predicting the power balance state of the light storage and charging micro-grid after switching to off-grid operation based on the operation state data by using the pre-configured micro-grid digital twin in the edge side controller; Formulating a load regulation strategy according to the power balance state, and executing the load regulation strategy after obtaining an off-grid instruction.

2. The off-grid operation control method of the light storage and charging micro-grid according to claim 1, wherein the micro-grid digital twin is pre-configured with a photovoltaic power generation model, an energy storage model and a load management model, and the step of simulating and predicting the power balance state of the light storage and charging micro-grid after switching to off-grid operation based on the operation state data by using the pre-configured micro-grid digital twin in the edge side controller comprises the following steps: Simulating and predicting the power generation in a set time period after off-grid operation by the photovoltaic power generation model according to the operation state data to obtain power generation prediction data; Simulating and predicting the power load in a set time period after off-grid operation by the load management model according to the operation state data to obtain load prediction data; Simulating and predicting the power adjustment range of the energy storage device in a set time period after off-grid operation by the energy storage model according to the operation state data to obtain energy storage prediction data; Determining the power balance state according to the power generation prediction data, the load prediction data and the energy storage prediction data. The step of formulating the load regulation strategy according to the power balance state comprises the following steps:

3. The off-grid operation control method of the optical storage and charging micro-grid according to claim 2, characterized in that, Calculating the power shortage of the light storage and charging micro-grid in the power balance state; Analyzing the power load of each load level in the light storage and charging micro-grid according to the load prediction data; Formulating the adjustment mode of the power consumption device of each load level according to the power shortage to obtain the load regulation strategy, wherein the adjustment mode of the power consumption device comprises reducing the load before off-grid operation, cutting off the load before off-grid operation, reducing the load once after off-grid operation, reducing the load gradually after off-grid operation, limiting the load growth and allowing the load to be freely adjusted. The step of analyzing the power load of each load level in the light storage and charging micro-grid according to the load prediction data comprises the following steps:

4. The off-grid operation control method of the optical storage and charging micro-grid according to claim 3, characterized in that, Clustering the power load in the light storage and charging micro-grid; Determining the load level of each type of power load according to the operation state data; Analyzing the size of the power load of each load level from the load prediction data. The step of clustering the power load in the light storage and charging micro-grid comprises the following steps:

5. The off-grid operation control method of the optical storage and charging micro-grid according to claim 4, characterized in that, Obtaining the historical operation data of the power load and extracting the operation regularity characteristics; Clustering the power load according to the operation regularity characteristics to obtain the type to which each power load belongs. The step of determining the load level of each type of power load according to the operation state data comprises the following steps:

6. The off-grid operation control method of the optical storage and charging micro-grid according to claim 4, characterized in that, Determining the adjustment range and / or limitation condition of each type of power load according to the operation state data; Determining the load level according to the adjustment range and / or limitation condition. After formulating the load regulation strategy according to the power balance state, the method further comprises the following steps:

7. The off-grid operation control method of the optical storage and charging micro-grid according to claim 2, characterized in that, ​ simulate, by the energy storage model, a state of the energy storage device after the load regulation strategy is executed, to obtain an expected operation state of the energy storage device; output a report interface corresponding to the expected operation state of the energy storage device.

8. The off-grid operation control method of the optical storage and charging micro-grid according to claim 7, characterized in that, After the load regulation strategy is executed, further comprising: obtaining an actual operation state of the energy storage device; comparing the actual operation state with the expected operation state; after a deviation of the actual operation state from the expected operation state exceeds a preset threshold, modifying the load regulation strategy according to the actual operation state.

9. The off-grid operation control method of the optical storage and charging micro-grid according to claim 8, characterized in that, After the deviation of the actual operation state from the expected operation state exceeds the preset threshold, further comprising: collecting off-grid operation data of the light storage and charging micro-grid; using the off-grid operation data to dynamically update the power generation prediction data and the load prediction data.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the off-grid operation control method of the light storage and charging micro-grid according to any one of claims 1 to 9.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the off-grid operation control method of the light storage and charging micro-grid according to any one of claims 1 to 9.

12. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-11. The processor executes the computer program to implement the steps of the off-grid operation control method of the light storage and charging micro-grid according to any one of claims 1 to 9.

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