Multi-mode switching control method, device, medium and equipment for optical storage and charging station area

Abstract

The application discloses a kind of multi-mode switching control method, device, medium and equipment of light storage and charging area, belong to control field.The application is by obtaining the multi-dimensional dynamic parameter of light storage and charging area, by mode mapping matrix matching target operation mode, and according to this, internal power distribution instruction and external power interaction instruction are generated simultaneously, the former controls photovoltaic, energy storage, charging equipment in table area and carries out power output or absorption according to strategy, the latter controls table area and external power grid and carries out power exchange according to corresponding direction and magnitude.The application realizes the adaptive seamless switching and efficient coordination of resources of light storage and charging area under multiple working conditions.

Description

Technical Field

[0001] This invention relates to the field of control, and in particular to a multi-mode switching control method, apparatus, medium and equipment for a photovoltaic energy storage and charging station area. Background Technology

[0002] With the deepening of the "dual carbon" goals and the accelerated construction of new power systems, the scale of distributed photovoltaic power generation, user-side energy storage systems, and electric vehicle charging piles connected to distribution substations continues to expand. Integrated photovoltaic-energy storage-charging substations have become an important component of the new power distribution network. Under this architecture, photovoltaic, energy storage, and charging facilities share the transformer capacity of the same distribution substation, forming a regional energy unit integrating power generation, energy storage, and electricity consumption. However, the randomness and volatility of photovoltaic output, the time-varying state of charge of energy storage, and the suddenness and uncertainty of charging loads combine to make the operating conditions of photovoltaic-energy storage-charging substations complex and variable, posing severe challenges to the safe operation, energy utilization efficiency, and economy of the substations.

[0003] To address these challenges, various methods for controlling transformer substation operation have been proposed in existing technologies. For example, some solutions use energy management systems to monitor substation power in real time, enabling switching between grid-connected and islanded modes; others use power hierarchical control strategies to coordinate the scheduling of photovoltaic, energy storage, and charging piles; still others utilize flexible interconnection devices to achieve power sharing among multiple substations. Regarding mode switching, existing technologies trigger different operating modes (such as economic operation mode, load balancing mode, and power outage transfer mode) based on single or a few conditions such as substation load rate. These methods improve the controllability of substation operation to some extent.

[0004] However, existing technologies still have the following shortcomings. First, existing control methods mostly make isolated decisions for single or a small number of parameters, lacking the ability to comprehensively collect and collaboratively sense multi-dimensional dynamic parameters such as photovoltaic output, load demand, and energy storage status, making it difficult to accurately capture the overall operating status of the system. Second, existing mode switching mechanisms are usually based on fixed trigger thresholds or simple logical judgments, failing to establish a systematic mapping relationship between multi-dimensional parameter states and operating modes. When multiple parameters change simultaneously or operating conditions conflict, it is difficult to quickly and reasonably determine the optimal operating mode. Third, existing flexible interconnection methods lack deep coupling with mode decision-making at the power interaction level, often only achieving rigid power transmission or simple power transfer, failing to form a complete closed-loop control chain from state perception to mode decision-making, and then to power execution and interaction.

[0005] These shortcomings make it difficult for photovoltaic, energy storage, and charging stations to accurately and efficiently achieve adaptive switching of operating modes when faced with complex and ever-changing operating conditions. Summary of the Invention

[0006] This invention provides a multi-mode switching control method, device, medium, and equipment for a photovoltaic-storage-charging area, in order to solve the problem that it is difficult to accurately and efficiently achieve adaptive switching of operating modes when the photovoltaic-storage-charging area faces complex and ever-changing operating conditions.

[0007] Firstly, this application provides a multi-mode switching control method for an optical storage and charging station area, including: Obtain multi-dimensional dynamic parameters of the photovoltaic-storage-charging area; Based on the multi-dimensional dynamic parameters, a preset mode mapping matrix is ​​queried to match the target operation mode of the optical storage and charging station area; Power scheduling instructions are generated based on the target operating mode; the power scheduling instructions include internal power allocation instructions and external power interaction instructions. According to the internal power allocation command, the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area are controlled to output or absorb power according to the power allocation strategy corresponding to the target operating mode. According to the external power interaction command, the photovoltaic, energy storage and charging station area and the external power grid exchange power in accordance with the power interaction direction and power interaction amount corresponding to the target operation mode.

[0008] This application acquires multi-dimensional dynamic parameters of the photovoltaic-storage-charging area, enabling comprehensive perception of multi-dimensional information such as photovoltaic output, load demand, energy storage state of charge, and grid dispatch signals within a unified time frame. This provides a comprehensive and real-time data foundation for subsequent decision-making. Based on this, a target operating mode matching the current conditions is determined by querying a preset mode mapping matrix using the multi-dimensional dynamic parameters. This allows different state codes of the multi-dimensional parameters to quickly converge to a unique operating mode through matrix mapping, avoiding decision conflicts and response delays caused by relying on fixed thresholds or simple logical judgments when multiple parameters change simultaneously. Furthermore, internal power allocation instructions and external power interaction instructions are synchronously generated in the power dispatch command according to the target operating mode, controlling the photovoltaic equipment, energy storage equipment, and charging equipment within the photovoltaic-storage-charging area to operate according to the corresponding power allocation strategies. The system outputs or absorbs power and executes external power interaction commands through a preset flexible interconnection interface to achieve power exchange between the distribution area and the external power grid according to the corresponding power interaction direction and amount. This simultaneously maps the state perception results to the coordinated control of internal equipment and the flexible interaction of external interfaces, forming a direct and complete linkage between mode decision-making and power execution. Throughout the process, from parameter acquisition, mode determination, command generation to internal execution and external interaction, a closed data flow and control flow loop is formed. This ensures that every mode switch has a corresponding perception input as a trigger, a corresponding matrix mapping as a decision-making basis, and corresponding internal power allocation commands and external power interaction commands to drive the coordinated action of internal equipment and external interfaces, thereby realizing adaptive seamless switching and efficient resource coordination of the photovoltaic, energy storage and charging distribution area under multiple operating conditions.

[0009] The acquisition of multidimensional dynamic parameters of the photovoltaic-storage-charging area specifically includes: The photovoltaic power output forecast, the load forecast of the photovoltaic storage and charging area, the state of charge of the energy storage system, and the grid-side energy dispatch signal are obtained.

[0010] This application acquires the predicted photovoltaic output, predicted load, state of charge of the energy storage system, and grid-side energy dispatch signals of a photovoltaic-storage-charging substation. It integrates the predicted photovoltaic output (reflecting source-side power generation capacity), the predicted load (reflecting load-side electricity demand), the state of charge of the energy storage system (reflecting storage-side adjustability margin), and the energy dispatch signals (reflecting grid-side dispatch intentions) at the same time point. This allows the heterogeneous information originally scattered across the four stages of generation, consumption, storage, and grid to be converged into a unified multi-dimensional dynamic parameter set, providing complete input features for subsequent operational condition sensing. Based on this, this four-dimensional parameter... The data covers the randomness dimension of photovoltaic output, the time-varying dimension of load fluctuation, the constraint dimension of energy storage status, and the guiding dimension of grid dispatch. These four dimensions complement and constrain each other, enabling the collected data to fully depict the operating status of the photovoltaic-storage-charging area at any given time. This avoids the perception bias and decision blind spots caused by collecting only a single or a few parameters in traditional solutions. As a result, it provides a comprehensive and reliable data foundation for subsequent accurate operating condition matching and operating mode decision-making through mode mapping matrix, ensuring that every mode switch is based on real operating conditions perceived through multi-dimensional collaborative sensing.

[0011] Furthermore, the step of querying a preset mode mapping matrix based on the multi-dimensional dynamic parameters to match the target operating mode of the optical storage and charging station area specifically involves: The predicted photovoltaic output is discretized into photovoltaic output status levels according to a preset photovoltaic output classification rule; The predicted load values ​​of the transformer area are discretized into load status levels according to a preset load classification rule; The state of charge of the energy storage system is discretized into state of charge levels according to a preset charge classification rule; The grid-side energy dispatch signal is discretized into dispatch signal status levels according to a preset dispatch signal classification rule; The photovoltaic output status level, load status level, charge status level, and dispatch signal status level are combined to generate the status code of the current operating condition. The preset pattern mapping matrix is ​​queried according to the status code; the running mode corresponding to the status code is extracted as the target running mode. The mode mapping matrix stores the mapping relationship between each state code and each operating mode, and each row of the mode mapping matrix stores a set of state codes and the corresponding operating mode number.

[0012] This application discretizes photovoltaic power output forecast, transformer load forecast, energy storage system state of charge, and grid-side energy dispatch signals into unified state levels according to their respective hierarchical rules. This transforms four-dimensional continuous parameters with different dimensions and numerical ranges into comparable and combinable discretized expressions within the same state space, thus establishing a unified semantic foundation for the joint processing of multi-dimensional parameters. Based on this, the four-dimensional state levels are combined to generate a state code for the current operating condition. This state code, in a unique and compact form, represents the specific value combination of the four-dimensional parameters in their respective state spaces, enabling any set of multi-dimensional dynamic parameters to be processed. All can be mapped to a deterministic state code value, avoiding matching ambiguity caused by continuous parameter fluctuations. Furthermore, based on the state code query pattern mapping matrix, since the matrix uses the state code as the row index and the running mode number as the row storage content, a one-to-one correspondence between the state code and the running mode is established. This reduces the decision-making process to a simple table lookup operation rather than a complex multi-condition logical judgment. As a result, it can quickly and deterministically converge to the unique target running mode when multiple parameters change simultaneously, overcoming the decision conflict and response lag problems caused by relying on fixed threshold combinations or step-by-step condition judgments in traditional schemes.

[0013] Furthermore, the pattern mapping matrix is ​​optimized and updated in the following way: Record multiple sets of operating data of the photovoltaic storage and charging station area within a preset historical period. Each set of operating data includes the status code of the historical multidimensional dynamic parameters, the historical operating mode corresponding to the status code, and the system comprehensive loss, mode switching voltage fluctuation amplitude and early warning trigger number generated by the photovoltaic storage and charging station area during the operation of the historical operating mode. The multiple sets of operational data are grouped according to the state code, and all operational data belonging to the same state code are grouped into the same data group. For each data group, all running data within the data group are input into a preset cost function to calculate the mapping cost evaluation value corresponding to each running data, and the average value of the mapping cost evaluation values ​​of all running data within the data group is used as the mapping cost evaluation value of the state code corresponding to the data group. The mapping cost evaluation value of each state code is compared with a preset cost threshold, and the state codes whose mapping cost evaluation value is higher than the cost threshold are selected as the state codes to be optimized. For each state code to be optimized, the historical operating mode with the lowest overall system loss is selected from all the operating data of the data group belonging to that state code as the backup operating mode for that state code. Replace the running mode in the mapping relationship between the mode mapping matrix and the state code to be optimized with the backup running mode to obtain the optimized mode mapping matrix. This application records multiple sets of operational data for a photovoltaic-storage-charging station area within a preset historical period, including status codes, corresponding operating modes, overall system losses, voltage fluctuation amplitudes, and the number of early warning triggers. The operational data is grouped by status code, ensuring that multiple historical response records belonging to the same status code are grouped into the same data group. This establishes a structured data foundation indexed by status codes for subsequent refined evaluations targeting specific operating conditions. Based on this, a preset cost function is used to calculate the mapped cost evaluation value of each operational data point within each group, and the average value is taken as the overall cost evaluation value for that status code. Using the average value rather than a single extreme value to represent the overall performance of that status code effectively avoids excessive interference from single abnormal data points in the evaluation results, objectively reflecting the average cost level of the aggregate's long-term operation under that condition. Furthermore… The cost evaluation value of each state code is compared with a preset threshold, and the state codes with cost evaluation values ​​higher than the threshold are selected as the state codes to be optimized. This allows the optimization target to be accurately locked into the specific operating conditions where the current matrix mapping effect is poor. Then, for each state code to be optimized, the operating mode with the minimum comprehensive loss is selected from the historical data of its data group as the backup operating mode to replace the original mapping relationship in the matrix. This completes the iterative closed loop of "identifying the operating conditions with exposed problems → determining the optimal strategy verified under the operating conditions → writing it into the matrix". This enables the mode mapping matrix to be automatically optimized based on the continuous accumulation of actual operating data, and continuously approach the optimal scheduling strategy of the optical storage and charging area under all operating conditions. This overcomes the problem of decision performance degradation caused by the fixed mapping matrix being unable to adapt to long-term evolution factors such as equipment aging and load characteristic changes in the area.

[0014] Furthermore, after generating the power scheduling instruction according to the target operating mode, the method further includes: Obtain the target power value corresponding to the target operating mode; the target power value includes the target output power of the photovoltaic inverter, the target charging and discharging power of the energy storage converter, the target distribution power of the charging pile, and the target interaction power of the flexible interconnection interface. Before issuing the power scheduling command to the photovoltaic inverter, the energy storage converter, the charging pile, and the flexible interconnect interface, the current power reference value of the energy storage converter is adjusted according to the target charging and discharging power of the energy storage converter, and the current power reference value of the flexible interconnect interface is adjusted according to the target interaction power of the flexible interconnect interface, so that the current power reference value of the energy storage converter and the current power reference value of the flexible interconnect interface move closer to their respective target power values. Based on the target interaction power of the flexible interconnect interface and the adjusted current power reference value of the flexible interconnect interface, a power disturbance prediction value is calculated. Based on the power disturbance prediction value, the adjusted current power reference value of the flexible interconnect interface is adjusted again with feedforward compensation to pre-compensate for the power gap before a power surge occurs.

[0015] This application provides clear power adjustment targets for each executing device during mode switching by acquiring target power values ​​for the photovoltaic inverter, energy storage converter, charging pile, and flexible interconnect interface, ensuring that the energy storage converter and flexible interconnect interface know their final state before switching. Based on this, before issuing power dispatch commands, the current power reference value of the energy storage converter is adjusted for the first time according to its target charging and discharging power, and the current power reference value of the flexible interconnect interface is adjusted for the first time according to its target interaction power, bringing their current power reference values ​​closer to their respective target power values. At this point, the power reference values ​​of the energy storage converter and flexible interconnect interface have been pre-shifted from their original values ​​before switching towards their target values. This decomposes the power surge amplitude that would normally need to be overcome all at once after issuing the command into a switching step. The system employs two phases: pre-adjustment and switching, which reduces the power jump gradient at the moment of command execution. Furthermore, it calculates the power disturbance prediction value based on the difference between the target interactive power of the flexible interconnect interface and its current power reference value. This difference directly quantifies the amount of power change that the external power interaction channel needs to bear before and after mode switching. Based on this, the current power reference value of the flexible interconnect interface is adjusted with feedforward compensation, so that the power gap on the external power interaction channel is pre-filled before the disturbance actually occurs, rather than waiting for voltage drop or frequency shift to occur before feedback compensation. Thus, through the combined effect of reference value pre-adjustment before command issuance and feedforward compensation, the impact of power mutation on DC bus voltage and AC bus voltage is synergistically suppressed from two aspects: "reducing the jump amplitude" and "pre-eliminating disturbance sources". This overcomes the voltage fluctuation and regulation lag problems caused by responding after command issuance in traditional schemes.

[0016] Furthermore, the step of controlling the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operating mode based on the external power exchange command specifically includes: During the execution of the external power interaction command, the voltage of the DC bus and the voltage of the AC bus are monitored in real time; the DC bus is the DC connection bus between the preset energy storage system and the preset energy storage converter, and the AC bus is the AC coupling bus between the photovoltaic energy storage and charging station area and the preset external power grid. When the voltage of the DC bus deviates from the preset DC voltage stability range, a first power regulation amount is generated, and the power output value of the energy storage converter is adjusted according to the first power regulation amount so that the voltage of the DC bus is restored to the DC voltage stability range. When the voltage of the AC bus deviates from the preset AC voltage stability range, a second power regulation amount is generated, and the power output value of the preset flexible interconnection interface is adjusted according to the second power regulation amount so that the voltage of the AC bus returns to the AC voltage stability range. After the external power interaction command is executed, the current value of the state of charge of the energy storage system is obtained, and the current voltage of the DC bus and the current voltage of the AC bus are obtained. Based on the current value of the state of charge of the energy storage system, the current voltage of the DC bus, and the current voltage of the AC bus, determine whether the photovoltaic-storage-charging station area has reached a preset stable state. If a stable state is not reached, a preset fine-tuning program is initiated to fine-tune the current power reference value of the energy storage converter or the current power reference value of the flexible interconnection interface until the photovoltaic-storage-charging area reaches a stable state.

[0017] This application monitors the DC bus voltage between the energy storage system and the energy storage converter, as well as the AC bus voltage between the photovoltaic-storage-charging station area and the external power grid, in real time during the execution of external power interaction commands. When either bus voltage deviates from its corresponding stable range, a corresponding power regulation is generated to adjust the power output value of the energy storage converter or the power output value of the flexible interconnection interface. This ensures that voltage fluctuations on both the DC and AC sides are detected in real time and immediately suppressed by the corresponding equipment when deviations occur, preventing power quality deterioration or equipment tripping caused by the continuous accumulation of voltage deviations during switching. Furthermore, after the external power interaction command is executed, the current value of the energy storage system's state of charge, the current voltage of the DC bus, and the current voltage of the AC bus are used together as a stability criterion. According to reports, the system simultaneously verifies whether it has reached a steady state from both the energy storage energy dimension and the electrical quantity dimension, rather than relying solely on a single parameter for judgment, thereby improving the accuracy and reliability of steady-state determination. If a steady state is not reached, a fine-tuning program is initiated to precisely correct the power reference value of the energy storage converter or flexible interconnection interface. This ensures that the system does not directly enter the next scheduling cycle when it fails to meet the standard, but completes the final deviation elimination until it reaches a completely stable state through a fine-tuning closed loop. Thus, the real-time voltage suppression during execution, the multi-dimensional steady-state determination after execution, and the fine-tuning correction when the standard is not met constitute a hierarchical control system for the entire process of external power interaction command execution. This overcomes the problem of continuous accumulation of steady-state deviation or misjudgment caused by the lack of phased adjustment and multi-dimensional verification mechanisms after command execution in traditional schemes.

[0018] Furthermore, the step of initiating a preset fine-tuning program to fine-tune the current power reference value of the energy storage converter or the current power reference value of the flexible interconnect interface specifically involves: Identify the target device that needs fine-tuning, wherein the target device is the energy storage converter or the flexible interconnect interface; Obtain the current actual power value and the target power value of the target device, and calculate the power deviation value of the target device based on the difference between the target power value and the current actual power value; Obtain the equipment operating margin of the target device and the system power balance compensation amount of the optical storage and charging station area; calculate the power reference calibration parameters of the target device based on the power deviation value, the equipment operating margin, and the system power balance compensation amount. Obtain the device response coefficient of the target device and the system coupling strength coefficient between the target device and another device. Calculate the dynamic response coordination parameters of the target device based on the device response coefficient and the system coupling strength coefficient. Based on the power reference calibration parameters and the dynamic response coordination parameters, a fine-tuning instruction for the target device is generated, and the fine-tuning instruction is sent to the target device to fine-tune the target settings.

[0019] This application, when executing the fine-tuning procedure, first identifies the target device to be fine-tuned as an energy storage converter or a flexible interconnect interface, ensuring that all subsequent fine-tuning steps are calculated for the same device, avoiding potential control command conflicts that might arise from applying corrections to two devices simultaneously. Based on this, a power deviation value is calculated according to the difference between the target power value and the current actual power value of the target device. This difference directly quantifies the degree of deviation between the current power and the target power of the target device, providing an accurate direction and amplitude basis for generating fine-tuning commands. Furthermore, when calculating the power reference calibration parameters, the operating margin of the target device and the system power balance compensation amount of the photovoltaic-storage-charging area are considered simultaneously. This ensures that the generated power reference calibration parameters not only reflect the remaining adjustable space of the target device itself but also take into account the overall power balance requirements of the area, thereby avoiding fine-tuning commands failing to execute due to exceeding device capabilities or causing new power imbalances due to ignoring global system power constraints. Simultaneously, by obtaining... The dynamic response coordination parameters are calculated using the device response coefficient of the target device and the system coupling strength coefficient between the target device and another device. This allows the fine-tuning command to consider the response speed characteristics of the target device and the impact of its power adjustment on the operating state of the other device during the generation stage. This enables the two devices to coordinate their actions according to their different response rhythms during the fine-tuning process, avoiding secondary power oscillations caused by differences in device response rates. Finally, the fine-tuning command for the target device is generated and executed based on the power reference calibration parameters and the dynamic response coordination parameters. This gives the fine-tuning command the ability to "correct accuracy based on power deviation", "ensure feasibility based on device margin and system balance", and "coordination constraints based on response characteristics and coupling relationships". Under multiple constraints, the power reference value of the target device is finely corrected, overcoming the problem of repeated adjustments or adjustment failures caused by ignoring the coupling relationship between devices or system power balance constraints in the fine-tuning stage of traditional solutions.

[0020] Secondly, this application provides a multi-mode switching control device for an optical storage and charging station area, the multi-mode switching control device for the optical storage and charging station area comprising: The acquisition module is used to acquire multi-dimensional dynamic parameters of the photovoltaic storage and charging area; The matching module is used to query a preset mode mapping matrix based on the multi-dimensional dynamic parameters in order to match the target operating mode of the optical storage and charging station area. The generation module is used to generate power scheduling instructions based on the target operating mode; the power scheduling instructions include internal power allocation instructions and external power interaction instructions. The first control module is used to control the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area to output or absorb power according to the power distribution strategy corresponding to the target operating mode, based on the internal power distribution instructions. The second control module is used to control the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operation mode, based on the external power exchange command.

[0021] This application acquires multi-dimensional dynamic parameters of the photovoltaic-storage-charging area through an acquisition module, enabling the convergence of multi-dimensional information such as photovoltaic output, load demand, energy storage state of charge, and grid dispatch signals at the data entry point, providing a unified data input foundation for subsequent decision-making modules. The matching module queries a preset mode mapping matrix based on these multi-dimensional dynamic parameters, matching the current operating condition with the multi-dimensional parameter state codes stored in the matrix and outputting the target operating mode. This simplifies the complex decision-making problem involving multiple coupled parameters to a matrix lookup operation, avoiding logical conflicts and decision delays caused by multi-condition step-by-step judgments. The generation module synchronously generates power dispatch instructions containing internal power allocation instructions and external power interaction instructions based on the target operating mode, simultaneously decomposing a single decision result into power allocation constraints for internal equipment and power interaction constraints for the external grid, thus enabling internal power... The scheduling and external power exchange are linked at the command level. The first control module controls the photovoltaic equipment, energy storage equipment and charging equipment to output or absorb power according to the corresponding power allocation strategy based on the internal power allocation command. The second control module controls the photovoltaic, energy storage and charging stations to exchange power with the external power grid according to the corresponding power exchange direction and amount based on the external power interaction command. The two control modules respectively receive the two types of sub-commands in the power scheduling command and drive the corresponding execution objects to coordinate actions. This enables the power allocation of internal equipment and the power interaction of external interfaces to be executed synchronously under the constraints of the same target operation mode. Thus, a complete hardware closed loop from multi-dimensional parameter perception to mode decision and then to internal and external coordinated execution is realized at the device level, overcoming the coordination delay and command mismatch problems caused by the separate operation of perception, decision and control modules in traditional devices.

[0022] Thirdly, this application provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a multi-mode switching control method for an optical storage and charging station area as described above. Its beneficial effects are the same as those of the multi-mode switching control method for an optical storage and charging station area provided in the first aspect of this application.

[0023] Fourthly, this application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement any of the multi-mode switching control methods for optical storage and charging stations as described in the first aspect. Attached Figure Description

[0024] Figure 1: A schematic flowchart of an embodiment of the multi-mode switching control method for the photovoltaic storage and charging station area provided in this application; Figure 2 : A schematic diagram of an embodiment of the multi-mode switching control device for the photovoltaic storage and charging station area provided in this application. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1 Please refer to Figure 1 In order to solve the problem that existing technologies cannot accurately and efficiently achieve adaptive switching of operating modes, this invention provides a multi-mode switching control method for a photovoltaic storage and charging station area, including steps S01-S05.

[0027] S01: Obtain multi-dimensional dynamic parameters of the photovoltaic-storage-charging area; In a preferred embodiment of this invention, the acquisition of the multidimensional dynamic parameters of the optical storage and charging area specifically involves: Four types of dynamic parameters for the photovoltaic-storage-charging area are acquired. Specifically, the photovoltaic power output is predicted based on historical power generation data of the photovoltaic array, real-time irradiance, ambient temperature, and weather forecast information. A pre-trained prediction model continuously outputs the photovoltaic power output curve for future periods, with a sampling frequency of 1 time / minute. The prediction model can employ time series prediction models such as Long Short-Term Memory Networks (LSTM) or Gated Cyclic Units (GCUs). The area load prediction value is generated based on historical user electricity consumption data, time period characteristics, seasonal factors, and charging pile reservation information. A load decomposition algorithm is used to generate predicted values ​​for the total load and categorized loads of the area, with an update cycle of 5 minutes. The state of charge (SOC) of the energy storage system is calculated in real time by collecting the total voltage, individual cell voltage, charging and discharging current, and temperature of the energy storage battery pack, using the ampere-hour integral method combined with open-circuit voltage calibration. The rate of change of SOC is monitored simultaneously to predict charging and discharging trends. The grid-side energy dispatch signal is received in real time through the communication interface with the grid dispatch system. This signal is a technical dispatch instruction issued by the grid dispatch system to instruct the photovoltaic-storage-charging area to participate in grid interaction, including but not limited to peak shaving and valley filling instructions, demand response instructions, or source-grid-load-storage coordinated control instructions.

[0028] After the above four types of dynamic parameters are collected, they are filtered and validated to remove abnormal data that clearly exceeds the reasonable range, and missing data is interpolated to ensure the continuity and accuracy of each parameter. The preprocessed four-dimensional dynamic parameters are used as input data for subsequent pattern mapping matrix queries.

[0029] S02: Based on the multi-dimensional dynamic parameters, query the preset mode mapping matrix to match the target operation mode of the optical storage and charging station area; In a preferred embodiment of this invention, the step of querying a preset mode mapping matrix based on the multi-dimensional dynamic parameters to match the target operating mode of the optical storage and charging station area specifically involves: The aforementioned grading rules and their corresponding grading thresholds are pre-set based on the rated parameters of the equipment in the photovoltaic-storage-charging area, historical operating data, and power grid dispatching specifications. During actual operation, they can be dynamically corrected according to the adaptive adjustment mechanism described later. First, each dimension of the four-dimensional dynamic parameters is discretized and mapped to its corresponding finite state level. Specifically, the photovoltaic (PV) power output is discretized into three state levels according to a preset PV power output classification rule: insufficient output, moderate output, and excess output. Insufficient output indicates that the PV power output is lower than the local load demand or a preset lower output threshold. Moderate output indicates that the PV power output is basically matched with the local load demand or is within a preset moderate range. Excess output indicates that the PV power output is higher than the local load demand or a preset upper output threshold. The local load forecast is discretized into three state levels according to a preset load classification rule: light load, normal load, and heavy load. Light load indicates that the local load forecast is lower than a preset light load threshold. Normal load indicates that the local load forecast is within a preset normal load range. Heavy load indicates that the local load forecast is higher than a preset heavy load threshold. The energy storage system's state of charge is... According to the preset charge classification rules, the energy storage system is discretized into three state levels: low charge, medium charge, and high charge. The low charge state indicates that the energy storage system's charge is below the preset low charge threshold, the medium charge state indicates that the energy storage system's charge is within the preset medium charge range, and the high charge state indicates that the energy storage system's charge is above the preset high charge threshold. The grid-side energy dispatch signal is discretized into three state levels according to the preset dispatch signal classification rules: valley dispatch, flat dispatch, and peak dispatch. These correspond to the valley dispatch instructions, flat dispatch instructions, and peak dispatch instructions issued by the grid dispatch system, respectively. The valley dispatch state indicates that the grid side indicates that the photovoltaic-storage-charging area is in a valley dispatch scenario, the flat dispatch state indicates that the grid side indicates that the photovoltaic-storage-charging area is in a flat dispatch scenario, and the peak dispatch state indicates that the grid side indicates that the photovoltaic-storage-charging area is in a peak dispatch scenario.

[0030] After each of the four-dimensional parameters is discretized into three state levels, the photovoltaic output state level, load state level, charge state level, and dispatch signal state level are combined in a preset order to generate the state code for the current operating condition. Since each of the four-dimensional parameters has three state levels, there are a total of 81 state codes, and each group of state codes corresponds to a unique state code.

[0031] The mode mapping matrix is ​​a pre-constructed two-dimensional lookup table matrix, where each row stores a set of state codes and the corresponding operating mode number. The pre-constructed preset operating modes include emergency power supply mode, anomaly recovery mode, photovoltaic (PV) consumption priority mode, and economic operation mode. Emergency power supply mode has the highest priority, ensuring power supply to emergency vehicle charging piles when the energy storage system has sufficient charge and receives an emergency charging command. Anomaly recovery mode has the next highest priority, quickly switching to islanded or support operation when anomalies such as voltage exceeding limits, frequency fluctuations, or momentary power outages are detected in the distribution network. PV consumption priority mode has a lower priority than anomaly recovery mode, prioritizing PV power supply to local loads and charging piles during periods of sufficient PV output, and using the remaining power to charge the energy storage system. Economic operation mode has the lowest priority, optimizing power flow with the goal of minimizing the overall operating losses of the PV-storage-charging area and the distribution network. Each operating mode is pre-configured with a corresponding operating mode number. For example, the emergency power supply mode is numbered M0, the abnormal recovery mode is numbered M1, the photovoltaic consumption priority mode is numbered M2, and the economic operation mode is numbered M3.

[0032] After the state level combination of each dynamic parameter dimension generates the state code for the current operating condition, this state code is matched one by one with the state codes in each row of the mode mapping matrix. The operating mode corresponding to the operating mode number stored in the row that matches successfully is extracted as the target operating mode. When multiple operating modes are triggered simultaneously, arbitration is performed according to a preset priority rule. That is, the highest priority operating mode is selected as the final target operating mode in the following order: emergency power supply mode has higher priority than abnormal recovery mode, abnormal recovery mode has higher priority than photovoltaic power consumption priority mode, and photovoltaic power consumption priority mode has higher priority than economic operating mode. If both photovoltaic power consumption priority mode and economic operating mode meet the conditions among the multiple modes triggered in the current operating condition, the photovoltaic power consumption priority mode is executed first according to the above priority rule. The lowest priority economic operating mode is only allowed to be used when there is no emergency demand, no grid abnormality, and all equipment is within the safety margin range.

[0033] It should also be noted that, in another optional implementation, the mode mapping matrix is ​​constructed using a 4th-order tensor as its underlying representation. Specifically, the photovoltaic output status level is denoted as 'a', the transformer load status level as 'b', the energy storage system charge status level as 'c', and the grid-side energy dispatch signal status level as 'd'. Any status code of the four-dimensional parameters corresponds to a 4th-order tensor element, and the value of this tensor element is the operating mode number corresponding to that status code. In actual engineering implementation, this 4th-order tensor is flattened and expanded into a two-dimensional lookup table matrix. Each row of the matrix corresponds to a set of status codes, and the row sequentially stores the set of status codes and their corresponding operating mode numbers. This allows for rapid matching of matrix row indices through status codes during runtime, achieving efficient lookup output.

[0034] In another optional implementation, the pattern mapping matrix supports real-time dynamic correction based on parameter change trends. In this implementation, the trend feature vector of each dynamic parameter is first calculated. The trend feature vector describes the comprehensive characteristics of the parameter's rate of change and degree of fluctuation within a preset sliding time window. It is calculated as follows: multiple historical sample values ​​of each dynamic parameter within the preset sliding time window are obtained; the difference between the initial parameter value and the current parameter value is divided by the sliding window duration to obtain the parameter's rate of change; simultaneously, the sum of the squared deviations of each sample value within the window from the window's parameter mean is divided by the number of sampling points in the window, and the square root is taken to obtain the parameter's degree of fluctuation; the rate of change and the degree of fluctuation are multiplied to obtain the parameter's trend feature vector. The larger the absolute value of the rate of change and the higher the degree of fluctuation, the larger the absolute value of the trend feature vector, indicating a more drastic change trend in the parameter.

[0035] It also obtains the current safety margin level of the photovoltaic, energy storage, and charging station area. The safety margin is equal to the difference between the preset critical value and the current operating value divided by the preset critical value. Based on different values ​​of the safety margin, it is divided into three levels: Level 1 is stable operation with a safety margin greater than or equal to 15%; Level 2 is parameters approaching the critical point with a safety margin greater than or equal to 5% and less than 15%; Level 3 is the system approaching the safety boundary with a safety margin less than 5%.

[0036] Based on the aforementioned trend feature vector and safety margin level, the threshold for classifying the state level of each dynamic parameter is adaptively adjusted, so that the currently acquired multi-dimensional dynamic parameters are re-discretized into updated state levels according to the adjusted threshold. Specifically, when the trend feature vector of a parameter shows a large rate of change and drastic fluctuations, and the corresponding safety margin level is level two or three, the interval judgment threshold for that parameter is tightened in advance, so that the parameter is classified into the next state level closer to the safety boundary before the change is significant, thereby triggering the safety-related operation mode switch in advance; when the parameter change trend is gentle and the safety margin level is level one, the original threshold remains unchanged.

[0037] The state levels of the re-discretized dynamic parameters are combined again to form an updated state code, and the pattern mapping matrix is ​​re-queried based on the updated state code.

[0038] Furthermore, when a distribution network voltage exceeds its limit, the energy storage system approaches its charging / discharging safety boundary, or an emergency charging command is received, the operating mode number in the row corresponding to the current operating condition in the mode mapping matrix is ​​directly rewritten, forcibly adopting a higher-level priority operating mode to override the original mapping result. For example, when the energy storage system's state of charge continues to decline during peak hours and the safety margin enters the emergency level, the row that was originally matched to the economic operating mode in the operating condition matrix is ​​directly rewritten, forcibly switching to the abnormal recovery mode, prioritizing the restriction of energy storage discharge and ensuring voltage stability in the distribution area.

[0039] It should also be noted that during the mode decision-making process, the decision results are verified and corrected based on scenario-specific dynamic constraints. These dynamic constraints are adaptively adjusted according to the real-time characteristics of the equipment and the system's operating conditions, specifically including two categories: equipment characteristic-oriented dynamic constraints and scenario-condition-oriented dynamic constraints.

[0040] Equipment-characteristic-oriented dynamic constraints are used to dynamically adjust the permissible range of operating parameters of each device based on its real-time operating status. For energy storage converters, the maximum charge / discharge rate and state-of-charge (SOC) operating range are dynamically adjusted based on the battery cell temperature, voltage inconsistency, and cycle count reported by the battery management system. When the battery temperature difference exceeds a preset temperature difference threshold or the cell voltage deviates from the average value by more than a preset voltage deviation threshold, the maximum charge / discharge rate is reduced to decrease the polarization effect and thermal runaway risk caused by high-current charging and discharging, and the SOC operating range is narrowed to avoid deep charging and discharging. For photovoltaic inverters, the maximum safe output of the photovoltaic inverter under the current operating conditions is calculated in real time based on the temperature and irradiance change rate of the photovoltaic modules. When the module temperature exceeds a preset temperature threshold, the maximum allowable output of the photovoltaic inverter is reduced according to a preset derating factor. For charging piles, the maximum allowable output power of the charging pile is dynamically adjusted based on the charging interface temperature and line current monitoring values. When the interface temperature exceeds a preset temperature threshold or the line current exceeds a preset current carrying capacity threshold, the maximum allowable output power of the charging pile is reduced.

[0041] Scenario-based dynamic constraints are used to inject strategic limitations into decision-making based on the overall operational scenario of the photovoltaic-storage-charging distribution area. In the distribution network security scenario, the upper limit of the power fed from the distribution area to the external grid is dynamically adjusted based on the voltage monitoring value at the point of common coupling. When the voltage exceeds the preset upper voltage threshold, the upper limit of the power fed from the distribution area to the external grid is tightened, guiding the system to switch to local consumption or energy storage charging to suppress further voltage rise. When the frequency is lower than the preset lower frequency threshold, the energy storage system is forced to provide minimum active power support. In the economic operation scenario, the corresponding economic constraints are activated according to the dispatch scenario indicated by the grid-side energy dispatch signal. During valley dispatch, the energy storage system is forced to charge at a power not lower than the preset charging rate and is prohibited from purchasing electricity from the external grid at high prices. During peak dispatch, the energy storage system is forced to discharge at a power not lower than the preset discharging rate and the amount of electricity purchased from the external grid is limited. In the emergency power supply scenario, when an emergency command is activated, non-critical load proportion constraints are immediately applied to cut off non-critical loads, and emergency reserve power constraints are set to reserve power margin to cope with secondary load impacts.

[0042] The aforementioned dynamic constraint system ensures that every decision is within the real-time physical capabilities of the equipment and adapts to the current system operating scenario, thereby optimizing operational efficiency while ensuring safety.

[0043] In another optional implementation, the mode mapping matrix supports self-learning optimization based on a cost function. In this implementation, multiple sets of operational data for the photovoltaic-storage-charging area are first recorded within a preset historical time period. Each set of operational data includes the state code of historical multi-dimensional dynamic parameters, the historical operating mode corresponding to that state code, and the overall system loss, mode-switching voltage fluctuation amplitude, and number of early warning triggers generated by the photovoltaic-storage-charging area during operation under that historical operating mode. The mode-switching voltage fluctuation amplitude is the difference between the instantaneous maximum and minimum values ​​of the AC bus voltage during mode switching, used to quantify the impact of mode switching on power quality. The overall system loss L_total is equal to the sum of the distribution network interaction loss L_grid, the energy storage charging and discharging loss L_bat, the photovoltaic converter loss L_pv, and the charging equipment loss L_ch. Among them, the distribution network interaction loss L_grid is equal to the product of the equivalent resistance of the transformer area line and the square of the interaction power between the transformer area and the main grid; the energy storage charging and discharging loss L_bat is equal to (1 / ηb-1) multiplied by the energy storage charging and discharging power, where ηb is the comprehensive efficiency of the energy storage converter; the photovoltaic converter loss L_pv is equal to (1 / ηp-1) multiplied by the actual photovoltaic output, where ηp is the photovoltaic inverter efficiency; and the charging equipment loss L_ch is equal to (1 / ηc-1) multiplied by the total charging power of the charging pile, where ηc is the charging pile conversion efficiency. The optimization objective of the system's comprehensive loss is to minimize the sum of the above four losses. Next, multiple sets of operating data are grouped according to the state code, and all operating data belonging to the same state code are grouped into the same data group. For each data group, all operating data in the data group are input into a preset cost function to calculate the mapping cost evaluation value corresponding to each operating data, and the average of the mapping cost evaluation values ​​of all operating data in the data group is used as the mapping cost evaluation value of the state code corresponding to the data group. The cost function is obtained by weighted summation of system overall loss, mode switching voltage fluctuation amplitude, and early warning trigger count, and is used to quantify the overall mapping effect of a set of operating data. Subsequently, the mapping cost evaluation value of each state code is compared with a preset cost threshold, and state codes with mapping cost evaluation values ​​higher than the cost threshold are selected as state codes to be optimized. For each state code to be optimized, the operating mode with the lowest system overall loss is selected from all operating data belonging to that state code's data group as the backup operating mode for that state code. Finally, the operating mode in the mapping relationship corresponding to the state code to be optimized in the mode mapping matrix is ​​replaced with the aforementioned backup operating mode to obtain the optimized mode mapping matrix, which is stored for subsequent querying.

[0044] S03: Generate a power scheduling instruction based on the target operating mode; the power scheduling instruction includes an internal power allocation instruction and an external power interaction instruction; In a preferred embodiment of this invention, the step of generating a power scheduling instruction based on the target operating mode includes an internal power allocation instruction and an external power interaction instruction, specifically: In this step, after determining the target operating mode, a corresponding power dispatch command is generated based on the target operating mode. Specifically, the target power value corresponding to the target operating mode is first obtained. This target power value includes the target output power of the photovoltaic inverter, the target charging and discharging power of the energy storage converter, the target allocation power of the charging piles, and the target interaction power of the flexible interconnection interface. Among them, the target output power of the photovoltaic inverter represents the active power value that the photovoltaic equipment should output under the current target operating mode; the target charging and discharging power of the energy storage converter represents the charging or discharging power value that the energy storage system should perform under the current target operating mode, with a positive value during charging and a negative value during discharging; the target allocation power of the charging piles represents the charging power value that each charging pile should output under the current target operating mode; the target interaction power of the flexible interconnection interface represents the power value that the photovoltaic-energy storage-charging area should exchange with the external power grid through the flexible interconnection interface under the current target operating mode, with a positive value indicating that the photovoltaic-energy storage-charging area absorbs power from the external power grid and a negative value indicating that the photovoltaic-energy storage-charging area feeds power to the external power grid.

[0045] When determining the target power values ​​mentioned above, the corresponding power allocation strategy is invoked for calculation based on the type of the target operating mode. For example, in the photovoltaic (PV) consumption priority mode, following the allocation logic of "PV output prioritizes supplying local loads and charging piles, and surplus power is used to charge energy storage," the remaining power after subtracting the predicted load value of the distribution area and the power demand of the charging piles from the predicted PV output is used as the target charging and discharging power of the energy storage converter. If the remaining power is positive, the energy storage is charged; if the remaining power is negative, the energy storage is discharged or supplemented from the grid. In the economic operation mode, the optimization objective is to minimize the comprehensive operating loss of the PV-storage-charging distribution area and the distribution network. Combining the scheduling scenario indicated by the real-time grid-side energy dispatch signal, the optimization algorithm solves the power allocation scheme that minimizes the comprehensive loss, and obtains the target power value of each device. In the emergency power supply mode, the power supply of emergency vehicle charging piles is prioritized, and the target charging and discharging power of the energy storage converter is configured to discharge to meet emergency charging needs. In the abnormal recovery mode, the corresponding power support target is generated according to the type of distribution network abnormality (such as voltage over-limit, frequency fluctuation, or instantaneous power outage), and the output support power of the energy storage converter or flexible interconnection interface is controlled to maintain the voltage and frequency stability of the distribution area.

[0046] Power dispatch commands include internal power allocation commands and external power interaction commands. Internal power allocation commands are generated based on the target output power of the photovoltaic inverters, the target charging and discharging power of the energy storage converters, and the target allocated power of the charging piles. These commands contain the target power setpoints for each internal device and are used to control the photovoltaic inverters, energy storage converters, and charging piles within the photovoltaic-energy storage-charging area to output or absorb power according to their respective target power values, thereby achieving autonomous balancing and optimized power allocation within the area. External power interaction commands are generated based on the target interaction power of the flexible interconnection interface. These commands contain the interaction power setpoint and the interaction direction identifier and are used to control the flexible interconnection interface to perform power exchange between the photovoltaic-energy storage-charging area and the external power grid according to the value and direction of the target interaction power. The interaction direction identifier is determined by the sign of the target interaction power; a positive value indicates power absorption from the external power grid, and a negative value indicates power feedback to the external power grid. Internal power allocation commands and external power interaction commands are generated synchronously in the same power scheduling command, enabling power regulation within the transformer area and power interaction outside the transformer area to work together within the same control cycle, avoiding power imbalance or regulation lag caused by the step-by-step execution of internal and external commands.

[0047] During the generation of internal power allocation commands and external power interaction commands, the target power values ​​are also verified and corrected based on the real-time operating status of each device. Specifically, the current power reference value, current actual power value, and device operating margin of the energy storage converter are obtained. If the target charging and discharging power of the energy storage converter exceeds the adjustable range limited by its device operating margin, the target charging and discharging power is limited within the device operating margin range, and the target output power of the photovoltaic inverter or the target allocation power of the charging pile is adjusted accordingly to maintain power balance in the distribution area. Simultaneously, the current power reference value, current actual power value, and device operating margin of the flexible interconnection interface are obtained. If the target interaction power of the flexible interconnection interface exceeds the adjustable range limited by its device operating margin, the target interaction power is limited within the device operating margin range, and the target charging and discharging power of the energy storage converter is adjusted accordingly to compensate for insufficient external power interaction. Through the above verification and correction, the generated power scheduling commands are ensured to be executable under the current device status.

[0048] After the power dispatch command is generated, the internal power allocation command is sent to the local controllers of the photovoltaic inverter, energy storage converter and charging pile through a standardized communication protocol, and the external power interaction command is sent to the local controller of the flexible interconnection interface, so that each device can perform power output, power absorption or power exchange operations according to the corresponding target power value.

[0049] S04: According to the internal power allocation command, control the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area to output or absorb power according to the power allocation strategy corresponding to the target operating mode; In a preferred embodiment of this invention, the step of controlling the photovoltaic equipment, energy storage equipment, and charging equipment within the photovoltaic-energy storage-charging area to output or absorb power according to the power allocation strategy corresponding to the target operating mode based on the internal power allocation command specifically includes: In this step, after the internal power allocation commands are sent to the local controllers of the photovoltaic inverter, energy storage converter, and charging pile respectively via a standardized communication protocol, each device performs corresponding power output or power absorption operations according to the target power setting value contained in the internal power allocation command. Specifically, the photovoltaic inverter adjusts its power output value according to the target output power of the photovoltaic inverter in the internal power allocation command to control the active power output of the photovoltaic array to the AC bus of the distribution area; the energy storage converter absorbs power from the AC bus of the distribution area to charge the energy storage battery pack in charging mode according to the target charging and discharging power of the energy storage converter in the internal power allocation command, and converts the DC power of the energy storage battery pack into AC power to output power to the AC bus of the distribution area in discharging mode; the charging pile adjusts its output power according to the target allocation power of the charging pile in the internal power allocation command to control the charging power provided to the electric vehicle.

[0050] Under different target operating modes, the power allocation strategies corresponding to the internal power allocation commands differ, and each device executes different power output or power absorption operations. In the photovoltaic (PV) consumption priority mode, the internal power allocation command is generated according to the allocation logic of "PV output prioritizes supplying local loads and charging piles, with surplus power used for energy storage charging": the PV inverter outputs power according to the predicted PV output value, which is first supplied to the local loads and charging piles in the distribution area. If the PV output power is greater than the sum of the local load and charging pile demands, the surplus power is absorbed by the energy storage converter in charging mode and stored in the energy storage system; if the PV output power is less than the sum of the local load and charging pile demands, the insufficient portion is supplemented by the energy storage converter releasing stored energy in discharging mode. In the PV consumption priority mode, when the energy storage system reaches a high state of charge, it indicates that the energy storage system can no longer absorb the remaining PV power. At this time, the internal power allocation command controls the PV inverter to reduce its output power, or triggers the external power interaction command (this request will be executed in subsequent step S05).

[0051] Under the economic operation mode, the internal power allocation command generates a power allocation strategy with the goal of minimizing the overall operating losses of the photovoltaic, energy storage, and charging substations and the distribution network. Based on the scheduling scenario (valley scheduling, flat scheduling, or peak scheduling) indicated by the real-time acquired grid-side energy dispatch signals, and combined with the photovoltaic predicted output, substation load prediction values, and energy storage system state of charge, the power allocation scheme that minimizes the overall operating losses is solved. Specifically, when the grid-side energy dispatch signal is in valley dispatch mode, the internal power distribution command controls the energy storage converter to absorb power from the AC bus of the distribution area in charging mode, storing the electrical energy in the energy storage system. At the same time, it controls the photovoltaic inverter to supply local loads and charging piles with power according to the photovoltaic forecast output. When the grid-side energy dispatch signal is in peak dispatch mode, the internal power distribution command controls the energy storage converter to release the stored electrical energy in discharging mode, prioritizing the power demand of local loads and charging piles. When the energy storage discharge power is sufficient, it further outputs power to the external grid through external power interaction commands. When the grid-side energy dispatch signal is in flat dispatch mode, the internal power distribution command dynamically adjusts the charging and discharging state of the energy storage converter according to the relative magnitude of the photovoltaic forecast output and the distribution area load forecast, so that the energy storage system charges during periods of photovoltaic excess output and discharges during periods of insufficient photovoltaic output, thereby maintaining the power balance of the distribution area and reducing overall operating losses.

[0052] In emergency power supply mode, internal power allocation commands are generated according to a strategy that prioritizes emergency charging needs. Upon receiving an emergency charging command, the power supply priority for emergency vehicle charging stations is set to the highest. The energy storage converter is controlled to release power in discharge mode, and the released power is preferentially allocated to emergency vehicle charging stations. The remaining power is then allocated to other regular charging stations and local loads. In this mode, if the state of charge of the energy storage system falls below a preset low-charge threshold, an abnormal recovery mode is automatically triggered, limiting energy storage discharge and switching to substation voltage support operation.

[0053] In abnormal recovery mode, internal power allocation commands are generated according to the allocation strategy of "maintaining stable voltage and frequency in the distribution area". When abnormalities such as voltage exceeding limits, frequency fluctuations, or momentary power outages are detected in the distribution network, corresponding power adjustment commands are generated according to the type of abnormality: when voltage exceeds limits, the energy storage converter is controlled to adjust reactive power output to support the voltage in the distribution area; when frequency fluctuates, the energy storage converter is controlled to adjust active power output to maintain stable frequency in the distribution area; when there is a momentary power outage, the energy storage converter is controlled to switch to islanded operation mode to provide continuous power supply to critical loads in the distribution area.

[0054] During the process of each device executing power output or power absorption according to the internal power allocation command, the actual power output value of each device is collected in real time through a high-speed sampling module. The actual power value of each device is compared with the corresponding target power setpoint in the internal power allocation command, and the deviation value is calculated. The power command of each device is dynamically corrected through a PID controller, so that the actual power value of each device continuously tracks the target power setpoint, avoiding the accumulation of power deviation caused by device response delay or external disturbances. At the same time, the change trend of the energy storage system's state of charge during the execution of the power allocation strategy is monitored. When the energy storage system's state of charge approaches the low charge threshold or high charge threshold, the target charging and discharging power of the energy storage converter is adjusted in advance to prevent the energy storage system from overcharging or over-discharging. Through the above real-time monitoring and dynamic correction, it is ensured that each device continuously and stably executes power output or power absorption operations under the constraints of the power allocation strategy of the target operating mode.

[0055] S05: Control the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operation mode, based on the external power exchange command.

[0056] In a preferred embodiment of this invention, the step of controlling the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operating mode based on the external power exchange command specifically includes: In this step, external power exchange commands are executed through a flexible interconnection interface, enabling the photovoltaic-storage-charging area and the external power grid to exchange power according to the power exchange direction and amount corresponding to the target operating mode. The flexible interconnection interface includes at least one voltage source converter, which connects the AC bus of the photovoltaic-storage-charging area (i.e., the AC coupling bus between the photovoltaic-storage-charging area and the external power grid) to the external power grid, supporting independent control of active and reactive power. Based on the exchange power setting value and exchange direction identifier contained in the external power exchange command, the voltage source converter of the flexible interconnection interface is controlled to output power to or absorb power from the external power grid according to the set power exchange direction and amount. Specifically, when the interaction direction in the external power interaction command is identified as absorbing power from the external power grid, the voltage source converter of the flexible interconnection interface operates in rectification mode, converting the AC power of the external power grid into DC power or directly injecting power into the AC bus of the photovoltaic-storage-charging area; when the interaction direction in the external power interaction command is identified as feeding power to the external power grid, the voltage source converter operates in inverter mode, feeding the power on the AC bus of the photovoltaic-storage-charging area to the external power grid.

[0057] The flexible interconnection interface also employs droop control and virtual synchronous machine technology to simulate the active-frequency droop and reactive-voltage droop characteristics of a synchronous generator in the control loop of the voltage source converter. This enables the voltage source converter to autonomously respond to voltage and frequency changes in the external power grid during power interaction, avoiding impact on the distribution network.

[0058] During the execution of external power interaction commands via the flexible interconnection interface, the DC bus voltage and AC bus voltage are monitored in real time. The DC bus is the DC connection bus between the energy storage system and the energy storage converter, serving as the channel for DC power transmission between the two systems. The AC bus is the AC coupling bus between the photovoltaic-energy storage-charging area and the external power grid, acting as the common connection point for AC power exchange between the various AC devices within the area and the external power grid. Voltage sensors deployed on the DC bus are used to collect the DC bus voltage value in real time, while voltage transformers or sensors deployed on the AC bus are used to collect the AC bus voltage amplitude and frequency in real time.

[0059] The system pre-stores DC voltage stability range and AC voltage stability range. The DC voltage stability range is determined based on the rated DC voltage of the energy storage system and the allowable operating voltage range of the energy storage converter. The AC voltage stability range is determined based on the rated voltage of the distribution network and national power quality standards. When the DC bus voltage deviates from the preset DC voltage stability range, a first power regulation amount is generated based on the difference between the current DC bus voltage value and the upper or lower limit of the DC voltage stability range. The power output value of the energy storage converter is adjusted according to this first power regulation amount, so that the energy storage converter suppresses the deviation of the DC bus voltage by adjusting the power absorbed from or output to the DC bus, until the DC bus voltage returns to the DC voltage stability range. For example, when the DC bus voltage is higher than the upper limit of the DC voltage stability range, a negative first power regulation is generated to control the energy storage converter to increase the power absorbed from the DC bus (i.e., increase the charging power) to lower the DC bus voltage. When the DC bus voltage is lower than the lower limit of the DC voltage stability range, a positive first power regulation is generated to control the energy storage converter to increase the power output to the DC bus (i.e., increase the discharging power) to raise the DC bus voltage. When the AC bus voltage deviates from the preset AC voltage stability range, a second power regulation is generated based on the difference between the current AC bus voltage value and the upper or lower limit of the AC voltage stability range. The power output value of the flexible interconnect interface is adjusted according to this second power regulation, so that the flexible interconnect interface suppresses the deviation of the AC bus voltage by adjusting the power absorbed from or output to the AC bus until the AC bus voltage returns to the AC voltage stability range. For example, when the AC bus voltage is higher than the upper limit of the AC voltage stability range (i.e., voltage rise), a negative second power regulation is generated to control the flexible interconnection interface to reduce the power fed to the external grid or increase the power absorbed from the external grid, thereby suppressing further rises in the AC bus voltage. When the AC bus voltage is lower than the lower limit of the AC voltage stability range (i.e., voltage drop), a positive second power regulation is generated to control the flexible interconnection interface to increase the power fed to the external grid or decrease the power absorbed from the external grid, thereby supporting the AC bus voltage. Specifically, the calculation method for the second power regulation is as follows: first, the deviation between the current AC bus voltage value and the center value of the AC voltage stability range is obtained; this deviation is multiplied by a preset proportional coefficient to obtain a preliminary regulation; and then, the second power regulation is determined based on this preliminary regulation. When adjusting the power output value of the flexible interconnection interface according to the second power adjustment amount, if the second power adjustment amount is positive, the flexible interconnection interface is controlled to increase the active or reactive power injected into the AC bus; if the second power adjustment amount is negative, the flexible interconnection interface is controlled to decrease the active or reactive power injected into the AC bus. The preset proportional coefficient is pre-set according to the rated capacity of the flexible interconnection interface and the voltage regulation sensitivity of the distribution network.

[0060] The aforementioned DC bus voltage regulation and AC bus voltage regulation are logically independent and can be executed in parallel in terms of timing, thereby ensuring that the voltage on both the DC and AC sides is maintained within their respective stable ranges throughout the entire process of executing external power interaction commands.

[0061] After the external power interaction command is executed, the current value of the energy storage system's state of charge (SOC) in the multi-dimensional dynamic parameters is obtained, along with the current voltages of the DC bus and AC bus. Then, based on the current SOC, DC, and AC bus voltages, it is determined whether the photovoltaic-storage-charging station area has reached a preset stable state. Specifically, it is determined whether the current SOC is within a preset normal charging range (e.g., 20% to 90%), the DC bus voltage is within a preset DC voltage stability range, and the AC bus voltage is within a preset AC voltage stability range. If all three conditions are met, the photovoltaic-storage-charging station area is determined to have reached a stable state; otherwise, it is determined that the photovoltaic-storage-charging station area has not reached a stable state. Among them, the state of charge of the energy storage system is used to characterize whether the remaining available energy of the energy storage system is within the safe operating range, and the DC bus voltage and AC bus voltage are used to characterize whether the power quality of the DC side and AC side is within the qualified range, respectively. The three together constitute a multi-dimensional steady-state criterion from the dimensions of energy and electrical quantity. Only when the parameters of the three dimensions simultaneously meet the stability conditions can it be confirmed that the photovoltaic-storage-charging station area has entered a steady state after the external power interaction command is executed.

[0062] If it is determined that the photovoltaic-storage-charging area has not reached a stable state, a preset fine-tuning program will be initiated to fine-tune the current power reference value of the energy storage converter or the current power reference value of the flexible interconnection interface until the photovoltaic-storage-charging area reaches a stable state.

[0063] As a preferred embodiment of this example, after the internal power allocation command and the external power interaction command are sent to each device, the dynamic process of mode switching is further refined through a three-order progressive control logic of "pre-adjustment before switching - suppression during switching - calibration after switching".

[0064] Before issuing power dispatch commands to each device, a pre-adjustment phase is first initiated. The target power value corresponding to the target operating mode is obtained. The power deviation value of the energy storage converter is calculated based on the target charging / discharging power of the energy storage converter and its current power reference value. Similarly, the power deviation value of the flexible interconnect interface is calculated based on the target interaction power of the flexible interconnect interface and its current power reference value. According to a preset deviation compensation ratio (e.g., 30% to 50%), the current power reference values ​​of both the energy storage converter and the flexible interconnect interface are adjusted for the first time, bringing them closer to their respective target power values. During the pre-adjustment process, the operating margins of the energy storage converter and the flexible interconnect interface are also obtained. The pre-adjusted power reference values ​​are verified to ensure they are within the adjustable range defined by their respective operating margins. If they exceed the adjustable range, the corresponding adjustment amount is automatically reduced. Meanwhile, a power disturbance prediction value is calculated based on the difference between the target interactive power of the flexible interconnect interface and the current power reference value of the flexible interconnect interface. The current power reference value of the flexible interconnect interface is then adjusted by feedforward compensation based on this power disturbance prediction value, so as to pre-compensate for the power gap before a power surge occurs.

[0065] After the power dispatch command is issued and the various devices switch to respond to the command, the process control phase begins. The voltages of the DC bus and AC bus are monitored in real time via a high-speed sampling module. When the DC bus voltage deviates from the preset DC voltage stability range, the proportional-integral (PI) regulator is activated, generating a first power regulation value. Based on this first power regulation value, the power output value of the energy storage converter is adjusted to restore the DC bus voltage to its stable range. When the AC bus voltage deviates from the preset AC voltage stability range, the PI regulator is activated, generating a second power regulation value. Based on this second power regulation value, the power output value of the flexible interconnect interface is adjusted to restore the AC bus voltage to its stable range. Specifically, the proportional term of the PI regulator is used to quickly output a corresponding power regulation value to offset the voltage deviation, while the integral term is used to slowly eliminate steady-state errors to prevent sustained voltage deviation after fluctuations.

[0066] After the mode switching process is completed, the calibration phase begins. In this phase, if the photovoltaic-storage-charging station area has not reached a stable state as determined by the aforementioned criteria based on the energy storage system's state of charge, DC bus voltage, and AC bus voltage, a fine-tuning program is initiated to adjust the current power reference value of the energy storage converter or the current power reference value of the flexible interconnect interface. Specifically, the process begins by identifying the target device requiring fine-tuning, which may be an energy storage converter or a flexible interconnect interface. The current actual power value and target power value of the target device are then obtained. The power deviation value of the target device is calculated based on the difference between the target power value and the current actual power value. The device's operating margin and the system power balance compensation amount for the photovoltaic-storage-charging area are also obtained. Based on the power deviation value, operating margin, and system power balance compensation amount, the power reference calibration parameters for the target device are calculated. Specifically, the power reference calibration parameters equal the power deviation value multiplied by the operating margin plus the system power balance compensation amount. The device response coefficient of the target device is obtained, along with the system coupling strength coefficient between the target device and another of the energy storage converter and flexible interconnect interface. Based on the device response coefficient and system coupling strength coefficient, the dynamic response coordination parameters of the target device are calculated. Specifically, the dynamic response coordination parameters equal the device response coefficient multiplied by the system coupling strength coefficient. Finally, a fine-tuning command for the target device is generated based on the power reference calibration parameters and the dynamic response coordination parameters, and this command is sent to the target device. During the fine-tuning process, following a preset step-by-step adjustment strategy, the power reference value is adjusted at a predetermined ratio according to the deviation value each time, and fine-tuning continues until the photovoltaic-storage-charging area reaches a stable state. If stability cannot be achieved after fine-tuning, an alarm signal is triggered, and the operating mode is reassessed.

[0067] In summary, this application acquires multi-dimensional dynamic parameters of the photovoltaic-storage-charging area, enabling comprehensive perception of multi-dimensional information such as photovoltaic output, load demand, energy storage state of charge, and grid dispatch signals within a unified time frame. This provides a comprehensive and real-time data foundation for subsequent decision-making. Based on this, a preset mode mapping matrix is ​​queried using the multi-dimensional dynamic parameters to determine the target operating mode matching the current operating condition. This allows different state codes of the multi-dimensional parameters to quickly converge to a unique operating mode through matrix mapping, avoiding decision conflicts and response delays caused by relying on fixed thresholds or simple logical judgments when multiple parameters change simultaneously. Furthermore, internal power allocation instructions and external power interaction instructions are synchronously generated in the power dispatch command according to the target operating mode, controlling the photovoltaic equipment, energy storage equipment, and charging equipment within the photovoltaic-storage-charging area according to the corresponding power allocation policies. The system performs power output or absorption and executes external power interaction commands through a preset flexible interconnection interface to achieve power exchange between the distribution area and the external power grid according to the corresponding power interaction direction and amount. This simultaneously maps the state perception results to the coordinated control of internal equipment and the flexible interaction of external interfaces, forming a direct and complete linkage between mode decision-making and power execution. Throughout the process, from parameter acquisition, mode determination, command generation to internal execution and external interaction, a closed data flow and control flow loop is formed. This ensures that each mode switch has a corresponding perception input as a trigger, a corresponding matrix mapping as a decision-making basis, and corresponding internal power allocation commands and external power interaction commands to drive the coordinated action of internal equipment and external interfaces, thereby realizing adaptive seamless switching and efficient resource coordination of the photovoltaic, energy storage and charging distribution area under multiple operating conditions.

[0068] Example 2 Please refer to Figure 2 This is a multi-mode switching control device for the optical storage and charging station area provided in the embodiments of this application.

[0069] In this embodiment, the multi-mode switching control device for the optical storage and charging station area includes an acquisition module 10, a matching and distribution module 20, a generation module 30, a first control module 40, and a second control module 50.

[0070] Module 10 is used to acquire multi-dimensional dynamic parameters of the photovoltaic-storage-charging area; The matching module 20 is used to query a preset mode mapping matrix based on the multi-dimensional dynamic parameters in order to match the target operation mode of the optical storage and charging station area. The generation module 30 is used to generate power scheduling instructions according to the target operating mode; the power scheduling instructions include internal power allocation instructions and external power interaction instructions; The first control module 40 is used to control the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area to output or absorb power according to the power distribution strategy corresponding to the target operating mode, based on the internal power distribution instructions. The second control module 50 is used to control the photovoltaic energy storage and charging station area to exchange power with the external power grid according to the power exchange direction and power exchange amount corresponding to the target operation mode, based on the external power exchange command.

[0071] For ease of description and brevity, the embodiments of the present invention include all the implementation methods in the above embodiments of the multi-mode switching control method for the optical storage and charging station area, and will not be repeated here.

[0072] Example 3: This application provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute a multi-mode switching control method for an optical storage and charging station area when it is running. The multi-mode switching control method for the optical storage and charging station area, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0073] Example 4 This embodiment provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any one of the multi-mode switching control methods for optical storage and charging stations as described in Embodiment 1.

[0074] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A multi-mode switching control method for a photovoltaic, energy storage, and charging station area, characterized in that, include: Obtain multi-dimensional dynamic parameters of the photovoltaic-storage-charging area; Based on the multi-dimensional dynamic parameters, a preset mode mapping matrix is ​​queried to match the target operation mode of the optical storage and charging station area; Power scheduling instructions are generated based on the target operating mode; the power scheduling instructions include internal power allocation instructions and external power interaction instructions. According to the internal power allocation command, the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area are controlled to output or absorb power according to the power allocation strategy corresponding to the target operating mode. According to the external power interaction command, the photovoltaic, energy storage and charging station area and the external power grid exchange power in accordance with the power interaction direction and power interaction amount corresponding to the target operation mode.

2. The multi-mode switching control method for the photovoltaic-storage-charging area according to claim 1, characterized in that, The acquisition of multidimensional dynamic parameters of the photovoltaic-storage-charging area specifically includes: The photovoltaic power output forecast, the load forecast of the photovoltaic storage and charging area, the state of charge of the energy storage system, and the grid-side energy dispatch signal are obtained.

3. The multi-mode switching control method for the photovoltaic-storage-charging area according to claim 2, characterized in that, The step of querying a preset mode mapping matrix based on the multi-dimensional dynamic parameters to match the target operating mode of the optical storage and charging station area specifically involves: The predicted photovoltaic output is discretized into photovoltaic output status levels according to a preset photovoltaic output classification rule; The predicted load values ​​of the transformer area are discretized into load status levels according to a preset load classification rule; The state of charge of the energy storage system is discretized into state of charge levels according to a preset charge classification rule; The grid-side energy dispatch signal is discretized into dispatch signal status levels according to a preset dispatch signal classification rule; The photovoltaic output status level, load status level, charge status level, and dispatch signal status level are combined to generate the status code of the current operating condition. The preset pattern mapping matrix is ​​queried according to the status code; the running mode corresponding to the status code is extracted as the target running mode. The mode mapping matrix stores the mapping relationship between each state code and each operating mode, and each row of the mode mapping matrix stores a set of state codes and the corresponding operating mode number.

4. The multi-mode switching control method for the photovoltaic-storage-charging station area according to claim 3, characterized in that, The pattern mapping matrix is ​​optimized and updated in the following way: Record multiple sets of operating data of the photovoltaic storage and charging station area within a preset historical period. Each set of operating data includes the status code of the historical multidimensional dynamic parameters, the historical operating mode corresponding to the status code, and the system comprehensive loss, mode switching voltage fluctuation amplitude and early warning trigger number generated by the photovoltaic storage and charging station area during the operation of the historical operating mode. The multiple sets of operational data are grouped according to the state code, and all operational data belonging to the same state code are grouped into the same data group. For each data group, all running data within the data group are input into a preset cost function to calculate the mapping cost evaluation value corresponding to each running data, and the average value of the mapping cost evaluation values ​​of all running data within the data group is used as the mapping cost evaluation value of the state code corresponding to the data group. The mapping cost evaluation value of each state code is compared with a preset cost threshold, and the state codes whose mapping cost evaluation value is higher than the cost threshold are selected as the state codes to be optimized. For each state code to be optimized, the historical operating mode with the lowest overall system loss is selected from all the operating data of the data group belonging to that state code as the backup operating mode for that state code. The running mode in the mapping relationship between the mode mapping matrix and the state code to be optimized is replaced with the backup running mode to obtain the optimized mode mapping matrix.

5. The multi-mode switching control method for the photovoltaic-storage-charging area according to claim 1, characterized in that, After generating the power scheduling instruction according to the target operating mode, the method further includes: Obtain the target power value corresponding to the target operating mode; the target power value includes the target output power of the photovoltaic inverter, the target charging and discharging power of the energy storage converter, the target distribution power of the charging pile, and the target interaction power of the flexible interconnection interface. Before issuing the power scheduling command to the photovoltaic inverter, the energy storage converter, the charging pile, and the flexible interconnect interface, the current power reference value of the energy storage converter is adjusted according to the target charging and discharging power of the energy storage converter, and the current power reference value of the flexible interconnect interface is adjusted according to the target interaction power of the flexible interconnect interface, so that the current power reference value of the energy storage converter and the current power reference value of the flexible interconnect interface move closer to their respective target power values. Based on the target interaction power of the flexible interconnect interface and the adjusted current power reference value of the flexible interconnect interface, a power disturbance prediction value is calculated. Based on the power disturbance prediction value, the adjusted current power reference value of the flexible interconnect interface is adjusted again with feedforward compensation to pre-compensate for the power gap before a power surge occurs.

6. The multi-mode switching control method for the photovoltaic-storage-charging area according to claim 1, characterized in that, The step of controlling the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operation mode, based on the external power exchange command, specifically involves: During the execution of the external power interaction command, the voltage of the DC bus and the voltage of the AC bus are monitored in real time; the DC bus is the DC connection bus between the preset energy storage system and the preset energy storage converter, and the AC bus is the AC coupling bus between the photovoltaic energy storage and charging station area and the preset external power grid. When the voltage of the DC bus deviates from the preset DC voltage stability range, a first power regulation amount is generated, and the power output value of the energy storage converter is adjusted according to the first power regulation amount so that the voltage of the DC bus is restored to the DC voltage stability range. When the voltage of the AC bus deviates from the preset AC voltage stability range, a second power regulation amount is generated, and the power output value of the preset flexible interconnection interface is adjusted according to the second power regulation amount so that the voltage of the AC bus returns to the AC voltage stability range. After the external power interaction command is executed, the current value of the state of charge of the energy storage system is obtained, and the current voltage of the DC bus and the current voltage of the AC bus are obtained. Based on the current value of the state of charge of the energy storage system, the current voltage of the DC bus, and the current voltage of the AC bus, determine whether the photovoltaic-storage-charging station area has reached a preset stable state. If a stable state is not reached, a preset fine-tuning program is initiated to fine-tune the current power reference value of the energy storage converter or the current power reference value of the flexible interconnection interface until the photovoltaic-storage-charging area reaches a stable state.

7. The multi-mode switching control method for the photovoltaic-storage-charging area according to claim 6, characterized in that, The step of initiating a preset fine-tuning program to fine-tune the current power reference value of the energy storage converter or the current power reference value of the flexible interconnect interface specifically involves: Identify the target device that needs fine-tuning, wherein the target device is the energy storage converter or the flexible interconnect interface; Obtain the current actual power value and the target power value of the target device, and calculate the power deviation value of the target device based on the difference between the target power value and the current actual power value; Obtain the equipment operating margin of the target device and the system power balance compensation amount of the optical storage and charging station area; calculate the power reference calibration parameters of the target device based on the power deviation value, the equipment operating margin, and the system power balance compensation amount. Obtain the device response coefficient of the target device and the system coupling strength coefficient between the target device and another device. Calculate the dynamic response coordination parameters of the target device based on the device response coefficient and the system coupling strength coefficient. Based on the power reference calibration parameters and the dynamic response coordination parameters, a fine-tuning instruction for the target device is generated, and the fine-tuning instruction is sent to the target device to fine-tune the target settings.

8. A multi-mode switching control device for a photovoltaic storage and charging station area, characterized in that, include: The acquisition module is used to acquire multi-dimensional dynamic parameters of the photovoltaic storage and charging area; The matching module is used to query a preset mode mapping matrix based on the multi-dimensional dynamic parameters in order to match the target operating mode of the optical storage and charging station area. The generation module is used to generate power scheduling instructions based on the target operating mode; the power scheduling instructions include internal power allocation instructions and external power interaction instructions. The first control module is used to control the photovoltaic equipment, energy storage equipment and charging equipment in the photovoltaic-storage-charging area to output or absorb power according to the power distribution strategy corresponding to the target operating mode, based on the internal power distribution instructions. The second control module is used to control the photovoltaic-storage-charging station area and the external power grid to exchange power according to the power exchange direction and power exchange amount corresponding to the target operation mode, based on the external power exchange command.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the multi-mode switching control method for the optical storage and charging station area as described in any one of claims 1 to 7.

10. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the multi-mode switching control method for the optical storage and charging station area as described in any one of claims 1 to 7.

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