Distributed energy storage charging multifunctional cooperative control method and system

Abstract

The invention discloses a distributed energy storage charging multifunctional cooperative control method and system, and relates to the technical field of electrical engineering. The method comprises the steps of collecting original data of a power grid side, an energy storage side, a charging side and a photovoltaic side, performing abnormal value elimination, protocol conversion and data compression processing on the original data, and outputting standardized data; based on the standardized data, firstly performing energy storage safety hard constraint check to determine a safety state, then performing scene threshold adaptation according to a power grid load rate and a user charging emergency degree to output a scene type and basic charging power, and then generating a preliminary power instruction through a lightweight reinforcement learning algorithm; and finally, performing compliance judgment on the initial power instruction, and outputting a final power instruction. According to the invention, a closed-loop control architecture of data acquisition, dynamic decision, power execution and feedback correction is constructed to balance the three-party requirements of the power grid, charging and energy storage, so that the effects of improving the interaction reliability of the power grid and the charging experience of a user are achieved.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering technology, specifically to a multifunctional collaborative control method and system for distributed energy storage charging. Background Technology

[0002] With the rapid development of distributed energy storage and new energy charging technologies, integrated photovoltaic-energy storage-charging systems have become an important component of new power systems, widely used in industrial and commercial photovoltaic-energy storage-charging integration, virtual power plants, and vehicle-to-grid (V2G) scenarios. Existing photovoltaic-energy storage-charging systems mostly employ fixed priority strategies. When the grid issues peak-shaving / frequency regulation commands, user charging is often forcibly interrupted. If charging is prioritized, the grid command responsiveness is insufficient. Traditional reinforcement learning algorithms use redundant state parameters, leading to data transmission delays and decision lags. Furthermore, the experience replay pool uses global random sampling, resulting in insufficient learning of emergency grid scenarios. Existing solutions embed SOC checks into the post-decision execution stage, which can easily lead to over-discharge of energy storage and lacks layered protection combined with SOH, shortening the lifecycle of distributed energy storage. When the sum of the grid command power and the charging demand power exceeds the available energy storage power, existing technologies adopt a "one-size-fits-all" strategy without clear quantitative reduction logic. Summary of the Invention

[0003] The purpose of this invention is to provide a multifunctional collaborative control method and system for distributed energy storage charging, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a multi-functional collaborative control method for distributed energy storage charging, comprising the following steps:

[0005] Step S100: Collect raw data from the grid side, energy storage side, charging side and photovoltaic side, perform outlier removal, protocol conversion and data compression on the raw data, and output standardized data;

[0006] Step S200: Based on the standardized data, first perform a hard constraint check on energy storage safety to determine the safety status, then perform scenario threshold adaptation based on grid load rate and user charging urgency to output scenario type and basic charging power, then generate preliminary power command through lightweight reinforcement learning algorithm, and finally perform compliance judgment on the preliminary power command to output final power command.

[0007] Step S300: Calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to the scenario type, and output the actual operating data after execution;

[0008] Step S400: Calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds a preset threshold, adjust the energy storage power allocation parameters and push the status information to the power grid, users, and the next round of decision-making to form a closed-loop control.

[0009] Preferably, step S100 further includes the following sub-steps:

[0010] Step S110: Collect the frequency deviation on the power grid side using the power grid data acquisition component. Load factor and command power ;

[0011] The state of charge of each energy storage unit on the energy storage side is collected by the energy storage data acquisition component. Health status and rated power Then the average state of charge is calculated. Minimum health status and total available power ,in, and In this context, i = 1, 2, ..., N, where N is the total number of energy storage units;

[0012] The charging data acquisition component collects the charging demand of each charging pile on the charging side. Level 1 user count and number of emergency users The total charging demand is calculated. ,in, In the context, j = 1, 2, ..., M, where M is the total number of charging stations;

[0013] Real-time power output from the photovoltaic side is collected through photovoltaic data acquisition components. ;

[0014] Step S120: Use the 3σ criterion to remove data from the original data that exceeds a reasonable range. The data exceeding a reasonable range includes... , or , or , For the rated power of photovoltaic power, the data after elimination is replaced with data from the previous period;

[0015] Convert the data in the original data of different protocol formats to the IEC 61850 standard format; , , and Differential compression is used, with a compression ratio of no less than 1:5;

[0016] Step S130: Integrate the data processed in step S120 and output the data containing... , , , , , , , , , The standardized data mentioned above.

[0017] Preferably, step S110 further includes the following sub-steps:

[0018] In step S111, the real-time frequency of the power distribution network is collected by a frequency sensor. The frequency deviation was calculated. 50Hz is the rated frequency of the power grid;

[0019] Real-time power of the 10kV bus is collected using voltage and current sensors. Combined with the rated capacity of the busbar The load factor was calculated. ;

[0020] The commanded power is obtained through the power grid dispatching interface. The command power Discharging is positive, charging is negative;

[0021] Step S112: Collect the data from N distributed energy storage units via the CAN bus. With the The calculation yielded the The above ; Collect the data from each energy storage unit The total available power is calculated. ;

[0022] Step S113: Collect the data from the M charging piles via the charging protocol interface. The total charging demand is calculated. The user priority is identified through the user identification component, and the number of Level 1 users is then calculated. ;

[0023] Based on the user's reported estimated car usage time The number of emergency users was obtained by statistics. The number of emergency users correspond Level 1 users;

[0024] Step S114: Collect the real-time output of the photovoltaic inverter through the photovoltaic communication interface. The photovoltaic communication interface supports the Modbus TCP protocol, and the data acquisition delay does not exceed 30ms.

[0025] Preferably, step S200 further includes the following sub-steps:

[0026] Step S210, if the In this mode, the system switches to energy storage protection priority mode, prohibiting all energy storage from participating in grid auxiliary operations and allocating basic charging power only to Level 1 users. and total charging power 0.2 represents the maximum permissible depth of discharge;

[0027] like This allows energy storage to participate in grid auxiliary operations, but the discharge power... 0.3 represents the maximum permissible depth of discharge, allocating basic charging power to Level 1 users. Charging for Level 2 users is suspended.

[0028] like If so, continue with step S220;

[0029] Step S220, based on the load rate Adjusting frequency deviation The emergency threshold and normal threshold are used to output either the emergency scenario or the normal scenario of the power grid.

[0030] Based on the proportion of emergency users Adjust the base charging power for Level 1 users ;

[0031] Step S230: Construct a state space based on the 6-dimensional core parameters in the standardized data. The 6-dimensional core parameters include... , , , , , ;

[0032] The reward value is calculated using a three-stage reward function. ,in Incentives for grid compliance and grid responsiveness hour , hour , hour , As a reward for charging protection, the Level 1 interruption rate is included. hour , hour , hour , Penalties for energy storage protection, among which hour , hour , hour ;

[0033] The algorithm model is trained using a scenario-layered experience replay mechanism, and an initial power command is output. The initial power command includes the command power under power grid emergency scenarios. With initial charging power and preliminary grid auxiliary power under normal grid scenarios ;

[0034] Step S240: Check whether the preliminary power command meets the energy storage safety constraints and grid standard constraints, wherein the energy storage safety constraints are the total commanded power. The power grid standard constraint is the power grid command power. , This is the maximum allowable response power of the power grid;

[0035] If satisfied, output the final power command;

[0036] If the conditions are not met, return to step S230 to readjust the reward function weights and generate a new preliminary power command. The number of retries is ≤3. If the conditions are still not met after 3 retries, the energy storage protection priority mode is triggered.

[0037] Preferably, step S220 further includes the following sub-steps:

[0038] Step S221, if ,but The emergency threshold is set to ±0.3Hz, the normal threshold is set to ±0.2Hz, and the response trigger delay is ≤20ms;

[0039] like ,but The emergency threshold is set to ±0.5Hz, the normal threshold is set to ±0.3Hz, and the response trigger delay is ≤30ms;

[0040] like ,but The emergency threshold is set to ±0.8Hz, the normal threshold is set to ±0.5Hz, and the response trigger delay is ≤50ms;

[0041] Step S222, if The basic charging power Prioritize the allocation of the real-time output ;

[0042] like The basic charging power ,use Combined supply with energy storage;

[0043] like The basic charging power The remaining energy storage capacity will be used for grid auxiliary applications.

[0044] Preferably, step S300 further includes the following sub-steps:

[0045] Step S310: If it is a power grid emergency scenario, then the total discharge power requirement is... , For the final grid command power, This refers to the final charging power.

[0046] like Then according to the power allocation coefficient Power allocation for each energy storage unit ,in i=1,2,…,N;

[0047] like Then calculate the charging power reduction ratio. To obtain the actual charging power ;

[0048] If it is a normal power grid scenario, the total power consumption demand is... , As auxiliary power to the final power grid;

[0049] like Then priority will be given to satisfying. The surplus power serves as auxiliary power for the actual power grid. ;like Then calculate the reduction ratio of auxiliary power in the power grid. To obtain the actual auxiliary power of the power grid ;

[0050] Step S320: The power of each energy storage unit is sent to each energy storage unit through the energy storage control component. or The drive energy storage inverter adjusts its power according to the PWM control signal, outputting the actual grid response power. ; issues commands to each charging station via the charging control component Adjust the charging pile's output current to output the actual charging power. ;

[0051] If an overcurrent or overtemperature anomaly is detected, the emergency stop switch is triggered, power distribution is suspended, and the system switches to emergency mode. The emergency mode only guarantees 5kW / pile charging for Level 1 users. The overcurrent is defined as current > 1.2 × rated current, and the overtemperature is defined as temperature > 65℃.

[0052] Preferably, step S400 further includes the following sub-steps:

[0053] Step S410: Calculate the power grid response deviation. ,like but ;

[0054] Calculate charging power deviation ,like but ;

[0055] Calculate photovoltaic output deviation The To contribute to the actual development of photovoltaics;

[0056] Step S420, if Then improve energy storage unit The coefficient is 10%, that is And return to step S200 for feedback. Correction factor;

[0057] like and Then from An additional 5kW / pile will be allocated to Level 1 users, and feedback will be returned to step S200. Fluctuation warning and reservation As backup power;

[0058] Upload to the power grid dispatch center With remaining total available power Push charging progress to users ,cost And abnormal alerts, push equipment health reports to the operation and maintenance terminal, the equipment health reports contain Decline trend Inverter temperature profile During the charging progress This is the amount of charge already applied. To meet user power needs, It is a time-of-use electricity price.

[0059] The present invention also provides a multi-functional collaborative control system for distributed energy storage charging, comprising:

[0060] The data acquisition module is used to collect raw data from the grid side, energy storage side, charging side and photovoltaic side, and to perform outlier removal, protocol conversion and data compression on the raw data to output standardized data.

[0061] The central decision-making module is used to first perform a hard constraint check on energy storage safety to determine the safety status based on the standardized data, then perform scenario threshold adaptation based on the grid load rate and the user's charging urgency to output the scenario type and basic charging power, then generate a preliminary power command through a lightweight reinforcement learning algorithm, and finally perform compliance judgment on the preliminary power command to output the final power command.

[0062] The distributed execution module is used to calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to the scenario type, and output the actual operation data after execution.

[0063] The feedback module is used to calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds the preset threshold, the energy storage power allocation parameters are adjusted and the status information is pushed to the power grid, users and the next round of decision-making to form a closed-loop control.

[0064] The present invention also provides an electronic device, which is a physical device, comprising:

[0065] The processor and the memory are communicatively connected.

[0066] The memory is used to store at least one executable instruction executed by the processor, which executes the executable instruction to implement the multi-functional collaborative control method for distributed energy storage charging as described above.

[0067] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-functional collaborative control method for distributed energy storage charging as described above.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] By constructing a closed-loop control architecture encompassing data acquisition, dynamic decision-making, power execution, and feedback correction, the system balances the needs of the power grid, charging, and energy storage, thereby improving the reliability of grid interaction and the user charging experience. Through lightweight reinforcement learning algorithms and scenario-based threshold adaptation, the system optimizes decision-making efficiency and adaptability, reducing system response latency and enhancing multi-device compatibility. Furthermore, by employing hard constraint checks and hierarchical protection mechanisms for energy storage safety, the system avoids over-discharge and health degradation of energy storage, extending the energy storage lifespan and reducing equipment replacement costs. Attached Figure Description

[0070] Figure 1 The main flowchart of a multi-functional collaborative control method for distributed energy storage charging provided in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the structure of a multi-functional collaborative control system for distributed energy storage charging provided in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0073] 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.

[0074] Please see Figure 1 This invention provides a multi-functional collaborative control method for distributed energy storage charging, comprising:

[0075] Step S100: Collect raw data from the grid side, energy storage side, charging side, and photovoltaic side; perform outlier removal, protocol conversion, and data compression on the raw data; and output standardized data.

[0076] Among them, "raw data from the grid side" refers to the initial data related to power operation collected from the distribution network, including but not limited to the real-time frequency, voltage, current, and power of peak-shaving and frequency regulation commands issued by the grid; "raw data from the energy storage side" refers to the initial data collected from distributed energy storage units, including but not limited to the state of charge (SOC), state of health (SOH), rated power, and real-time operating temperature of each energy storage unit; "raw data from the charging side" refers to the initial data collected from charging piles and user interaction links, including but not limited to the current charging demand power of each charging pile, user priority identifier, and user's expected vehicle usage time; "raw data from the photovoltaic side" refers to the initial data collected from photovoltaic inverters, mainly the real-time output power of the photovoltaic system and the operating status of photovoltaic modules.

[0077] Additionally, it should be noted that "outlier removal" refers to removing invalid data from the original data that exceeds the normal range, such as frequency deviations exceeding ±2Hz, or energy storage unit state of charge less than 0% or greater than 100%, which are clearly inconsistent with actual operating patterns, by using preset data rationality judgment rules to avoid abnormal data interfering with subsequent decision-making; "protocol conversion" refers to uniformly converting communication protocol data output from different acquisition objects and in different formats into a standard protocol format (such as the IEC 61850 standard format) that meets the parsing requirements of the system's central decision-making module, solving the protocol compatibility problem between different devices; "data compression processing" refers to reducing the amount of data through specific algorithms (such as differential compression algorithms), especially for slowly changing data (such as energy storage unit health status and rated power), reducing the pressure of data transmission and storage while ensuring data accuracy; "standardized data" refers to the final data set that has a unified format, accurate content, and no redundancy after outlier removal, protocol conversion, and data compression processing, and this data set can be directly called by the central decision-making module.

[0078] In one possible implementation, the data acquisition is performed by the data acquisition module in the system. This module connects to the power grid, energy storage, charging, and photovoltaic equipment through different acquisition components. For example, it acquires grid-side data through an IEC 61850 smart gateway, energy storage-side data through a CAN bus, charging-side data through a communication module supporting the OCPP 1.6 protocol, and photovoltaic-side data through a Modbus TCP protocol interface, ensuring real-time acquisition of data from all dimensions.

[0079] Step S100 further includes the following sub-steps:

[0080] Step S110: Collect the frequency deviation on the power grid side using the power grid data acquisition component. Load factor and command power ;

[0081] The state of charge of each energy storage unit on the energy storage side is collected by the energy storage data acquisition component. Health status and rated power Then the average state of charge is calculated. Minimum health status and total available power ,in, and In this context, i = 1, 2, ..., N, where N is the total number of energy storage units;

[0082] The charging data acquisition component collects the charging demand of each charging pile on the charging side. Level 1 user count and number of emergency users The total charging demand is calculated. ,in, In the context, j = 1, 2, ..., M, where M is the total number of charging stations;

[0083] Real-time power output from the photovoltaic side is collected through photovoltaic data acquisition components. ;

[0084] Step S120: Use the 3σ criterion to remove data from the original data that exceeds a reasonable range. The data exceeding a reasonable range includes... , or , or , For the rated power of photovoltaic power, the data after elimination is replaced with data from the previous period;

[0085] Convert the data in the original data of different protocol formats to the IEC 61850 standard format; , , and Differential compression is used, with a compression ratio of no less than 1:5;

[0086] Step S130: Integrate the data processed in step S120 and output the data containing... , , , , , , , , , The standardized data mentioned above.

[0087] The "3σ criterion" refers to eliminating outlier data (such as frequency deviations) that exceed the range of "mean ± 3 standard deviations" based on the normal distribution characteristics of the data. >±2Hz, energy storage <0% or >100%) to ensure data validity; "IEC61850 standard format" refers to the universal data exchange format in the field of power system automation, used to unify the protocols of different devices such as power grid, energy storage, and charging, and avoid data parsing errors caused by protocol incompatibility; "differential compression processing" refers to the... , For data that changes slowly (update cycle 1 minute), only the difference between the current value and the previous cycle value is stored, achieving a data compression ratio of no less than 1:5 and reducing transmission pressure.

[0088] In one possible implementation, in addition to using the 3σ criterion, outlier removal can also employ the "adjacent cycle comparison method" for sudden extreme data (such as a sudden drop in photovoltaic output to 0). If the difference between the current data and the average data of the previous 3 cycles exceeds 50%, it is determined to be an anomaly and the data from the previous cycle is used as a substitute, further improving data reliability. In addition to supporting the IEC 61850 format, the protocol conversion stage can also be compatible with the Modbus RTU format, adapting to the communication needs of some older charging piles without the need for additional protocol converters.

[0089] For example, in one feasible implementation, the power grid data acquisition component acquires the real-time frequency of the distribution network via a frequency sensor. =50.35Hz (calculated) =+0.35Hz), real-time power of the 10kV bus is collected through voltage and current sensors. =9.2MW (bus rated capacity) ,calculate Peak shaving instructions are obtained through the power grid dispatch interface. =200kW; the energy storage data acquisition component collects data from 10 energy storage units via CAN bus. (Range 26%-30%, calculation) =28%) (All are 92%-95%, calculated) =92%), combined with rated power =30kW, calculate =10×28%×30=84kW; The charging data acquisition component collects data from 20 charging piles via the OCPP 1.6 protocol. (Total demand) =180kW), the number of Level 1 users is identified through the RFID card reader module. =3 vehicles (all reported estimated usage time) =30min, calculate =3 units); the photovoltaic data acquisition module collects data from the 1MW photovoltaic inverter via Modbus TCP protocol. =150kW. In the preprocessing stage, one energy storage unit was excluded due to communication interference. =105% outlier (replace with 28% from the previous period), convert all data to IEC 61850 format, for , After differential compression, the output contains , , , , , , , , , Standardized data provides input for subsequent decision-making.

[0090] Furthermore, step S110 also includes the following sub-steps:

[0091] In step S111, the real-time frequency of the power distribution network is collected by a frequency sensor. The frequency deviation was calculated. 50Hz is the rated frequency of the power grid;

[0092] Real-time power of the 10kV bus is collected using voltage and current sensors. Combined with the rated capacity of the busbar The load factor was calculated. ;

[0093] The commanded power is obtained through the power grid dispatching interface. The command power Discharging is positive, charging is negative;

[0094] Step S112: Collect the data from N distributed energy storage units via the CAN bus. With the The calculation yielded the The above ; Collect the data from each energy storage unit The total available power is calculated. ;

[0095] Step S113: Collect the data from the M charging piles via the charging protocol interface. The total charging demand is calculated. The user priority is identified through the user identification component, and the number of Level 1 users is then calculated. ;

[0096] Based on the user's reported estimated car usage time The number of emergency users was obtained by statistics. The number of emergency users correspond Level 1 users;

[0097] Step S114: Collect the real-time output of the photovoltaic inverter through the photovoltaic communication interface. The photovoltaic communication interface supports the Modbus TCP protocol, and the data acquisition delay does not exceed 30ms.

[0098] Among them, "frequency sensor" refers to the detection equipment used to collect the real-time operating frequency of the distribution network, with an accuracy of ±0.01Hz and a sampling frequency of 500Hz to capture instantaneous frequency fluctuations; "voltage and current sensor" refers to the device installed at the 10kV bus to collect the real-time voltage and current of the bus, and then calculate the real-time power P_bus of the bus, with a measurement error ≤0.5%; "CAN bus" refers to the communication bus connecting the local controller of each distributed energy storage unit with the data acquisition module, with a baud rate of 500kbps to ensure data transmission delay ≤10ms to avoid lag in energy storage status data; "charging protocol interface" refers to the communication interface supporting the OCPP 1.6 protocol, used to establish data interaction with charging piles and obtain the charging power demand of each charging pile in real time; "user identification component" refers to the component integrating an RFID card reader module and user APP data receiving function, which identifies user priority (Level 1 / 2) by reading user cards or receiving information reported by the APP; "photovoltaic communication interface" refers to the component supporting Modbus The TCP protocol interface is used to obtain real-time power output data from the photovoltaic inverter, with a data acquisition delay of ≤30ms, ensuring that the photovoltaic power output data can be used in power balance calculations in a timely manner.

[0099] In one possible implementation, power grid data acquisition, in addition to being obtained through the power grid dispatch interface, also involves... In addition, edge computing nodes can parse data from the power distribution network's SCADA system to obtain more detailed regional load distribution information (such as real-time load of each factory building within the park), providing a more accurate grid-side reference for subsequent power allocation; in the energy storage data acquisition stage, besides collecting... , In addition, it can collect the real-time temperature of each energy storage unit (accuracy ±1℃) to determine whether there is a risk of overheating in the energy storage and further enhance the safety protection of energy storage.

[0100] Step S200: Based on the standardized data, first, a hard constraint check of energy storage safety is performed to determine the safety status. Then, the scenario threshold is adaptively adjusted according to the grid load rate and the user's charging urgency to output the scenario type and basic charging power. Subsequently, a preliminary power command is generated through a lightweight reinforcement learning algorithm. Finally, the compliance of the preliminary power command is judged, and the final power command is output.

[0101] In this embodiment of the application, step S200 is the "decision center" of the entire control method. Its purpose is to dynamically balance the relationship between "grid auxiliary demand, user charging demand, and energy storage protection demand" based on the standardized data output in step S100, so as to avoid functional conflicts (such as grid peak shaving and user charging competing for energy storage resources), and finally generate safe, compliant, and executable power commands. The core effect is to ensure that the grid response meets the standards, user charging is guaranteed, and energy storage is not damaged. Among them, "safety status" refers to the energy storage operation status determined after passing the energy storage safety hard constraint check, including three categories: "energy storage protection priority status," "restricted participation status," and "normal participation status"; "grid load factor" refers to the ratio of the current real-time power of the distribution network to the rated capacity of the bus, which is a core indicator for judging the grid operation pressure; "user charging urgency" refers to the urgency of the user's charging needs determined based on the user's reported expected vehicle usage time or user priority; "scenario type" refers to the grid operation scenario determined based on the relationship between frequency deviation and corresponding threshold, mainly including two categories: "grid emergency scenario" and "grid normal scenario"; "basic charging power" refers to the minimum charging power guarantee value set for Level 1 users (high-priority users) to ensure the basic charging needs of emergency users; "preliminary power command" refers to the power allocation command generated by a lightweight reinforcement learning algorithm that has not undergone compliance verification; and "final power command" refers to the executable power command that has been confirmed to be safe and compliant after compliance judgment.

[0102] Additionally, it should be noted that step S200 first delineates the safety boundary of energy storage participation through hard constraint checks on energy storage safety to avoid decisions that could harm energy storage; then, it adapts the scenario based on the actual situation of the power grid and users to ensure that the decisions fit the current operating environment; next, it generates preliminary instructions through a lightweight reinforcement learning algorithm to achieve multi-objective optimization; and finally, it eliminates unsafe and non-compliant instructions through compliance judgment to ensure that the instructions can be implemented.

[0103] In one possible implementation, the lightweight reinforcement learning algorithm adopts the DQN-Lite algorithm, whose state space contains only 6 core parameters (frequency deviation, grid command power, average state of charge of energy storage, minimum health state of energy storage, number of Level 1 users, and total charging demand). The reward function adopts a three-stage approach (grid compliance reward, charging guarantee reward, and energy storage protection penalty). The experience replay pool is divided into "grid emergency sub-pool", "grid normal sub-pool" and "energy storage protection sub-pool" according to the scenario to ensure balanced learning in each scenario and control the decision delay to within 35ms.

[0104] Step S200 further includes the following sub-steps:

[0105] Step S210, if the In this mode, the system switches to energy storage protection priority mode, prohibiting all energy storage from participating in grid auxiliary operations and allocating basic charging power only to Level 1 users. and total charging power 0.2 represents the maximum permissible depth of discharge;

[0106] like This allows energy storage to participate in grid auxiliary operations, but the discharge power... 0.3 represents the maximum permissible depth of discharge, allocating basic charging power to Level 1 users. Charging for Level 2 users is suspended.

[0107] like If so, continue with step S220;

[0108] Step S220, based on the load rate Adjusting frequency deviation The emergency threshold and normal threshold are used to output either the emergency scenario or the normal scenario of the power grid.

[0109] Based on the proportion of emergency users Adjust the base charging power for Level 1 users ;

[0110] Step S230: Construct a state space based on the 6-dimensional core parameters in the standardized data. The 6-dimensional core parameters include... , , , , , ;

[0111] The reward value is calculated using a three-stage reward function. ,in Incentives for grid compliance and grid responsiveness hour , hour , hour , As a reward for charging protection, the Level 1 interruption rate is included. hour , hour , hour , Penalties for energy storage protection, among which hour , hour , hour ;

[0112] The algorithm model is trained using a scenario-layered experience replay mechanism, and an initial power command is output. The initial power command includes the command power under power grid emergency scenarios. With initial charging power and preliminary grid auxiliary power under normal grid scenarios ;

[0113] Step S240: Check whether the preliminary power command meets the energy storage safety constraints and grid standard constraints, wherein the energy storage safety constraints are the total commanded power. The power grid standard constraint is the power grid command power. , This is the maximum allowable response power of the power grid;

[0114] If satisfied, output the final power command;

[0115] If the conditions are not met, return to step S230 to readjust the reward function weights and generate a new preliminary power command. The number of retries is ≤3. If the conditions are still not met after 3 retries, the energy storage protection priority mode is triggered.

[0116] Among them, "hard constraint check for energy storage safety" refers to the check based on... and This involves determining whether energy storage meets the safety conditions for participating in grid auxiliary power or meeting charging needs, and avoiding over-discharge or excessive degradation of energy storage health; "scenario threshold adaptation" refers to adjusting based on grid load factor. Adjusting frequency deviation Emergency / normal thresholds, based on the proportion of emergency users. Adjusting the base charging power for Level 1 users allows decisions to adapt to real-time grid and user status; the "lightweight reinforcement learning algorithm" refers to the DQN-Lite algorithm, which reduces the state space from the traditional 10-15 dimensions to 6 dimensions of core parameters. , , , , , The system employs scenario-based experience replay (with a grid emergency / normal / energy storage protection sub-pool ratio of 3:5:2) to improve decision-making efficiency (delay ≤ 35ms) and accuracy (≥ 98%). "Compliance judgment" refers to checking whether the initial power command meets the requirement of "total command power ≤..." (Energy storage security constraints) and "Grid command power ≤ (Power grid standard constraints,) The maximum allowable response power of the power grid is pre-configured by the dispatch center to avoid commands exceeding the hardware or power grid capacity.

[0117] In one possible implementation, the energy storage safety hard constraint check is based on and In addition, it can also be combined with the real-time discharge depth of each energy storage unit ( If any energy storage unit (correspond If the power consumption is less than 20%, then the discharge power of that energy storage unit is limited individually (≤20% of the rated power), rather than limiting all energy storage units as a whole, thus improving the utilization rate of energy storage resources; in the reward function of the lightweight reinforcement learning algorithm, a "photovoltaic consumption reward R4" can be introduced—if Utilization rate ( Actual consumption / If ≥90%, then R4=3, further incentivizing the system to prioritize the use of photovoltaic energy and reduce energy storage consumption.

[0118] For example, in one feasible implementation, step S210, =22%≥20%, =91%, enter scene adaptation; step S220, =85% (60%≤ ≤90%) =+0.4Hz (exceeds the normal threshold of ±0.3Hz, indicating a power grid emergency scenario). ( >50%) =15kW / pile; Step S230, construct the state space based on 6-dimensional core parameters, and calculate the three-segment reward function. =10 ( ≥95%) + 8 ( =0)+0( ≥25%) = 18, output initial instructions: =180kW =5×15=75kW; Step S240, =200kW, total commanded power =180 + 75 = 255kW =260kW (constraints satisfied), output final command: =180kW =75kW, completing dynamic priority decision-making.

[0119] Furthermore, step S220 also includes the following sub-steps:

[0120] Step S221, if ,but The emergency threshold is set to ±0.3Hz, the normal threshold is set to ±0.2Hz, and the response trigger delay is ≤20ms;

[0121] like ,but The emergency threshold is set to ±0.5Hz, the normal threshold is set to ±0.3Hz, and the response trigger delay is ≤30ms;

[0122] like ,but The emergency threshold is set to ±0.8Hz, the normal threshold is set to ±0.5Hz, and the response trigger delay is ≤50ms;

[0123] Step S222, if The basic charging power Prioritize the allocation of the real-time output ;

[0124] like The basic charging power ,use Combined supply with energy storage;

[0125] like The basic charging power The remaining energy storage capacity will be used for grid auxiliary applications.

[0126] in," The "emergency threshold" refers to the critical frequency deviation value used to determine whether the power grid enters an emergency scenario. Exceeding this threshold requires priority to ensure grid auxiliary power. "Normal threshold" refers to the frequency deviation range when the power grid is in a stable operating state, at which time charging demand can be prioritized; "Emergency user ratio" "This refers to the ratio of Level 1 users whose expected usage time is ≤1 hour to the total number of Level 1 users, which directly reflects the urgency of users' charging needs."

[0127] In one possible implementation, the grid-side threshold adjustment is based on... In addition, it can be combined with the "peak shaving level" (such as level 1 peak shaving, level 2 peak shaving) issued by the power grid dispatch center - level 1 peak shaving (power grid is extremely overloaded). The emergency threshold can be further tightened to ±0.2Hz, and the response trigger delay ≤15ms, improving the grid emergency response speed; the user-side basic charging power adjustment is based on... In addition, it can also be combined with the user's charging mode (such as fast charging / slow charging). When an emergency user selects fast charging, It can be increased to 20kW / pile, further shortening the charging time for emergency users.

[0128] For example, in one feasible implementation, step S221, =92% >90%) The emergency threshold is set to ±0.3Hz, the normal threshold is set to ±0.2Hz, and the response trigger delay is set to 18ms (≤20ms). Real-time... =+0.32Hz (Exceeds the emergency threshold, determined to be a power grid emergency scenario); Step S222, Level 1 user count =4 units, of which 3 =35min (emergency), 1 unit =120min (non-emergency) =75% (>50%) =15kW / pile, and priority is given to allocating photovoltaic power output. =120kW (current photovoltaic output is sufficient), avoiding direct consumption of energy storage. This adjustment ensures both rapid grid response in emergencies and guarantees charging needs for emergency users.

[0129] Step S300: Calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to scenario type, and output actual operating data after execution.

[0130] In this embodiment, "total power demand" refers to the total power currently required by the system, calculated according to the final power command, including the sum of the power required for grid auxiliary power and the power required for user charging; "charging power adjustment" refers to adjusting the output power of each charging pile according to the charging power determined in the final power command to ensure that the charging demand is met as instructed; "actual operating data" refers to the actual operating parameters collected from the energy storage unit and charging pile after the power allocation and adjustment are completed, including but not limited to the actual response power of the energy storage unit, the actual charging power of the charging pile, and the equipment operating temperature.

[0131] Additionally, it should be noted that step S300 first calculates the total power demand based on the final power command, clarifying the current power consumption or total output of the system; then, it determines the priority according to the scenario type (prioritizing grid commands in emergency grid scenarios and prioritizing charging needs in normal grid scenarios); finally, it allocates power to each energy storage unit, adjusts the charging pile power, and collects actual operating data. The execution entity is the distributed execution module in the system, which includes energy storage control components (such as bidirectional inverters and local power controllers) and charging control components (such as charging pile power adjustment units) to ensure accurate execution of commands.

[0132] In one possible implementation, the energy storage power allocation adopts a "capacity-health weighted algorithm", that is, the power allocation coefficient is equal to the product of the state of charge and health of a single energy storage unit, divided by the sum of the products of the state of charge and health of all energy storage units. This algorithm can ensure that energy storage units with high state of charge and good health can bear more power, avoid overloading of a single unit, and extend the overall energy storage life.

[0133] Step S300 further includes the following sub-steps:

[0134] Step S310: If it is a power grid emergency scenario, then the total discharge power requirement is... , For the final grid command power This refers to the final charging power.

[0135] like Then according to the power allocation coefficient Power allocation for each energy storage unit ,in i=1,2,…,N;

[0136] like Then calculate the charging power reduction ratio. To obtain the actual charging power ;

[0137] If it is a normal power grid scenario, the total power consumption demand is... , As auxiliary power to the final power grid;

[0138] like Then priority will be given to satisfying. The surplus power serves as auxiliary power for the actual power grid. ;like Then calculate the reduction ratio of auxiliary power in the power grid. To obtain the actual auxiliary power of the power grid ;

[0139] Step S320: The power of each energy storage unit is sent to each energy storage unit through the energy storage control component. or The drive energy storage inverter adjusts its power according to the PWM control signal, outputting the actual grid response power. ; issues commands to each charging station via the charging control component Adjust the charging pile's output current to output the actual charging power. ;

[0140] If an overcurrent or overtemperature anomaly is detected, the emergency stop switch is triggered, power distribution is suspended, and the system switches to emergency mode. The emergency mode only guarantees 5kW / pile charging for Level 1 users. The overcurrent is defined as current > 1.2 × rated current, and the overtemperature is defined as temperature > 65℃.

[0141] Among them, "power balance calculation" refers to calculating the actual power allocation value of each device based on the final power command, the total available power of energy storage, and photovoltaic output, so as to avoid the total power exceeding the system's carrying capacity; "power allocation coefficient" "Refers to based on each energy storage unit" and The calculated weighting coefficients ensure balanced load across all energy storage units (power deviation ≤ 5%), preventing overload of any single unit. A "bidirectional inverter" refers to a device that converts stored DC power into AC power (discharging) or vice versa (charging), with an efficiency ≥ 98.5% and supports PWM (pulse width modulation) control for continuously adjustable power. An "emergency stop switch" is a protection device installed on both the energy storage inverter and the charging pile, with a response time ≤ 100ms. It triggers when overcurrent (current > 1.2 times rated current) or overtemperature (temperature > 65℃) is detected, cutting off power output to protect the equipment.

[0142] In one possible implementation, the power balance calculation step, in addition to considering and In addition, "diesel generator backup power" (such as the 2MW diesel generator configured in the park) can be introduced. When the power grid is in an emergency and the energy storage + photovoltaic power is insufficient, the diesel generator can be started to supplement the power, further improving the grid's auxiliary response capability; in the distributed execution link, in addition to feedback and In addition, it can also provide feedback on the real-time operating temperature and current of each device, providing more comprehensive device status data for subsequent feedback and correction steps.

[0143] For example, in one feasible implementation, step S310, in a power grid emergency scenario, =250kW, =60kW (3 Level 1 users, =20kW / pile), total demand =310kW, =280kW, =50kW, total available =330kW≥310kW, according to Allocate 250kW of grid power ( Range 0.09-0.11 (load balancing), 60kW charging power; Step S320, the energy storage control component sends power commands (23-27kW / unit) to 10 energy storage units, the inverter controls the output power through PWM, and feedback... =250kW; The charging control component sends a 20kW / unit command to each of the three charging piles, and provides feedback. =60kW; During execution, the temperature of one inverter rose to 68℃ (>65℃), triggering the emergency stop switch. This energy storage unit stopped operating, and the system automatically allocated its 25kW power to the other 9 energy storage units. (Recalculate) to ensure the total power remains unchanged, and complete the power allocation execution.

[0144] Step S400: Calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds a preset threshold, adjust the energy storage power allocation parameters and push the status information to the power grid, users, and the next round of decision-making to form a closed-loop control.

[0145] In this embodiment, step S400 is the core link to achieve "continuous optimization" of the system. Its purpose is to identify and adjust the execution deviation by comparing the difference between the actual operating data and the final power command, and push the status information to the relevant parties, so as to form a closed-loop control of "collection-decision-execution-feedback-optimization". The core effect is to improve the stability and accuracy of the system operation and reduce the deviation of subsequent decisions. Among them, "deviation value" refers to the degree of difference between the actual operating data and the final power command, usually expressed as a percentage, used to quantify the magnitude of the execution deviation; "preset threshold" refers to the upper limit of the allowable deviation range set in advance by the system. If the deviation value exceeds this threshold, it needs to be corrected; "energy storage power allocation parameters" refers to the parameters used in the energy storage power allocation process, such as the power allocation coefficient and the charging power reduction ratio; "status information" refers to the current operating status data of the system, including grid response, user charging progress, and remaining energy storage capacity; "next round decision-making" refers to the process of the next execution step S200 (dynamic priority decision-making step); "closed-loop control" refers to the transmission of execution deviation information to the next round of decision-making through feedback correction, so that the next round of decision-making can make targeted adjustments, forming a cyclically optimized control mode.

[0146] Additionally, it should be noted that step S400 first calculates various deviation values ​​(such as grid response deviation and charging power deviation) based on actual operating data and the final power command; then, it compares the deviation values ​​with preset thresholds to determine whether correction is needed; if correction is needed, it adjusts the energy storage power allocation parameters (such as adjusting the power allocation coefficient) and pushes the status information to the grid dispatch center, users, and the next round of decision-making; if correction is not needed, it only pushes the status information to ensure that relevant parties are aware of the system's operating status. The execution entity is the feedback module in the system, which realizes deviation calculation, correction, and information push through interaction with the distributed execution module and the central decision-making module.

[0147] In one possible implementation, the preset threshold is set to 10%, that is, if the grid response deviation or charging power deviation exceeds 10%, a correction is triggered; the status information is pushed through multiple channels: the actual response power and remaining energy storage capacity are uploaded to the grid dispatch center, the charging progress and cost are pushed to users, and the equipment health report (such as the energy storage SOH decay trend) is pushed to the operation and maintenance end, so as to ensure that different entities obtain the information they need.

[0148] Step S400 further includes the following sub-steps:

[0149] Step S410: Calculate the power grid response deviation. ,like but ;

[0150] Calculate charging power deviation ,like but ;

[0151] Calculate photovoltaic output deviation The To contribute to the actual development of photovoltaics;

[0152] Step S420, if Then improve energy storage unit The coefficient is 10%, that is And return to step S200 for feedback. Correction factor;

[0153] like and Then from An additional 5kW / pile will be allocated to Level 1 users, and feedback will be returned to step S200. Fluctuation warning and reservation As backup power;

[0154] Upload to the power grid dispatch center With remaining total available power Push charging progress to users ,cost And abnormal alerts, push equipment health reports to the operation and maintenance terminal, the equipment health reports contain Decline trend Inverter temperature profile During the charging progress This is the amount of charge already applied. To meet the user's power demand, It is a time-of-use electricity price.

[0155] in," "Correction factor" refers to the power allocation factor for high SOC energy storage units when the grid response deviation exceeds the limit. Adjustments (such as a 10% increase) will be made to supplement the grid response power; "Fluctuation warning" refers to sending an early warning to the central decision-making module when the photovoltaic output deviation exceeds 15%, prompting the next round of decision-making to reserve more energy storage backup power to cope with photovoltaic fluctuations; "Remaining total available power" refers to the remaining capacity after subtracting the actual grid response power and actual charging power from the total available energy storage power. This is used to provide feedback to the power grid regarding subsequent response capabilities.

[0156] In one possible implementation, the deviation calculation step, in addition to calculating... , , In addition, it can also calculate the "energy storage power distribution deviation". ( ),in This refers to the actual power output of the i-th distributed energy storage unit (i=1,2,…,N, where N is the total number of energy storage units) in step S300. This refers to the power allocation command value issued to the i-th distributed energy storage unit, which is the target value calculated based on the final power command output in step S200. If a certain energy storage unit... If the failure rate is greater than 10%, the unit is considered to be potentially faulty, and a maintenance reminder is sent to the operations and maintenance team; the execution process is corrected, except for adjustments. In addition to reserving backup power, the weight of the reward function of the lightweight algorithm can be dynamically adjusted. For example, when photovoltaic fluctuations are large, the weight of R4 (photovoltaic consumption reward) can be increased to further optimize subsequent decisions.

[0157] For example, in one feasible implementation, step S410, =200kW, =185kW, (≤10%, qualified); =80kW, =72kW, =10% (critical pass); =150kW, =120kW, =20% (>15%, unacceptable); Step S420, because... >15%, from An additional 5kW / pile will be allocated from the 300kW to 2 Level 1 users. Adjusted to 80kW), feedback was sent to the central decision-making module. Fluctuation warning, reserve 10% × 300 = 30kW of standby power; in the status push phase, upload data to the power grid. =185kW =300-185-80=35kW; Push charging progress (80% charged) and cost (80kW×1.2 yuan / kWh=96 yuan) to the user; Push equipment health report to the maintenance department ( The decline trend is 0.5% / month (normal). Feedback and correction have been completed, providing an optimization basis for the next round of decision-making.

[0158] In this embodiment, by constructing a closed-loop control architecture encompassing data acquisition, dynamic decision-making, power execution, and feedback correction, the needs of the power grid, charging, and energy storage are balanced, thereby improving the reliability of grid interaction and the user charging experience. Through lightweight reinforcement learning algorithms and scenario-based threshold adaptation, decision-making efficiency and adaptability are optimized, reducing system response latency and enhancing multi-device compatibility. Furthermore, through hard constraint checks and hierarchical protection mechanisms for energy storage safety, over-discharge and health degradation of energy storage are avoided, extending the energy storage lifespan and reducing equipment replacement costs.

[0159] Based on the above embodiments, such as Figure 2 As shown, the present invention also provides a multi-functional collaborative control system for distributed energy storage charging, used to support the multi-functional collaborative control method for distributed energy storage charging in the above embodiments. The multi-functional collaborative control system for distributed energy storage charging includes:

[0160] The data acquisition module is used to collect raw data from the grid side, energy storage side, charging side and photovoltaic side, and to perform outlier removal, protocol conversion and data compression on the raw data to output standardized data.

[0161] The central decision-making module is used to first perform a hard constraint check on energy storage safety to determine the safety status based on the standardized data, then perform scenario threshold adaptation based on the grid load rate and the user's charging urgency to output the scenario type and basic charging power, then generate a preliminary power command through a lightweight reinforcement learning algorithm, and finally perform compliance judgment on the preliminary power command to output the final power command.

[0162] The distributed execution module is used to calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to the scenario type, and output the actual operation data after execution.

[0163] The feedback module is used to calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds the preset threshold, the energy storage power allocation parameters are adjusted and the status information is pushed to the power grid, users and the next round of decision-making to form a closed-loop control.

[0164] In this embodiment, by constructing a closed-loop control architecture encompassing data acquisition, dynamic decision-making, power execution, and feedback correction, the needs of the power grid, charging, and energy storage are balanced, thereby improving the reliability of grid interaction and the user charging experience. Through lightweight reinforcement learning algorithms and scenario-based threshold adaptation, decision-making efficiency and adaptability are optimized, reducing system response latency and enhancing multi-device compatibility. Furthermore, through hard constraint checks and hierarchical protection mechanisms for energy storage safety, over-discharge and health degradation of energy storage are avoided, extending the energy storage lifespan and reducing equipment replacement costs.

[0165] Furthermore, the multi-functional collaborative control system for distributed energy storage charging can run the aforementioned multi-functional collaborative control method for distributed energy storage charging. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0166] Based on the above embodiments, such as Figure 3 As shown, the present invention also provides an electronic device, the electronic device comprising:

[0167] The processor 22 includes at least one processor 22, at least one memory 21, a communication interface 23, and a communication bus 24, wherein the processor 22 is communicatively connected to the memory 21.

[0168] In this embodiment, the memory 21 can be implemented in any suitable manner, for example, the memory 21 can be a read-only memory, a hard disk drive, a solid-state drive, or a USB flash drive, etc.; the memory 21 is used to store at least one executable instruction executed by the processor;

[0169] In this embodiment, the processor 22 can be implemented in any suitable manner. For example, the processor 22 can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) that can be executed by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc.; the processor is used to execute the executable instructions to implement the multifunctional cooperative control method for distributed energy storage charging as described above.

[0170] Based on the above embodiments, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-functional collaborative control method for distributed energy storage charging as described above.

[0171] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, equipment, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or equipment, and may be electrical, mechanical, or other forms.

[0174] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0176] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program instructions, such as USB flash drives, portable hard drives, read-only storage servers, random access storage servers, magnetic disks, or optical disks.

[0177] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

[0178] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. 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.

Claims

1. A multi-functional collaborative control method for distributed energy storage charging, characterized in that, Includes the following steps: Step S100: Collect raw data from the grid side, energy storage side, charging side and photovoltaic side, perform outlier removal, protocol conversion and data compression on the raw data, and output standardized data; Step S200: Based on the standardized data, first perform a hard constraint check on energy storage safety to determine the safety status, then perform scenario threshold adaptation based on grid load rate and user charging urgency to output scenario type and basic charging power, then generate preliminary power command through lightweight reinforcement learning algorithm, and finally perform compliance judgment on the preliminary power command to output final power command. Step S300: Calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to the scenario type, and output the actual operating data after execution; Step S400: Calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds a preset threshold, adjust the energy storage power allocation parameters and push the status information to the power grid, users, and the next round of decision-making to form a closed-loop control.

2. The multi-functional collaborative control method for distributed energy storage charging according to claim 1, characterized in that, Step S100 further includes the following sub-steps: Step S110: Collect the frequency deviation on the power grid side using the power grid data acquisition component. Load factor and command power ; The state of charge of each energy storage unit on the energy storage side is collected by the energy storage data acquisition component. Health status and rated power Then the average state of charge is calculated. Minimum health status and total available power ,in, and In this context, i = 1, 2, ..., N, where N is the total number of energy storage units; The charging data acquisition component collects the charging demand of each charging pile on the charging side. Level 1 user count and number of emergency users The total charging demand is calculated. ,in, In the context, j = 1, 2, ..., M, where M is the total number of charging stations; Real-time power output from the photovoltaic side is collected through photovoltaic data acquisition components. ; Step S120: Use the 3σ criterion to remove data from the original data that exceeds a reasonable range. The data exceeding a reasonable range includes... , or , or , For the rated power of photovoltaic power, the data after elimination is replaced with data from the previous period; Convert the data in the original data of different protocol formats to the IEC 61850 standard format; , , and Differential compression is used, with a compression ratio of no less than 1:5; Step S130: Integrate the data processed in step S120 and output the data containing... , , , , , , , , , The standardized data mentioned above.

3. The multi-functional collaborative control method for distributed energy storage charging according to claim 2, characterized in that, Step S110 further includes the following sub-steps: In step S111, the real-time frequency of the power distribution network is collected by a frequency sensor. The frequency deviation was calculated. 50Hz is the rated frequency of the power grid; Real-time power of the 10kV bus is collected using voltage and current sensors. Combined with the rated capacity of the busbar The load factor was calculated. ; The commanded power is obtained through the power grid dispatching interface. The command power Discharging is positive, charging is negative; Step S112: Collect the data from N distributed energy storage units via the CAN bus. With the The calculation yielded the The above ; The data collected from each energy storage unit The total available power is calculated. ; Step S113: Collect the data from the M charging piles via the charging protocol interface. The total charging demand is calculated. The user priority is identified through the user identification component, and the number of Level 1 users is then calculated. ; Based on the user's reported estimated car usage time The number of emergency users was obtained by statistics. The number of emergency users correspond Level 1 users; Step S114: Collect the real-time output of the photovoltaic inverter through the photovoltaic communication interface. The photovoltaic communication interface supports the Modbus TCP protocol, and the data acquisition delay does not exceed 30ms.

4. The multi-functional collaborative control method for distributed energy storage charging according to claim 1, characterized in that, Step S200 further includes the following sub-steps: Step S210, if the In this mode, the system switches to energy storage protection priority mode, prohibiting all energy storage from participating in grid auxiliary operations and allocating basic charging power only to Level 1 users. and total charging power 0.2 represents the maximum permissible depth of discharge; like This allows energy storage to participate in grid auxiliary operations, but the discharge power... 0.3 represents the maximum permissible depth of discharge, allocating basic charging power to Level 1 users. Charging for Level 2 users is suspended. like If so, continue with step S220; Step S220, based on the load rate Adjusting frequency deviation The emergency threshold and normal threshold are used to output either the emergency scenario or the normal scenario of the power grid. Based on the proportion of emergency users Adjust the base charging power for Level 1 users ; Step S230: Construct a state space based on the 6-dimensional core parameters in the standardized data. The 6-dimensional core parameters include... , , , , , ; The reward value is calculated using a three-stage reward function. ,in Incentives for grid compliance and grid responsiveness hour , hour , hour , As a reward for charging protection, the Level 1 interruption rate is included. hour , hour , hour , Penalties for energy storage protection, among which hour , hour , hour ; The algorithm model is trained using a scenario-layered experience replay mechanism, and an initial power command is output. The initial power command includes the command power under power grid emergency scenarios. With initial charging power and preliminary grid auxiliary power under normal grid scenarios ; Step S240: Check whether the preliminary power command meets the energy storage safety constraints and grid standard constraints, wherein the energy storage safety constraints are the total commanded power. The power grid standard constraint is the power grid command power. , This represents the maximum allowable response power of the power grid. If satisfied, output the final power command; If the conditions are not met, return to step S230 to readjust the reward function weights and generate a new preliminary power command. The number of retries is ≤3. If the conditions are still not met after 3 retries, the energy storage protection priority mode is triggered.

5. The multi-functional collaborative control method for distributed energy storage charging according to claim 4, characterized in that, Step S220 further includes the following sub-steps: Step S221, if ,but The emergency threshold is set to ±0.3Hz, the normal threshold is set to ±0.2Hz, and the response trigger delay is ≤20ms; like ,but The emergency threshold is set to ±0.5Hz, the normal threshold is set to ±0.3Hz, and the response trigger delay is ≤30ms; like ,but The emergency threshold is set to ±0.8Hz, the normal threshold is set to ±0.5Hz, and the response trigger delay is ≤50ms; Step S222, if The basic charging power Prioritize the allocation of the real-time output ; like The basic charging power ,use Combined supply with energy storage; like The basic charging power The remaining energy storage capacity participates in grid auxiliary operations.

6. The multi-functional collaborative control method for distributed energy storage charging according to claim 1, characterized in that, Step S300 further includes the following sub-steps: Step S310: If it is a power grid emergency scenario, then the total discharge power requirement is... , For the final grid command power, This refers to the final charging power. like Then according to the power allocation coefficient Power allocation for each energy storage unit ,in i=1,2,…,N; like Then calculate the charging power reduction ratio. To obtain the actual charging power ; If it is a normal power grid scenario, the total power consumption demand is... , For final grid auxiliary power; like Then priority will be given to satisfying. The surplus power serves as auxiliary power for the actual power grid. ;like Then calculate the reduction ratio of auxiliary power in the power grid. To obtain the actual grid auxiliary power ; Step S320: The power of each energy storage unit is sent to each energy storage unit through the energy storage control component. or The drive energy storage inverter adjusts its power according to the PWM control signal, outputting the actual grid response power. ; The charging control component sends commands to each charging station. Adjust the charging pile's output current to output the actual charging power. ; If an overcurrent or overtemperature anomaly is detected, the emergency stop switch is triggered, power distribution is suspended, and the system switches to emergency mode. The emergency mode only guarantees 5kW / pile charging for Level 1 users. The overcurrent is defined as current > 1.2 × rated current, and the overtemperature is defined as temperature > 65℃.

7. The multi-functional collaborative control method for distributed energy storage charging according to claim 1, characterized in that, Step S400 further includes the following sub-steps: Step S410: Calculate the power grid response deviation. ,like but ; Calculate charging power deviation ,like but ; Calculate photovoltaic output deviation The To contribute to the actual development of photovoltaics; Step S420, if Then improve energy storage unit The coefficient is 10%, that is And return to step S200 for feedback. Correction factor; like and Then from An additional 5kW / pile will be allocated to Level 1 users, and feedback will be returned to step S200. Fluctuation warning and reservation As backup power; Upload to the power grid dispatch center With remaining total available power Push charging progress to users ,cost And abnormal alerts, push equipment health reports to the operation and maintenance terminal, the equipment health reports contain Decline trend Inverter temperature profile During the charging progress This is the amount of charge already applied. To meet user power needs, It is a time-of-use electricity price.

8. A multi-functional collaborative control system for distributed energy storage and charging, characterized in that, include: The data acquisition module is used to collect raw data from the grid side, energy storage side, charging side and photovoltaic side, and to perform outlier removal, protocol conversion and data compression on the raw data to output standardized data. The central decision-making module is used to first perform a hard constraint check on energy storage safety to determine the safety status based on the standardized data, then perform scenario threshold adaptation based on the grid load rate and the user's charging urgency to output the scenario type and basic charging power, then generate a preliminary power command through a lightweight reinforcement learning algorithm, and finally perform compliance judgment on the preliminary power command to output the final power command. The distributed execution module is used to calculate the total power demand according to the final power command, allocate energy storage power and adjust charging power according to the scenario type, and output the actual operation data after execution. The feedback module is used to calculate the deviation value based on the actual operating data and the final power command. If the deviation value exceeds the preset threshold, the energy storage power allocation parameters are adjusted and the status information is pushed to the power grid, users and the next round of decision-making to form a closed-loop control.

9. An electronic device, characterized in that, The electronic device includes: The processor and the memory are communicatively connected. The memory is used to store at least one executable instruction executed by the processor, the processor being used to execute the executable instruction to implement the multi-functional collaborative control method for distributed energy storage charging as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-functional collaborative control method for distributed energy storage charging as described in any one of claims 1 to 7.

Images