A source, network, load and storage integrated photovoltaic power generation and energy storage system

Abstract

The application relates to the technical field of photovoltaic systems, and discloses a source-grid-load-storage integrated photovoltaic power generation energy storage system which comprises a photovoltaic power generation unit, an energy storage unit, a photovoltaic controller, an energy storage converter, an on-grid and off-grid switching unit, further comprises a hybrid intelligent management architecture, a hierarchical thermal management protection system and a task execution monitoring mechanism; the hybrid intelligent management architecture comprises a central coordination layer, a grouping autonomous layer and a communication network layer. The source-grid-load-storage integrated photovoltaic power generation energy storage system can effectively break the scheduling bottleneck of the traditional architecture by constructing the central coordination layer, the grouping autonomous layer and the communication network layer, the dynamic grouping mechanism based on the real-time health degree of the battery can maximize the performance value of the batteries with different health states, and finally realizes all-round improvement of the system operation efficiency, the battery service life and the safety protection capability, thereby providing technical support for the large-scale and high-reliable operation of the source-grid-load-storage integrated system.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic system technology, and in particular to an integrated photovoltaic power generation and energy storage system. Background Technology

[0002] With the acceleration of the global energy transition and the deepening of the "dual carbon" goal, photovoltaic (PV) power generation, with its advantages of being clean, low-carbon, and readily available, has seen its share in the energy supply system continue to rise. However, PV power generation is inherently intermittent and volatile, making it difficult to directly match the stable operation of the power grid and the electricity demand of users. Therefore, integrated PV power generation and energy storage systems, which combine power generation, energy storage, load regulation, and grid interaction, have become a core technological solution to address these issues and achieve efficient consumption of new energy sources. These systems can smooth out fluctuations in PV output and participate in grid peak shaving and frequency regulation through energy storage units, while also enabling flexible responses to load-side electricity demand. They are key infrastructure for building new power systems and have been widely applied in various scenarios such as centralized PV power plants, industrial park microgrids, and electric vehicle charging stations.

[0003] However, the existing management architecture and operation mechanism of integrated photovoltaic power generation and energy storage systems still have many problems. Most systems adopt a traditional centralized management architecture, where the central control layer is responsible for the scheduling and monitoring of all battery arrays. When facing large-scale battery clusters, it is prone to problems such as computational overload and response delay, making it difficult to achieve refined collaborative scheduling. Therefore, the task redistribution is relatively crude, and simple allocation is mostly based on the load rate without comprehensively considering key indicators such as battery health and temperature margin. This can easily lead to excessive wear and tear on healthy batteries or overload operation of low-healthy batteries, which not only reduces the overall efficiency of the system but also creates potential safety hazards. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology has the disadvantage of slow management architecture response. To address this, we propose an integrated photovoltaic power generation and energy storage system.

[0005] To achieve the above objectives, this application adopts the following technical solution: an integrated photovoltaic power generation and energy storage system, comprising a photovoltaic power generation unit, an energy storage unit, a photovoltaic controller, an energy storage converter, and a grid-connected / off-grid switching unit, and further comprising a hybrid intelligent management architecture, a hierarchical thermal management and protection system, and a task execution monitoring mechanism; the hybrid intelligent management architecture comprises a central coordination layer, a group autonomy layer, and a communication network layer; the central coordination layer is used to receive grid commands and load demands, generate a global scheduling strategy, and issue power allocation tasks to the group autonomy layer; the central coordination layer is also used to reselect other groups based on the global system status when it detects that a group unit in the group autonomy layer cannot complete the task. The system comprises battery grouping units and task reassignment; the grouping autonomous layer includes multiple battery grouping units, used to dynamically group batteries according to their real-time status such as battery health and remaining capacity, and to allocate and execute tasks from the central coordination layer based on the status of each battery cell within the group; the communication network layer adopts a hybrid network architecture combining wired and wireless communication, used to realize data and command transmission between the central coordination layer and the grouping autonomous layer, as well as between each battery grouping unit; the hierarchical thermal management protection system is configured in the energy storage unit to protect the temperature of the battery cells; and the task execution monitoring mechanism is used to monitor the operating status of the task execution units in real time.

[0006] Preferably, the central coordination layer uses a collaborative scheduling algorithm based on battery health weights to generate scheduling strategies, the group autonomous layer uses a clustering algorithm to dynamically group tasks based on battery health, and the central coordination layer uses a multi-objective optimization algorithm to select target group units when reallocating tasks.

[0007] Preferably, the clustering algorithm is a density-weighted clustering algorithm, which is used to divide battery cells into different levels according to their health status and mix them into groups to achieve balanced performance within the group.

[0008] Preferably, the grouped autonomous layer is configured with an edge computing unit for locally performing battery health assessment and dynamic grouping; when the assessed battery health change exceeds a preset threshold, dynamic adjustment of the grouping is triggered.

[0009] Preferably, the communication network layer includes a CAN bus for communication within the battery group unit, an industrial Ethernet for inter-layer communication, and a private wireless network as a backup link.

[0010] Preferably, the central coordination layer issues instructions to the target group unit through a primary and backup dual communication channel, and the evaluation indicators of the multi-objective optimization algorithm include the real-time health status, remaining capacity, load level, and temperature status of the group unit.

[0011] Preferably, the energy storage unit is equipped with a graded thermal management protection system, which is configured to sequentially perform protective actions such as enhanced cooling, power limitation, and shutdown when the battery temperature reaches multiple incremental preset temperature thresholds.

[0012] Preferably, when the central coordination layer reallocates tasks, it encapsulates the task information into structured data packets conforming to the IEC 61850 standard for transmission.

[0013] Preferably, the system further includes a task execution monitoring mechanism, wherein the central coordination layer is configured to periodically collect real-time operating parameters of the task execution unit, and execute instruction correction or task reassignment when the parameters deviate from the instruction value by more than a preset tolerance.

[0014] Preferably, the real-time operating parameters include output power and state of charge; when the deviation between the real-time power and the command continues for more than a preset time, a correction command is issued; when the state of charge is lower than the discharge protection threshold or higher than the charging protection threshold, the current task is terminated and a reassignment is initiated.

[0015] The technical effects and advantages of this invention are as follows: By constructing a central coordination layer, a group autonomy layer, and a communication network layer, this invention effectively overcomes the scheduling bottleneck of traditional architectures. Its dynamic grouping mechanism based on real-time battery health maximizes the performance value of batteries in different health states, avoiding the "weakest link" effect. The improved distributed consensus algorithm enables efficient collaborative scheduling of large-scale battery arrays, reducing the load on the central control layer. The multi-objective optimized task redistribution mechanism balances system security, equipment lifespan, and load balancing, while the hybrid communication architecture ensures the real-time performance and reliability of data transmission. Ultimately, this invention achieves a comprehensive improvement in system operating efficiency, battery lifespan, and security protection capabilities, providing technical support for the large-scale, highly reliable operation of integrated power generation, grid, load, and energy storage systems. Attached Figure Description

[0016] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0019] Reference Figure 1 As shown, the present invention provides a technical solution: an integrated photovoltaic power generation and energy storage system, comprising a photovoltaic power generation unit, an energy storage unit, a photovoltaic controller, an energy storage converter, a grid-connected / off-grid switching unit, and a hybrid intelligent management architecture; the hybrid intelligent management architecture comprises a central coordination layer, a group autonomy layer, and a communication network layer; the group autonomy layer dynamically groups the batteries according to their real-time health status and grants each group local decision-making authority based on its health status; when a task cannot be processed within a group, the task is assigned to the globally optimal unit.

[0020] Specifically, the working principle of the hybrid intelligent management architecture is as follows: The central coordination layer is responsible for global energy management and macro-level scheduling. This layer receives grid scheduling instructions and load demand information, formulates global energy management strategies, issues task instructions to the group autonomous layers, and monitors the operating status of the entire system. The central coordination layer adopts an improved distributed consensus algorithm, introducing a health weight factor and a dynamic adjacency matrix, which enables efficient management of large-scale battery energy storage arrays. The formula is as follows: Let the state vector of the i-th battery cluster be... Where P is the output power, SOH is the battery health status, and T is the temperature. At time t+1, the state is updated to... ,in Let be the set of neighboring nodes of the i-th node; For dynamic weights, The higher the battery health level, the greater the weight, to prevent battery health nodes from being over-scheduled. For global reference state, Let t be the health status of the j-th battery cluster. It is the sum of the health scores of all neighboring nodes of the i-th battery cluster at time t.

[0021] When the dynamic adjacency matrix The system converges to the global reference state, and the convergence time is shortened to <20ms, thereby meeting the real-time scheduling requirements of large-scale battery arrays.

[0022] The grouped autonomous layer comprises multiple battery grouping units. Each grouping unit consists of 2-4 individual battery cells, dynamically grouped according to battery health (SOH). The grouping strategy employs a density-based fuzzy C-means (DBFCM) clustering algorithm, grouping batteries with high health (SOH ≥ 90%) with those with medium health (80% ≤ SOH < 90%) to ensure complementary battery performance within each group.

[0023] The specific steps are as follows: Step 1, select the core parameters for battery health assessment and normalize each parameter: ,in This represents the original value of the j-th parameter of the i-th battery. The maximum value of the j-th parameter. It is the minimum value of the j-th parameter.

[0024] Step two: Introduce a density function to measure the degree of local clustering of samples (batteries), avoid isolated samples interfering with clustering, and calculate the density factor of the i-th battery. This characterizes the degree of aggregation of the samples within the sample space. , where the indicator function Defined as: ,in The Euclidean distance between the i-th and k-th batteries is calculated when the normalized parameter is 6: , This is the cutoff distance.

[0025] Step 3: Construct a density-weighted fuzzy C-means (FCM) objective function to achieve cluster optimization: Define the objective function of DBFCM and introduce a density factor. Modify traditional FCM to improve clustering robustness: The termination condition is , Let be the membership degree of the i-th battery belonging to class C. , The membership degree is updated through alternating iterations, representing fuzzy coefficients. With cluster center Until the objective function converges: Membership update formula Cluster center update formula The j-th parameter cluster center of class C; .

[0026] Step four: After iterative convergence, cluster centers for the three types of batteries are obtained; finally, each group contains two healthy battery cells and two good battery cells to ensure that the battery performance within the group is matched.

[0027] The communication network layer adopts a hybrid architecture of CAN bus, industrial Ethernet, and 5G or wireless communication to ensure the reliability and real-time performance of data transmission. Intra-group communication uses CAN bus with a communication rate of up to 500kbps, ensuring a response time of <10ms; inter-group communication uses industrial Ethernet to support high-speed data transmission; and global communication uses 5G or wireless communication as a backup link.

[0028] The dynamic grouping mechanism based on real-time battery health achieves accurate battery health assessment through a multi-parameter fusion algorithm. This algorithm integrates voltage, current, temperature, and internal resistance, employing an improved LSTM neural network model to control the SOH estimation error within ±2%. The system regroups batteries every 24 hours or when any battery health change exceeds 5%, ensuring the rationality and effectiveness of the grouping.

[0029] When a task group is unable to complete its assigned power task on time due to unforeseen circumstances or resource constraints, the system quickly triggers a built-in intelligent reallocation process. The central coordination layer first automatically encapsulates the unprocessed tasks into a standardized, structured "task description package," containing key information such as task requirements, execution conditions, and deadlines. Subsequently, based on a continuously updated global status table that records the operating parameters of each group and the overall system resource status in real time, the system initiates a multi-objective optimization matching algorithm. This algorithm, with the core optimization goal of achieving system operational safety and balanced equipment lifespan, comprehensively evaluates multiple key status indicators for each available group in the system during the decision-making process, including real-time health status, remaining capacity, current load level, and temperature margin. Through mathematical modeling and real-time optimization calculations, the algorithm automatically generates an optimal task allocation strategy from a global perspective, ensuring reasonable load distribution while minimizing the risk of single-point overload. Finally, the system generates precise power commands based on this and distributes them to one or more selected, best-performing group nodes, which then take over and execute the remaining tasks, thereby ensuring the system's continuous, stable, and efficient operation.

[0030] The system first uses a dual-end hierarchical threshold verification mechanism to accurately identify abnormal task execution states of groups and avoid false triggering. When the ratio of the current real-time load power of a group to its rated carrying power reaches or exceeds 85% and this state lasts for more than 10 seconds, it is determined that the resources are overloaded and cannot continue to undertake tasks. If any level 2 or above fault occurs in the group, such as the voltage of a single cell exceeding the normal range, the CAN bus data frame loss rate reaching 15% or above, or the battery management unit module failure (there are three levels of faults, with level 2 being a fault that directly affects task execution), it is determined that the task is interrupted due to equipment failure.

[0031] When the average operating temperature of a group reaches 45℃ (Level 2 temperature warning threshold), or the difference between the current temperature of the group and the maximum safe temperature (50℃) (temperature margin) is less than 5℃, in order to protect battery life and system safety, the group is automatically determined to lack the safety margin for task execution. The edge computing unit of the group first completes the local anomaly judgment and synchronously uploads a snapshot of the operating parameters within 10 seconds to the central coordination layer. The central coordination layer reviews the uploaded data and, after confirming that the anomaly status is real and valid, formally triggers the task reassignment process. The overall trigger response time does not exceed 20 milliseconds. After the central coordination layer confirms the trigger, it immediately disassembles and standardizes the unfinished tasks, generating a structured task description package that conforms to the IEC61850 communication standard to ensure that the task information is complete and traceable, consisting of letters and timestamps. The combined proprietary code includes the task generation time and sequence number, used for full-process traceability of the task, clearly defining the specific power value required by the task, and marking the power direction. It includes the duration, start time, and end time of the task, defines the time window for task execution, and marks the voltage level required by the task and the allowable 5% voltage fluctuation range to ensure voltage matching of the receiving group. It is divided into 1-5 levels, with level 1 being the highest priority. For example, the power grid frequency regulation task is level 1, and the peak-valley arbitrage task is level 3. Priority is given to ensuring the landing of high-priority tasks. It records the fault type, fault occurrence time, and key parameter snapshots of the original group to help the new group avoid similar risks. The encapsulated task description packet is transmitted via industrial Ethernet in the form of real-time priority data frames, equipped with CRC cyclic redundancy check function to ensure no data loss.

[0032] After receiving the task description packet, the system scheduling module immediately retrieves the global group status table, which is updated every 500 milliseconds by the central coordination layer and stored in a distributed database. It then performs availability filtering on all groups, removing those that do not meet the criteria. The status table covers 12 real-time operational data items for all groups, with core parameters categorized into four types: Health parameters: average group health (SOH), increase in average internal resistance relative to the initial value, and cumulative battery cycle count; Capacity parameters: remaining charge capacity of the group (remaining capacity), and average state of charge (SOC); Load parameters: current load rate, and the difference between rated power and real-time load (idle power margin); Safety parameters: average operating temperature, temperature margin, cooling system operating status, communication status, and hardware health status.

[0033] Basic function screening directly removes groups with abnormal communication status or hardware failures; health and safety screening removes low-health, high-risk groups with a health level below 80% or an average operating temperature of 45℃; capacity margin screening removes groups with idle power margin less than 80% of the total task requirements, ensuring that the receiving groups have sufficient resource margin.

[0034] For the available groups after screening, the system starts a multi-objective optimization matching algorithm to generate a globally optimal task allocation strategy. It prioritizes groups with high health and large temperature margin to avoid safety risks caused by task execution, takes into account the current load level of each group to avoid long-term overload of a single group, ensures the overall equipment life balance, and prioritizes groups with operating temperature close to 25°C to reduce the damage of high temperature to the battery.

[0035] In the algorithm's execution flow, the scheme is first initialized, generating 50 initial task allocation schemes, each specifying the power ratio of the subtasks to be undertaken by each available group. Next, the schemes are evaluated, and each scheme is comprehensively scored according to the weight ratio of the three-dimensional objectives. The lower the score, the better the scheme. Then, the schemes are iteratively optimized for 50 generations through selection, crossover, mutation, and other algorithmic operations. The iteration stops when the score fluctuation for 5 consecutive generations does not exceed 0.01. The final optimal scheme must satisfy two core constraints: first, the idle power margin of all undertaking groups can cover the subtask requirements; second, the SOC of the discharge task group is not less than 20%, the SOC of the charging task group is not higher than 80%, and the total power of all subtasks is equal to the total requirement of the original task.

[0036] If the optimal solution is a single group taking over the task, the instruction should clearly indicate the task power, execution time, compatible voltage, and upper temperature limit threshold of 45°C; if multiple groups are coordinating to take over, the instruction should be split into multiple sub-instructions according to the power ratio in the solution, and the synchronous execution time of each sub-task should be marked to ensure that the start-up time deviation of each group does not exceed 100 milliseconds. Instructions are simultaneously transmitted via industrial Ethernet and 5G private network. After receiving the instruction, the target group must return an instruction reception receipt within 5 milliseconds. If the main channel does not receive the receipt, the system automatically switches to the backup channel to retransmit, with no more than two retransmissions to ensure instruction delivery. After the target group takes over the task, the system initiates real-time monitoring and closed-loop management to ensure smooth task execution. The central coordination layer collects the target group's real-time power, operating temperature, SOC, and other core parameters every 200 milliseconds. If any abnormality occurs, immediate intervention is initiated. If the real-time execution power deviates from the instruction requirement by more than 10% for more than 5 seconds, a power correction instruction is immediately issued. When the group temperature rises to 45°C, enhanced heat dissipation is automatically activated (liquid cooling system flow rate increased by 50%), while the task power is reduced by 20%. If the discharge group's SOC drops below 20%, or the charging group's SOC rises above 80%, the task for that group is immediately terminated and a secondary redistribution is initiated.

[0037] After the task is completed, the target group needs to upload a task completion report that includes the actual execution power, total duration, and changes in battery status. The central coordination layer will store the report data in the task log and update the global group status table in sync, thus completing the closed-loop management of the entire task redistribution process.

[0038] Example 1: This example is for a large-scale centralized photovoltaic power station energy storage system with a total capacity of 100MW / 400MWh, using lithium iron phosphate battery technology.

[0039] The system hardware configuration includes a photovoltaic power generation unit using 200,000 500Wp monocrystalline silicon photovoltaic modules, forming 400 photovoltaic arrays. The energy storage unit consists of 200 battery clusters, each containing 192 280Ah lithium iron phosphate cells, arranged in a 48-series, 4-parallel configuration. The photovoltaic controller is a centralized MPPT controller. The energy storage converter uses a three-phase, three-wire topology with a power density of 2.5kW / kg.

[0040] The hybrid intelligent management architecture is deployed through a central coordination layer in the central control room of the energy storage power station. A high-performance server serves as the main controller, and a dual-machine hot backup system is configured to ensure system reliability. The central coordination layer is responsible for receiving grid dispatch instructions, photovoltaic power forecast information, and load demand information, and for formulating 24-hour rolling optimization dispatch strategies.

[0041] The grouped autonomous layer divides the 200 battery clusters into 50 grouping units, with each group containing 4 battery clusters. Each group is configured with an independent edge computing unit, using the RK3568 chip, which integrates an NPU (Neural Processing Unit) to support the local execution of deep learning models.

[0042] The communication network layer adopts a three-layer architecture: intra-group communication uses CANFD bus with a communication rate of 2Mbps; inter-cluster communication uses industrial Ethernet with a rate of 1000Mbps; and global communication uses a 5G private network to ensure the real-time performance and reliability of data transmission.

[0043] Based on the dynamic grouping implementation of health status, when the system is initially running, the initial health status benchmark of each battery is established through full capacity testing. The health status assessment algorithm adopts an improved LSTM neural network model, which integrates characteristic parameters of voltage, current, temperature and internal resistance, and the SOH estimation accuracy reaches ±2%.

[0044] The grouping strategy employs a density-based fuzzy C-means (DBFCM) clustering algorithm to categorize batteries into three health levels: healthy batteries (SOH ≥ 90%), good batteries (80% ≤ SOH < 90%), and average batteries (70% ≤ SOH < 80%). The grouping principle is based on complementary health levels, meaning each group contains two clusters of healthy batteries and two clusters of good batteries. The system performs a health assessment and grouping adjustment every 24 hours. Immediate grouping adjustments are triggered when the health level of any battery changes by more than 5%.

[0045] When the power grid issues a peak-shaving task, the central coordination layer first decomposes the task, initially allocating the total task volume according to the number of batteries. After each group receives the task, it performs a secondary allocation based on the health status of the batteries within the group: batteries with higher health status undertake more tasks, and the task sharing ratio is proportional to the health status.

[0046] When a group is unable to complete a task due to battery failure or overload, it sends a task reassignment request to the central coordination layer. The central coordination layer then safely isolates the group, reclaims the incomplete tasks, and generates a detailed task requirement package. From the currently idle or lightly loaded groups, the scheduler automatically selects the most suitable group to take over the task. The core criterion for selection is a comprehensive evaluation value of the group's status; the scheduler directly assigns the task to the group with the best comprehensive evaluation.

[0047] The thermal management system employs an indirect liquid cooling system, with each battery cluster having its own independent liquid cooling circuit. The cooling medium is a 50% ethylene glycol aqueous solution, with the flow rate dynamically adjusted based on battery temperature. The system features three levels of temperature protection: Level 1 warning: Temperature > 40℃, activates the liquid cooling system; Level 2 warning: Temperature > 45℃, reduces charging and discharging power by 50%; Level 3 warning: Temperature > 50℃, stops operation of the affected cluster.

[0048] Example 2: This example is for a distributed photovoltaic energy storage system applied in an industrial park, with a total capacity of 10MW / 20MWh, using lithium battery technology.

[0049] In terms of system hardware configuration, the photovoltaic power generation units are distributed on rooftops and open spaces within the park, with a total installed capacity of 15MW. They utilize string inverters, with a total of 30 500kW inverters. The energy storage units are distributed across six distribution rooms within the park, with each distribution room equipped with one battery container.

[0050] The central coordination layer adopts a cloud-edge collaborative architecture, with the cloud deployed at the park's energy management center and edge computing nodes distributed in various power distribution rooms. The cloud is responsible for global optimization scheduling and strategy formulation, while the edge nodes are responsible for local control and data processing.

[0051] The grouped autonomous layer divides the six battery containers into three group units, with each group containing two containers. Due to the distributed deployment, the battery containers within each group communicate via a 5G network to ensure real-time performance.

[0052] The communication network layer adopts a hybrid architecture, with intra-group communication using CAN bus and 5G backup; inter-group communication and global communication mainly use 5G private network to ensure reliable communication in a distributed environment.

[0053] Considering the characteristics of distributed systems, a lightweight algorithm is used for health assessment, which runs locally on edge nodes. The algorithm integrates basic parameters such as voltage, current and temperature, and achieves rapid assessment through a simplified neural network model. The accuracy of SOH estimation is kept within ±3%.

[0054] The grouping strategy adopts the principle of proximity, prioritizing the grouping of battery containers that are geographically close together to reduce communication latency. It also considers the balance of battery health, ensuring that each group contains batteries in different health states.

[0055] The dynamic task reallocation mechanism of a distributed system has the following characteristics: it supports cross-regional task allocation, fully utilizes the resources of each group, considers transmission losses, incorporates transmission costs during task allocation, supports real-time electricity pricing, and performs deep balancing during off-peak electricity periods. When the energy storage system in a distribution room cannot meet demand due to sudden load changes, it can request support from energy storage systems in other distribution rooms. The support price is dynamically calculated based on factors such as real-time electricity price, transmission distance, and battery health.

[0056] Example 3: This example focuses on an energy storage system for an electric vehicle charging station, with a total capacity of 2MW / 4MWh, employing ternary lithium battery technology. In terms of system hardware configuration, the charging station is equipped with 20 120kW fast charging piles and 10 60kW slow charging piles. The energy storage system uses high-power ternary lithium batteries, supporting high-rate charging and discharging.

[0057] Considering the highly dynamic nature of charging station loads, the system employs a more flexible grouping strategy. Batteries are divided into four groups, each capable of operating independently or collaboratively. A central coordination layer is integrated into the charging station's operation and management system, monitoring charging demand, grid status, and battery status in real time and dynamically adjusting energy storage strategies. For the specific needs of charging stations, the system uses a more refined health assessment method, focusing on battery power characteristics and cycle life. The grouping strategy considers not only health but also battery power capacity and response speed.

[0058] It supports rapid response to demand, with task allocation time of <100ms, takes charging priority into account, reserves capacity for emergency charging needs, and provides support during peak grid periods.

[0059] When multiple charging demands arrive simultaneously, the system intelligently allocates power based on factors such as charging power requirements, battery health, and SOC status. Batteries with high health and sufficient SOC are given priority for high-power charging tasks.

[0060] Example 4: This example combines the technical features of Examples 1, 2, and 3 to provide a comprehensive implementation plan for a photovoltaic, energy storage, and charging microgrid system.

[0061] This system integrates photovoltaic power generation, energy storage, charging, and microgrid control functions, and adopts a hierarchical distributed architecture. The total system capacity is 50MW / 100MWh, including 30MW of photovoltaic power generation, 100MWh of energy storage, and 50 charging piles.

[0062] Building upon traditional methods, this embodiment of the hybrid intelligent management architecture introduces a multi-stakeholder collaboration mechanism. The system involves multiple stakeholders, including photovoltaic operators, energy storage operators, charging station operators, and power grid companies, utilizing blockchain technology to achieve data sharing and profit distribution. Dynamic grouping optimization based on health status includes:

[0063] The system employs a multi-layered grouping strategy. The first layer groups by technology type, such as lithium iron phosphate and ternary lithium batteries, with different groups for each. The second layer groups by health status. The third layer groups by geographical location. Each group is configured with an independent edge computing unit to support local decision-making and rapid response.

[0064] Building upon traditional methods, an options contract mechanism has been introduced. Entities can lock in future energy storage services by signing options contracts. This mechanism improves the predictability of system revenue and provides price protection for users.

[0065] Through the detailed description of the four embodiments above, it can be seen that the present invention proposes a system architecture of centralized coordination and group autonomy, which retains the global optimization capabilities of centralized management while possessing the high reliability and rapid response characteristics of a distributed architecture. Through hierarchical design and intelligent grouping, efficient management of large-scale energy storage systems is achieved. Based on health-based dynamic grouping technology, battery health is used as the core basis for grouping and task allocation. Through real-time health assessment and dynamic grouping adjustment, optimal allocation of battery resources is achieved, improving system efficiency, extending battery life, and realizing optimal resource allocation.

[0066] This invention establishes a multi-dimensional safety protection system from individual units to the entire system, and achieves effective prevention and control of thermal runaway risk through temperature monitoring, health assessment, and fault early warning.

[0067] The technical solution of this invention is not only applicable to newly built energy storage systems, but also to the intelligent transformation of existing energy storage systems. With the continuous development of new energy technologies and the deepening of power market reforms, this system has broad application prospects and promotional value. In the future, with the continuous maturation of new technologies such as artificial intelligence, blockchain, and 5G, the technical solution of this invention will be further improved and upgraded, providing important support for building a clean, efficient, and safe new power system.

[0068] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A photovoltaic power generation and energy storage integrated system, comprising a photovoltaic power generation unit, an energy storage unit, a photovoltaic controller, an energy storage converter, and a grid-connected / off-grid switching unit, characterized in that, It also includes a hybrid intelligent management architecture, a hierarchical thermal management and protection system, and a task execution monitoring mechanism. The hybrid intelligent management architecture includes a central coordination layer, a group autonomy layer, and a communication network layer. The central coordination layer is used to receive power grid instructions and load demands, generate a global scheduling strategy, and issue power allocation tasks to the group autonomy layer. The central coordination layer is also used to reselect other group units and reassign tasks based on the global system status when it detects that a group unit in the group autonomy layer cannot complete the task. The central coordination layer uses a collaborative scheduling algorithm based on battery health weights to generate the scheduling strategy, the group autonomy layer uses a clustering algorithm to dynamically group according to battery health, and the central coordination layer uses a multi-objective optimization algorithm to select target group units when reassigning tasks. The central coordination layer is responsible for overall energy management and macro-level scheduling. It receives grid dispatch instructions and load demand information, formulates global energy management strategies, issues task instructions to the group autonomous layers, and monitors the overall system operation status. The central coordination layer employs an improved distributed consensus algorithm, introducing a health weighting factor and a dynamic adjacency matrix, enabling efficient management of large-scale battery energy storage arrays. The formula is as follows: Let the state vector of the i-th battery cluster be... Where P is the output power, SOH is the battery health status, and T is the temperature. At time t+1, the state is updated to... ,in Let be the set of neighboring nodes of the i-th node; For dynamic weights, The higher the battery health level, the greater the weight, to prevent battery health nodes from being over-scheduled. For global reference state, Let t be the health status of the j-th battery cluster. The sum of the health scores of all neighboring nodes of the i-th battery cluster at time t; When the dynamic adjacency matrix The system converges to the global reference state with a convergence time of <20ms, thus meeting the real-time scheduling requirements of large-scale battery arrays. The clustering algorithm is a density-weighted clustering algorithm, used to divide battery cells into different levels according to their health status and mix them into groups to achieve performance balance within each group. The grouping autonomous layer includes multiple battery grouping units, used to dynamically group the battery cells in the energy storage unit according to their real-time status of battery health and remaining capacity, and to allocate and execute tasks from the central coordination layer according to the status of each battery cell within the group. The communication network layer adopts a hybrid network architecture combining wired and wireless communication, used to realize data and command transmission between the central coordination layer and the grouping autonomous layer, as well as between each battery grouping unit. The hierarchical thermal management protection system is configured in the energy storage unit to protect the temperature of the battery cells, and the task execution monitoring mechanism is used to monitor the operating status of the task execution units in real time.

2. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: The grouped autonomous layer is equipped with an edge computing unit for locally performing battery health assessment and dynamic grouping; when the assessed battery health change exceeds a preset threshold, dynamic adjustment of the grouping is triggered.

3. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: The communication network layer includes a CAN bus for communication within the battery group units, an industrial Ethernet for inter-layer communication, and a private wireless network as a backup link.

4. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: The central coordination layer issues instructions to the target group unit through primary and backup dual communication channels. The evaluation indicators of the multi-objective optimization algorithm include the real-time health status, remaining capacity, load level and temperature status of the group unit.

5. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: The energy storage unit is equipped with a graded thermal management protection system, which is configured to sequentially perform protective actions such as enhanced cooling, power limitation, and shutdown when the battery temperature reaches multiple incremental preset temperature thresholds.

6. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: When redistributing tasks, the central coordination layer encapsulates task information into structured data packets conforming to the IEC 61850 standard for transmission.

7. The integrated photovoltaic power generation and energy storage system according to claim 1, characterized in that: The system also includes a task execution monitoring mechanism. The central coordination layer is configured to periodically collect real-time operating parameters of the task execution units and execute instruction correction or task reassignment when the parameters deviate from the instruction value by more than a preset tolerance.

8. The integrated photovoltaic power generation and energy storage system according to claim 7, characterized in that: The real-time operating parameters include output power and state of charge; when the deviation between the real-time output power and the command continues for more than a preset time, a correction command is issued; when the state of charge is lower than the discharge protection threshold or higher than the charging protection threshold, the current task is terminated and a reassignment is initiated.

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