A method for controlling an energy storage power plant

Abstract

The application provides a kind of energy storage power station control method, it is related to energy storage power station application technical field, can realize the total output power of energy storage power station according to the power indicated by frequency modulation instruction Operation, and keep the equalization of battery pack and energy storage unit state of charge in station, improve the available capacity and service life of energy storage power station.The specific technical scheme is: receiving frequency modulation instruction;With the sum of the total output power of all battery packs equal to the total output power indicated by frequency modulation instruction as constraint, with the equalization of state of charge between each battery pack as target, power optimization allocation is carried out based on the operating state information of each battery pack, to determine the target output power of each battery pack;For each battery pack, according to the target output power corresponding to the battery pack, with the equalization of state of charge between each energy storage unit in the battery pack as target, control each energy storage unit in the battery pack to carry out distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

Description

Technical Field

[0001] This application relates to the field of energy storage power station application technology, and in particular to an energy storage power station control method. Background Technology

[0002] With the continuous advancement of new power system construction, energy storage power stations, with their millisecond to second-level response speed and bidirectional regulation capabilities, are gradually becoming an important resource for grid-side power command tracking. Large-scale energy storage power stations consist of multiple battery packs and multiple energy storage units. Currently, when allocating power in energy storage power stations, power is distributed among the battery packs based on the total output power indicated by the frequency regulation command. However, due to differences in capacity, efficiency, aging, and health status among the battery packs and energy storage units, some battery packs may experience safety issues because their condition is incompatible with the allocated power. Summary of the Invention

[0003] This application provides a control method for an energy storage power station, which can enable the total output power of the energy storage power station to operate according to the power indicated by the frequency regulation command issued by the grid side, while maintaining the balanced state of charge of the battery packs and energy storage units in the station, thereby improving the available capacity and overall cycle life of the energy storage power station.

[0004] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a control method for an energy storage power station is provided, applied to an energy storage power station control system. The energy storage power station control system includes at least one battery pack, and each battery pack includes at least one energy storage unit. The method includes: receiving a frequency modulation command, which indicates the total output power of the energy storage power station; taking the sum of the total output power of all battery packs as equal to the total output power indicated by the frequency modulation command as a constraint, and taking the balance of the state of charge among the battery packs as an objective, optimizing power allocation based on the operating status information of each battery pack to determine the target output power of each battery pack; the operating status information includes the current state of charge of the battery pack; for each battery pack, according to the target output power corresponding to the battery pack, and with the goal of balancing the state of charge of each energy storage unit within the battery pack, controlling the energy storage units within the battery pack to perform distributed coordination, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack.

[0005] This scheme first uses the hard constraint that the sum of the total output power of all battery packs equals the total output power indicated by the frequency regulation command for power optimization allocation. This ensures that the total output power of the energy storage power station operates according to the power indicated by the frequency regulation command issued by the grid side. Simultaneously, the frequency regulation command is first allocated to each battery pack, and then the target output power of each battery pack is further refined and allocated to each energy storage unit within the pack. This hierarchical and progressive allocation method reduces the overall computational complexity of the solution, thereby achieving a fast and accurate response to the frequency regulation power command. Furthermore, power optimization allocation based on the operating status information of each battery pack allows for the allocation of power adapted to its own state, thus preventing safety issues.

[0006] Secondly, this scheme allocates power at the battery pack level with the goal of balancing the state of charge (SOC) among the battery packs, and at the energy storage unit level with the goal of balancing the SOC among the energy storage units within a pack. This maintains a balanced SOC among the battery packs and energy storage units within the energy storage power station. This mechanism effectively suppresses power distribution imbalances and SOC imbalances caused by factors such as capacity differences, efficiency differences, aging, and inconsistent health conditions. It also avoids uneven aging of energy storage units due to differences in charge and discharge depths, thereby improving the lifespan of the battery packs and energy storage units, and ultimately enhancing the usable capacity and lifespan of the energy storage power station.

[0007] In one possible implementation of the first aspect, the target output power of each battery pack is determined by constraining that the sum of the total output power of all battery packs equals the total output power indicated by the frequency modulation command, and aiming at balancing the state of charge among the battery packs. This includes optimizing power allocation based on the operating state information of each battery pack to determine the target output power of each battery pack. The objective function is constructed based on the operating state information of each battery pack, with the goal of balancing the state of charge among the battery packs. Under the condition of satisfying preset constraints, the objective function is solved to obtain the target output power of each battery pack. The preset constraints include that the sum of the total output power of all battery packs equals the total output power indicated by the frequency modulation command.

[0008] In one possible implementation of the first aspect, the preset constraints further include at least one of the following: the discharge power of each battery pack satisfies the constraints of its upper limit and lower limit discharge power; the charging power of each battery pack satisfies the constraints of its upper limit and lower limit charging power; and the state of charge (SOC) value of each battery pack is maintained between the preset lower limit and upper limit SOC value.

[0009] In another possible implementation of the first aspect, the objective function is obtained by weighted summation of the state-of-charge (POC) equalization term and the power fluctuation penalty term; the POC equalization term is related to the deviation of the POC value of each battery pack from the average POC value of all battery packs; the power fluctuation penalty term is related to the variation of the output power of the same battery pack in different control periods.

[0010] In another possible implementation of the first aspect, before solving the objective function, the method further includes: setting a battery state of charge region, and setting corresponding charging and discharging power limits for different state of charge regions; dynamically adjusting the charging and discharging power limits of the battery pack according to the state of charge region in which the battery pack is currently located; and ensuring that the output power of the battery pack meets the dynamically adjusted charging and discharging power limits based on preset constraints.

[0011] In another possible implementation of the first aspect, with the goal of balancing the state of charge (SOC) among the energy storage units within the battery pack, the energy storage units within the battery pack are controlled to perform distributed coordination, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack. This includes: constructing a connectivity relationship representing a neighborhood communication network based on the communication connection relationship between the energy storage units within the battery pack; determining the neighborhood set of each energy storage unit in the neighborhood communication network according to the connectivity relationship; controlling each energy storage unit within the battery pack to exchange SOC values ​​with energy storage units in its respective neighborhood set through the neighborhood communication network; and controlling each energy storage unit to iteratively update its current SOC value and the SOC values ​​of the exchanged neighboring energy storage units using a preset consensus algorithm until the SOC values ​​of each energy storage unit converge to equilibrium, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack.

[0012] In another possible implementation of the first aspect, the consensus algorithm includes a first-order discrete-time consensus algorithm that introduces a power constraint term; the power constraint term is related to the deviation between the total output power of the energy storage units in the battery pack and the target output power of the battery pack, and is used to drive the deviation to converge to zero during the iteration process; the initial value of the consensus algorithm is set as follows: the sum of the initial output power of each energy storage unit is equal to the target output power, and the power deviation estimate of each energy storage unit is initialized to zero.

[0013] In another possible implementation of the first aspect, the energy storage power station control system includes a battery pack control module and a corresponding energy storage unit control module. Receiving frequency modulation commands includes: receiving frequency modulation commands through the battery pack control module.

[0014] With the total output power of all battery packs equal to the total output power indicated by the frequency modulation command as a constraint, and with the goal of balanced state of charge among the battery packs, power optimization allocation is performed based on the operating status information of each battery pack to determine the target output power of each battery pack. This includes: through the battery pack control module, with the total output power of all battery packs equal to the total output power indicated by the frequency modulation command as a constraint, and with the goal of balanced state of charge among the battery packs, power optimization allocation is performed based on the operating status information of each battery pack to determine the target output power of each battery pack, and the target output power of each battery pack is sent to the energy storage unit control module corresponding to each battery pack.

[0015] For each battery pack, based on the target output power corresponding to the battery pack, and with the goal of balancing the state of charge among the energy storage units within the battery pack, the energy storage units within the battery pack are controlled to perform distributed coordination, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack. This includes: for each battery pack, receiving the target output power of the battery pack from the battery pack control module through the corresponding energy storage unit control module, and based on the target output power corresponding to the battery pack, with the goal of balancing the state of charge among the energy storage units within the battery pack, controlling the energy storage units within the battery pack to perform distributed coordination, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack.

[0016] Secondly, a power storage station control system is provided, comprising: a battery pack control module and an energy storage unit control module corresponding to the battery pack; the battery pack control module is used to receive frequency modulation commands; with the total output power of all battery packs equal to the total output power indicated by the frequency modulation commands as a constraint, and with the balance of state of charge among the battery packs as the goal, to optimize power allocation based on the operating status information of each battery pack, determine the target output power of each battery pack, and send the target output power of each battery pack to the energy storage unit control module corresponding to each battery pack; the energy storage unit control module is used to, for each battery pack, receive the target output power of the battery pack sent by the battery pack control module, and, based on the target output power of the battery pack, with the balance of state of charge among the energy storage units in the battery pack as the goal, control the energy storage units in the battery pack to perform distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0017] Thirdly, an electronic device is provided, comprising: a memory and one or more processors; the memory and the processors are coupled; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the energy storage power station control method described in any of the first aspects above.

[0018] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform the energy storage power station control method described in any of the first aspects.

[0019] Fifthly, a computer program product is provided, which, when run on a computer, causes the computer to execute the energy storage power station control method described in any of the first aspects above.

[0020] It is understood that the beneficial effects achieved by the energy storage power station control system described in the second aspect, the electronic equipment described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect can be referred to the beneficial effects in the first aspect and any of its possible design embodiments, and will not be repeated here. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a control method for an energy storage power station provided in an embodiment of this application; Figure 2 A schematic diagram of a hierarchical collaborative control architecture for an energy storage power station provided in an embodiment of this application; Figure 3 A schematic diagram illustrating a range of states of charge provided in an embodiment of this application; Figure 4 A schematic diagram illustrating how the actual output power of an energy storage power station follows the commanded power, as provided in an embodiment of this application. Figure 5 A schematic diagram illustrating the change in the state of charge of an energy storage unit within a battery pack during a scheduling period, provided as an embodiment of this application; Figure 6 This is a schematic diagram comparing the standard deviation of a distributed control method and a centralized control method as a function of the scheduling period, provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0023] With the continuous advancement of new power system construction, energy storage power stations, with their millisecond to second-level rapid response capabilities and flexible bidirectional adjustment characteristics, are gradually becoming an important resource for grid-side power command tracking. Large-scale energy storage power stations typically adopt a multi-level control architecture, including multiple battery packs and multiple energy storage units. Due to multiple factors such as capacity differences, efficiency differences, inconsistencies in aging and health status, communication latency, and information packet loss, simply allocating the total output power indicated by the frequency regulation command among the battery packs can easily lead to safety issues caused by some battery packs not being able to match the allocated power due to their own condition. Therefore, this application provides a control method for an energy storage power station, applied to an energy storage power station control system. The energy storage power station control system includes at least one battery pack, and each battery pack includes at least one energy storage unit. The method includes: receiving a frequency modulation command, which indicates the total output power of the energy storage power station; taking the sum of the total output power of all battery packs as equal to the total output power indicated by the frequency modulation command as a constraint, and taking the balance of the state of charge among the battery packs as the goal, optimizing the power allocation based on the operating status information of each battery pack to determine the target output power of each battery pack; the operating status information includes the current state of charge of the battery pack; for each battery pack, according to the target output power corresponding to the battery pack, taking the balance of the state of charge among the energy storage units in the battery pack as the goal, controlling the energy storage units in the battery pack to perform distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0024] This energy storage power station control method first uses the hard constraint that the sum of the total output power of all battery packs equals the total output power indicated by the frequency regulation command for power optimization allocation. This ensures that the total output power of the energy storage power station operates according to the power indicated by the frequency regulation command issued by the grid side. Simultaneously, the frequency regulation command is first allocated to each battery pack, and then the target output power of each battery pack is further refined and allocated to each energy storage unit within the pack. This hierarchical and progressive allocation method reduces the overall computational complexity of the solution, thereby achieving a fast and accurate response to the frequency regulation power command. Furthermore, power optimization allocation based on the operating status information of each battery pack allows for the allocation of power adapted to its own state, thus preventing safety issues.

[0025] Secondly, this energy storage power station control method aims to achieve balanced state of charge (SOC) among battery packs at the battery pack level and balanced SOC among energy storage units at the energy storage unit level, thereby maintaining a balanced SOC between battery packs and energy storage units within the energy storage power station. This mechanism effectively suppresses power distribution imbalances and SOC imbalances caused by factors such as capacity differences, efficiency differences, aging, and inconsistent health conditions, and avoids uneven aging of energy storage units due to differences in charge and discharge depths, thus improving the service life of battery packs and energy storage units, and ultimately increasing the usable capacity and overall cycle life of the energy storage power station.

[0026] In addition, other technical solutions have been proposed in other implementations. Among these solutions, a two-layer multi-objective optimization strategy for battery energy storage systems considering frequency regulation response characteristics has been proposed. The upper layer of this strategy decomposes the frequency regulation command according to its frequency components, assigning the high-frequency components to energy storage devices with faster response speeds. The lower layer establishes a multi-objective optimization model for each energy storage power station, comprehensively considering frequency regulation economy and state-of-charge balance, and introducing an immunity coefficient related to the remaining frequency regulation capacity to suppress lifespan loss caused by long-term full-load or idle operation of individual power stations. Finally, a multi-objective genetic algorithm is used to obtain the Pareto solution set, from which the optimal output of each power station is selected.

[0027] This strategy measures its performance using a comprehensive performance index comprised of frequency modulation relevance, accuracy, and latency. A comparison with dynamic proportional allocation methods is presented using multiple types of battery energy storage systems as examples. Results show that this method can improve net profit and reduce the standard deviation of the state of charge (SOC) among power stations. However, the key optimization variables and equilibrium objectives of this scheme are concentrated at the power station level, primarily addressing the SOC differences and economic efficiency among multiple power stations. It does not provide a unit-level distributed coordination and real-time balancing strategy for high-dimensional heterogeneous groups of numerous energy storage units within a single energy storage power station. Therefore, while satisfying station-level power commands, it is difficult to effectively constrain power jumps among energy storage units within the station, nor can it prevent overcharging or over-discharging of local energy storage units. Overall, this method emphasizes two-layer optimization and inter-station balancing at the multi-power station level, but it lacks sufficient coverage for online consistent convergence of SOC and fine-grained power allocation within a single power station.

[0028] Several alternative approaches exist for achieving power command tracking and state of charge (SCC) coordination. These include, but are not limited to: Approach 1: Employing a centralized optimization model across the entire site, solving for command tracking and SCC balance as a joint objective in a single step to directly obtain the power command for all energy storage units; Approach 2: Using a rule-based method based on droop control, capacity-weighted allocation, and SCC deviation correction. While these two approaches can achieve basic command response or inter-site balancing under certain conditions, they generally suffer from the following shortcomings: the computational complexity of the centralized optimization model increases dramatically with the number of energy storage units, making real-time performance and scalability difficult to guarantee; although the rule-based method is computationally simple, it is difficult to embed strict unit-level safety constraints, failing to ensure online consistent convergence of the SCC of all energy storage units within the site. Especially in engineering scenarios where external frequency-modulated power commands are the sole input, and real-time tracking and synchronous realization of unit-level SCC balance within the battery pack are required, these two alternative approaches struggle to simultaneously meet comprehensive indicators such as accurate tracking, online balancing, and constraint feasibility, thus failing to achieve the technical effects and application value of the aforementioned energy storage power station control methods.

[0029] Compared with other technical solutions, the energy storage power station control method and system provided in this application achieves an organic integration of centralized optimization and distributed coordination by constructing a two-layer collaborative architecture of battery pack control layer and energy storage unit control layer. In the upper layer (i.e., the battery pack control layer), the battery pack is used as the optimization object for centralized solution, with minimizing command tracking error as the core objective. Simultaneously, it embeds the state of charge (SOC) balancing objective and power change smoothing objective. Under multiple constraints such as charging and discharging power limits, SOC safety boundaries, and dynamic adjustment of SOC zones, the target output power and state reference of each group are obtained, ensuring the accuracy and consistency of the station-level external power response at the global level. In the lower layer (i.e., the energy storage unit control layer), a distributed consistency mechanism is introduced with energy storage units as the control object. Each unit only needs to exchange local information through neighborhood communication to finely allocate the group-level target power to each unit. Under the condition of strictly meeting power limits and energy boundaries, online consistent convergence of the SOC of each energy storage unit within the group and rapid execution of power commands are achieved. The aforementioned layered collaborative method can achieve high-precision, low-latency power command tracking and online balancing of the entire station's state of charge without changing the external interface of the energy storage power station. This effectively improves available capacity and battery life utilization, reduces operation and maintenance costs, and enhances scalability and engineering applicability in large-scale sites and complex operating environments. It provides a unified and robust technical path for the real-time control and large-scale deployment of energy storage power stations.

[0030] The energy storage power station control method provided in this application embodiment can be applied to the energy storage power station control system, which may include a battery pack control device and an energy storage unit control device, forming a two-level collaborative control architecture.

[0031] The energy storage power station comprises multiple battery packs, each containing multiple energy storage units. A battery pack control device communicates with each battery pack to collect operational status information and issue control commands. An energy storage unit control device is deployed within each battery pack and communicates with each energy storage unit within that pack to collect operational status information and control the output power of each energy storage unit.

[0032] Specifically, the battery pack control device serves as a unified access point for frequency regulation commands. It acquires the operating status information of each battery pack and receives the frequency regulation commands issued by the scheduler. It takes the total output power of all battery packs being equal to the total output power indicated by the frequency regulation command as a constraint, and aims at balancing the state of charge among the battery packs. Based on the operating status information of each battery pack, it performs power optimization allocation, determines the target output power of each battery pack, and publishes the target output power of each battery pack to the energy storage unit control device through a unified interface and receives the operating feedback data of each battery pack.

[0033] The energy storage unit control device can be deployed inside each battery pack. It can receive the target output power of the battery pack from the battery pack control device, and control the distributed coordination of each energy storage unit in the battery pack based on the target output power of the corresponding battery pack, with the goal of balancing the state of charge among the energy storage units in the battery pack. The sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0034] In the control system of this energy storage power station, through the coordinated operation of the battery pack control device and the energy storage unit control device, the energy storage power station can accurately respond to the grid frequency regulation command while maintaining the balance of the state of charge of the batteries throughout the station from between battery packs to within the packs (between energy storage units). In one possible implementation, the energy storage unit control device may not be deployed inside each battery pack, but rather as a centralized control node that communicates with each energy storage unit within each battery pack to collect and control the operating status information of all energy storage units.

[0035] The following describes in detail, with reference to the accompanying drawings, the energy storage power station control method provided in this application. This method integrates a distributed consensus and coordination mechanism, which can simultaneously achieve precise tracking of frequency regulation commands and online balancing of the state of charge of energy storage units. For example, as shown... Figure 1 As shown, the method includes steps S101 to S103.

[0036] S101, The energy storage power station control system receives a frequency modulation command, which is used to indicate the total output power of the energy storage power station.

[0037] For example, the battery pack control device in the energy storage power station control system can receive frequency regulation commands issued by the grid side. When the power system frequency fluctuates, the grid dispatch center can generate frequency regulation commands based on the frequency deviation and issue them to the energy storage power station. The battery pack control device in the energy storage power station control system, as a unified access point for the frequency regulation commands, can receive and parse the commands to obtain the total active power value that the energy storage power station needs to output in the current control cycle, which serves as the total benchmark for subsequent power optimization allocation.

[0038] S102. The energy storage power station control system takes the sum of the total output power of all battery packs as a constraint that it is equal to the total output power indicated by the frequency regulation command, takes the balance of the state of charge among the battery packs as the goal, performs power optimization allocation based on the operating status information of each battery pack, and determines the target output power of each battery pack.

[0039] The following example will be used to explain step S102 in detail.

[0040] In some embodiments, the energy storage power station control system is logically divided into a battery pack control layer and an energy storage unit control layer, forming a hierarchical collaborative control architecture. The functions of the battery pack control layer can be executed by the battery pack control device; the functions of the energy storage unit control layer can be executed by the energy storage unit control device.

[0041] The battery pack control layer, serving as the unified access point for frequency regulation commands in the energy storage power station, undertakes station-level coordination and optimization decision-making functions. Within this layer, the battery pack control device acquires the operating status information of each battery pack (including operating parameters and state variables), which may include, but is not limited to, the current state of charge, available energy, power limits, and efficiency coefficient. Based on the operating parameters and state variable information, the battery pack control device establishes and solves a centralized optimization problem, calculating the target output power that each battery pack should undertake in the current control cycle, and publishes the target output power of each battery pack to the lower layer (i.e., the energy storage unit control layer) using a unified command interface. Simultaneously, the battery pack control device also receives execution feedback from the energy storage unit control layer. This feedback mainly includes information such as the actual total output power of the energy storage units within each battery pack, the average state of charge, and whether power or energy boundaries have been reached, which is used for closed-loop correction of optimization decisions in the next optimization cycle.

[0042] In the energy storage unit control layer, the energy storage unit control device can be located inside each battery pack and consists of multiple energy storage units with local measurement and execution functions. Each energy storage unit is responsible for collecting its own state of charge and adhering to its own preset power and energy safety boundaries. After the energy storage unit control device receives the group-level power command (i.e., the target output power of the battery pack issued by the battery pack control layer), the energy storage units exchange information through a neighborhood communication network and use a consensus algorithm to iteratively adjust their respective output power, ultimately making the state of charge of all energy storage units in the group tend to be consistent, and the total output power equal to the target output power of the battery pack issued by the battery pack control device.

[0043] In terms of communication, a centralized connection can be used between layers (between the battery pack control layer and the energy storage unit control layer) to ensure the rapid issuance of pack-level power commands. Within a pack (i.e., between energy storage units within the battery pack), a neighborhood connection is used (i.e., each energy storage unit only communicates with its adjacent energy storage units), and a unified clock is used for timing synchronization and timestamp management to ensure the consistency and real-time performance of data and commands during cross-layer transmission.

[0044] For example, this application provides a schematic diagram of a hierarchical collaborative control architecture for an energy storage power station, such as... Figure 2As shown, the architecture includes a battery pack control layer and an energy storage unit control layer. In this example, the battery pack control layer includes X battery packs, numbered sequentially from battery pack 1 to battery pack X, where X is an integer greater than 1, such as X = 2, ..., i, ..., 10; each battery pack includes multiple energy storage units, numbered sequentially from energy storage unit 1 to energy storage unit Y, such as Y = 1, ..., i, ..., m.

[0045] First, the energy storage power station receives a frequency regulation command. This command is then directly sent to the control node of the battery pack control layer, such as battery pack i. As the hub for power distribution and control, battery pack i, after receiving the overall frequency regulation command, transmits it level by level to battery pack 1 and battery pack X through the information transmission link, thereby completing the command connection between all X battery packs in the station.

[0046] Each battery pack interacts with its underlying energy storage units via a bidirectional data exchange link. Taking battery pack 1 as an example, battery pack 1 is responsible for sending control parameters to energy storage units 1 to m within battery pack 1, and simultaneously receiving operational data transmitted back from the energy storage units; battery pack i and battery pack X independently manage their own energy storage units in the same manner.

[0047] In the energy storage unit control layer, energy storage units exchange information through a neighborhood communication network. Each energy storage unit communicates only with its neighboring energy storage units. Taking battery pack 1 as an example, battery pack 1 contains Y energy storage units, numbered sequentially from energy storage unit 1 to energy storage unit Y, where Y is an integer greater than 1. In this neighborhood communication network, energy storage unit 1 is adjacent to energy storage unit 2, energy storage unit 2 is adjacent to energy storage unit 1 and energy storage unit 3, and so on. Energy storage unit i is adjacent to energy storage unit i-1 and energy storage unit i+1 (where 1 < i < Y), and energy storage unit Y is adjacent to energy storage unit Y-1. Each energy storage unit exchanges state information such as state of charge with its neighboring energy storage units through this neighborhood communication network, and iteratively updates based on a consensus algorithm to drive the state of charge of each energy storage unit in the group to gradually approach equilibrium.

[0048] The topology of the neighborhood communication network is not limited to a chain-like sequential connection. The neighborhood set of each energy storage unit can be flexibly determined according to the actual communication wiring or preset communication relationships. As long as the entire communication network remains connected, the consensus algorithm can drive the state convergence of the entire group of energy storage units through neighborhood information exchange. For example, Figure 2 As shown, energy storage unit 1 can be adjacent to energy storage unit i and energy storage unit m, where 1 < i < m ≤ Y. In this example, the neighborhood set of energy storage unit 1 includes energy storage unit i and energy storage unit m, and energy storage unit 1 exchanges state information such as state of charge values ​​with energy storage unit i and energy storage unit m bidirectionally.

[0049] The entire control process can be summarized as follows: the dispatching side sends frequency modulation commands to the battery pack control layer. The battery pack control layer, through centralized optimization, breaks down the total output power into the target output power of each battery pack and sends them to all 10 battery packs one by one. Each battery pack then sends control parameters to each of its own energy storage units through bidirectional data interaction within the group. The energy storage units then execute charging and discharging actions in layers to jointly achieve the target output power. At the same time, each lower-level energy storage unit sends its own operating data, such as voltage, state of charge, and actual output power, back to its respective battery pack, forming a complete closed-loop management system.

[0050] In some embodiments, the battery layer control device in the energy storage power station control system can construct an objective function with the goal of balancing the state of charge among battery packs based on the operating status information of each battery pack, and solve the objective function under preset constraints to obtain the target output power of each battery pack. The preset constraints include: the sum of the total output power of all battery packs is equal to the total output power indicated by the frequency modulation command.

[0051] In this way, by solving the objective function that aims at balancing the state of charge among battery packs, the output power that each battery pack should bear at the current moment can be obtained, thus providing a clear power allocation for the lower energy storage unit control layer, ensuring charge balance among battery packs, and meeting the total output power requirements indicated by the frequency modulation command.

[0052] For example, the embodiments of this application are aimed at the operation scenario of large-scale energy storage power stations participating in grid frequency regulation. Combining battery operation mechanism and safe operation criteria, with the overall goal of accurate response of the entire station to frequency regulation commands, a centralized optimization model of the battery pack control layer is established by comprehensively considering the balance of state of charge among battery packs and the smoothness of changes in the output power of each battery pack.

[0053] This centralized optimization model includes an objective function and constraints. By solving the objective function in each control cycle, the battery pack control layer can determine the output power command for each battery pack in the current control cycle and issue it to the corresponding battery pack through a unified command interface. Specific details include S201~S203: S201. Construct the objective function. This objective function consists of two weighted terms: a state-of-charge equilibrium term and a power jump penalty term.

[0054] The state-of-charge (POC) balancing term measures the deviation between the current POC of each battery pack and the average POC of all battery packs in the entire station. POC balancing among energy storage battery packs refers to adjusting the output power of each battery pack during power optimization based on the deviation between its current POC and the average POC of all battery packs. This ensures that battery packs with higher POCs discharge first when the energy storage station outputs power, and battery packs with lower POCs charge first when absorbing power.

[0055] In other words, when an energy storage power station needs to output power (discharge), the objective function prompts battery packs with higher state of charge (SOC) to undertake more discharging tasks; when the energy storage power station needs to absorb power (charge), it prompts battery packs with lower SOC to undertake more charging tasks. By continuously adjusting the output power of each battery pack, the gap between its SOC and the average value is narrowed, thereby achieving SOC balance among the energy storage battery packs.

[0056] As operating time increases, the state of charge of each battery pack will gradually become more consistent, thereby avoiding situations such as overcharging or over-discharging of individual battery packs and premature exit from power optimization allocation.

[0057] The power jump penalty term is used to suppress drastic fluctuations in the output of the same battery pack between adjacent control periods. This term is introduced because if the power allocated to the battery pack surges or drops abruptly, causing frequent switching between charge and discharge states, it will not only accelerate the loss of active materials in the battery's electrodes but also affect the lifespan of the power electronic devices controlling the battery pack, thus shortening the battery pack's lifespan. Therefore, while pursuing a balanced state of charge, the objective function forces the output power of each battery pack to remain relatively smooth over time by penalizing power jumps.

[0058] The objective function for optimal power allocation is established by a weighted sum of the state-of-charge equilibrium term and the power jump penalty term. The calculation expression for the objective function for optimal power allocation can be: (1); In equation (1), Weighting coefficients for energy storage state of charge balance; N This represents the total number of battery packs. For the collected battery pack j Current state of charge; For battery pack j The state of charge value at the beginning of the current control cycle. This represents the average state of charge (SOC) of all battery packs. The weighting coefficient for the battery pack power jump penalty; For the collected battery pack j Current active power output value; This is the average value of the current active power output of all collected battery packs.

[0059] min: The minimum value operator. This means finding a solution in the given constraints that makes the objective function... f The solution that takes the smallest value. e: natural constant, the base of the exponential function. || is the absolute value operator.

[0060] To operate at the power obtained according to the frequency modulation control strategy, the battery pack at the next moment... j The calculated expression for the acquired state of charge value can be: (2); In equation (2), For battery pack j Rated capacity, The control period is the time interval between the current adjustment and the next adjustment.

[0061] The objective function, whose variables are in absolute values, is difficult to solve. Therefore, a linearization approach is used to transform the objective function into a mixed-integer linear programming problem without absolute values. The linearization approach involves introducing two new variables. , This replaces the absolute value part of the objective function, and corresponding constraints are set for the two new variables based on their absolute values. Simultaneously, let... ,in For discharge power, The charging power. The corresponding constraint can be expressed as: (3); (4); (5); (6); At this point, the variables , , , Substituting the original objective function (1) into the equation, we can obtain the transformed objective function: (7); S202. Construct preset constraints.

[0062] To ensure the feasibility and safety of the optimization results, the battery pack control layer also needs to meet the following constraints when performing power allocation optimization: the sum of the output power of all battery packs is equal to the command power issued by the scheduling command; the discharge power and charging power of each battery pack have maximum / minimum limits; and the SOC of each battery pack is between the upper and lower limits of the allowable range.

[0063] In other words, the preset constraints also include at least one of the following: the discharge power of each battery pack meets the constraints of its upper and lower discharge power limits; the charging power of each battery pack meets the constraints of its upper and lower charging power limits; and the state of charge (SOC) value of each battery pack is maintained between the preset lower and upper SOC values.

[0064] By setting upper and lower limits for the discharge and charging power of each battery pack, safety issues such as overheating and overcurrent caused by overload charging and discharging can be effectively prevented, power electronic devices can be protected from damage due to overload, and the battery pack can be guaranteed to operate safely within its rated operating range. By constraining the state of charge (SOC) value of each battery pack within preset upper and lower limits, overcharging or over-discharging can be prevented, reducing irreversible losses of active materials in the battery electrodes, thereby extending the battery pack's lifespan and improving the available capacity and operational reliability of the energy storage power station.

[0065] For example, in addition to the newly added variable constraints mentioned above, constraints for the actual operation of the battery pack are also required. These constraints can be expressed as follows: (8); In equation (8): This is the sum of the output power of all battery packs, which is the power that the dispatch command requires the energy storage power station to output (i.e., the commanded power). and Battery packs j The minimum and maximum values ​​of the discharge power; and Battery packs j The minimum and maximum values ​​of the charging power; and These represent the minimum and maximum values ​​of the battery pack's state of charge, respectively.

[0066] The above constraints together constitute the basic constraints of the centralized optimization model for the battery pack control layer. By combining the objective function and the constraints, and solving the multi-objective function in each control cycle, the output power that each battery pack should bear at the current moment can be obtained, thus providing a clear power allocation for the lower-level energy storage unit control layer.

[0067] S203, Dynamic adjustment of power boundaries based on SOC partitioning.

[0068] To further extend battery life, this application implements refined zoning management of the battery pack's operating conditions based on its state of charge (SOC). Batteries with an SOC consistently maintained between 30% and 50% have a longer lifespan than those consistently maintained between 70% and 90% or 20% and 40%. Based on this characteristic, battery SOC zones can be set, with corresponding charge / discharge power limits for each zone. The charge / discharge power limits are dynamically adjusted according to the current SOC zone of the battery pack. Under preset constraints, the battery pack's output power meets the dynamically adjusted charge / discharge power limits. Thus, the actual upper limit of the battery pack's charge / discharge power is no longer a fixed constant, but rather an adaptive boundary value based on its own SOC zone, thereby improving the battery pack's lifespan.

[0069] For example, the state of charge range of a battery pack can be divided into multiple intervals, such as... Figure 3 As shown, the states of charge (SOC) are, in order: extremely high range (90%~100%), high range (80%~90%), standard range (20%~80%), low range (10%~20%), and extremely low range (0%~10%). Based on the battery's current SOC range, the maximum allowable charge and discharge power is dynamically set to keep the battery's operating point as close as possible to the standard range.

[0070] Understandably, not only battery packs can set their own state of charge (SOC) zones, but energy storage units can also have their SOC zones set, with corresponding charge and discharge power limits for each zone. For example, energy storage units can also be configured with SOC zones such as... Figure 3 The diagram shows five charging zones. Taking charging as an example, energy storage units in the extremely high value zone are prohibited from charging to prevent overcharging damage. For energy storage units in the high value zone, to prevent excessive charging power from causing the state of charge to exceed the boundary and enter the extremely high value zone, their maximum charging power is limited to a preset value. For example, the maximum charging power of the energy storage unit... For energy storage units operating within the standard, low, and extremely low value ranges, the charging power can be relaxed to the rated maximum value to fully utilize their energy receiving capabilities. For example, the charging power of the energy storage unit... .

[0071] Through the aforementioned dynamic adjustment strategy, the upper limit of the actual charging and discharging power of each energy storage unit is no longer a fixed constant, but rather a boundary value that adaptively changes according to the range of its own state of charge. This boundary value serves as the basis for correcting the upper and lower power limits in S202. Substituting it into the lumped optimization model ensures that the optimization results, while satisfying the total power command, naturally guide each battery pack back to the standard operating range, effectively improving battery lifespan.

[0072] Combining S201 to S203, the battery pack control layer aims to minimize the objective function in each control cycle. Under multiple constraints such as power balance, charge and discharge limits, state of charge safety boundary, and dynamic power boundary based on SOC partitioning, it solves the centralized optimization model to obtain the target output power undertaken by each battery pack and sends it to the corresponding battery pack, thus completing the optimization decision task of this layer.

[0073] S103. For each battery pack, the energy storage power station control system controls the distributed coordination of each energy storage unit in the battery pack based on the target output power of the battery pack and with the goal of balancing the state of charge among the energy storage units in the battery pack. The sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0074] For example, after the energy storage unit control device obtains the target output power of each battery pack issued by the battery pack control device, for each battery pack, the energy storage unit control device in the energy storage power station control system can control the energy storage units in the battery pack to perform distributed coordination based on the target output power corresponding to the battery pack and with the goal of balancing the state of charge among the energy storage units in the battery pack, so that the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0075] The distributed coordination process specifically includes the following steps: First, the energy storage unit control device in the energy storage power station control system can construct a connectivity relationship representing a neighborhood communication network based on the communication connection relationship between each energy storage unit within the battery pack, and determine the neighborhood set of each energy storage unit in the neighborhood communication network according to this connectivity relationship. Each energy storage unit only exchanges information bidirectionally with energy storage units within its neighborhood set.

[0076] Then, the energy storage unit control device in the energy storage power station control system can control each energy storage unit in the battery pack to exchange state of charge (SOC) values ​​with energy storage units in their respective neighborhood sets through the neighborhood communication network. After obtaining its own current SOC value and the SOC values ​​of the neighboring energy storage units obtained through the exchange, each energy storage unit uses a preset consensus algorithm to iteratively update and gradually adjust its output power.

[0077] This consensus algorithm employs a first-order discrete-time consensus algorithm that incorporates a power constraint term. This power constraint term is related to the deviation between the total output power of the energy storage units within the battery pack and the target output power of the battery pack. It is used to drive this deviation to gradually converge to zero during the iteration process, thereby ensuring that the total output power within the pack strictly matches the target command issued from the upper layer.

[0078] The initial values ​​for the consensus algorithm are set as follows: the sum of the initial output power of each energy storage unit is equal to the target output power of the battery pack, and the power deviation estimate of each energy storage unit is initialized to zero.

[0079] Through the above iterative coordination process, the state of charge of each energy storage unit in the group gradually converges to equilibrium. At the same time, the sum of the output power of all energy storage units in the battery group is exactly equal to the target output power of the battery group, realizing the distributed and refined execution of group-level power commands and the online autonomous equilibrium of the state of charge.

[0080] The following example illustrates step S103 in detail. In some examples, the energy storage power station control system can establish a power allocation model based on a consensus algorithm at the energy storage unit control layer, based on the group-level optimization results (i.e., the output power responsible for each battery group) issued by the battery group control layer. The purpose is to refine the allocation of the output power responsible for the battery group to each energy storage unit within the group, while simultaneously achieving online balancing of the state of charge among the energy storage units.

[0081] A consensus algorithm refers to the process where the states of various agents gradually converge through continuous information exchange. This embodiment, tailored to the application characteristics of energy storage power stations, uses the state of charge (SOC) of each energy storage unit as the state variable for the consensus algorithm. Relying on an intra-group neighborhood communication network, it enables each energy storage unit to gradually achieve SOC convergence through iterative coordination, while satisfying the total power constraint. Specific details include S301~S303: S301. Construct the communication matrix.

[0082] First, the power station control system can construct a connectivity relationship representing the neighborhood communication network based on the communication connection relationship between each energy storage unit in the battery pack; and determine the neighborhood set of each energy storage unit in the neighborhood communication network according to the connectivity relationship.

[0083] For example, the connectivity between energy storage units within a group can be represented by connectivity relationships (e.g., a connectivity graph). G(V,E) To provide a mathematical description. Among them... V Represents a set of vertices, including connectivity. G All energy storage unit nodes in the group (i.e., within the group), each node represents an energy storage unit with communication and computing capabilities; E Represents an edge set, containing connectivity relationships. GThe edges connecting all nodes in the array represent the bidirectional information exchange between the corresponding two energy storage units. Based on the connectivity... G The Laplace matrix can be obtained. L Its calculation expression is as follows: (9); In equation (9): diagonal elements Non-diagonal elements; This represents the element in the i-th row and j-th column of the Laplace matrix L; a ij Let be the element in the i-th row and j-th column of the adjacency matrix A. The adjacency matrix A is a sparse square matrix with all diagonal elements being 0 and the number of rows equal to the total number of nodes. It is used to determine whether there is an edge connecting corresponding nodes. The expression is as follows: (10); In formula (10): a ij =1, node i At node j There are connections between them; a ij =0, node i At node j There is no connection between them.

[0084] 302. Construct a consensus algorithm.

[0085] There are many ways to express consensus algorithms, including continuous-time and discrete-time forms, first-order and second-order forms, etc. Considering cost and feasibility, communication in large-scale energy storage power stations is generally triggered once at fixed time intervals. Therefore, this embodiment selects a first-order discrete-time consensus algorithm.

[0086] In the process of power optimization allocation, the goal of this level is to achieve a balance of the state of charge (SOC) of each energy storage unit within the group. Therefore, the SOC of each energy storage unit is directly used as the state variable of the algorithm. In summary, the embodiments of this application design a first-order discrete-time consensus algorithm, the calculation expression of which is: (11); In equation (11): k It is discrete time; For energy storage units j In the k The charged state value (i.e., state variable) at the next iteration; For energy storage units i The k Sub-state value In the k+1 The update of the next iteration; M is the total number of energy storage units; State matrixW The i Line number j The elements of a column are represented as: (12); In each iteration step, the energy storage unit i Collect all its adjacent energy storage units j The system calculates the difference between its current state and the states of each adjacent energy storage unit, and then updates its state according to a preset weighting coefficient. The weighting coefficient is determined by the state matrix. W Given the elements w of the matrix ij It can be obtained by normalizing the absolute values ​​of each element of the Laplace matrix L.

[0087] However, the aforementioned basic consensus algorithm can only guarantee that the state of charge of each energy storage unit converges to the same value, but it cannot guarantee that the total output power of all energy storage units in the group is equal to the output power value that the battery pack needs to bear as issued by the battery pack control layer. P D To address this issue, a power constraint term needs to be explicitly introduced into the basic consensus algorithm, and its calculation expression can be: (13); In equation (13): A sufficiently small gain coefficient; For energy storage units i In the k The local estimation (i.e., bias) of the mismatch between the total output power and the transmitted power value during step iteration is as follows: (14); In equation (14): Energy storage unit i In the k The output power estimated during step iteration is expressed as: (15); By uniformly expressing the iterative rules of each unit in matrix form, the dynamic equations of the entire energy storage unit group can be obtained. This set of equations fully describes the evolution of state variables, power mismatch estimates, and output power during the iterative process. That is: (16); Through the improved consistency iterative algorithm described above, the state of charge of each energy storage unit within the group gradually converges under the drive of adjacent information interaction. At the same time, the local mismatch estimate gradually decays to zero, ensuring that the total output power of the entire group matches the output power value that the battery pack needs to bear. P D Consistency is achieved, ultimately realizing online autonomous balancing of power distribution within the group.

[0088] 303. Determine the initial values ​​for the consensus algorithm.

[0089] To ensure that the consensus algorithm can start correctly and eventually converge to a state that satisfies power balance, the initial values ​​of each variable in the algorithm need to be set reasonably.

[0090] For example, Definition 1 T Unit row vectors, state matrix W The condition is that the sum of all column vectors is 1, i.e., 1. T W =1 T Multiply both sides of the second term in equation (15) by 1 on the left. T ,get: (17); Right now For all k All values ​​are constants. That is, during the iteration process, the sum of the sum of the estimated local power mismatch values ​​of all energy storage units and the sum of the total output power always remain a constant value. This constant value is equal to the sum of the corresponding values ​​of each energy storage unit at the initial moment of the iteration.

[0091] This constant property reveals a key constraint on the choice of initial values ​​for the algorithm. This is crucial for achieving the optimal balance between the total output power of the energy storage unit and the power delivered by the battery pack at final convergence. To achieve a precise balance between them, the initial values ​​are set as follows: First, the sum of the initial output power of each energy storage unit should be directly equal to the power command value issued by the battery pack (i.e., the sum of the initial output power of each energy storage unit in the battery pack should be equal to the target output power of the battery pack). ,make In order to make Any value that is true, such as This condition ensures that the iteration satisfies the macroscopic matching of total power from the very beginning.

[0092] Secondly, the local power mismatch estimate (i.e., power deviation estimate) of each energy storage unit is initialized to zero, that is, for all i =1,2,……,M, choose This setting means that at the initial moment, each unit has zero local perception of the global power deviation.

[0093] Therefore, the initial value for the consensus algorithm is chosen as follows: (18); Based on the above conservation properties and initial settings, it can be guaranteed that For any iteration step kThis holds true consistently. As the number of iterations increases, when the consistency process converges, the state of charge of each energy storage unit tends to be consistent, and the power mismatch estimate decreases. Gradually decaying. Ultimately, in k As the value approaches infinity, all energy storage units All values ​​approach zero. At this point, the total output power of the energy storage units in the group is equal to the output power that the battery pack needs to bear. The distributed power allocation and SOC balancing tasks of the energy storage unit control layer are completed simultaneously.

[0094] The energy storage power station control method provided in this application decomposes the overall station solution task into two levels: group-level centralized optimization and intra-group parallel distributed updates. This significantly reduces the increase in computational and communication burden as the power station scales up. After the frequency regulation command arrives, it can complete the optimization solution of group-level output and the refined allocation of unit-level output in a short time. This effectively reduces the time delay and convergence uncertainty caused by centralized overall iteration, ensuring small error tracking and stable output under rapid power regulation conditions.

[0095] Meanwhile, the energy storage power station control method provided in this application introduces the state of charge balance target and related constraints between battery packs in the upper-level optimization, and promotes the state of charge of each unit in the pack to be consistent through consistency coordination in the lower level. This effectively suppresses the drift and unevenness of the state of charge, reduces the protection action and rebalancing overhead caused by the state of charge exceeding the limit, improves the available capacity and charging and discharging efficiency, and slows down the battery degradation process. Thus, it improves the economy of the whole life cycle while meeting the frequency regulation performance requirements.

[0096] Furthermore, regarding system scalability and robustness, the hierarchical collaborative architecture adopted in this application supports modular expansion of multiple battery packs and multiple energy storage units. It can adapt to differences in capacity, efficiency, and health status among different devices, and supports on-demand access of battery packs and energy storage units, flexibly responding to topology adjustments and capacity supplementation needs. The distributed consensus control strategy employed can maintain convergence even under complex engineering conditions such as communication latency and local measurement anomalies, thereby improving the reliability and environmental adaptability of system operation.

[0097] The energy storage power station control method provided in this application will be further described in detail below with reference to specific embodiments.

[0098] In some examples, a large-scale energy storage power station with an installed capacity of 25MW / 100MWh (i.e., rated power of 25MW and rated energy capacity of 100MWh) is taken as an example. This energy storage power station includes 10 energy storage battery packs, each containing 10 energy storage units, for a total of 100 energy storage units. Each energy storage unit consists of two battery cells and is equipped with a power conversion system (PCS) with a rated power of 500kW. The main parameters of each energy storage unit are shown in Table 1: Table 1

[0099] Considering that high-power charging and discharging can easily lead to overheating inside the energy storage unit, thus affecting its operating status and service life, the maximum charging and discharging power of each energy storage unit is limited to 1 / 4 of its rated capacity (i.e., 250kW). To simulate the real differences in the energy stored by the energy storage units, the initial state of charge (SOC) of the 100 energy storage units is randomly set within the range of 0.15 to 0.85.

[0100] In the power system, in order to mitigate the fluctuations in the output power (or output) of new energy sources such as wind power and photovoltaics, as well as the uncertainty of load power, the rapid response characteristics of energy storage power stations can be used to fill the power gap between the day-ahead plan and the actual output power.

[0101] For example, the power deficit (i.e., the commanded power of the frequency modulation command) is set to dynamically vary between -12000kW and 12000kW. After adopting the hierarchical control method proposed in the embodiments of this application, the actual output power curve of the energy storage power station is as follows: Figure 4 As shown.

[0102] like Figure 4 As shown, the horizontal axis represents the scheduling period, and the vertical axis represents power (in kW). The actual output power of the energy storage power station follows the commanded power well throughout the scheduling period, regardless of whether the commanded power is too high or too low, or even when switching between charging and discharging states at zero point. The control strategy can control the energy storage power station to output the corresponding power (kW). Although there is a slight deviation between the actual output power and the commanded power, it is within the allowable error range, thus verifying the effectiveness and accuracy of the energy storage power station control method provided in this application.

[0103] To deeply analyze the power distribution and SOC balancing process among the energy storage units within the group, battery pack 1 is used as an example. For instance, the initial SOC of the 10 energy storage units in battery pack 1 is set as [0.34, 0.62, 0.33, 0.49, 0.47, 0.61, 0.56, 0.42, 0.80, 0.29], exhibiting significant dispersion. Under the coordination of the lower-level distributed consensus algorithm, the SOC variation curves of each energy storage unit (e.g., SOC of energy storage unit 1, SOC of energy storage unit 2, SOC of energy storage unit 3, SOC of energy storage unit 4, SOC of energy storage unit 5, SOC of energy storage unit 6, SOC of energy storage unit 7, SOC of energy storage unit 8, SOC of energy storage unit 9, and SOC of energy storage unit 10) are as follows: Figure 5 As shown, its trend is consistent with the power allocation results. Figure 5 As shown, the horizontal axis represents the scheduling period, and the vertical axis represents the state of charge (SOC). Throughout the process, the output power of each energy storage unit changes smoothly without any sudden increases or decreases, thus demonstrating the effectiveness of the power jump penalty in the upper-level optimization. Simultaneously, the SOC balancing process progresses steadily, with the SOC curves of each energy storage unit smoothly converging as the scheduling period increases, eventually becoming essentially consistent.

[0104] To more intuitively demonstrate the advantages of the energy storage power station control method provided in this application embodiment, a centralized control method that does not employ the SOC distributed control mechanism provided in this application embodiment is used as a comparison benchmark. The SOC standard deviation of each energy storage unit within battery pack 1 is determined throughout the entire scheduling process, and the comparison curves are shown below. Figure 6 As shown.

[0105] like Figure 6 As shown, Figure 6 In the diagram, (a) and (b) represent the scheduling period on the horizontal axis, and the vertical axis represents the standard deviation of SOC. At the beginning of the scheduling period, Figure 6 The centralized control method shown in (a) and Figure 6 The standard deviation of SOC under the distributed control method shown in (b) is 0.1502. At the end of the scheduling period, Figure 6 In (a) the centralized control method, the standard deviation of SOC decreased to 0.0339, a reduction of 0.1163; while in this application, Figure 6 In (b), the standard deviation of SOC under distributed control decreased to 0.0021, a reduction of 0.1481. This comparative result shows that the distributed control method of this application embodiment can achieve a better state-of-charge balancing effect than the centralized control method. This means that the energy storage power station control method provided by this application embodiment can effectively reduce the number of overcharging or over-discharging of energy storage units, effectively ensuring the long-term operational safety of the energy storage power station.

[0106] In summary, through a comprehensive comparative analysis of frequency modulation command tracking performance, smooth power distribution within a group, and SOC balancing indicators, this application fully verifies that the method in the embodiments of this application can enable the actual output power of the energy storage power station to quickly and accurately follow the command power of the frequency modulation command, and effectively achieve the state of charge balancing of each energy storage unit. This effectively reduces the risk of overcharging and over-discharging of energy storage units, improves the available capacity and lifespan utilization of the energy storage power station, and has good engineering applicability and promotion value.

[0107] It is understood that the various methods, embodiments, or logical steps described above can be combined or referenced to construct more complex implementation methods, provided that they do not conflict with each other. This application does not impose any restrictions on this and aims to provide flexible technical solutions to support different actual needs.

[0108] This application provides a power station control device, which can be understood as a collection of functional modules that implement the above-described energy storage power station control method. This power station control device can be integrated into an electronic device to execute the various functions or steps performed by the energy storage power station control system in the above-described method embodiments.

[0109] This application provides an energy storage power station control system, including: a battery pack control module and an energy storage unit control module corresponding to the battery pack; the battery pack control module is used to receive frequency modulation commands; with the total output power of all battery packs equal to the total output power indicated by the frequency modulation commands as a constraint, and with the balance of state of charge among the battery packs as the goal, it performs power optimization allocation based on the operating status information of each battery pack, determines the target output power of each battery pack, and sends the target output power of each battery pack to the energy storage unit control module corresponding to each battery pack; the energy storage unit control module is used to receive the target output power of the battery pack sent by the battery pack control module for each battery pack, and according to the target output power corresponding to the battery pack, with the balance of state of charge among the energy storage units in the battery pack as the goal, control the energy storage units in the battery pack to perform distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

[0110] This application provides an electronic device that may include a memory and one or more processors. The memory and processors are coupled. The memory stores computer program code, including computer instructions. When the processor executes the computer instructions, the electronic device can perform various functions or steps performed by the energy storage power station control system in the above method embodiments.

[0111] This application also provides a computer-readable storage medium including computer instructions that, when executed on an electronic device, cause the electronic device to perform various functions or steps performed by the energy storage power station control system in the above method embodiments.

[0112] This application also provides a computer program product that, when run on a computer, causes the computer to perform various functions or steps executed by the energy storage power station control system in the above method embodiments.

[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0114] The electronic devices, computer-readable storage media, or computer program products provided in this application are all used to perform the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially or in other words, the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control method for an energy storage power station, characterized in that, An application to an energy storage power station control system, the energy storage power station control system including at least one battery pack, the battery pack including at least one energy storage unit, the method comprising: Receive frequency modulation command, the frequency modulation command being used to indicate the total output power of the energy storage power station; With the total output power of all battery packs equal to the total output power indicated by the frequency modulation command as a constraint, and with the goal of balancing the state of charge among the battery packs, power optimization allocation is performed based on the operating status information of each battery pack to determine the target output power of each battery pack; the operating status information includes the current state of charge of the battery pack. For each battery pack, based on the target output power corresponding to the battery pack, with the goal of balancing the state of charge among the energy storage units in the battery pack, the energy storage units in the battery pack are controlled to perform distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

2. The method according to claim 1, characterized in that, The step of determining the target output power of each battery pack by using the constraint that the sum of the total output power of all battery packs equals the total output power indicated by the frequency modulation command, aiming at the balance of the state of charge among the battery packs, and optimizing power allocation based on the operating state information of each battery pack, includes: Based on the operating status information of each battery pack, an objective function is constructed with the goal of balancing the state of charge among the battery packs, and the objective function is solved under the condition of satisfying the preset constraints to obtain the target output power of each battery pack. The preset constraint condition includes: the sum of the total output power of all battery packs is equal to the total output power indicated by the frequency modulation command.

3. The method according to claim 2, characterized in that, The preset constraints also include at least one of the following: The discharge power of each battery pack satisfies the constraints of its upper and lower discharge power limits. The charging power of each battery pack satisfies the constraints of its upper limit and lower limit charging power. The state of charge (SOC) value of each battery pack is maintained between a preset SOC lower limit and a preset SOC upper limit.

4. The method according to claim 2 or 3, characterized in that, The objective function is obtained by weighted summation of the state-of-charge equilibrium term and the power fluctuation penalty term. The state of charge balancing term is related to the deviation of the state of charge value of each battery pack from the average state of charge value of all battery packs. The power fluctuation penalty term is related to the variation in output power of the same battery pack during different control periods.

5. The method according to claim 3, characterized in that, Before solving the objective function, the method further includes: Set the battery state of charge regions, and set corresponding charging and discharging power limits for different state of charge regions; The charging and discharging power limits of the battery pack are dynamically adjusted according to the current state of charge region of the battery pack. Based on the preset constraints, the output power of the battery pack is made to meet the dynamically adjusted charge and discharge power limits.

6. The method according to claim 1, characterized in that, The step of controlling the energy storage units within the battery pack to perform distributed coordination, with the goal of achieving a balanced state of charge among the energy storage units, and ensuring that the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack, includes: Based on the communication connection relationship between each energy storage unit in the battery pack, a connectivity relationship representing the neighborhood communication network is constructed. The neighborhood set of each energy storage unit in the neighborhood communication network is determined based on the connectivity relationship. The system controls each energy storage unit within the battery pack to exchange state of charge values ​​with energy storage units in their respective neighborhood sets via the neighborhood communication network. Each energy storage unit is controlled to iteratively update its current state of charge (SOC) value and the SOC values ​​of neighboring energy storage units obtained through exchange, using a preset consensus algorithm, until the SOC values ​​of each energy storage unit converge to equilibrium, and the sum of the output power of all energy storage units in the battery pack equals the target output power of the battery pack.

7. The method according to claim 6, characterized in that, The consensus algorithm includes a first-order discrete-time consensus algorithm that incorporates a power constraint term; The power constraint term is related to the deviation between the total output power of the energy storage units in the battery pack and the target output power of the battery pack, and is used to drive the deviation to converge to zero during the iteration process; The initial value of the consensus algorithm is set as follows: the sum of the initial output power of each energy storage unit is equal to the target output power, and the power deviation estimate of each energy storage unit is initialized to zero.

8. The method according to claim 1, characterized in that, The energy storage power station control system includes a battery pack control module and an energy storage unit control module corresponding to the battery pack. The receiving of the frequency modulation command includes: receiving the frequency modulation command through the battery pack control module; The step of determining the target output power of each battery pack by using the constraint that the sum of the total output power of all battery packs equals the total output power indicated by the frequency modulation command, aiming at the balance of the state of charge among the battery packs, and optimizing power allocation based on the operating state information of each battery pack, includes: The battery pack control module uses the constraint that the sum of the total output power of all battery packs is equal to the total output power indicated by the frequency modulation command, and takes the balance of the state of charge among the battery packs as the goal. Based on the operating status information of each battery pack, it performs power optimization allocation, determines the target output power of each battery pack, and sends the target output power of each battery pack to the energy storage unit control module corresponding to each battery pack. For each battery pack, based on the target output power corresponding to the battery pack, and with the goal of balancing the state of charge among the energy storage units within the battery pack, the energy storage units within the battery pack are controlled to perform distributed coordination, and the sum of the output power of all energy storage units within the battery pack equals the target output power of the battery pack, including: For each battery pack, the target output power of the battery pack is received from the battery pack control module through the energy storage unit control module corresponding to the battery pack. Based on the target output power of the battery pack, and with the goal of balancing the state of charge among the energy storage units in the battery pack, the energy storage units in the battery pack are controlled to perform distributed coordination, and the sum of the output power of all energy storage units in the battery pack is equal to the target output power of the battery pack.

9. An electronic device, characterized in that, include: A memory, one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the energy storage power station control method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the energy storage power station control method as described in any one of claims 1-8.

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