Distributed Online Optimization Method and Device for Corrosion Degradation Balancing in Microgrid Energy Storage Systems

Abstract

This invention provides a distributed online optimization method and apparatus for balancing corrosion degradation in microgrid energy storage systems, relating to the field of distributed control technology for microgrids. The method includes: constructing a dynamic communication topology model of the microgrid battery energy storage system, using a time-varying directed graph to represent the dynamic communication process; constructing a distributed online energy dispatch optimization model considering the balanced lifetime of the battery energy storage system; transforming it into an instantaneous optimization form that can be iteratively calculated online; initializing relevant parameters and introducing coupling variables to nodes; exchanging dual and coupling variables of nodes based on dynamic communication and real-time information, and updating decision variables, dual variables, and coupling variables to obtain the power dispatch decision for the next time step. This invention can achieve a balance between the economic operating cost of the system and the lifetime of the battery energy storage system under complex conditions such as stochastic new energy access, time-varying and unstable communication network topology, and significant differences in the health status of distributed energy storage units.

Description

Technical Field

[0001] This invention relates to the field of distributed control and energy dispatch optimization technology for microgrids, and in particular to a distributed online optimization method and device for corrosion degradation balancing of microgrid energy storage systems. Background Technology

[0002] As the penetration rate of renewable energy in microgrids continues to increase, distributed battery energy storage systems, due to their flexible power regulation capabilities, have become a key component in ensuring stable system operation and improving the level of new energy consumption. However, in actual deployment and operation, energy storage systems face a severe challenge closely related to materials science: electrochemical corrosion and material degradation.

[0003] During repeated charge-discharge cycles, the electrode materials, current collectors, and electrolytes within energy storage units inevitably undergo complex electrochemical reactions and physical damage. This process essentially boils down to accelerated corrosion and fatigue aging of materials under electrochemical conditions. Due to differences in initial material properties, commissioning time, operating temperature, and historical operating conditions, the health status of distributed energy storage units generally exhibits significant imbalances. This imbalance is essentially due to differences in the corrosion rates and degradation degrees of materials within each unit. If the scheduling strategy prioritizes only instantaneous economic optimization, severely corroded battery units will be over-utilized, accelerating their material structure failure and ultimately causing a precipitous drop in the overall available capacity of the system.

[0004] Secondly, the operating environment of microgrids is highly dynamic and uncertain. On the one hand, the output of renewable energy sources (such as photovoltaic and wind power) is highly intermittent and random, requiring energy management and dispatch strategies to have the ability to optimize online in real time to cope with rapid fluctuations in source load. On the other hand, the communication network between devices within a microgrid often experiences random link disconnections or time-varying topology due to electromagnetic interference, equipment failures, or maintenance, which poses a severe challenge to the reliability of traditional distributed optimization methods that rely on fixed communication topologies.

[0005] Furthermore, existing distributed optimization frameworks also have limitations at the information exchange level. To achieve global coordination, current methods often require nodes to share detailed operating cost functions or power setpoints. However, the precise health status, internal resistance models, and degradation curves of each energy storage site are core private data belonging to the owners. Due to considerations of commercial information protection and security, it is not convenient to publicly transmit such data over a centralized communication network. This creates barriers for many optimization methods that rely on global models or full information exchange in practical applications.

[0006] While current research in this field has made progress in addressing individual challenges, such as designing robust consensus protocols to tolerate partial communication interruptions or introducing battery degradation costs into distributed scheduling, a solution that can systematically integrate the aforementioned multiple constraints is still lacking. Particularly in time-varying topology communication environments, how to minimize the total system operating cost and proactively balance the corrosion rate (i.e., lifetime degradation) of heterogeneous energy storage unit materials through distributed online collaboration without aggregating sensitive battery data from each node remains a critical technical challenge that urgently needs to be overcome.

[0007] In existing technologies, various centralized or distributed scheduling and control methods have been proposed for energy management of microgrids containing multiple distributed power sources and battery energy storage systems. These include optimization methods based on economic scheduling, secondary control methods based on droop control and consensus algorithms, and lifetime-related scheduling methods that consider the cost of battery corrosion and degradation. These methods can typically achieve a certain degree of operating cost reduction and power allocation optimization under given forecast conditions, playing a positive role in the safe and stable operation of microgrids. However, most existing technologies only focus on short-term economics and power balance, failing to adequately consider the differences in the health status of different energy storage units, the uncertainty of microgrid operation, and the time-varying nature of communication networks. This makes it difficult to meet the comprehensive requirements of long-term lifetime and online robustness in practical engineering. (1) Centralized or "quasi-centralized" economic scheduling and power allocation. This type of method usually involves a higher-level control center collecting operating parameters and forecast information from each distributed power source and energy storage unit to solve optimization problems for a single time period or multiple time periods, in order to achieve minimum operating costs or average power allocation. Although some literature has simply linearly added the battery capacity degradation cost to the objective function, it generally does not explicitly model the dynamic evolution of battery SOH (State of Health) and the differences in lifespan between different energy storage units, and also lacks constraints or penalties for the long-term goal of "lifespan balance". As a result, batteries with higher internal resistance and poorer health status may still be frequently used in global economic optimization, leading to premature failure of individual batteries and a sudden drop in the overall available capacity of the system. At the same time, this type of centralized method requires uploading relatively detailed local operating data and battery model parameters to the central controller, which presents problems in terms of data leakage prevention and communication bandwidth.

[0008] (2) Energy management strategies based on offline optimization or predictive control. These methods typically rely on day-ahead or intraday load forecasting and renewable energy output forecasting to construct multi-timescale optimization models. Power allocation schemes for a future period are obtained through linear programming, quadratic programming, or model predictive control. These methods perform well when forecasts are relatively accurate and the operating environment is relatively stable. However, in microgrids containing a large amount of renewable energy such as photovoltaics and wind power, the output exhibits strong randomness and non-stationarity, causing offline or fixed-prediction-based optimization schemes to deviate from the optimal in actual operation. Existing schemes generally lack a truly online adaptive scheduling mechanism, making it difficult to respond promptly to real-time operating condition changes and the accumulation of prediction errors, thus affecting economic efficiency and equipment lifespan utilization.

[0009] (3) Distributed secondary control and multi-agent coordinated control. Related technologies typically achieve frequency / voltage recovery, power or SOC (State of Charge) sharing and balancing among energy storage units through droop control, consensus algorithms or event-triggered control. Some works also consider the impact of switching topologies or communication delays. The advantages of this type of method are its decentralized structure, suitability for engineering implementation, and certain robustness to fixed or slowly changing communication networks. However, most existing distributed control methods: on the one hand, only use SOC or power balance as an approximate lifetime indicator, without introducing explicit SOH state and convex battery degradation models, and cannot achieve quantitative balancing of different battery lifetime consumption from a mechanistic and long-term perspective; on the other hand, they often assume that the communication topology is static or undirected, lacking systematic convergence analysis and guarantees for frequent link interruptions, recovery, and directed asymmetric connections in reality. In addition, this type of method often directly exchanges relatively complete state or gradient information in the network, which is insufficient for protecting the sensitive physical data of each energy storage site.

[0010] In summary, existing technologies struggle to simultaneously meet the following comprehensive requirements: ensuring real-time electricity demand from users and meeting grid operational constraints; adaptively responding online to random fluctuations in renewable energy output and load; coordinating generator sets, battery storage, and public grid transactions under a distributed, time-varying communication topology to minimize total operating costs and extend battery lifespan in a balanced manner; and protecting sensitive data such as local battery degradation curves. Therefore, it is necessary to propose a distributed online collaborative scheduling method for lifetime balancing of battery storage systems in distributed time-varying topologies of microgrids to overcome these shortcomings. Summary of the Invention

[0011] To address the challenge of existing microgrid dispatching methods in coordinating and optimizing system economy and balancing the lifespan of heterogeneous energy storage batteries under time-varying communication topologies and highly stochastic environments, this invention provides a distributed online optimization method and apparatus for balancing corrosion degradation in microgrid energy storage systems. The technical solution is as follows: On the one hand, a distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems is provided. This method is implemented by a distributed online optimization device for corrosion degradation balancing in microgrid energy storage systems, and includes: S1. Construct a dynamic communication topology model for a microgrid battery energy storage system, and use a time-varying directed graph to represent the dynamic communication process of the dynamic communication topology model; wherein, the time-varying directed graph includes nodes, edges and node communication weight matrix.

[0012] S2. Based on the dynamic communication topology model, considering the balanced lifespan of the microgrid battery energy storage system, a distributed online energy dispatch optimization model for the microgrid battery energy storage system is constructed. The distributed online energy dispatch optimization model includes: the power generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and the constraints, and introduces auxiliary variables for balancing battery lifespan.

[0013] S3. Transform the distributed online energy dispatch optimization model into a distributed solution Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online.

[0014] S4. Initialize the relevant parameters of the power generation cost function, transaction cost function, corrosion and aging cost function and constraints, initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem, and introduce coupling variables for each node.

[0015] S5. Based on the dynamic communication and real-time information of the microgrid battery energy storage system, the dual and coupled variables of the nodes are exchanged, and the decision variables, dual variables and coupled variables are updated to obtain the power dispatch decision at the next moment, so as to realize the online dispatch that balances economy and battery life.

[0016] Optionally, the nodes of the time-varying directed graph in S1 include: multiple battery nodes, generator nodes, and public network nodes.

[0017] Time-varying directed graphs allow for network interruptions or incomplete connections at a single moment, but information can still be passed through paths within any given finite time window.

[0018] Optionally, the distributed online energy dispatch optimization model in S2 is as shown in equation (1): (1) In the formula, Represents auxiliary variables. This indicates the output power of the generator set. Indicates the power exchanged with the public network. Indicates the battery's charging and discharging power. Indicates the total number of batteries. Indicates a time range. This indicates the cost of generating electricity from the generator set. This represents the transaction cost of exchanging power with the public network. Indicates the cost of health degradation, This indicates the minimum charging and discharging power of the battery. This indicates the maximum charging and discharging power of the battery. This indicates the charge power required by the user. This indicates the maximum power that the generator set can provide. This indicates the minimum battery level. Indicates the initial battery level. This indicates the maximum battery level.

[0019] Optionally, S3 includes: S31. Based on the distributed online energy dispatch optimization model, the Lagrange dual optimization problem is obtained; the Lagrange dual optimization problem includes multiple dual variables and regularization parameters.

[0020] S32. According to the Carlow-Kuhn-Tucker conditions, the derivative of the objective function of the Lagrange dual optimization problem with respect to the auxiliary variables is 0. Therefore, the Lagrange dual optimization problem is transformed to obtain the equivalent problem of the Lagrange dual optimization problem.

[0021] S33. Based on the fact that the constraints of dual variables are coupling constraints in a fully distributed network, the equivalent problem of the Lagrange dual optimization problem is transformed to obtain a distributed solution to the Lagrange dual form.

[0022] S34. Based on the Lagrange dual form, obtain the instantaneous optimized form of the Lagrange dual form that can be iteratively computed online.

[0023] Optionally, coupling variables are introduced for each node in S4, including: A first coupling variable and a second coupling variable are introduced for each battery node, and a third coupling variable and a fourth coupling variable are introduced for the generator node and the public grid node. The first coupling variable is used to estimate the sum of the dual variables, and the second, third and fourth coupling variables are used to estimate the sum of the generator output power, the power exchanged with the public grid and the charging and discharging power of the battery.

[0024] Optionally, in S5, the dual and coupling variables of the nodes are swapped, and the decision variables, dual variables, and coupling variables are updated, including: For each node in the time-varying directed graph, the dual and coupling variables of the node are exchanged with the node's neighbor nodes at the current time.

[0025] Update the decision variables based on the dual and coupling variables of the node and its neighbors.

[0026] Update the dual and coupling variables based on the updated decision variables.

[0027] On the other hand, a distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system is provided. This device is applied to the distributed online optimization method for corrosion degradation balancing in a microgrid energy storage system. The device includes: The topology model construction module is used to construct a dynamic communication topology model for a microgrid battery energy storage system. A time-varying directed graph is used to represent the dynamic communication process of the dynamic communication topology model. The time-varying directed graph includes nodes, edges, and node communication weight matrices.

[0028] The optimization model construction module is used to construct a distributed online energy dispatch optimization model for microgrid battery energy storage systems based on a dynamic communication topology model and considering the balanced lifetime of the microgrid battery energy storage system. The distributed online energy dispatch optimization model includes: the generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and constraints, and introduces auxiliary variables for balancing battery lifetime.

[0029] The model transformation module is used to transform the distributed online energy dispatch optimization model into a distributed solvable Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online.

[0030] The initialization module is used to initialize the parameters related to the power generation cost function, transaction cost function, corrosion and aging cost function, and constraints; initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem; and introduce coupling variables for each node.

[0031] The dispatch decision output module is used to exchange dual and coupled variables of nodes based on the dynamic communication and real-time information of the microgrid battery energy storage system, and update the decision variables, dual variables and coupled variables to obtain the power dispatch decision for the next moment, so as to realize online dispatch that balances economy and battery life.

[0032] On the other hand, a distributed online optimization device for corrosion degradation balancing of a microgrid energy storage system is provided. The distributed online optimization device for corrosion degradation balancing of a microgrid energy storage system includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the methods described above for distributed online optimization of corrosion degradation balancing of a microgrid energy storage system is implemented.

[0033] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the above-described distributed online optimization methods for corrosion degradation equilibrium in microgrid energy storage systems.

[0034] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, a distributed online optimization model that integrates the dynamic evolution of battery health status is constructed, and a distributed coordination algorithm that does not rely on globally sensitive data and adapts to dynamic topology switching is designed to achieve long-term minimization of the total system operating cost and balance the lifespan of heterogeneous energy storage batteries, thereby improving the overall service life of battery energy storage systems in microgrids. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a distributed online optimization method for corrosion degradation balancing in a microgrid energy storage system, provided by an embodiment of the present invention. Figure 2 This refers to the time-averaged cumulative cost of the microgrid during the optimization iteration process provided in this embodiment of the invention. Figure 3 This is the time-averaged degradation cost of the microgrid battery energy storage system during the optimization iteration process provided in the embodiments of the present invention; Figure 4 This is a block diagram of a distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of a distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, provided in an embodiment of the present invention. Detailed Implementation

[0037] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0038] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0039] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0040] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0041] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0042] This invention provides a distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems. This method can be implemented using a distributed online optimization device for corrosion degradation balancing in microgrid energy storage systems, which can be a terminal or a server. Figure 1 The flowchart shown is a distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems. The processing flow of this method may include the following steps: S1. Construct a dynamic communication topology model for a microgrid battery energy storage system, and use a time-varying directed graph to characterize the dynamic communication process of the dynamic communication topology model.

[0043] The time-varying directed graph includes nodes, edges, and a node communication weight matrix. Nodes include: multiple battery nodes, generator set nodes, and public network nodes.

[0044] Time-varying directed graphs allow for network interruptions or incomplete connections at a single moment, but information can still be passed through paths within any given finite time window.

[0045] In one feasible implementation, a dynamic communication topology model of the microgrid system energy storage system is constructed, and its dynamic communication process is characterized by a time-varying directed graph, as well as its dynamic connection properties and information transmission conditions are defined.

[0046] Specifically, the communication topology of a microgrid battery energy storage system is viewed as a time-varying directed graph. ,in This represents the set of nodes in a microgrid battery energy storage system. To account for the total number of batteries, two additional nodes are introduced. and This represents the interface node between the microgrid's own adjustable generator units and the public grid (upstream grid / electricity market). Let be the set of edges. express, This is the node communication weight matrix. (Weight matrix) It is a double random matrix with each weight being non-negative, i.e. In the network Represents a node To the node Communication links, express The weight, if ,but ,otherwise .

[0047] The change path of the inter-node communication link in a microgrid battery energy storage system is to satisfy - Jointly connected, that is, for any There exists a positive integer Make the graph It is strongly connected. This means that the network can be disconnected at any given moment, greatly enhancing its ability to model real-world dynamic networks, as it only requires information to be available within a finite time window. Internal information can be transmitted through paths, which ensures that in the long run, no node will be permanently isolated, and all local information can ultimately influence global decisions.

[0048] This invention employs the criterion of "global connectivity within a time window" to model the dynamically changing communication links within a microgrid. This criterion allows for interruptions or incomplete connections in the communication network at a single moment, but guarantees that the joint communication graph of the entire network remains connected within any given finite time window. This modeling method provides the theoretical basis for ensuring that subsequent optimization algorithms can operate effectively in real-world dynamic networks and achieve global consistency.

[0049] A dynamic communication topology model based on a "Q-joint connectivity" time-varying directed graph was constructed, enhancing the system's adaptability and robustness to dynamic and incomplete communication environments. This model does not require the communication network to be fully connected at every moment, but only that information can be transmitted to all nodes within a finite time window. This design significantly reduces the stringent requirements on communication hardware reliability and network stability, enabling the proposed scheduling method to adapt to real-world dynamic and incomplete communication scenarios such as intermittent communication link interruptions and temporary node offlineness, greatly improving the engineering applicability and operational robustness of microgrid systems under complex communication conditions.

[0050] S2. Based on the dynamic communication topology model, considering the balanced lifespan of the microgrid battery energy storage system, a distributed online energy dispatch optimization model for the microgrid battery energy storage system is constructed. The distributed online energy dispatch optimization model includes: the power generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and the constraints, and introduces auxiliary variables for balancing battery lifespan.

[0051] In one feasible implementation, under dynamic communication environment, considering the balanced lifespan of the battery energy storage system, a distributed online energy dispatch optimization model for the microgrid battery energy storage system is established.

[0052] Specifically, this invention considers the balanced lifespan of the battery energy storage system and establishes a distributed online energy dispatch optimization model for the microgrid battery energy storage system, which includes three parts: the power generation cost of the generator set, the transaction cost between the microgrid and the public grid, and the corrosion and aging (degradation) cost of the battery energy storage system.

[0053] 1) Power generation cost of generator sets: The generator set is The output power at time t is expressed as The following function represents the generator set within a time range. Cumulative electricity generation costs within: (1) In the formula, Indicates that the generator set is Output power at any time is The cost of electricity generation, It is a cost (benefit) coefficient that varies with generator characteristics. It is the maximum power that the generator set can provide. To obtain the actual output power optimized by this invention, the preliminary analysis and theoretical derivation focused on the cumulative results of variables over the entire time axis. Writing it as a time-invariant quantity can be seen as a simplified notation; however, after completing the theoretical derivation to obtain the instantaneous optimization problem, when it comes to specific optimization steps, the more appropriate notation is used. The time-varying notation, because at this point the invention has already been made Real-time decision-making.

[0054] 2) Public network transaction costs: Will The power exchanged with the public network at any given time is expressed as ,in Positive values ​​represent electricity purchases, and negative values ​​represent electricity sales. This is related to the electricity purchased / sold through the public grid within a specific time frame. The cumulative transaction costs within the period are expressed as: (2) In the formula, Indicates in The power exchanged with the public network at any time is Transaction costs, To obtain the actual switching power optimized by this invention, It is the real-time electricity price that changes over time.

[0055] 3) Corrosion and aging (degradation) costs of battery energy storage systems: Battery exist The charging and discharging power at a given time is expressed as ,in Positive values ​​indicate charging, and negative values ​​indicate discharging. (Regarding the battery...) The rated energy is expressed as The time step is (e.g., 5 minutes or 10 minutes), battery In time Equivalent charge-discharge cycle count for: (3) Battery In time The equivalent discharge depth is: (4) Using a quadratic ampere-hour flux model, the battery In time The damage function for the depth of discharge is: (5) In the formula, Indicates battery In time The damage function of the depth of discharge. Represents the coefficient of the constant term. Denotes the coefficient of the linear term. This represents the coefficient of the quadratic term.

[0056] Battery In time The degradation cost is: (6) In the formula, It is a battery Replacement costs.

[0057] The cumulative corrosion aging (degradation) cost of a microgrid battery energy storage system is expressed as follows: (7) 4) Constraints: Power balance: The sum of generator output and purchased electricity from the public grid must equal the sum of user demand and battery charging power, that is: (8) In the formula, This indicates the charge power required by the user.

[0058] Generator output limits: .

[0059] Battery state of charge dynamic balance, description Time's up The evolution of electrical charge over time: (9) In the formula, Indicates in Time of the first The amount of power in each battery.

[0060] Each battery Charge and discharge power limits: .

[0061] Battery limit: This prevents the battery from being overcharged or over-discharged.

[0062] Combining the battery's state of charge dynamic balance and capacity limit, we can obtain... .

[0063] 5) Establish a distributed online energy dispatch and optimization model for lifetime balancing of microgrid battery energy storage systems: Based on the cost function and constraints of the above three parts, this invention establishes the following distributed online energy dispatch and optimization model for lifetime balancing of microgrid battery energy storage systems: (10) In the formula, This indicates the introduction of auxiliary variables to balance battery life.

[0064] This invention constructs a multi-objective optimization model that includes power generation costs, grid transaction costs, and battery corrosion and aging costs. Its core innovation lies in introducing a shared "system lifetime budget" variable and comparing and constraining the cumulative corrosion and aging cost of each battery (based on its damage model calculated through charge-discharge cycles and depth) with this budget variable. Through this model design, the long-term goal of "extending overall battery life" is directly transformed into a collaborative constraint condition that must be met in online scheduling, thereby forcing all batteries to automatically tend towards balanced degradation during operation.

[0065] This paper innovatively integrates the battery corrosion aging cost model (based on the quadratic ampere-hour flux model) with the global optimization objective and introduces auxiliary variables. A lifetime balancing constraint was constructed, enabling proactive balancing and optimization of battery energy storage lifespan and reducing long-term system operating costs. This allows the optimization process to consider not only short-term economic operation (power generation and transaction costs) but also the cumulative corrosion and aging damage of each battery as an optimization variable for proactive management. The scheduling strategy obtained through this model can automatically avoid overuse of a few batteries, promoting coordinated and balanced participation of all batteries in charging and discharging, thereby extending the overall lifespan of the battery energy storage system and significantly reducing premature replacement and maintenance costs caused by battery pack inconsistencies.

[0066] S3. Transform the distributed online energy dispatch optimization model into a distributed solution Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online.

[0067] Optionally, step S3 above may include the following steps S31-S34: S31. Based on the distributed online energy dispatch optimization model, the Lagrange dual optimization problem is obtained; the Lagrange dual optimization problem includes multiple dual variables and regularization parameters.

[0068] In one feasible implementation, based on the distributed online energy dispatch and optimization model of the microgrid battery energy storage system lifetime balance, a formal representation of the optimization method that can be iteratively calculated is further derived. For the above optimization model (10), consider the following Lagrange dual optimization problem (11): (11) in, , , and Dual variables, functions , ride .

[0069] S32. According to the Carlow-Kuhn-Tucker conditions, the derivative of the objective function of the Lagrange dual optimization problem with respect to the auxiliary variables is 0. Therefore, the Lagrange dual optimization problem is transformed to obtain the equivalent problem of the Lagrange dual optimization problem.

[0070] In one feasible implementation, for this Lagrange dual problem (11), according to the KKT (Karush-Kuhn-Tucker) conditions, the objective function of the problem is related to... The derivative is 0, therefore we can obtain ,in It is a vector whose elements are all 1s. The equivalent problem of this Lagrange problem is: (12) S33. Based on the fact that the constraints of dual variables are coupling constraints in a fully distributed network, the equivalent problem of the Lagrange dual optimization problem is transformed to obtain a distributed solution to the Lagrange dual form.

[0071] In one feasible implementation, dual variables constraints In a fully distributed network, due to coupling constraints, problem (12) is further transformed into the following Lagrange dual form (13): (13) in, and Dual variables, multipliers .

[0072] S34. Based on the Lagrange dual form, obtain the instantaneous optimized form of the Lagrange dual form that can be iteratively computed online.

[0073] In one feasible implementation, based on the above problem, the instantaneous optimization problem (14) required for its iterative optimization can be obtained: (14) This invention transforms the original centralized optimization problem, which includes long-term lifetime constraints and global power balance constraints, into a series of instantaneous optimization subproblems that can be solved online and in a distributed manner by introducing a series of dual variables (or Lagrange multipliers). The key to this transformation lies in cleverly handling the global coupling relationship between variables, especially transforming the complex relationship that "all battery lifetime constraints must jointly satisfy a total budget" into a form in which each node can make collaborative decisions using local information.

[0074] S4. Initialize the relevant parameters of the power generation cost function, transaction cost function, corrosion and aging cost function and constraints, initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem, and introduce coupling variables for each node.

[0075] In one feasible implementation, this invention proposes a primal-dual method to solve the aforementioned instantaneous optimization problem and obtain online scheduling decisions. The initialization parameters required for the method of this invention are as follows: 1) Renewable energy generator sets: The quadratic term in the quadratic cost function (1) reflects the tolerance for wind and solar power curtailment; therefore, the coefficient of the quadratic term is... The coefficient is increased during periods of tight performance targets and significant carbon market pressure, and decreased during periods of low local load. The primary term coefficient is essentially the revenue from grid trading; therefore, it equals the real-time electricity price. .

[0076] 2) Public network transaction costs: Public network transaction cost function (2) Real-time electricity price parameters The term is restricted to non-negative values ​​to avoid duplicating the electricity sales portion with the primary term of the generator set cost. .

[0077] 3) Corrosion and aging costs of battery energy storage systems: The first in equations (3) and (4) Each battery has a rated energy of Time step (e.g., 5 minutes or 10 minutes, etc.) depends on the time interval of the algorithm implementation; battery The coefficients of the three terms (constant term, primary term, and secondary term) in the damage function (5) for depth of discharge are set as follows. For lead-acid batteries, , =-0.5762, =3.529. Since lithium batteries exhibit different performance in the early and long term, two sets of coefficients are typically determined for two different regions; that is, in the early stage... , =89.6, =-62.1, long-term value , =0.1, =0.0524. Additionally, the cost of replacing the battery in equation (6) The price was determined based on the actual purchase price of lead-acid and lithium batteries in the market at that time.

[0078] 4) Constraint parameters: User-required charge power Depend on The upper limit of generator output is determined by the actual electricity demand of users at any given time. Each battery Upper and lower limits of charging and discharging power and and power limit and It depends on the battery type and capacity. The amount of electricity required to execute the initial method is determined.

[0079] 5) Decision variables (power), dual variables, and regularization parameters: For the decision variable to be optimized, the initial output of the generator set and each battery Initial output If the microgrid has already generated its output power before the algorithm is executed (the microgrid has been started), then that power is its initial output. If it has not generated output power (the microgrid has not yet been started), then these two power components are randomly generated in their respective feasible regions to complete the cold start. Similarly, for the decision variable to be optimized, the power purchased from the public grid, if the microgrid has already started generating it, then the final purchased power is... Otherwise, its initial value is 0.

[0080] For dual variables, , , , , =0.

[0081] Regularization parameters This reflects the penalty for the absolute value of the dual variable, set as follows: , , .

[0082] 6) Define coupling variables for information coordination in a distributed environment: Because of the fully distributed network communication topology, each node can only exchange information with its neighbors and cannot know information about all nodes. Therefore, coupling variables need to be introduced to estimate the information for each battery node. Moment and , where vector Each dimension component is constrained to a closed interval. within, that is For the first Each node introduces coupling variables. and To estimate separately and ,in and The initial values ​​are respectively and Introducing coupling variables for generator nodes and public network nodes. and estimate Moment ,in and The initial values ​​are respectively and .

[0083] S5. Based on the dynamic communication and real-time information of the microgrid battery energy storage system, the dual and coupled variables of the nodes are exchanged, and the decision variables, dual variables and coupled variables are updated to obtain the power dispatch decision at the next moment, so as to realize the online dispatch that balances economy and battery life.

[0084] In one feasible implementation, based on dynamic communication and real-time information, the power dispatch decision for the next moment is obtained through online optimization by information exchange between multiple nodes, power decision update, dual variable adjustment and coupling variable synchronization, thereby realizing online power dispatch optimization.

[0085] Specifically, based on the above initial parameters and instantaneous problem (14), the online optimization of microgrid power dispatch considering the balanced lifetime of the balanced battery energy storage system under dynamic conditions is as follows, wherein, for the sake of presenting the method process, the following will be used. and They represent and ,Will and They represent and ,Other , , , .

[0086] 1) Exchange of information: For each node Exchange information about dual variables: (15) and exchange coupling variable information: (16) 2) Update decision: For each node The decision is updated using the following formula: (17) Among them, those that meet For projection operation, soon To Domain projection, Let be the derivative function, and be the step size. .

[0087] 3) Update the dual variable: (18) 4) Update coupling variables: (19) Furthermore, prediction and output scheduling decisions: for microgrid battery energy storage systems in Given the information at a given moment, the method described above can be used to predict and output the information at the next moment. Power dispatch decision (i.e. The system integrates power generation, energy storage, and grid interaction commands to simultaneously balance low cost and battery life, achieving online scheduling that balances economic efficiency and battery life.

[0088] This invention designs an iterative computation process in parallel execution by all participating nodes (battery, generator, grid interface). Each node performs only three steps at any given time: First, it exchanges limited collaborative information (including estimates of dual and coupling variables) with its current communicating neighbors; second, based on local information and the received neighbor information, it independently updates its own charging / discharging or power generation decisions; third, it synchronously updates the local dual and coupling variables used for collaboration. The entire process requires no central controller and achieves global optimization entirely through local interactions between nodes.

[0089] A set of "coupling variables" is creatively defined and maintained, with each node holding its own copy. This is used to distribute the estimation of key global states of the system (such as the sum of all relevant dual variables and the total power imbalance of the system) even when global information is unavailable. This mechanism is updated through a set of weighted averages and local correction rules based on dynamic communication topology, and is the core hub for ensuring consistent behavior among nodes in a decentralized coordination environment.

[0090] A complete set of systematic parameter and variable initialization methods is provided, covering everything from setting cost function coefficients and selecting battery corrosion and aging model parameters to determining the initial values ​​of all power decision variables, dual variables, and coupling variables. This scheme ensures that the proposed distributed online algorithm can quickly and reliably enter the effective collaborative optimization process from any reasonable initial state (including system cold start).

[0091] This invention successfully transforms the centralized optimization problem into a distributed solvable form based on Lagrange duality (Equation 14), introducing coupling variables ( (etc.) An iterative rule for "information exchange-decision update" based on dynamic communication topology was designed, realizing fully distributed online optimization computation and improving the algorithm's scalability and data protection. The entire optimization process does not require a central controller: each node (battery, generator set, public network interface) only needs to exchange limited coupling and dual variable information with its communication neighbors to independently update its own power decision. This architecture avoids the computational bottleneck and single point of failure risk of a central node, and the system has strong scalability (adding a new node only requires connecting to the communication network); at the same time, sensitive information such as the local cost function and state parameters of each node does not need to be reported, preventing data leakage.

[0092] The method proposed in this invention is an online optimization algorithm with real-time online decision-making capabilities, effectively tracking and responding to dynamic changes in the system. It operates at each scheduling time... Utilize the latest information at the current moment (such as real-time electricity prices) ,load Network topology (etc.), to quickly calculate the next moment in one iteration ( ) optimal scheduling instructions ( , , This method does not require all future information and can respond in real time to uncertainties such as fluctuations in renewable energy output, load changes, and electricity price changes, enabling rolling optimization and decision-making in dynamic environments. It is more practical and adaptable than traditional methods that rely on global offline forecasting.

[0093] Ultimately, through the synergistic effect of the aforementioned models and algorithms, this invention simultaneously optimizes multiple objectives while ensuring power balance and equipment safety constraints. It comprehensively optimizes economic benefits and equipment lifespan, improving the overall operational efficiency of the microgrid: minimizing the short-term economic costs of power generation and purchase, maximizing the long-term value of energy storage assets through balanced battery charging and discharging strategies, and ensuring the reliability and real-time nature of the decision-making process through a distributed architecture. This provides microgrid operators with an efficient dispatch solution that balances economy, reliability, and sustainability throughout the entire lifecycle, enhancing the overall operational efficiency and market competitiveness of the microgrid.

[0094] like Figure 2 , Figure 3 As shown, this invention can simultaneously optimize generator sets, multiple heterogeneous battery energy storage units, and energy trading with the public grid through a fully distributed online coordination mechanism, while protecting the battery data of each node from leakage. This enables the minimization of the total long-term operating cost of the system and the proactive balancing of the service life of the battery energy storage system.

[0095] In view of the shortcomings of the existing technology, the present invention mainly aims to solve the following technical problems: 1) In terms of problem modeling, there is a lack of a unified and solvable model that simultaneously reflects network non-stationarity, SOH dynamics, and lifetime equilibrium. Existing microgrid energy management models mostly focus only on short-term economics and power balance, at most adding battery degradation as a simple linear cost to the objective function, rarely treating battery SOH as a state variable evolving over time. Furthermore, the strong stochasticity of renewable energy output and load is often handled offline or based on fixed predictions, making it difficult to support a truly online, adaptive decision-making process. The resulting technical problem is: how to construct a unified scheduling model that is mathematically convex and can be solved iteratively online, capable of simultaneously characterizing microgrid operating costs, network constraints, stochastic renewable energy output and load changes, as well as the dynamic evolution of SOH and lifetime equilibrium objectives of each battery, thus providing a rigorous optimization foundation for subsequent distributed online methods. 2) In a fully distributed architecture, the global coupling constraints introduced to achieve battery lifetime balancing are difficult to handle. To prevent batteries with poor health from being overused, a lifetime balancing term reflecting the differences in State of Health (SOH) among different energy storage units needs to be introduced into the objective function or constraints. For example, penalizing each battery's SOH from deviating from the system average. This type of design is essentially a cross-node global coupling constraint or coupling cost term. In a fully distributed architecture, each node only has local SOH and operating data, and local decay curves and other sensitive information should not be uploaded. Traditional centralized solutions or distributed algorithms that require global information are difficult to apply. Therefore, the technical problem is: how to effectively handle the cross-node coupling constraints introduced by the lifetime balancing objective without relying on a central controller and without leaking the detailed decay models and SOH trajectories of each node, so that each battery can achieve global lifetime balancing scheduling under the condition of exchanging only limited aggregated information; 3) Under time-varying communication topologies and fluctuating communication quality, there is a lack of distributed scheduling mechanisms that balance stability coordination with engineering feasibility. In actual microgrids, the wireless or wired communication links between energy storage units can experience link interruptions, delay fluctuations, and frequent topology switching due to electromagnetic interference, communication module failures, or maintenance. This results in incomplete, asynchronous, or even temporary disconnections of information received by different nodes within the same scheduling cycle. Most existing distributed control and scheduling methods assume a stable and connected communication network topology, lacking specific mechanisms for handling link interruptions and topology switching recovery processes. This can easily lead to control command oscillations, decreased coordination efficiency, or even local node "loss of control." Therefore, one technical problem this invention aims to solve is: how to achieve stable power coordination and lifetime balancing scheduling between generator sets and multiple batteries under conditions where the communication topology changes over time and some links are intermittently available, while maintaining good engineering feasibility with limited computing and communication resources.

[0096] In this embodiment of the invention, a distributed online optimization model that integrates the dynamic evolution of battery health status is constructed, and a distributed coordination algorithm that does not rely on globally sensitive data and adapts to dynamic topology switching is designed to achieve long-term minimization of the total system operating cost and balance the lifespan of heterogeneous energy storage batteries, thereby improving the overall service life of battery energy storage systems in microgrids.

[0097] Figure 4 This is a block diagram illustrating a distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, according to an exemplary embodiment. This device is used in a distributed online optimization method for corrosion degradation balancing in a microgrid energy storage system. (Refer to...) Figure 4 The device includes a topology model construction module 310, an optimization model construction module 320, a model transformation module 330, an initialization module 340, and a scheduling decision output module 350. Wherein: The topology model construction module 310 is used to construct a dynamic communication topology model of a microgrid battery energy storage system. A time-varying directed graph is used to represent the dynamic communication process of the dynamic communication topology model. The time-varying directed graph includes nodes, edges, and node communication weight matrices.

[0098] The optimization model construction module 320 is used to construct a distributed online energy dispatch optimization model for the microgrid battery energy storage system based on the dynamic communication topology model and considering the balanced lifespan of the microgrid battery energy storage system. The distributed online energy dispatch optimization model includes: the generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and the constraints, and introduces auxiliary variables for balancing battery lifespan.

[0099] The model transformation module 330 is used to transform the distributed online energy dispatch optimization model into a distributed solution Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online.

[0100] The initialization module 340 is used to initialize the parameters related to the power generation cost function, transaction cost function, corrosion and aging cost function and constraints, initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem, and introduce coupling variables for each node.

[0101] The scheduling decision output module 350 is used to exchange dual and coupled variables of nodes based on the dynamic communication and real-time information of the microgrid battery energy storage system, and update the decision variables, dual variables and coupled variables to obtain the power scheduling decision at the next moment, so as to realize online scheduling that balances economy and battery life.

[0102] In this embodiment of the invention, a distributed online optimization model that integrates the dynamic evolution of battery health status is constructed, and a distributed coordination algorithm that does not rely on globally sensitive data and adapts to dynamic topology switching is designed to achieve long-term minimization of the total system operating cost and balance the lifespan of heterogeneous energy storage batteries, thereby improving the overall service life of battery energy storage systems in microgrids.

[0103] Figure 5 This is a schematic diagram of the structure of a distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, as provided in an embodiment of the present invention. Figure 5 As shown, the distributed online optimization device for corrosion degradation balancing in microgrid energy storage systems may include the above-mentioned... Figure 4 The illustrated microgrid energy storage system corrosion degradation balancing distributed online optimization device. Optionally, the microgrid energy storage system corrosion degradation balancing distributed online optimization device 410 may include a first processor 2001.

[0104] Optionally, the distributed online optimization device 410 for corrosion degradation balancing of microgrid energy storage system may also include a memory 2002 and a transceiver 2003.

[0105] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0106] The following is combined with Figure 5 The components of the distributed online optimization device 410 for corrosion degradation balancing in microgrid energy storage systems are described in detail below: The first processor 2001 is the control center of the distributed online optimization device 410 for corrosion degradation balancing in the microgrid energy storage system. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0107] Optionally, the first processor 2001 can execute various functions of the microgrid energy storage system corrosion degradation balancing distributed online optimization device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0108] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 are shown in the diagram.

[0109] In a specific implementation, as one example, the distributed online optimization device 410 for corrosion degradation balancing in a microgrid energy storage system may also include multiple processors, such as... Figure 5 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0110] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0111] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the microgrid energy storage system corrosion degradation balancing distributed online optimization device 410. Figure 5 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0112] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0113] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 5(Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0114] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently, and its interface circuit is connected to the distributed online optimization device 410 for corrosion degradation balancing in a microgrid energy storage system. Figure 5 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0115] It should be noted that, Figure 5 The structure of the distributed online optimization device 410 for corrosion degradation balancing of microgrid energy storage system shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0116] Furthermore, the technical effects of the distributed online optimization device 410 for corrosion degradation balancing of microgrid energy storage system can be referred to the technical effects of the distributed online optimization method for corrosion degradation balancing of microgrid energy storage system described in the above method embodiments, and will not be repeated here.

[0117] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0118] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0119] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0120] It should be understood that the term "and / or" in this article 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 existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0121] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of 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 a single item or multiple items.

[0122] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0123] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0125] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 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.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, the functional units in the various embodiments of the present invention 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.

[0128] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A micro-grid energy storage system corrosion degradation equalization distributed online optimization method, characterized in that, The method includes: S1. Construct a dynamic communication topology model for a microgrid battery energy storage system, and use a time-varying directed graph to represent the dynamic communication process of the dynamic communication topology model; wherein, the time-varying directed graph includes nodes, edges and node communication weight matrices; S2. Based on the dynamic communication topology model, considering the balanced lifespan of the microgrid battery energy storage system, a distributed online energy dispatch optimization model for the microgrid battery energy storage system is constructed. The distributed online energy dispatch optimization model includes: the power generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and the constraints. An auxiliary variable for balancing battery lifespan is also introduced. S3. Transform the distributed online energy dispatch optimization model into a distributed solution Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online. S4. Initialize the relevant parameters of the power generation cost function, transaction cost function, corrosion and aging cost function and constraints; initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem; and introduce coupling variables for each node. S5. Based on the dynamic communication and real-time information of the microgrid battery energy storage system, the dual and coupled variables of the nodes are exchanged, and the decision variables, dual variables and coupled variables are updated to obtain the power dispatch decision at the next moment, so as to realize the online dispatch that balances economy and battery life.

2. The microgrid energy storage system corrosion degradation equalization distributed online optimization method of claim 1, wherein, The nodes of the time-varying directed graph in S1 include: multiple battery nodes, generator set nodes, and public network nodes; The time-varying directed graph allows for network interruptions or incomplete connections at a single moment, but information can be transmitted through paths within any given finite time window.

3. The method of claim 1, wherein, The distributed online energy dispatch optimization model in S2 is shown in equation (1) below: （1） In the formula, Represents auxiliary variables. This indicates the output power of the generator set. Indicates the power exchanged with the public network. Indicates the battery's charging and discharging power. Indicates the total number of batteries. Indicates a time range. This indicates the cost of generating electricity from the generator set. This represents the transaction cost of exchanging power with the public network. Indicates the cost of health degradation, This indicates the minimum charging and discharging power of the battery. This indicates the maximum charging and discharging power of the battery. This indicates the charge power required by the user. This indicates the maximum power that the generator set can provide. This indicates the minimum battery level. Indicates the initial battery level. This indicates the maximum battery level.

4. The distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems according to claim 1, characterized in that, The S3 includes: S31. Based on the distributed online energy dispatch optimization model, the Lagrange dual optimization problem is obtained; wherein, the Lagrange dual optimization problem includes multiple dual variables and regularization parameters; S32. According to the Carlow-Kuhn-Tucker conditions, the derivative of the objective function of the Lagrange dual optimization problem with respect to the auxiliary variables is 0. Then, the Lagrange dual optimization problem is transformed to obtain the equivalent problem of the Lagrange dual optimization problem. S33. Constraints based on dual variables are coupled constraints in a fully distributed network. The equivalent problem of the Lagrange dual optimization problem is transformed to obtain a distributed solution to the Lagrange dual form. S34. Based on the Lagrange dual form, obtain the instantaneous optimized form of the Lagrange dual form that can be iteratively computed online.

5. The distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems according to claim 1, characterized in that, The introduction of coupling variables for each node in S4 includes: A first coupling variable and a second coupling variable are introduced for each battery node, and a third coupling variable and a fourth coupling variable are introduced for the generator node and the public grid node. The first coupling variable is used to estimate the sum of the dual variables, and the second, third and fourth coupling variables are used to estimate the sum of the generator output power, the power exchanged with the public grid and the charging and discharging power of the battery.

6. The distributed online optimization method for corrosion degradation balancing in microgrid energy storage systems according to claim 1, characterized in that, The process in S5 involves exchanging the dual and coupling variables of nodes and updating the decision variables, dual variables, and coupling variables, including: For each node in the time-varying directed graph, the dual and coupling variables of the node are swapped with those of its current neighbor nodes. Update the decision variables based on the dual and coupling variables of the node and its neighbors; Update the dual and coupling variables based on the updated decision variables.

7. A distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, wherein the distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system is used to implement the distributed online optimization method for corrosion degradation balancing in a microgrid energy storage system as described in any one of claims 1-6, characterized in that, The device includes: The topology model construction module is used to construct a dynamic communication topology model for a microgrid battery energy storage system. A time-varying directed graph is used to represent the dynamic communication process of the dynamic communication topology model. The time-varying directed graph includes nodes, edges, and node communication weight matrices. The optimization model construction module is used to construct a distributed online energy dispatch optimization model for a microgrid battery energy storage system based on a dynamic communication topology model and considering the balanced lifetime of the microgrid battery energy storage system. The distributed online energy dispatch optimization model includes: the generation cost function of the generator set, the transaction cost function between the microgrid and the public grid, the corrosion and aging cost function of the battery energy storage system, and constraints, and introduces auxiliary variables for balancing battery lifetime. The model transformation module is used to transform the distributed online energy dispatch optimization model into a distributed solution Lagrange dual form, and derive the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online. The initialization module is used to initialize the parameters related to the power generation cost function, transaction cost function, corrosion and aging cost function and constraints, initialize the decision variables, dual variables and regularization term parameters in the instantaneous optimization problem, and introduce coupling variables for each node. The dispatch decision output module is used to exchange dual and coupled variables of nodes based on the dynamic communication and real-time information of the microgrid battery energy storage system, and update the decision variables, dual variables and coupled variables to obtain the power dispatch decision for the next moment, so as to realize online dispatch that balances economy and battery life.

8. The distributed online optimization device for corrosion degradation balancing in microgrid energy storage systems according to claim 7, characterized in that, The process of transforming the distributed online energy dispatch optimization model into a distributed solvable Lagrange dual form, and deriving the instantaneous optimization form of the Lagrange dual form that can be iteratively computed online, includes: S31. Based on the distributed online energy dispatch optimization model, the Lagrange dual optimization problem is obtained; wherein, the Lagrange dual optimization problem includes multiple dual variables and regularization parameters; S32. According to the Carlow-Kuhn-Tucker conditions, the derivative of the objective function of the Lagrange dual optimization problem with respect to the auxiliary variables is 0. Then, the Lagrange dual optimization problem is transformed to obtain the equivalent problem of the Lagrange dual optimization problem. S33. Constraints based on dual variables are coupled constraints in a fully distributed network. The equivalent problem of the Lagrange dual optimization problem is transformed to obtain a distributed solution to the Lagrange dual form. S34. Based on the Lagrange dual form, obtain the instantaneous optimized form of the Lagrange dual form that can be iteratively computed online.

9. A distributed online optimization device for corrosion degradation balancing in a microgrid energy storage system, characterized in that, The distributed online optimization device for corrosion degradation balancing in the microgrid energy storage system includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 6.

Images