Elastic power grid recovery method and system integrating multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solution

Abstract

The invention provides an elastic power grid recovery method and system fusing multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving, and the method comprises the steps: constructing a power system model according to the state data of a power grid; according to the power and voltage data of each node, establishing a normal-post-disaster double-layer power flow constraint comprising a node power balance equation; according to the state-of-charge equation, the charge-discharge power and state limitation constraint and the energy storage life loss cost, establishing an energy storage equipment dynamic constraint; establishing a post-disaster line power association equation and a unit time load recovery upper limit constraint based on the designed post-disaster recovered line power and load recovery constraint; taking minimization of total elastic recovery cost in a given operation period as an optimization objective, and constructing an objective function of the power system model; and solving the target function through a distributed algorithm to obtain a global consistency optimization result. According to the method, the robustness and the collaboration of rapid self-healing after the power grid disaster are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system equipment, and particularly relates to an elastic power grid recovery method and system fusing multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving. BACKGROUND

[0002] In recent years, extreme natural disasters such as typhoons, earthquakes and ice disasters occur frequently, which poses unprecedented challenges to the resilience of power systems. The traditional post-disaster recovery of power grid mainly relies on centralized optimization control method, that is, the dispatch center uniformly collects global information such as power grid topology, load demand and device state, and generates a recovery strategy by solving a large-scale mathematical programming model. However, in the extreme scenarios of communication link interruption, local monitoring data loss or large-scale node withdrawal, the centralized system is paralyzed due to the inability to obtain complete data in real time, which may cause delay in generating recovery strategies, or even completely lose the recovery ability due to the damage of the control center itself.

[0003] To break through the bottleneck of the centralized architecture, academia and industry gradually turn to distributed optimization technology, trying to realize node localization decision through multi-agent system, distributed model predictive control and other frameworks. For example, the "regional autonomous collaborative recovery" method allows each node to generate power generation output and load reduction scheme based on local information, and exchanges boundary power constraints with adjacent nodes through limited communication. However, the existing distributed methods still have multiple bottlenecks: firstly, the coordination of physical constraints is insufficient, most models regard network power flow, energy storage action and load recovery as independent optimization variables, which leads to conflicts between local strategies when integrated. Typical problems include excessive use of local energy storage by nodes leading to power over-limit of adjacent lines, or mismatched load recovery timing causing voltage frequency fluctuation; secondly, the existing distributed framework mostly relies on fixed partitioning cooperation mechanism, which requires nodes to pre-allocate computing tasks before optimization by dividing cooperation groups, when some nodes exit due to communication interruption or mobile devices such as emergency power supply vehicles are temporarily connected, the current optimization process needs to be interrupted and re-partitioned, which causes unpredictable delay of the recovery process. In addition, although some researches try to introduce data-driven methods such as reinforcement learning to improve decision speed, the black box model lacks explicit embedding of power grid physical laws, which makes it difficult to strictly guarantee the safety constraints such as line capacity and energy storage SOC, and there is a risk of action over-limit in actual deployment.

[0004] Further analysis reveals that the shortcomings of existing technologies stem from a deep-seated contradiction between model construction and solution methods. At the model level, most studies employ simplified single-timescale optimization or steady-state power flow assumptions, neglecting key dynamic constraints such as post-disaster grid topology dynamic reconfiguration, equipment derating, and energy storage charge / discharge rate limitations. This leads to a severe disconnect between the generated strategies and the actual physical responses. Simultaneously, existing objective functions often focus on maximizing load recovery or minimizing recovery time, lacking a coordinated quantification of energy storage lifetime loss, soaring generation costs, and economic losses from power outages. This frequently results in a dilemma between economic efficiency and serviceability during actual recovery. The interplay of these problems severely restricts the rapid self-healing capability of power systems under extreme disaster scenarios, necessitating an innovative method that deeply integrates dynamic physical constraints, supports resilient node collaboration, and considers multi-objective collaborative optimization. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for resilient power grid restoration that integrates multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solution, in order to solve the above problems.

[0006] This invention provides a resilient power grid recovery method that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution. The method includes: constructing a power system model based on power grid state data; establishing a normal-post-disaster two-layer power flow constraint, including node power balance equations, based on the power and voltage data of each node; establishing dynamic constraints for energy storage devices based on the state-of-charge equation, charging and discharging power and state-limit constraints, and energy storage lifetime loss costs; establishing post-disaster line power correlation equations and unit-time load recovery upper limit constraints based on the designed post-disaster recovery line power and load recovery constraints; constructing an objective function for the power system model with the optimization objective of minimizing the total cost of resilient recovery within a given operating cycle. This total cost, including economic losses caused by severe power system fluctuations, energy storage costs, and generation costs, is coordinated through a multi-objective optimization function to achieve a globally consistent optimization result.

[0007] In another implementation of the present invention, the normal-post-disaster two-layer power flow constraint is:

[0008] in, Represents a node i Total power generation and They represent energy storage facilities. m exist t Discharge and charging power at any given time, post-disaster power flow Indicates the line i - j Real-time power during dynamic reconfiguration process denotes the pre-disaster baseline of the node i at time t , denotes the node i at t total load shedding amount at time is the line i - j active power under the reference operating condition.

[0009] In another implementation of the present application, the dynamic constraint of the energy storage device is:

[0010]

[0011] wherein, is the post-disaster recovery state of the node i at time t , denotes the voltage amplitude of the node i at time t .

[0012] In another implementation of the present application, the objective function is:

[0013] wherein, , and are weight coefficients respectively measuring the importance of the three costs; is the economic loss of the load k ; is the unit time loss cost of the energy storage m at time t ; is the generation cost.

[0014] Another aspect of the present invention provides a resilient power grid recovery system that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution, comprising: a model building module for constructing a power system model based on power grid state data; a constraint establishment module for establishing a normal-post-disaster two-layer power flow constraint including node power balance equations based on the power and voltage data of each node; establishing dynamic constraints for energy storage devices based on the state of charge equation, charging and discharging power and state limitation constraints, and energy storage lifetime loss cost; establishing post-disaster line power correlation equations and unit time load recovery upper limit constraints based on the designed post-disaster recovery line power and load recovery constraints; and an objective function construction module for constructing an objective function for the power system model with the optimization objective of minimizing the total cost of resilient recovery within a given operating cycle. The total cost is coordinated by a multi-objective optimization function to implement a global recovery strategy, including economic losses caused by severe power system fluctuations, energy storage costs, and power generation costs. Optimization and solution module: The objective function is solved using a distributed algorithm to obtain a globally consistent optimization result.

[0015] In another implementation of the present invention, the normal-post-disaster two-layer power flow constraint is:

[0016] in, Represents a node i Total power generation and They represent energy storage facilities. m exist t Discharge and charging power at any given time, post-disaster power flow Indicates the line i - j Real-time power during dynamic reconfiguration process Indicates nodes in the pre-disaster baseline i In time t The original load demand, Represents a node i exist t Total load shedding at any given time, normal power flow It is a line i - j Active power under reference operating conditions.

[0017] In another implementation of the present invention, the dynamic constraints of the energy storage device are:

[0018]

[0019] in, Nodes in post-disaster recovery status i In timet voltage amplitude, This indicates a node in normal operating condition. i In time t The voltage amplitude.

[0020] In another implementation of the present invention, the objective function is:

[0021] in, , and These are the weighting coefficients for measuring the importance of the three costs; For load k Economic losses; For energy storage m In time t The cost per unit time lost; For the cost of generating electricity.

[0022] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a resilient power grid restoration method integrating multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of a resilient power grid restoration method integrating multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution as described in any of the preceding claims.

[0023] This invention presents a resilient power grid recovery method that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution. It constructs a two-layer power flow variable for normal and post-disaster recovery states, forming a dynamic constraint system through node power balance equations, line derating constraints, a full lifecycle model of energy storage, and voltage recovery rate limits. A multi-objective optimization function is designed, incorporating economic losses in power supply, energy storage lifetime losses, and generation costs. The model is decoupled and solved in parallel using the Alternating Direction Multiplier Method (ADMM). The physical feasibility of the strategy is ensured through post-disaster line power correlation equations and load recovery rate constraints. Furthermore, a dynamic topology management module allows for rapid restart of optimization iterations when nodes are added or removed, providing highly robust, highly collaborative, and economically balanced technical support for rapid self-healing of the power grid after disasters. Attached Figure Description

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Through reading the following detailed description of the embodiments, the advantages and benefits in the solutions will become clear to those skilled in the art. The drawings are only for the purpose of illustrating the preferred embodiments, and are not considered as limiting the present application. In the drawings: Figure 1 The flowchart of the elastic power grid recovery method of an embodiment of the present application is shown in the figure.

[0025] Figure 2 The control block diagram of the elastic power grid recovery system of an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0026] In order to make the person skilled in the art better understand the technical solutions in the embodiments of the present application, the following will combine the drawings in the embodiments of the present application, and clearly and detailedly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those skilled in the art should belong to the protection scope of the embodiments of the present application.

[0027] The elastic power grid refers to a technical system that maintains the power supply to key loads under extreme disturbance conditions through active defense, rapid adaptation and efficient recovery. Its core is to build a dynamic self-healing architecture that adapts to uncertain environments to achieve the "impact resistance, damage reduction and accelerated recovery" elastic triangle target. Elastic defense covers real-time state perception, risk assessment, collaborative control and self-healing decision-making. Traditional defense strategies mainly rely on relay protection devices and offline emergency plans, but face two major challenges: one is that extreme disturbances (such as multiple node outages caused by hurricanes) exceed the preset fault set, causing the protection system to fail; two is that under high penetration of distributed energy, the dynamic interaction complexity of source-grid-load increases dramatically, and traditional centralized optimization is difficult to respond in real time.

[0028] Figure 1 The flowchart of the elastic power grid recovery method of an embodiment of the present application is shown in the figure. Figure 1 The present embodiment mainly includes: S101, constructing a power system model according to power grid state data.

[0029] For example, before making post-disaster recovery decisions, a dynamic power grid model including nodes, lines, loads, and distributed resources is constructed. To clearly describe this multi-agent collaborative power system post-disaster distributed decision-making and response model, a simplified power system model is first established, which includes a... n A network of nodes, defining the set of power grid nodes. With line set ,node i and j The lines between them are marked as i - j Each line is configured with pre-disaster rated capacity parameters. and post-disaster capacity reduction coefficient Generally, the post-disaster derating factor is determined by historical disaster data or real-time monitoring; for example, it is set to 0.6 when lightning strikes cause insulation aging.

[0030] Based on this, for each node i Configure load set Each load The rated power is Energy storage device collection Assignment to nodes based on geographical location, property ownership, and other relationships. i That is, for each node i Energy storage devices m It can be recorded as having The parameters of each energy storage device include maximum charging and discharging power. and Charge and discharge efficiency and Capacity limits and and life loss coefficient .

[0031] In addition, a communication adjacency matrix is ​​generated based on the power grid physical topology. , specify nodes i Communicating only with directly electrically connected adjacent nodes, such as i - j If a line exists, the communication link is valid. In practice, nodes... i By recording the flow direction of its own data to determine valid connections, this dynamic communication topology allows for the automatic removal of associated links when a node fails and the expansion of adjacency relationships when a new node is added.

[0032] S102. Based on the power and voltage data of each node, establish a normal-post-disaster two-layer power flow constraint that includes the node power balance equation.

[0033] S103. Based on the state equation of charge, charging and discharging power and state constraints, and energy storage life loss cost, establish dynamic constraints for energy storage equipment.

[0034] S104. Based on the design constraints for line power and load recovery after disaster, establish the correlation equation for line power after disaster and the upper limit constraint for load recovery per unit time.

[0035] S105. With the goal of minimizing the total cost of elastic recovery within a given operating cycle, construct the objective function of the power system model. The total cost is coordinated by a multi-objective optimization function to implement the global recovery strategy, including economic losses, energy storage costs, and power generation costs caused by severe fluctuations in the power system.

[0036] S106. Solve the objective function using a distributed algorithm to obtain a globally consistent optimization result.

[0037] For example, matrix factorization is used to globally constrain equations. and The problem is decoupled to the local subproblems corresponding to each node. The nodes only need to exchange boundary variables (such as adjacent line power and energy storage capacity). Each node solves the local subproblems in parallel using the alternating direction multiplier method and iteratively updates the Lagrange multipliers and penalty phases to coordinate the consistency of cross-node constraints and achieve collaborative convergence.

[0038] This invention presents a resilient power grid recovery method that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution. It constructs a two-layer power flow variable for normal and post-disaster recovery states, forming a dynamic constraint system through node power balance equations, line derating constraints, a full lifecycle model of energy storage, and voltage recovery rate limits. A multi-objective optimization function is designed, incorporating economic losses in power supply, energy storage lifetime losses, and generation costs. The model is decoupled and solved in parallel using the Alternating Direction Multiplier Method (ADMM). The physical feasibility of the strategy is ensured through post-disaster line power correlation equations and load recovery rate constraints. Furthermore, a dynamic topology management module allows for rapid restart of optimization iterations when nodes are added or removed, providing highly robust, highly collaborative, and economically balanced technical support for rapid self-healing of the power grid after disasters.

[0039] In another implementation of the present invention, the normal-post-disaster two-layer power flow constraint is:

[0040] in, Represents a node i Total power generation and They represent energy storage facilities. m exist t Discharge and charging power at any given time, post-disaster power flow representing lines i - j Real-time power during dynamic reconstruction, representing nodes in pre-disaster baseline i At time t Original load demand, representing nodes i Total load shedding at t moment, normal power flow Line i - j Active power in reference operating condition, Load demand reference value for node i , Total load shedding for node i .

[0041] Exemplarily, the model integrates a grid-ES interaction architecture with multi-time scale coordination to represent power system constraints in a resilient grid. To represent normal and post-disaster operating states, first, a bi-flow state variable is defined, i.e., to achieve joint representation of pre-disaster normal state and post-disaster recovery state, a two-level constraint system containing the following core equations is established: Power flow balance equation: In the post-disaster state, there is:

[0042] On this basis, there is:

[0043] To convert the model into an optimizable second-order cone model, the Disflow model is used, and there is:

[0044] And subject to post-disaster line capacity constraint, there is:

[0045] Wherein, Reactive power shedding.

[0046] And in the normal state, there is:

[0047] And

[0048] At the same time, without load shedding, the power balance equation for node i is:

[0049] And the line capacity constraint is:

[0050] In the above model, normal power flow is the line i - j active power in the base case, while post-disaster power flow represents the real-time power in the dynamic reconfiguration process, and has the form i where j denotes the total generation of node , i denotes the original load demand (pre-disaster baseline) of node at time i , t denotes the total load shedding of node at time i . Meanwhile, t and denote the discharging power and charging power of energy storage facility at time m , respectively. In addition, t denotes the voltage amplitude of node at time i in the post-disaster recovery state, while t denotes the voltage amplitude of node at time i in the normal operating state. t

[0051] Meanwhile, to further characterize the node load, the load of node i is denoted as:

[0052] and the load shedding is quantified as:

[0053] where and denote the power demand of load k under normal conditions and the power demand reduction at time t in the post-disaster state, respectively.

[0054] The actual power supply of the load must satisfy the following relationship:

[0055] The above normal-post-disaster double-layer model has a topological correlation, i.e., there may be line disconnections in the post-disaster state, resulting in a different network topology from the normal state. Therefore, the switch state of the line is defined, and the line connectivity constraint is introduced when , line i -​j After disaster disconnection, the power flow is forced to be zero, i.e. .

[0056] In another implementation of the present application, the energy storage device dynamic constraint is:

[0057]

[0058] wherein, is the voltage amplitude of the node i at time t . represents the voltage amplitude of the node i at time t .

[0059] Exemplarily, for the energy storage device, a state of charge (SOC) dynamic equation and charging and discharging action constraints are introduced, i.e. in the charging and discharging process of a single energy storage device m , there are:

[0060] and there are limits:

[0061] On this basis, the charging and discharging power of the energy storage device is limited by the device capacity and the action exclusion condition:

[0062]

[0063] In addition, there is also a charging and discharging action exclusion of the energy storage device, denoted as:

[0064] wherein, and represent the charging and discharging states of the energy storage, respectively.

[0065] The charging and discharging power of the energy storage also needs to be compatible with the node voltage safety range, and the following two equations are used to indicate that the power output limit and the power output capability of the energy storage adaptively adjust with the node voltage fluctuation, respectively:

[0066]

[0067] Safety recovery rate constraint: At the same time, the load recovery rate and the energy storage output need to satisfy the real-time matching relationship, denoted as:

[0068] In addition, the post-disaster node voltage recovery needs to gradually approach the normal state to avoid sudden impact on the equipment, and there are:

[0069] Among them, is the maximum allowed instantaneous deviation, and is the recovery rate coefficient.

[0070] It should be noted that in the above formula:

[0071] for dynamic correlation of post-disaster line power.

[0072] In another implementation of the present application, the objective function is:

[0073] Among them, , and are weight coefficients for measuring the importance of the three costs, respectively; is the economic loss of the load k ; is the energy storage m cost in the unit time t ; is the generation cost.

[0074] Exemplarily, the cost of elastic recovery is mainly composed of three parts, which are the economic loss caused by the severe fluctuation of the power system, the energy storage cost and the generation cost, respectively.

[0075] The economic loss caused by the severe fluctuation of the power system can be measured by the following formula, that is:

[0076] Among them, is the economic loss of the load k , represents the power loss coefficient of the load k .

[0077] Considering the life cost of the energy storage device, the single energy storage device m has:

[0078] Among them, is the energy storage m cost in the unit time t , is the energy storage mThe life loss coefficient of the unit, unit: ￥ / kWh.

[0079] The generation cost can be recorded as:

[0080] where, , and are the coefficients of the generation cost of the nodes i .

[0081] Based on the above fast recovery constraints of the extreme situation of the power system, the minimization of the comprehensive cost in the whole cycle is proposed:

[0082] where, , and are the weight coefficients for measuring the importance of the three costs.

[0083] The global optimization problem is decomposed into parallel sub-problems of each node by the alternating direction multiplier method (ADMM). For this purpose, a general model of ADMM is first provided, in which the decision variables of each node x i The system decision variables are composed of:

[0084] where each , and the objective function of optimization is assumed to be:

[0085] where, is a closed convex function, , and the constant vector . Subsequently, the augmented Lagrangian function of the objective function is established, in which the penalty parameter , and the Lagrange multiplier , so that the augmented Lagrangian is:

[0086] Based on the above formula, the ADMM iteration process is: (1) Local sub-problem update: Starting from the initial value and , each node does

[0087] where, k indicates the iteration number, indicates the firstk The decision variable value after the secondary iteration.

[0088] (2) Update the multiplier:

[0089] (3) Determine whether the stopping criterion is met: If the stopping criterion is met after one iteration, the iteration is stopped and the optimization result is obtained, otherwise the iteration is continued. The stopping criterion includes the judgment of the primal residual and the dual residual. The primal residual is defined as:

[0090] And the dual residual is defined as:

[0091] Generally, the stopping criterion is as follows:

[0092] The present application aims at the failure of the traditional centralized recovery strategy in the communication interruption and node withdrawal scene, and the shortcomings of the existing distributed method in the physical constraint coordination, dynamic topology adaptability and multi-objective optimization ability, by constructing a pre-disaster / post-disaster double-layer power flow model to accurately describe the network reconstruction and resource interaction mechanism, establishing the joint optimization target of energy storage life, line capacity reduction and generation cost, and realizing model decoupling and parallel solving based on the alternating direction multiplier method. The method overcomes the multiple bottlenecks of traditional methods in coordination performance and dynamic performance, provides an innovative technical path for the rapid self-healing of power grid in extreme disaster scenarios, ensures the physical feasibility of the strategy through the post-disaster line power correlation equation and load recovery rate constraint, and quickly restarts the optimization iteration when the node is added or deleted through the dynamic topology management module, providing high robustness, strong coordination and economic balance for the rapid self-healing of power grid after disaster.

[0093] In another aspect of the present application, an elastic power grid recovery system integrating multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving is provided, comprising: A model construction module: constructing a power system model according to power grid state data.

[0094] A constraint establishment module: establishing normal-post-disaster double-layer power flow constraints including node power balance equations according to node power and voltage data; establishing dynamic constraints of energy storage devices according to state of charge equations, charging and discharging power and state limit constraints, energy storage life loss cost; establishing post-disaster line power correlation equations and unit time load recovery upper limit constraints based on designed post-disaster recovery line power and load recovery constraints.

[0095] Objective function construction module: construct the objective function of the power system model with the optimization goal of minimizing the total cost of elastic recovery in a given operating cycle, the total cost is coordinated by a multi-objective optimization function to global recovery strategy, including economic loss caused by power system severe fluctuation, energy storage cost and power generation cost.

[0096] Optimization solving module: solve the objective function by distributed algorithm to get global consistency optimization result.

[0097] Exemplarily, the system further comprises a node controller, a communication module, a global verification unit and a dynamic topology management module.

[0098] The communication module is used to send point-to-point based network broadcast node optimization results and receive adjacent node data.

[0099] Node controller: deployed at each power grid node, performs optimization modeling to generate local load reduction, energy storage charging and discharging and power generation output strategy.

[0100] The global verification unit is used for global verification unit compliance check, including: verifying whether the strategy leads to line power over-limit and verifying whether the energy storage charging and discharging violates the SOC safety range, and when any of the above conditions is not met, triggering the local optimization module to regenerate the strategy.

[0101] Global verification unit compliance check includes: Verifying whether the strategy leads to line power over-limit ( ); Verifying whether the energy storage charging and discharging violates the SOC safety range ( ); When any of the above conditions is not met, triggering the local optimization module to regenerate the strategy.

[0102] The dynamic topology management module is used to: detect node communication state, if node i fails, remove its decision variable in the next optimization cycle ; when a new node is added, its parameters , are added to the global model and the optimization iteration is restarted.

[0103] Can interact with grid equipment control: Charging and discharging power of energy storage converter and ; Load switch action to achieve control ; Output adjustment of generator set .

[0104] The system can interact with the grid equipment to control the charge-discharge power of the energy storage converter, the load switch action to realize control and the output adjustment of the generator set.

[0105] The system supports node local decision, cross-region constraint verification and dynamic topology reconstruction. The specific implementation of the post-disaster recovery system of the flexible power grid is based on Figure 2 The module architecture realizes distributed rapid recovery decision and safety control in extreme disaster scenarios through the collaborative operation of the node controller, communication module, global verification unit and dynamic topology management module. The core process of system implementation is as follows.

[0106] Based on the locally perceived grid state (including node voltage, line power, energy storage state of charge and load demand), each node controller performs optimization modeling, generates a preliminary strategy containing local load reduction , energy storage charge-discharge power , and generation output , and sends the boundary line power , adjacent node voltage and energy storage capacity state to the adjacent nodes directly connected in the form of point-to-point broadcast through the communication module.

[0107] The global verification unit verifies whether the strategy leads to line power overrun (i.e. ) or energy storage SOC exceeding the safe range (i.e. ) by receiving each node strategy in real time and embedding node power balance equation, energy storage dynamic constraint and line power correlation equation. If illegal behavior is detected in optimization, the system triggers an alarm and notifies the relevant node controller to regenerate the optimization strategy, while recording the illegal event.

[0108] The dynamic topology management module continuously monitors the node communication state. When it detects that node i is out of operation or communication interruption due to disaster, it immediately removes its decision variable and the power variables of the associated lines and in the next optimization cycle, and updates the adjacency matrix A to shield the failed node.

[0109] When an emergency power supply vehicle or mobile energy storage and other new nodes are connected, the dynamic topology management module automatically expands the decision variable set, initializes its parameters (including energy storage capacity , charge-discharge efficiency and and line connection relationship), and realizes collaborative optimization under the new topology by restarting ADMM iteration.

[0110] At the optimization solving level, the node controller converts the model into a standard convex optimization format and solves it in parallel using ADMM, whose decision variables cover generation, energy storage actions and load control instructions. The communication module uses a lightweight communication protocol to achieve low-latency data exchange between adjacent nodes in each iteration, only transmitting the boundary line power 、 , node voltage square and energy storage SOC state, avoiding the bandwidth pressure caused by global information synchronization. The update of the Lagrange multiplier in the ADMM framework ensures data integrity through cryptographic signatures, preventing malicious nodes from tampering with the optimization results.

[0111] The elastic power grid recovery system of the present application integrates multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving, builds a double-layer power flow variable of normal state and post-disaster recovery state, forms a dynamic constraint system through node power balance equation, line capacity reduction constraint, energy storage full life cycle model and voltage recovery rate limit; designs a multi-objective optimization function including power supply economic loss, energy storage life consumption and generation cost, and realizes model decoupling and parallel solving based on alternating direction multiplier method (ADMM); through the post-disaster line power correlation equation and load recovery rate constraint, the physical feasibility of the strategy is ensured, and through the dynamic topology management module, the optimization iteration is quickly restarted when nodes are added or deleted, providing high robustness, strong collaboration and economic balance for the fast self-healing of the power grid after the disaster.

[0112] In another implementation of the present application, the normal-post-disaster double-layer flow constraint is:

[0113] wherein, Pd(t) represents the total generation of node i , Pd(t) represents the total generation of node and respectively represent the discharge power and charge power of energy storage facility m at time t , post-disaster power flow represents the real-time power of line i - j in the dynamic reconstruction process, represents the original load demand of node i at time t in the pre-disaster baseline, represents the total load shedding of node i at time t , normal power flow is the active power of line i - j under the baseline working condition.

[0114] In another implementation of the present application, the dynamic constraint of the energy storage device is:

[0115]

[0116] wherein, is the voltage amplitude of the node i at time t in the post-disaster recovery state. is the voltage amplitude of the node i at time t in the normal operation state.

[0117] In another implementation of the present application, the objective function is:

[0118] wherein, , and are weight coefficients for measuring the importance of the three costs, respectively; is the economic loss of the load k ; is the unit time loss cost of the energy storage m at time t ; is the generation cost.

[0119] In another aspect of the present application, the electronic device comprises a processor, a memory, and a communication bus, and a communication interface.

[0120] wherein: the processor, the memory, and the communication interface complete mutual communication through the communication bus.

[0121] The communication interface is configured to communicate with other electronic devices or servers.

[0122] The processor is configured to execute a program, and specifically can execute the steps of the flexible power grid recovery method of any one of the above embodiments, which integrates multi-agent collaborative modeling, dynamically coupled and distributed efficient solving.

[0123] Specifically, the program can include program code, and the program code includes computer operation instructions.

[0124] The processor can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the application. The one or more processors included in the smart device can be of the same type, such as one or more CPUs; or can be of different types, such as one or more CPUs and one or more ASICs.

[0125] The memory is configured to store programs. The memory can include a high-speed RAM memory, and can further include a non-volatile memory, such as at least one disk memory.

[0126] The programs can be specifically configured to cause the processor to perform the steps of any of the embodiments of the method for restoring an elastic power grid by means of multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving. The specific implementation of each step in the programs can refer to the corresponding description of the steps and units performed by any of the embodiments of the method for restoring an elastic power grid by means of multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving described above, which will not be repeated here. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working processes of the devices and modules described above can refer to the corresponding process descriptions in the foregoing method embodiments.

[0127] The exemplary embodiments of the application further provide a non-transitory computer-readable storage medium having computer instructions stored therein, wherein the computer instructions are configured to cause a computer to execute the method of the embodiments of the application.

[0128] The method according to the embodiments of the application described above can be implemented in hardware, firmware, or as software stored in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk or an optical disk, or computer code downloaded over a network and originally stored in a remote recording medium or a non-transitory machine-readable medium and then stored in a local recording medium, so that the method described herein can be processed by such software on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware such as an ASIC or an FPGA. It can be understood that the computer, the processor, the microprocessor controller or the programmable hardware includes a storage component (for example, a RAM, a ROM, a flash memory, etc.) that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code will convert the general-purpose computer into a special-purpose computer for executing the method shown herein.

[0129] To this end, particular embodiments of the application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results.

[0130] It should be noted that all directional indications, such as upper, lower, left, right, front, back, rear, etc., are merely used for convenience of description and are not intended to limit the application to a particular orientation.

[0131] In the description of the present application, the terms "first", "second", etc., are used only to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features.

[0132] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0133] It should be noted that, although the specific embodiments of the present application are described in detail with reference to the accompanying drawings, it should not be understood as limiting the scope of protection of the present application. Various modifications and variations of the embodiments described in the claims within the scope of the present application can be made by those skilled in the art without creative labor.

[0134] The examples of the embodiments of the present application are intended to simply illustrate the technical features of the embodiments of the present application, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present application, and are not improper limitations of the embodiments of the present application.

[0135] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An elastic power grid restoration method that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solution, characterized in that, The method comprises the following steps: constructing a power system model according to power grid state data; establishing normal-post-disaster double-layer power flow constraints containing node power balance equations according to power and voltage data of each node; establishing dynamic constraints of energy storage equipment according to state of charge equations, charging and discharging power and state limit constraints, and energy storage life loss cost; establishing post-disaster line power correlation equations and per unit time load recovery upper limit constraints based on designed post-disaster recovery line power and load recovery constraints; constructing an objective function of the power system model with the optimization objective of minimizing total elastic recovery cost in a given operation period, wherein the total cost coordinates global recovery strategies through a multi-objective optimization function, and the total cost includes economic losses caused by severe fluctuations of the power system, energy storage cost and power generation cost; solving the objective function through a distributed algorithm to obtain a globally consistent optimization result.

2. The method of claim 1, wherein, The normal-post-disaster double-layer power flow constraints are: wherein, denotes the total generation of a node i , and denote the discharging power and charging power of an energy storage facility m at time t , the post-disaster power flow denotes the line i - j power in real-time during dynamic reconfiguration, denotes the original load demand of a node i at time t in the pre-disaster baseline, denotes the total load shed of a node i at time t , the normal power flow is the active power of a line i - j in the baseline operating condition.

3. The method of claim 2, wherein, The dynamic constraints of the energy storage equipment are: wherein, is the voltage amplitude at the node i in the time t of the post-disaster recovery state. is the voltage amplitude at the node i in the time t of the normal operation state.

4. The method of claim 1, wherein, The objective function is: wherein, , and are weight coefficients measuring the importance of the three costs, respectively; is the economic loss of the load k ; is the cost of the storage m in time t ; is the cost of power generation.

5. An elastic power grid restoration system that integrates multi-agent collaborative modeling, dynamic constraint coupling, and distributed efficient solving, characterized in that, The method comprises the following steps: a model construction module: constructing a power system model according to power grid state data; a constraint establishment module: establishing normal-post-disaster double-layer power flow constraints containing node power balance equations according to power and voltage data of each node; establishing dynamic constraints of energy storage equipment according to state of charge equations, charging and discharging power and state limit constraints, and energy storage life loss cost; and establishing post-disaster line power correlation equations and per unit time load recovery upper limit constraints based on designed post-disaster recovery line power and load recovery constraints; an objective function construction module: constructing an objective function of the power system model with the optimization objective of minimizing total elastic recovery cost in a given operation period, wherein the total cost coordinates global recovery strategies through a multi-objective optimization function, and the total cost includes economic losses caused by severe fluctuations of the power system, energy storage cost and power generation cost; an optimization solving module: solving the objective function through a distributed algorithm to obtain a globally consistent optimization result.

6. The method of claim 5, wherein, The normal-post-disaster double-layer power flow constraints are: wherein, denotes the nodes i of the total generation, and denotes the discharge power and the charge power of the energy storage facilities m at the time instant t , the post-disaster power flow denotes the lines i - j the real-time power in the dynamic reconfiguration process, denotes the original load demand of the nodes i at the time t in the pre-disaster baseline, denotes the total load shedding of the nodes i at the time instant t , the normal power flow is the active power of the lines i - j in the reference operating condition.

7. The method of claim 6, wherein, The dynamic constraints of the energy storage equipment are: wherein, is the voltage amplitude at the node i in the post-disaster recovery state t at time is the voltage amplitude at the node i in the normal operation state t at time 8. The method of claim 5, wherein, The objective function is: wherein, , and are weight coefficients measuring the importance of the three costs, respectively; is the economic loss of the load k ; is the cost of the storage m waste in time t ; is the cost of power generation.

9. An electronic device, comprising: The method comprises the following steps: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements steps of the elastic power grid recovery method of fused multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving according to any one of claims 1 to 4 when executing the computer program.

10. A computer storage medium, characterized in that, A computer program is stored on the computer storage medium, and the computer program is executed by the processor to implement steps in the elastic power grid recovery method of fused multi-agent collaborative modeling, dynamic constraint coupling and distributed efficient solving according to any one of claims 1 to 4.

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