Power distribution network power supply recovery method based on mobile energy storage and flexible load cooperation

Abstract

The invention provides a power distribution network power supply recovery method based on mobile energy storage and flexible load cooperation. The method comprises the following steps: considering multiple constraints such as a mobile energy storage space-time scheduling constraint and a distributed power supply power constraint, and establishing a power grid side main body game model taking the minimum operation cost of a power distribution network during a fault period as an upper objective function; multiple constraints such as a flexible load power constraint and an electric quantity conservation constraint are considered, and a flexible load side subordinate body game model with the maximum flexible load adjustment income of the pulverizer as a lower layer objective function is established; model solving is carried out based on the master-slave game model, the main body game model transmits the flexible load adjustment excitation electricity price to the slave body game model, the slave body game model transmits the power consumption behavior of the flexible load to the main body, and iteration is repeated until the model converges; and a power supply recovery strategy based on mobile energy storage and flexible load cooperation is obtained, and the strategy guides the power utilization behavior of the flexible load by exciting the electricity price, and gives consideration to the multi-subject benefits of the power grid side and the flexible load side.

Description

Technical Field

[0001] This invention relates to the field of power supply restoration in distribution networks, and more specifically, to a method for power supply restoration in distribution networks based on the coordination of mobile energy storage and flexible loads. Background Technology

[0002] Taking feed grinders as an example, power outages caused by disasters such as typhoons and rainstorms will directly lead to the accumulation of raw materials at the upstream end and the disruption of the downstream aquaculture chain, resulting in a non-linear surge in economic losses. Among related technologies, grinders have the potential for flexible adjustment. If this potential can be intelligently developed, it will provide a key breakthrough for achieving precise, efficient, and adaptive grinding operations, significantly improving industry efficiency.

[0003] New flexible resources, such as mobile energy storage, flexible loads, and distributed power sources, are being connected to the distribution network on a large scale. However, these new flexible resources lack sufficient automation capabilities and fail to fully consider the decision-making independence and differences in interests between grid operators and users as different stakeholders. Summary of the Invention

[0004] In view of this, the present invention provides a power supply restoration method for distribution networks based on the coordination of mobile energy storage and flexible loads.

[0005] One aspect of the present invention provides a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads. The controller of the power supply restoration system performs the following iterative operations: First, the initial flexible load incentive price is transmitted to a flexible load-side slave game model. Then, the flexible load-side slave game model is solved, with the lower-level objective function being maximizing the flexible load adjustment benefit of the crusher. This yields flexible load electricity consumption behavior information that maximizes the flexible load adjustment benefit of the crusher. The constraints of the flexible load-side slave game model include: flexible load constraints, total runtime constraints, energy conservation constraints, and flexible load power constraints. Next, the flexible load electricity consumption behavior information is transmitted to a grid-side principal game model. Then, the grid-side principal game model is solved, with the upper-level objective function being minimizing the distribution network operating cost during the fault period. This yields the power supply restoration scheme that minimizes the distribution network operating cost during the fault period and the target flexible load incentive price. Based on the initial electricity price search range, the constraints of the grid-side principal game model include: mobile energy storage scheduling constraints, mobile energy storage charging and discharging constraints, mobile energy storage state of charge constraints, grid radial topology constraints, load shedding power constraints, flexible load incentive price constraints, distributed power generation power constraints, and distribution network power flow constraints. Based on the initial flexible load incentive price and the target flexible load incentive price, the target electricity price search range is determined and used as the initial electricity price search range for the grid-side principal game model in the next iteration. The target flexible load incentive price is then used as the initial flexible load incentive price to be transmitted to the flexible load-side slave game model in the next iteration. If the iteration meets the convergence condition, the power supply restoration scheme is output based on the grid-side principal game model, and the flexible load electricity consumption behavior information is output based on the flexible load-side slave game model to achieve power supply restoration of the distribution network.

[0006] According to the above-described implementation scheme of the present invention, the model construction of master-slave game is conducive to realizing the strategic interaction and interest coordination between the grid side and the flexible load side, generating a source-grid-load-storage flexible multi-resource collaborative power supply restoration scheme that can be agreed upon by different stakeholders such as grid operators and crusher users. While ensuring the safe operation of the grid, it realizes the utility balance of multiple stakeholders and provides an efficient engineering decision-making tool for improving the resilience of the distribution network after disasters. Attached Figure Description

[0007] The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0008] Figure 1 An exemplary system architecture for a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads, according to an embodiment of the present invention, is shown.

[0009] Figure 2AAn architecture diagram of a master-slave game model for implementing a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads, according to an embodiment of the present invention, is shown.

[0010] Figure 2B A flowchart illustrating the overall process of a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown.

[0011] Figure 3 A schematic diagram of the spatiotemporal dynamic scheduling of mobile energy storage according to an embodiment of the present invention is shown;

[0012] Figure 4A A flowchart illustrating the model solution using a master-slave game model based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown.

[0013] Figure 4B A schematic diagram illustrating the iterative update of the upper and lower limits of the incentive electricity price based on the bisection method according to an embodiment of the present invention is shown;

[0014] Figure 5 A block diagram of a power distribution network restoration device based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown;

[0015] Figure 6 A block diagram of an electronic device suitable for implementing a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads, according to an embodiment of the present invention, is shown. Detailed Implementation

[0016] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0017] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0018] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0019] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0020] Figure 1 An exemplary system architecture for a power supply restoration method based on mobile energy storage and flexible load coordination, according to an embodiment of the present invention, is shown. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to embodiments of the present invention, in order to help those skilled in the art understand the technical content of the present invention, but do not mean that embodiments of the present invention cannot be used in other devices, systems, environments or scenarios.

[0021] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a server 101, a network 102, and a power distribution network-transportation network coupled architecture 103.

[0022] Network 102 serves as a medium for providing a communication link between server 101 and the power distribution network-transportation network coupled architecture 103. Network 102 may include various connection types, such as wired and / or wireless communication links, etc.

[0023] Server 101 can be a server that provides various services, such as a controller for a power restoration system or a back-end management server. The back-end management server can analyze and process the received data and feed the processing results back to the power distribution network-transportation network coupled architecture 103.

[0024] The power distribution network-transportation network coupling architecture 103 is used to provide server 101 with system parameters including resources such as power distribution network, transportation network, crusher, mobile energy storage, and distributed power source, and can receive the power consumption behavior of crusher and power distribution network power restoration plan returned by server 101.

[0025] It should be noted that the power supply restoration method for distribution networks based on the coordination of mobile energy storage and flexible loads provided in this embodiment of the invention can generally be executed by server 101. Alternatively, the power supply restoration method for distribution networks based on the coordination of mobile energy storage and flexible loads provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 101 but can communicate with server 101.

[0026] For example, the initial flexible load incentive price, the flexible load-side slave game model constructed for the crusher, and the grid-side principal game model constructed for the distribution network can be originally stored in server 101, or stored on an external storage device and imported into server 101. Then, server 101 can locally execute the distribution network power supply restoration method based on mobile energy storage and flexible load coordination provided in this embodiment of the invention, or send the initial flexible load incentive price, the flexible load-side slave game model, and the grid-side principal game model to other servers or server clusters, and have other servers or server clusters that receive the initial flexible load incentive price, the flexible load-side slave game model, and the grid-side principal game model execute the distribution network power supply restoration method based on mobile energy storage and flexible load coordination provided in this embodiment of the invention.

[0027] It should be understood that Figure 1 The number of networks and servers shown is merely illustrative. Depending on implementation needs, there can be any number of networks and servers.

[0028] Mobile energy storage (MES) can accurately match post-disaster load demand through spatial transfer and rapid response capabilities, but related research still has significant limitations: most power restoration methods adopt a centralized single-objective optimization framework, which does not fully consider the decision-making independence and interest differences of grid operators and flexible load users such as crushers as different stakeholders; the modeling of typical loads in rural distribution networks does not fully explore the adjustability of flexible loads and lacks a price-incentive-based pricing mechanism; and the failure to establish a master-slave game relationship between the grid and flexible loads, such as crushers, results in the failure to fully realize the emergency potential of mobile energy storage and flexible loads, making it difficult to achieve incentive compatibility and game equilibrium that minimizes grid-side operating costs and maximizes crusher-side adjustment benefits. Therefore, a multi-resource collaborative recovery method is needed that can integrate the coupling characteristics of distribution network and transportation network with mobile energy storage and utilize load temporal elasticity. Based on the master-slave game framework of the Stackelberg competition model, this method can achieve strategic interaction and utility equilibrium among multiple stakeholders, including the distribution network and the power grid, through a hierarchical decision-making mechanism in which the grid side formulates flexible load incentive prices and the flexible load side optimizes electricity consumption behavior by setting flexible load incentive prices. This will effectively improve the post-disaster resilience of the distribution network.

[0029] Figure 2A An architecture diagram of a master-slave game model for implementing a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads, according to an embodiment of the present invention, is shown.

[0030] like Figure 2AAs shown, the master-slave game model 200 is characterized by constructing a two-layer game framework: the upper layer is the grid-side master game model 210, which aims to minimize the operating cost during a fault (load shedding cost + load regulation subsidy cost) and decides on the location and power of mobile energy storage, the power of distributed power sources, the active power reduction power of loads, the reactive power reduction power, etc., in relation to the flexible load incentive price. The lower layer is the flexible load-side slave game model 220, which aims to maximize the flexible load regulation revenue and decides on the flexible load's electricity consumption behavior. The load regulation subsidy cost and the load regulation subsidy profit can be jointly reflected by the flexible load incentive price and the flexible load's electricity consumption behavior.

[0031] Furthermore, by constructing a power distribution network-transportation network coupling model that takes into account traffic congestion coefficients, the spatiotemporal dynamic behavior of mobile energy storage under a master-slave game environment can be accurately characterized.

[0032] According to the above embodiments of the present invention, the grid side transmits incentive prices to the flexible load side to guide the electricity consumption behavior of the flexible load, and the flexible load side feeds back its electricity consumption behavior to the grid side. The two sides achieve Stackelberg game equilibrium through strategic interaction and information feedback. By simulating the real-time impact of post-disaster traffic conditions on mobile energy storage dispatch, and combining the flexible load shifting characteristics to carry out collaborative optimization, the optimal dispatch path and power restoration scheme for mobile energy storage are generated, which can maintain the power supply to critical loads as much as possible and reduce load reduction power. Applying the method to multi-point fault scenarios caused by extreme disasters such as typhoons and icing, based on the master-slave game model, it can provide effective multi-agent coordinated decision support for the post-disaster recovery of distribution networks.

[0033] Figure 2B A flowchart illustrating the overall process of a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown.

[0034] like Figure 2B As shown, the method includes operations S210 to S250.

[0035] In operation S210, determine the system parameters.

[0036] According to an embodiment of the present invention, it is first necessary to determine various parameters such as distribution network topology, line parameters, distributed power generation capacity, mobile energy storage technology parameters, crusher parameters, and flexible / ordinary load parameters to provide data support for post-disaster power restoration.

[0037] In operation S220, a grid-side principal game model is established with the goal of minimizing the operating cost of the distribution network during a fault as the upper-level objective function.

[0038] According to an embodiment of the present invention, see Figure 2AAs shown, taking the minimization of the distribution network's operating cost during a fault as the upper-level objective function, a grid-side principal game model 210 can be constructed. Operating costs can include load reduction costs and flexible load adjustment costs. In the context of distribution network-transportation network coupling, the grid-side principal game model 210 considers the power transfer characteristics of mobile energy storage at both temporal and spatial scales, and accurately models the temporal and spatial scheduling model of mobile energy storage by taking into account multiple constraints such as mobile energy storage scheduling constraints, mobile energy storage charging and discharging constraints, and mobile energy storage state of charge constraints. Furthermore, the grid-side principal game model 210 also needs to consider multiple constraints such as the grid's radial topology constraints, load reduction power constraints, flexible load incentive price constraints, distributed generation power constraints, and distribution network power flow constraints.

[0039] Based on the above embodiments of the present invention, after an extreme event causes the distribution network to disconnect from the main grid, flexible resources such as mobile energy storage, distributed power sources, and flexible loads can be fully dispatched to restore power supply to as many critical loads as possible.

[0040] In operation S230, a flexible load side follower game model is established with the goal of maximizing the benefits of flexible load adjustment of the crusher as the lower objective function.

[0041] According to an embodiment of the present invention, see further. Figure 2A As shown, taking the maximization of the benefits of flexible load adjustment of the crusher as the lower-level objective function, a flexible load-side follower game model 220 can be constructed. The flexible load-side follower game model 220 can take into account multiple constraints such as flexible load constraints, total running time constraints, power conservation constraints, and flexible load power constraints.

[0042] In operating S240, a master-slave game model based on the coordination of mobile energy storage and flexible load is used to solve the model.

[0043] According to an embodiment of the present invention, a master-slave game strategy can be used to coordinate the interests of demand response loads between the distribution network and the crusher, formulate a reasonable flexible load incentive price, and guide the electricity consumption behavior of the flexible loads on the crusher side and the distribution network. The upper-level objective function of the master is set to minimize the operating cost of the distribution network, and the lower-level objective function of the slave is set to maximize the benefits of the flexible load. The master transmits the flexible load incentive price to the slave, and runs a slave game model with the objective function of maximizing the benefits of the flexible load to obtain the electricity consumption behavior of the flexible load. Then, the slave transmits the electricity consumption behavior of the flexible load back to the master, and runs a master game model with the objective function of minimizing the operating cost of the power grid to obtain the flexible load incentive price and the multi-resource collaborative power supply restoration scheme, updating the search range of the flexible load incentive price. The master and slave iterate repeatedly until an equilibrium solution is obtained.

[0044] By operating the S250, a power restoration strategy based on the coordination of mobile energy storage and flexible loads is obtained.

[0045] According to embodiments of the present invention, solving the master-slave game model can, under the condition that the above iterative operations satisfy the corresponding convergence conditions, yield power restoration schemes including distributed power generation schemes, mobile energy storage scheduling schemes, network reconfiguration schemes, load reduction power, and adjusted flexible load incentive prices, as well as flexible load electricity consumption behavior. This master-slave game model can fully coordinate the interests of the grid side and the flexible load side. On the grid side, it ensures the power supply to critical loads as much as possible by scheduling mobile energy storage and distributed power sources, and guides the electricity consumption behavior of flexible loads by setting reasonable incentive prices, thus balancing the dual objectives of minimizing grid-side load shedding costs and maximizing flexible load-side adjustment benefits.

[0046] The following is based on Figure 2A , Figure 2B The implementation process of the present invention will be further described in detail below.

[0047] According to an embodiment of the present invention, for the above operation S220, an upper-level objective function as shown in formula (1) can be constructed.

[0048] (1)

[0049] (2)

[0050] (3)

[0051] In formulas (1) to (3), This represents the cost of load shedding. This represents the total cost of flexible load incentives. Indicates the operating cost of the power distribution network. This represents the load shedding cost per unit of power reduction, where T represents the set of fault periods and N represents the set of load nodes in the distribution network. This represents the weight coefficient of node i. This represents the active power reduction at node i at time t. This represents the flexible load incentive price for the unit of electricity transferred during the m-th fault period. This represents the operating state of the crusher s during the m-th fault period, as output by the flexible load side from the volume game model. This represents the input power of the crusher s during the m-th fault period, as output by the flexible load side from the volume game model. This represents the operating time of the crusher s during the m-th fault period, as output by the flexible load side from the volume game model. All of them are decision variables.

[0052] According to an embodiment of the present invention, in a coupled distribution network and transportation network system, the charging and discharging power and location of mobile energy storage jointly determine the scheduling state of mobile energy storage. Therefore, a node travel distance adjacency matrix can be established, and the shortest path between node i and node j can be obtained through a shortest path algorithm. Considering the congestion of the transportation network, the dynamic real-time speed of mobile energy storage is obtained, and the travel time of mobile energy storage between two nodes is calculated based on the node travel distance and speed.

[0053] (4)

[0054] (5)

[0055] (6)

[0056] In formulas (4) to (6), This represents the static shortest travel distance between node i and node j in the power distribution network. This represents the actual shortest travel distance between node i and node j at time t under congestion conditions; This represents the actual speed at which the energy storage k moves at time t; The ideal speed of the mobile energy storage k is given without considering congestion; e is a natural constant, and c represents the congestion coefficient of the traffic network, which can be estimated based on the traffic flow. Let represent the actual shortest travel time of mobile energy storage k from node i to node j starting at time t. Formula (4) represents the actual shortest travel distance of mobile energy storage, formula (5) represents the dynamic real-time speed of mobile energy storage, and formula (6) represents the actual shortest travel time of mobile energy storage.

[0057] When the distribution network issues a dispatch command to the mobile energy storage, the mobile energy storage needs to select the optimal route based on the traffic network congestion situation and move from node i to node j for charging and discharging operations in the shortest travel time. A mobile energy storage can only be connected to one node at any given time. It is not in a connected state when traveling between node i and node j, and can only charge and discharge when it is connected to a node.

[0058] Based on this, see Figure 2A Constraints as shown in formulas (7) to (9) can be constructed as mobile energy storage time-spacing constraints in the grid-side main game model 210.

[0059] (7)

[0060] (8)

[0061] (9)

[0062] In formulas (7) to (9), This indicates the connection state between the mobile energy storage device k and node i at time t. It can be an adjustable 0 / 1 variable, where a value of 1 indicates that the mobile energy storage k is connected to node i, and a value of 0 indicates that the mobile energy storage k is not connected to node i. Indicates that mobile energy storage k is in The connection status with node j at any given time. Indicates time interval, Indicates the installation time of mobile energy storage. This is a 0 / 1 variable, representing the charging state of the mobile energy storage k at time t. A value of 1 indicates that it is in a charging state, and a value of 0 indicates that it is not in a charging state. The variable is 0 / 1, representing the discharge state of mobile energy storage k at time t. A value of 1 indicates that it is in the discharge state, and a value of 0 indicates that it is not in the discharge state. Formula (7) indicates that when the action time of mobile energy storage is less than the sum of the travel time of mobile energy storage between two nodes and the installation time of mobile energy storage, mobile energy storage is not connected to any node; Formula (8) indicates that a mobile energy storage can only be connected to one node; Formula (9) indicates the coupling relationship between the charging and discharging state and the location of mobile energy storage, which can only be charged and discharged when connected to node i or node j.

[0063] Since formula (9) contains bilinear terms, the Big M method can be used to linearize formula (9), introduce intermediate variables, and transform the bilinear constraint into a linear constraint as shown in formula (10).

[0064] (10)

[0065] In formula (10), Indicate that the mobile energy storage k and node i are in the range from t to t. The connection status during a time period can be a 0 / 1 variable. A value of 1 indicates that the mobile energy storage and the node are connected during the corresponding time period, while a value of 0 indicates that the mobile energy storage and the node are disconnected during the corresponding time period.

[0066] Figure 3 A schematic diagram of the spatiotemporal dynamic scheduling of mobile energy storage according to an embodiment of the present invention is shown.

[0067] like Figure 3 As shown, line 310 is a combined power line and transportation line. The dots on line 310 represent electrical load nodes. The mobile energy storage 320 implements dynamic scheduling on line 310 based on time information such as travel time and installation time.

[0068] According to an embodiment of the present invention, the mobile energy storage charge and discharge constraint is used to determine the charge and discharge state of the mobile energy storage and the active power output during charge and discharge, given the maximum active power allowed to be output by the mobile energy storage during charge and discharge; and to determine the charge and discharge state of the mobile energy storage and the reactive power output during charge and discharge, given the maximum reactive power allowed to be output by the mobile energy storage during charge and discharge.

[0069] Based on this, see further. Figure 2A The charging and discharging constraints of mobile energy storage in the grid-side main game model 210 can include constraints determined by formulas (11) to (14).

[0070] (11)

[0071] (12)

[0072] (13)

[0073] (14)

[0074] In formulas (11) to (14), Let represent the active power output of the mobile energy storage device k at time t during charging. This represents the maximum active power that the mobile energy storage device k is allowed to output during the charging process. Let represent the active power output of the mobile energy storage k at time t. This represents the maximum active power that the mobile energy storage k is allowed to output during discharge. This represents the reactive power output of the mobile energy storage device k during charging at time t. This represents the maximum reactive power that the mobile energy storage device k is allowed to output during the charging process. This represents the reactive power output of the mobile energy storage device k at time t. This represents the maximum reactive power that the mobile energy storage k is allowed to output during discharge. All are decision variables. Formulas (11) to (12) define the adjustment range of the active power of mobile energy storage during charging and discharging; Formulas (13) to (14) represent the upper and lower limits of the reactive power of mobile energy storage during charging and discharging.

[0075] According to an embodiment of the present invention, the state of charge constraint of mobile energy storage is used to determine the active power output of mobile energy storage at a single moment and the charge of mobile energy storage at a single moment, given the maximum capacity, minimum capacity and energy conversion efficiency of mobile energy storage during charging and discharging.

[0076] Based on this, see further. Figure 2AThe mobile energy storage charge state constraints in the grid-side main game model 210 may include constraints determined by formulas (15) to (16).

[0077] (15)

[0078] (16)

[0079] In formulas (15) to (16), This represents the charge of the mobile energy storage device k at time t. This represents the energy conversion efficiency of the mobile energy storage device k during the charging process. This represents the energy conversion efficiency of the mobile energy storage k during the discharge process. This represents the minimum capacity of mobile energy storage k. Let k represent the maximum capacity of the mobile energy storage. Formula (15) represents the charge constraint of the mobile energy storage, and formula (16) represents the upper and lower capacity constraints of the mobile energy storage.

[0080] According to an embodiment of the present invention, after a fault occurs in the distribution network, distributed generation can act as a black-start power source, and tie switches will also activate, forming an island to maintain power supply to critical loads. Virtual power node states are introduced as decision variables, and a virtual commodity flow model is used to ensure that the distribution network maintains its radial characteristics after a fault.

[0081] According to an embodiment of the present invention, the radial topology constraint of the power grid is used to determine the switching state of each branch given the initial topology connection relationship of the distribution network.

[0082] Based on this, see further. Figure 2A Constraints as shown in formulas (17) to (20) can be constructed as the grid radial topology constraints in the grid-side subject game model 210.

[0083] (17)

[0084] (18)

[0085] (19)

[0086] (20)

[0087] In formulas (17) to (20), This represents the distribution network branch located from node i to node j. It represents the set of all branches in the distribution network; The variable is 0 or 1, representing the branch at time t. The switch status is indicated by a value of 1, which means the circuit is on, and a value of 0, which means the circuit is off. This represents the total number of load nodes in the distribution network; It is a 0 / 1 variable, representing the virtual power node flag at time t. When the flag is 1, it means that the node is a virtual power node, and when the flag is 0, it means that the node is not a virtual power node. This represents the set of indices of the downstream nodes of node i. This represents the set of indices of the upstream nodes of node i. Represents the branch at time t Virtual current variables on the surface This represents the virtual power flow variable on branch ji at time t. This represents the virtual power injected into node i at time t; To presuppose a positive number, specifically, It can be a sufficiently large positive number. All of them are decision variables.

[0088] According to an embodiment of the present invention, when the supply and demand of the distribution network are unbalanced, it is necessary to reduce the load, and the load will reduce active power and reactive power proportionally.

[0089] According to an embodiment of the present invention, the load reduction power constraint is used to determine the active power and reactive power reduction of each node, given the maximum value of the active power and the maximum value of the reactive power of the load at each node.

[0090] Based on this, see further. Figure 2A Constraints as shown in formulas (21) to (22) can be constructed as load reduction power constraints in the main game model 210 on the grid side.

[0091] (twenty one)

[0092] (twenty two)

[0093] In formulas (21) to (22), This represents the active power reduction at node i at time t. This represents the maximum active power of the load at node i at time t. This represents the reactive power reduction at node i at time t. This represents the maximum reactive power of the load at node i at time t. All of them are decision variables.

[0094] According to an embodiment of the present invention, the flexible load incentive price constraint is used to determine the flexible load incentive price per unit of electricity transferred during a given single fault period, given the minimum and maximum values ​​of the flexible load incentive price per unit of electricity transferred during a single fault period, and the minimum average value of the flexible load incentive price per unit of electricity transferred during a single fault period across all fault periods.

[0095] See also Figure 2A The flexible load incentive price constraint in the grid-side main game model 210 can include constraints determined by formulas (23) to (24).

[0096] (twenty three)

[0097] To avoid minimizing the operating costs of the distribution network and thus reducing the incentive costs of flexible loads Always set to the minimum, the average value constraint shown in formula (24) can be added to the adjustment incentive cost of flexible load at different time periods.

[0098] (twenty four)

[0099] In formulas (23) and (24), This represents the minimum flexible load incentive price for a unit of electricity transferred during the m-th fault period. This represents the maximum flexible load incentive price for a unit of electricity transferred during the m-th fault period. This represents the minimum average value of the flexible load incentive electricity price.

[0100] According to an embodiment of the present invention, the distributed power constraint is used to determine the active power of each distributed power source given the maximum active power of each distributed power source; and to determine the reactive power of each distributed power source given the maximum reactive power of each distributed power source.

[0101] Based on this, see further. Figure 2A The distributed power constraints in the grid-side subject game model 210 may include constraints determined by formulas (25) to (26).

[0102] (25)

[0103] (26)

[0104] In formulas (25) to (26), This represents the active power of the distributed power source h at time t. This represents the maximum active power of the distributed power source h at time t. This represents the reactive power of the distributed power source h at time t. This represents the maximum reactive power of the distributed power source h at time t. All of them are decision variables.

[0105] According to an embodiment of the present invention, the power flow constraints of the distribution network include: power balance constraints of the grid, nonlinear constraints of voltage, current, and power, voltage difference constraints at nodes, upper and lower limit constraints of node voltages, and upper and lower limit constraints of branch currents. Specifically, in the voltage difference constraints at nodes, the square term of voltage is substituted with a variable; in the power balance constraints of the grid, the square term of current is substituted with a variable; and the nonconvex power flow equations are relaxed to be convex. The nonlinear constraints of voltage, current, and power are relaxed using a second-order cone relaxation, transforming them into a convex problem that can be solved efficiently. For network topology changes caused by switching operations, the Big M method is used to relax the voltage difference constraints.

[0106] According to an embodiment of the present invention, power flow constraints in a distribution network are used to determine the active power, reactive power, current value, and switching status of each branch, given the resistance and reactance values ​​of each branch, the maximum active power and the maximum reactive power of each node load, the maximum and minimum values ​​of the square voltage of each node, the connection status of distributed power sources and nodes, and the maximum value of the square current of each branch.

[0107] Based on this, see further. Figure 2A Constraints as shown in formulas (27) to (33) can be constructed as power flow constraints in the distribution network in the main game model 210 on the power grid side.

[0108] (27)

[0109] (28)

[0110] (29)

[0111] (30)

[0112] (31)

[0113] (32)

[0114] (33)

[0115] In formulas (27) to (33), This represents the active power transmitted by branch ij at time t. This represents the active power transmitted by branch ji at time t. This represents the reactive power transmitted by branch ij. This indicates the reactive power transmitted by branch ji. This indicates the resistance of branch ji. This represents the resistance of branch ij. This represents the reactance value of branch ji. This represents the reactance value of branch ij. This represents the square of the branch current. This represents the square of the current in branch ij. The variable is 0 or 1, representing the connection status between the distributed power source h and node i. A value of 1 indicates that the distributed power source h and node i are connected, and a value of 0 indicates that the distributed power source h and node i are not connected. Let represent the square of the voltage at node i at time t. Let represent the square of the voltage at node j at time t. This represents the minimum squared value of the voltage at node i at time t. This represents the maximum value of the squared voltage at node i at time t. This represents the maximum value of the square of the current in branch ij. For decision variables. Formulas (27) to (28) are the power balance constraints of the distribution network; formulas (29) to (30) are the voltage difference constraints of the nodes; formula (31) is the nonlinear constraint of voltage, current and power; formula (32) is the upper and lower limit constraint of node voltage; formula (33) is the upper and lower limit constraint of branch current.

[0116] After introducing new variables, formula (31) still has square terms of decision variables, which are quadratic nonlinear constraints. If the optimization objective is a strictly increasing function, then the following convex relaxation can be performed.

[0117] (34)

[0118] (35)

[0119] (36)

[0120] (37)

[0121] (38)

[0122] Formulas (34) to (38) provide a detailed mathematical process for obtaining the second-order cone constraint form by relaxing the non-convex constraint. Therefore, formula (31) can be replaced by formula (38).

[0123] According to an embodiment of the present invention, for the above operation S230, a lower-level objective function as shown in formula (39) can be constructed. Thus, by adjusting the electricity consumption behavior of the flexible load, the benefits of flexible load regulation can be maximized.

[0124] (39)

[0125] The input power controlled during the feed grinder test can be calculated according to formula (40).

[0126] (40)

[0127] In formulas (39) to (40), This indicates the benefits of flexible load adjustment for the crusher. This represents the flexible load incentive price for the unit shifted electricity during the determined m-th fault period; This represents the working status of the crusher s during the m-th fault period. It can be a 0 / 1 variable, where a value of 1 indicates that the crusher is working and a value of 0 indicates that the crusher is not working. This represents the input power of the crusher s during the m-th fault period. This represents the operating time of the crusher s during the m-th fault period. All of them are decision variables. This indicates the rated power input to the crusher. This indicates the efficiency with which the crusher converts electrical power into mechanical power. This indicates the output power of the crusher.

[0128] According to embodiments of the present invention, flexible loads possess time-series adjustment characteristics, enabling flexible migration of electricity demand across time periods while maintaining a constant total electricity consumption. As a typical flexible load in rural power grids, the pulverizer possesses both important and flexibly adjustable attributes. In the event of extreme disasters such as typhoons or torrential rains, if the pulverizer is shut down for more than 12 hours, it will lead to an interruption in the daily feed supply, triggering stress responses and reduced production risks in livestock, resulting in significant losses to the breeding area. Simultaneously, pulverizer processing is not continuous; the work arrangement for each specific operating period is highly flexible. When facing extreme disasters, the pulverizer operates at reduced capacity, rationally arranging electricity usage time while meeting the baseline electricity consumption. Incentivized by the flexible load adjustment subsidy mechanism, the pulverizer load can achieve the transfer of electricity usage time periods, but the total electricity consumption of the pulverizer load remains unchanged.

[0129] According to an embodiment of the present invention, a flexible load constraint is used to determine the operating state and input power of the crusher during a single fault period, given the total power consumption of the crusher during all fault periods.

[0130] Based on this, see further. Figure 2AConstraints as shown in formula (41) can be constructed as flexible load constraints in the flexible load side follower game model 220.

[0131] (41)

[0132] According to an embodiment of the present invention, the total runtime constraint is used to determine the working state of the crusher during a single fault period, given the total working time of the crusher during all fault periods and the preparation time before the crusher starts working.

[0133] Based on this, see further. Figure 2A Constraints as shown in formula (42) can be constructed as total runtime constraints in the flexible load-side slave game model 220.

[0134] (42)

[0135] According to an embodiment of the present invention, the power conservation constraint is used to determine the power consumption of the crusher during a single fault period, given the total power consumption of the crusher during all fault periods.

[0136] Based on this, see further. Figure 2A Constraints as shown in formula (43) can be constructed as energy conservation constraints in the flexible load side slave game model 220.

[0137] (43)

[0138] According to an embodiment of the present invention, to further explore the adjustability of flexible loads, a multi-level power adjustment knob can be integrated into the crusher control system. This knob is linked to a frequency converter or motor control unit, allowing the operator to intuitively select between preset levels such as "coarse crushing," "medium crushing," and "fine crushing" by rotating the physical knob. Each level corresponds to an optimized set of power, speed, and feeding logic, thus flexibly adapting to different raw materials and particle size requirements without complex parameter settings.

[0139] According to an embodiment of the present invention, a flexible load power constraint is used to determine the input power of the crusher during a single fault period, given the maximum and minimum input power of the crusher.

[0140] Based on this, see further. Figure 2A Constraints as shown in formula (44) can be constructed as flexible load power constraints in the flexible load side follower game model 220.

[0141] (44)

[0142] In formulas (41) to (44), This represents the total power consumption of the crusher s during all fault periods T. This indicates the preparation time before the crusher starts working. This represents the total operating time of the crusher s during all fault periods T. This represents the power consumption of the crusher s during the m-th fault period. As decision variables, This indicates the lower limit of the shiftable time period. This indicates the upper limit of the shiftable time period. This indicates the minimum input power of the crusher. This indicates the maximum input power of the crusher.

[0143] Regarding the above operation S240, Figure 4A A flowchart illustrating the model solution using a master-slave game model based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown.

[0144] like Figure 4A As shown, the method includes operations S410 to S440 being iteratively executed by the controller of the power restoration system.

[0145] In operation S410, the initial flexible load incentive price is transmitted to the flexible load side slave game model. The flexible load side slave game model with the goal of maximizing the flexible load adjustment benefit of the crusher is solved to obtain the flexible load electricity consumption behavior information that maximizes the flexible load adjustment benefit of the crusher. The constraints of the flexible load side slave game model include: flexible load constraints, total running time constraints, power conservation constraints, and flexible load power constraints.

[0146] According to an embodiment of the present invention, the specific method for solving the model to obtain flexible load electricity consumption behavior information in operation S410 may include: determining the working state and input power of the crusher during a single fault period based on flexible load constraints, total operating time constraints, power conservation constraints, and flexible load power constraints, thereby obtaining multiple candidate working state input power sequences; for each working state input power sequence, combining the lower-level objective function, and given the flexible load incentive price for a unit shifted power during a single fault period, solving for the crusher's flexible load adjustment benefit, thereby obtaining multiple crusher flexible load adjustment benefits; and determining the flexible load electricity consumption behavior information based on the working state input power sequence corresponding to the crusher flexible load adjustment benefit with the largest benefit among the multiple crusher flexible load adjustment benefits.

[0147] For this operation, see Figure 2A This means that the flexible load-side subject game model can optimize the scheduling of flexible load's electricity consumption behavior based on the obtained initial flexible load incentive price and with the goal of maximizing the adjustment benefits of flexible load, and transmit the information of flexible load's electricity consumption behavior to the grid-side subject game model.

[0148] In operation S420, the flexible load electricity consumption behavior information is transmitted to the grid-side principal game model. The grid-side principal game model is solved with the objective function of minimizing the distribution network operating cost during the fault period. The result is the power supply restoration scheme that minimizes the distribution network operating cost during the fault period and the target flexible load incentive price. The target flexible load incentive price is determined based on the initial price search range. The constraints of the grid-side principal game model include: mobile energy storage time-sharing scheduling constraints, mobile energy storage charging and discharging constraints, mobile energy storage state of charge constraints, grid radial topology constraints, load reduction power constraints, flexible load incentive price constraints, distributed power constraints, and distribution network power flow constraints.

[0149] According to an embodiment of the present invention, the specific method for solving the model in operation S420 to obtain the power restoration scheme and the target flexible load incentive price may include: determining the active power reduction of each node at each moment during all fault periods and the flexible load incentive price per unit of shifted electricity during a single fault period based on the time-scheduling constraints of mobile energy storage, the charging and discharging constraints of mobile energy storage, the state of charge constraints of mobile energy storage, the radial topology constraints of the power grid, the load reduction power constraints, the flexible load incentive price constraints, the distributed power constraints, and the power flow constraints of the distribution network, thereby obtaining multiple candidate active power reduction flexible load incentive price sequences; for each active power reduction flexible load incentive price sequence, combining the upper-level objective function, given the load shedding cost per unit of reduction power, the weight coefficients of each node in the distribution network, and the working state and input power of the crusher output by the flexible load side slave game model during each fault period, solving the distribution network operating cost, thereby obtaining multiple distribution network operating costs; and determining the power restoration scheme and the target flexible load incentive price based on the active power reduction flexible load incentive price sequence corresponding to the distribution network operating cost with the lowest cost among the multiple distribution network operating costs.

[0150] For this operation, please refer to [link / reference]. Figure 2A This indicates that the grid-side subject game model can optimize the scheduling of flexible resources such as mobile energy storage and distributed power sources based on the obtained flexible load electricity consumption behavior with the goal of minimizing grid operating costs, thereby obtaining a power supply restoration scheme that coordinates multiple flexible resources and a target flexible load incentive price.

[0151] In operation S430, based on the initial flexible load incentive price and the target flexible load incentive price, the target price search range is determined as the initial price search range used by the grid-side principal game model in the next round of iteration, and the target flexible load incentive price is used as the initial flexible load incentive price to be transmitted to the flexible load-side slave game model in the next round of iteration.

[0152] According to embodiments of the present invention, such as Figure 2AThe master-slave game model shown can be solved by the bisection method. The main idea of ​​the bisection method is to provide a feasible interval for system scheduling, and this interval always contains the optimal operation state of system scheduling. In each iteration, the lower or upper bound is updated according to the value in the interval to gradually narrow the interval range until it is less than or equal to the convergence value, thus obtaining the optimal result.

[0153] Based on this, an initial electricity price search range can be set first, so as to determine the initial flexible load incentive electricity price used at the start of the first round of iteration operation according to the initial electricity price search range.

[0154] For example, the initial flexible load incentive price can be set based on formula (45).

[0155] (45)

[0156] In formula (45), The initial value can be pre-defined according to business needs, based on the initial value. Calculated It can be used as an initial flexible load incentive price.

[0157] Then, based on the preset formulas shown in formulas (46) to (47), the update can be performed. This determines the initial electricity price search range to be used in the next round of iteration.

[0158] (46)

[0159] (47)

[0160] In formulas (46) and (47), This represents the target flexible load incentive price obtained from the d-th iteration operation, where d is a positive integer. When d equals 1... Take the initial flexible load incentive price.

[0161] Figure 4B A schematic diagram of the upper and lower limits of the incentive electricity price based on the bisection method is shown according to an embodiment of the present invention.

[0162] like Figure 4B As shown, by using the bisection method to continuously compress and update the search interval for the incentive electricity price, it is beneficial to accelerate the solution speed of the master-slave game model.

[0163] In operation S440, under the condition that the iterative operation meets the convergence condition, the power supply restoration scheme is output based on the main game model of the power grid side and the power consumption behavior information of the flexible load is output based on the slave game model of the flexible load side, so as to realize the power supply restoration of the distribution network.

[0164] According to an embodiment of the present invention, the convergence condition includes: the absolute value of the difference between the target flexible load incentive price adjusted in the current iteration and the target flexible load incentive price adjusted in the previous iteration is less than or equal to 0.0001.

[0165] Based on this, the convergence condition can be shown in formula (48).

[0166] (48)

[0167] In formula (48), It is a positive number.

[0168] According to an embodiment of the present invention, the power restoration scheme determined based on operation S430 can be used as the power restoration strategy on the grid side, and the flexible load power consumption behavior information determined based on operation S430 can be used as the power restoration strategy on the crusher side.

[0169] Through the above embodiments of the present invention, the model construction of master-slave game facilitates the strategic interaction and interest coordination between the power grid side and the flexible load side, generating a multi-resource collaborative recovery scheme of source, grid, load and storage that can be agreed upon by different stakeholders such as power grid operators and crusher users. While ensuring the safe operation of the power grid, it achieves the utility balance of multiple stakeholders and provides an efficient engineering decision-making tool for improving the resilience of the distribution network after disasters.

[0170] According to the above embodiments of the present invention, a two-layer Stackelberg game model is constructed: the upper layer aims to minimize the grid operating cost and decides on power restoration schemes such as the location and power of mobile energy storage, the power of distributed power sources, the switching status of distribution network lines, and the power of load reduction, as well as flexible load incentive prices; the lower layer aims to maximize the benefits of flexible load regulation and optimizes the flexible load electricity consumption period and power based on the flexible load incentive prices transmitted from the upper layer, thereby obtaining the flexible load electricity consumption behavior and transmitting the flexible load electricity consumption behavior to the upper layer; the two layers iterate repeatedly to obtain an equilibrium solution that adapts to the different interests of the flexible load side (represented by the crusher) and the distribution network side.

[0171] Figure 5 A block diagram of a power distribution network restoration device based on the coordination of mobile energy storage and flexible loads according to an embodiment of the present invention is shown.

[0172] like Figure 5 As shown, the power supply restoration device 500 based on mobile energy storage and flexible load coordination includes a flexible load-side slave game module 510, a grid-side master game module 520, a master-slave iteration module 530, and a scheme generation module 540.

[0173] The flexible load side slave game module 510 is used to transmit the initial flexible load incentive price to the flexible load side slave game model, solve the flexible load side slave game model with the goal of maximizing the flexible load adjustment benefit of the crusher, and obtain the flexible load electricity consumption behavior information that maximizes the flexible load adjustment benefit of the crusher. The constraints of the flexible load side slave game model include: flexible load constraints, total running time constraints, power conservation constraints, and flexible load power constraints.

[0174] The grid-side principal game module 520 is used to transmit flexible load electricity consumption behavior information to the grid-side principal game model. It solves the grid-side principal game model with the objective function of minimizing the distribution network operating cost during the fault period. It obtains the power supply restoration scheme that minimizes the distribution network operating cost during the fault period and the target flexible load incentive price. The target flexible load incentive price is determined based on the initial price search range. The constraints of the grid-side principal game model include: mobile energy storage time-space scheduling constraints, mobile energy storage charging and discharging constraints, mobile energy storage state of charge constraints, grid radial topology constraints, load reduction power constraints, flexible load incentive price constraints, distributed power constraints, and distribution network power flow constraints.

[0175] The master-slave iteration module 530 is used to determine the target electricity price search range based on the initial flexible load incentive price and the target flexible load incentive price, and use it as the initial electricity price search range used by the main game model on the grid side in the next round of iteration operation. The target flexible load incentive price is used as the initial flexible load incentive price to be transmitted to the slave game model on the flexible load side in the next round of iteration operation.

[0176] The scheme generation module 540 is used to output a power supply restoration scheme based on the main body game model of the power grid side and output the power consumption behavior information of the flexible load based on the slave body game model of the flexible load side, so as to realize the power supply restoration of the distribution network when the iterative operation meets the convergence condition.

[0177] Any one or more of the modules according to embodiments of the present invention, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules according to embodiments of the present invention can be implemented by splitting them into multiple modules. Any one or more of the modules according to embodiments of the present invention can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, and firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules according to embodiments of the present invention can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0178] For example, any plurality of the flexible load-side slave game module 510, the grid-side master game module 520, the master-slave iteration module 530, and the scheme generation module 540 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the flexible load-side slave game module 510, the grid-side master game module 520, the master-slave iteration module 530, and the scheme generation module 540 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in hardware or firmware, or in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the flexible load-side slave game module 510, the power grid-side master game module 520, the master-slave iteration module 530, and the scheme generation module 540 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0179] Figure 6 A block diagram of an electronic device suitable for implementing a power supply restoration method for a distribution network based on the coordination of mobile energy storage and flexible loads, according to an embodiment of the present invention, is shown. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0180] like Figure 6As shown, an electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0181] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0182] According to an embodiment of the present invention, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to a bus 604. The system 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A driver 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the driver 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0183] According to embodiments of the present invention, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the system of the embodiments of the present invention. According to embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0184] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0185] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0186] For example, according to embodiments of the present invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0187] Embodiments of the present invention also include a computer program product, which includes a computer program containing program code for executing the methods provided in the embodiments of the present invention. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the power supply restoration method for distribution networks based on the coordination of mobile energy storage and flexible loads provided in the embodiments of the present invention.

[0188] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this embodiment of the invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0189] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0190] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features recited in the various embodiments and / or claims of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not expressly stated in the present invention. In particular, the features described in the various embodiments and / or claims of this invention can be combined and / or combined in various ways without departing from the spirit and teachings of this invention. All such combinations and / or combinations fall within the scope of this invention.

[0192] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for power supply restoration in a distribution network based on the coordination of mobile energy storage and flexible loads, characterized in that, The controller of the power restoration system performs the following iterative operations: The initial flexible load incentive price is transmitted to the flexible load side slave game model. The flexible load side slave game model is solved with the goal of maximizing the flexible load adjustment benefit of the crusher as the lower objective function. The flexible load electricity consumption behavior information that maximizes the flexible load adjustment benefit of the crusher is obtained. The constraints of the flexible load side slave game model include: flexible load constraints, total running time constraints, power conservation constraints, and flexible load power constraints. The flexible load electricity consumption behavior information is transmitted to the grid-side principal game model. The grid-side principal game model is solved with the objective function of minimizing the distribution network operating cost during the fault period. The result is the power supply restoration scheme that minimizes the distribution network operating cost during the fault period and the target flexible load incentive price. The target flexible load incentive price is determined based on the initial price search range. The constraints of the grid-side principal game model include: mobile energy storage time-sharing scheduling constraints, mobile energy storage charging and discharging constraints, mobile energy storage state of charge constraints, grid radial topology constraints, load reduction power constraints, flexible load incentive price constraints, distributed power constraints, and distribution network power flow constraints. Based on the initial flexible load incentive price and the target flexible load incentive price, the target price search interval is determined and used as the initial price search interval for the main game model on the grid side in the next round of iteration. The target flexible load incentive price is used as the initial flexible load incentive price to be transmitted to the slave game model on the flexible load side in the next round of iteration. When the iterative operation meets the convergence condition, a power supply restoration scheme is output based on the main game model on the grid side and the power consumption behavior information of the flexible load is output based on the slave game model on the flexible load side, so as to realize the power supply restoration of the distribution network.

2. The method according to claim 1, characterized in that, The flexible load constraint is used to determine the working status and input power of the crusher during a single fault period, given the total power consumption of the crusher during all fault periods. The total runtime constraint is used to determine the working status of the crusher during a single fault period, given the total working time of the crusher during all fault periods and the preparation time before the crusher starts working. The energy conservation constraint is used to determine the energy consumption of the crusher during a single fault period, given the total energy consumption of the crusher during all fault periods. The flexible load power constraint is used to determine the input power of the crusher during a single fault period, given the maximum and minimum input power values ​​of the crusher.

3. The method according to claim 2, characterized in that, The solution to the flexible load side-entity game model, which takes maximizing the flexible load adjustment benefit of the crusher as the lower-level objective function, yields the following flexible load electricity consumption behavior information that maximizes the flexible load adjustment benefit of the crusher: Based on the flexible load constraint, the total running time constraint, the power conservation constraint, and the flexible load power constraint, the working state and input power of the crusher during a single fault period are determined, resulting in multiple candidate working state input power sequences. For each of the aforementioned working states, input power sequences are combined with the lower-level objective function. Given the flexible load incentive price for unit shift power during a single fault period, the flexible load adjustment revenue of the crusher is solved to obtain multiple flexible load adjustment revenues for the crusher. Based on the working state input power sequence corresponding to the pulverizer with the greatest flexible load adjustment benefit among the multiple pulverizers, the power consumption behavior information of the flexible load is determined.

4. The method according to claim 3, characterized in that, The flexible load incentive price constraint is used to determine the flexible load incentive price per unit of electricity transferred during a given single fault period, based on the minimum and maximum values ​​of the flexible load incentive price per unit of electricity transferred during a single fault period across all fault periods.

5. The method according to claim 4, characterized in that, The mobile energy storage scheduling constraint is used to constrain the following: when the mobile energy storage's operating time is less than the sum of the actual shortest travel time between two nodes and the mobile energy storage's installation time, the mobile energy storage will not connect to any node; a mobile energy storage can only connect to one node; the mobile energy storage can only charge and discharge when two nodes are connected; wherein, the actual shortest travel time is determined based on the actual shortest travel distance and actual speed of the mobile energy storage, the actual speed is determined based on the ideal vehicle speed of the mobile energy storage without considering traffic congestion and the congestion coefficient of the traffic network, and the actual shortest travel distance is determined based on the static shortest travel distance between two nodes in the distribution network and the actual speed; The mobile energy storage charge and discharge constraints are used to determine the charge and discharge state of the mobile energy storage and the active power output during charge and discharge, given the maximum active power allowed to be output during charge and discharge; and to determine the charge and discharge state of the mobile energy storage and the active power output during charge and discharge, given the maximum reactive power allowed to be output during charge and discharge. The state of charge constraint of the mobile energy storage is used to determine the active power output of the mobile energy storage at a single moment and the charge of the mobile energy storage at a single moment, given the maximum capacity, minimum capacity and energy conversion efficiency of the mobile energy storage during the charging and discharging process.

6. The method according to claim 5, characterized in that, The radial topology constraints of the power grid are used to determine the switching state of each branch given the initial topology connection relationship of the distribution network. The load reduction power constraint is used to determine the active power and reactive power reduction of each node, given the maximum value of the active power and the maximum value of the reactive power of the load at each node. The distributed power constraint is used to determine the active power of each distributed power source given its maximum active power; and to determine the reactive power of each distributed power source given its maximum reactive power.

7. The method according to claim 6, characterized in that, The power flow constraints of the distribution network include: power balance constraints of the network, nonlinear constraints of voltage, current and power, voltage difference constraints of nodes, upper and lower limit constraints of node voltage and upper and lower limit constraints of branch current. In the voltage difference constraint of the node, the square term of voltage is replaced by a variable, and in the power balance constraint of the power grid, the square term of current is replaced by a variable to relax the non-convex power flow equations into convex ones; the nonlinear constraints of voltage, current and power are relaxed by second-order cones to transform them into solvable convex problems; for the network topology changes caused by switching actions, the big M method is used to relax the voltage difference constraint of the node. The power flow constraints of the distribution network are used to determine the active power, reactive power, and current values ​​transmitted by each branch, the maximum active power and reactive power of each node load, the maximum and minimum values ​​of the square of the voltage of each node, the connection status of distributed power sources and nodes, and the maximum value of the square of the current of each branch, in order to decide the active power, reactive power, current value, and switching status of each branch, the active power and reactive power of each distributed power source, the active power and reactive power reduced by each node, the voltage of each node, the connection status of mobile energy storage and nodes, and the active power and reactive power output by mobile energy storage at each charging and discharging time.

8. The method according to claim 7, characterized in that, The solution to the grid-side principal game model, which uses the minimum operating cost of the distribution network during a fault as the upper-level objective function, yields the power restoration scheme and target flexible load incentive price that minimize the operating cost of the distribution network during a fault. Based on the mobile energy storage scheduling constraints, mobile energy storage charging and discharging constraints, mobile energy storage state of charge constraints, grid radial topology constraints, load reduction power constraints, flexible load incentive price constraints, distributed power constraints, and distribution network power flow constraints, the active power reduction of each node at each time during all fault periods and the flexible load incentive price per unit of shifted electricity during a single fault period are determined, resulting in multiple candidate active power reduction flexible load incentive price sequences. For each of the active power reduction flexible load incentive price sequences, combined with the upper-level objective function, given the load shedding cost per unit reduction power, the weight coefficient of each node in the distribution network, and the working state and input power of the crusher during each fault period output by the flexible load side slave game model, the distribution network operating cost is solved to obtain multiple distribution network operating costs. The power supply restoration scheme and the target flexible load incentive price are determined based on the active power reduction flexible load incentive price sequence corresponding to the distribution network operation cost with the lowest cost among the multiple distribution network operation costs.

9. The method according to claim 8, characterized in that, The convergence condition includes: the absolute value of the difference between the target flexible load incentive price adjusted in the current iteration and the target flexible load incentive price adjusted in the previous iteration is less than or equal to 0.0001.

10. The method according to any one of claims 1-9, characterized in that, The power restoration scheme includes: distributed power output scheme, mobile energy storage scheduling scheme, network reconfiguration scheme, load reduction power, and adjusted flexible load incentive price.

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