Multi-source cooperative power grid reconstruction optimization method and system based on mobile energy storage vehicle

Abstract

The invention provides a multi-source cooperative power grid reconstruction optimization method and system based on a mobile energy storage vehicle, and the method comprises the following steps: building a load shortest average power failure time objective function according to the principle of multi-source cooperation and considering the damage reconstruction requirements of the mobile energy storage vehicle and a power grid; constructing constraint conditions of power grid reconstruction according to the constructed objective function; and based on a random integer programming model, optimizing and solving the model by adopting a scene decomposition algorithm to obtain a power grid reconstruction optimization scheduling strategy set. According to the multi-source collaborative power grid reconstruction optimization method based on the mobile energy storage vehicle, special requirements of multi-source collaboration and power grid reconstruction of the mobile energy storage vehicle are considered, and the method can be applied to various damaged scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of application of mobile energy storage vehicles in power systems, and in particular to a multi-source collaborative power grid reconstruction optimization method and system based on mobile energy vehicles. Background Art

[0002] Mobile energy storage vehicles have become an important device for realizing non-stop distribution network maintenance, and play a key role in non-stop maintenance operations. However, there is currently a lack of effective scheduling methods to effectively support applications in complex scenarios such as disasters. These problems greatly limit the value and potential of mobile power vehicles in supply and maintenance applications. On the one hand, in typical damaged scenarios, a small number of power loads cannot be fully connected to the main power grid. At this time, mobile energy storage vehicles can play a key role and are the only key resource that can achieve emergency reconstruction (rapid restoration of power services across regions). On the other hand, in scenarios with very serious equipment defects and failures, and where serious cascading power outages have already occurred, multiple components of the power grid and even major core components have suffered severe blows, and network reconstruction is urgently needed at the system level.

[0003] However, current research on the structural reconstruction of damaged power grids mainly focuses on economical scheduling and efficient energy planning, and has not conducted in-depth research on the unique requirements for grid reconstruction optimization under damaged scenarios; there has been no research on grid reconstruction for multiple application scenarios based on real-time scheduling of mobile power vehicles.

[0004] CN202010968995.6A discloses a mobile energy storage system operation strategy, including the following steps: establishing a mobile energy storage vehicle transportation model based on the transmission lines in the distribution network; judging the voltage over-limit situation of each node in each time period, and establishing a power optimization model required for mobile energy storage voltage regulation; establishing an optimal distribution network benefit operation strategy model to obtain a mobile energy storage system operation strategy.

[0005] CN202211532118.X discloses a mobile energy storage scheduling method, system and storage medium, the method comprising: establishing models of electric vehicles, mobile energy storage vehicles and hydrogen fuel cell power generation vehicles respectively, and evaluating the dispatchable power of the three types of mobile energy storage in different time periods; establishing a multi-objective function of load peak-to-valley difference, distribution network loss and distribution network operation cost, and in order to reduce the probability of abnormal ranking, vector normalization is used to optimize the multiple objectives; a firefly and particle swarm hybrid optimization algorithm is used to solve the optimal solution for the joint participation of multiple types of mobile energy storage in scheduling.

[0006] The above two patents mainly involve the peak-shaving and valley-filling capabilities of mobile energy storage for the power grid. At this time, the power grid is in normal operation. By establishing a multi-objective cost function such as network loss and operating cost, the optimal economic operation plan of the power grid considering mobile energy storage is obtained. However, the scheduling problem of mobile energy storage vehicles when the power grid is damaged is not considered.

[0007] Therefore, there is an urgent need for a grid reconstruction optimization method that can consider the scheduling problem of mobile energy storage vehicles when the grid is damaged and can be applied in various damage scenarios. Summary of the invention

[0008] In order to solve the deficiencies in the prior art, the present invention provides a multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle. When reconstruction resources are relatively scarce, the power supply structure is adjusted, the voltage and power relationship between the feeder nodes and the mobile energy storage vehicle is coordinated, and the power supply of important loads is restored to the maximum extent.

[0009] In order to achieve the above-mentioned object of the invention, the present invention specifically adopts the following technical solutions.

[0010] A multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle, characterized in that it includes the following steps:

[0011] Step 1: Based on the principle of multi-source coordination, considering the requirements of mobile energy storage vehicles and damaged power grid reconstruction, the objective function of the shortest average power outage time of the load is constructed;

[0012] Step 2: Based on the objective function constructed in step 1, construct the constraints for power grid reconstruction;

[0013] Step 3: Based on the random integer programming model, the scenario decomposition algorithm is used to optimize the model and obtain the optimal dispatching strategy set for power grid reconstruction.

[0014] More preferably,

[0015] In step 1, based on the principle of multi-source coordination, considering the special requirements of mobile energy storage vehicles and damaged power grid reconstruction, the objective function of the shortest average power outage time of the load is constructed, including:

[0016] Construct the objective function of the shortest average power outage time of pre-disaster load;

[0017] And construct the objective function of the shortest average power outage time of load after the disaster.

[0018] More preferably,

[0019] Before the disaster, that is, in the pre-configuration stage, the construction formula of the objective function of the shortest average power outage time of the load is as follows:

[0020]

[0021] Among them, u n represents the probability of occurrence of scenario n, w i represents the priority of load i, p i represents the active power demand of load i, γ iknIndicates whether load i can be restored by the existing power source of feeder node k of load i in scenario n. If yes, γ ikn =1, otherwise, γ ikn =0; T in represents the normal recovery time required for load i in scenario n; y smkn Indicates whether the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s in scenario n. If so, then y smkn =1, otherwise, y smkn =0; t skn It represents the transportation time required for mobile energy storage vehicle m to maneuver from standby node s to node k in scenario n; N is the set of damaged scenarios; B is the set of load nodes; F is the set of feeder nodes; G is the set of candidate nodes for mobile energy storage vehicles; S is the set of standby nodes; and M is the set of the number of mobile energy storage vehicles.

[0022] More preferably,

[0023] After the disaster, that is, in the dynamic configuration stage, the construction formula of the objective function of the shortest average power outage time of load is as follows:

[0024]

[0025] Among them, u n represents the probability of occurrence of scenario n, w i represents the priority of load i, p i represents the active power demand of load i, γ ikn Indicates whether load i can be restored by the existing power source of feeder node k of load i in scenario n. If yes, γ ikn =1, otherwise, γ ikn =0; T in represents the normal recovery time required for load i in scenario n; y smkn Indicates whether the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s in scenario n. If so, then y smkn =1, otherwise, y smkn =0; t skn It represents the transportation time required for the mobile energy storage vehicle m to maneuver from the standby node s to the feeder node k in scenario n; N is the damaged scenario set; B is the load node set; F is the feeder node set; G is the mobile energy storage vehicle candidate node set; S is the standby node set; M is the number of mobile energy storage vehicles.

[0026] More preferably,

[0027] In step 2, the constraints for grid reconstruction include electrical constraints, topological constraints, power balance constraints, and damaged line constraints when constructing mobile energy storage vehicles in coordination;

[0028] The electrical constraints of the mobile energy storage vehicles during coordination include pre-configuration constraints and real-time scheduling constraints.

[0029] More preferably,

[0030] The pre-disaster constraints are pre-configured constraints, including:

[0031]

[0032] x sm represents the number of mobile energy storage vehicles on the standby node s, X s The upper limit of the number of mobile energy storage vehicles that each standby node can accommodate. Further preferably,

[0033] Post-disaster constraints include real-time scheduling constraints, topology constraints, power balance constraints, and damaged line constraints;

[0034] Among them, the real-time scheduling constraints are:

[0035]

[0036] y smkn Indicates whether, in scenario n, the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s.

[0037] More preferably,

[0038] Topology constraints include:

[0039]

[0040] Among them, z kn Indicates whether the feeder k as a candidate node is equipped with a power source that can restore the load under a given scenario n; v ikn Indicates whether load i is included in the local power grid formed by feeder node k in a given scenario n. If yes, it takes 1, otherwise it takes 0; c ijn Indicates whether the transmission line (i, j) is included in the local grid formed by the feeder node k under a given scenario n; θ k (i) represents the parent node set of load i in the local power grid composed of power source node k; ζ k (i,j) represents the set of child nodes of transmission line (i,j).

[0041] More preferably,

[0042] Power balancing constraints include:

[0043]

[0044] in, and They represent the active power and reactive power flowing out of node i in the local power grid composed of feeder node k under a given scenario n, respectively; and They represent the active power and reactive power flowing out of node j in the local power grid composed of feeder node k under a given scenario n, respectively; represents the set of nodes i in the local power grid composed of feeder nodes k supplied by standby nodes s; p i represents the active power demand of load i, q i represents the reactive power demand of load i, C represents a constant that can simultaneously meet the requirements of maximum power limit and narrowing the solution space, represents the maximum transmission power of the transmission line (i, j); V R , V , r ij , x ij They represent the node voltage, reference voltage, voltage upper limit, voltage lower limit, and resistance and reactance of the transmission line (i, j) respectively.

[0045] More preferably,

[0046] Impaired line constraints include:

[0047]

[0048] Among them, LO n represents the set of damaged lines in the power grid under a given scenario n, c ijn is a damage parameter, indicating whether the transmission line (i, j) is connected under a given scenario n; when the transmission line (i, j) is connected, it means that power can be transmitted, and the value is 1; when the transmission line (i, j) is damaged, the value is 0.

[0049] More preferably,

[0050] In step 3, based on the random integer programming model, the scenario decomposition algorithm is used to optimize the model and obtain the grid reconstruction optimization dispatch strategy set including:

[0051] The model objective function is constructed as follows:

[0052]

[0053] in, For all x n is the weighted average of all scenarios n belonging to N in the previous iteration; μ and α are cost coefficient vectors set according to the specific problem; l represents the lth element of the vector; ρ is the disturbance parameter.

[0054] At the same time, the present invention provides a multi-source collaborative power grid reconstruction optimization system based on a mobile energy storage vehicle, the system comprising: a power outage time objective function construction module, a constraint condition construction module and an optimization scheduling strategy set reconstruction module;

[0055] The power outage time objective function construction module builds the load shortest average power outage time objective function based on the principle of multi-source coordination, taking into account the requirements of mobile energy storage vehicles and damaged power grid reconstruction;

[0056] The constraint condition construction module constructs the constraint conditions for power grid reconstruction according to the constructed load shortest average power outage time objective function;

[0057] The module for reconstructing the optimal dispatching strategy set is based on the random integer programming model and adopts the scenario decomposition algorithm to optimize and solve the model and obtain the optimal dispatching strategy set for power grid reconstruction.

[0058] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of any one of the methods.

[0059] A computer-readable storage medium stores a computer program, which is used by a processor to execute the steps of any one of the methods.

[0060] The beneficial effect of the present invention lies in that, compared with the prior art, the multi-source collaborative power grid reconstruction optimization method based on mobile energy storage vehicles described in the present invention takes into account the real-time scheduling of mobile energy storage vehicles in damaged scenarios, and can maximize the shortening of the average power outage time of important loads; the multi-source collaborative power grid reconstruction optimization method based on mobile energy storage vehicles described in the present invention takes into account the special requirements of multi-source collaboration and power grid reconstruction of mobile energy storage vehicles, and can be applied in various damaged scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of a multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle in an embodiment of the present invention;

[0062] Figure 2 is a schematic diagram of multi-source collaboration in an embodiment of the present invention;

[0063] Figure 3 It is a flow chart based on the scene decomposition method in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0065] According to one aspect of the present application, a multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle is provided, such as Figure 1 The figure shows a flow chart of a multi-source coordinated power grid reconstruction optimization method based on a mobile energy storage vehicle, the method comprising the following steps:

[0066] Step 1: Based on the principle of multi-source coordination, considering the requirements of mobile energy storage vehicles and damaged power grid reconstruction, construct the objective function of the shortest average power outage time of the load.

[0067] In one embodiment of the present invention, in step 1, based on the principle of multi-source coordination, considering the special requirements of mobile energy storage vehicles and damaged power grid reconstruction, constructing the shortest average power outage time objective function of the load includes:

[0068] The objective function of the shortest average power outage time before the disaster is constructed according to the following formula:

[0069]

[0070] And the objective function of the shortest average power outage time of the load after the disaster is constructed according to the following formula:

[0071]

[0072] Among them, u n represents the probability of occurrence of scenario n, w i represents the priority of load i, p i represents the active power demand of load i, γ ikn Indicates whether load i is restored by power source k in scenario n; T in represents the normal recovery time required for load i in scenario n; y smkn Indicates whether node k is restored to power supply by mobile energy storage vehicle m that moves from standby node s in scenario n; t skn Indicates the transportation time required for mobile energy storage vehicle m to maneuver from standby node s to node k in scenario n; N is the damaged scenario set; B is the load node set; F is the feeder node set; G is the mobile energy storage vehicle candidate node set; S is the standby node set; M is the number of mobile energy storage vehicles.

[0073] When load i cannot be restored to power supply by the existing power source k, that is, γikn =0, load i needs to go through maintenance time T in To restore, the power outage time of load i corresponds to Conversely, if load i can restore power supply from feeder node k, that is, γ ikn =1, load i will not be powered off; if load i can be restored to power by mobile energy storage vehicle k, the power outage time of load i depends on the transportation time of the mobile energy storage vehicle, that is, the transportation time t required for mobile energy storage vehicle m to maneuver from standby node s to candidate node k in scenario n skn .

[0074] In this embodiment, the requirements for mobile energy storage vehicles and grid damage reconstruction include:

[0075] 1) The mobile energy storage vehicle has sufficient rated power supply, that is, it has sufficient logistical supplies and will not be unable to provide power due to battery energy shortage;

[0076] 2) Connectivity of damaged grid nodes, i.e., any two nodes can be reached by mobile energy storage vehicles;

[0077] The requirements for mobile energy storage vehicles and grid reconstruction after damage are reflected in:

[0078] 1) The probability that the mobile energy storage vehicle cannot reach a certain node to provide power is not considered in the shortest average power outage time objective function;

[0079] 2) The power supply constraints of mobile energy storage vehicles are not considered in the constraints

[0080] Step 2: Based on the objective function constructed in step 1, construct the constraints for power grid reconstruction.

[0081] In this embodiment, the shortest average power outage time objective function of important loads in two different stages before and after the disaster is adopted, and the scheduling process of mobile energy storage vehicles is divided into two steps, pre-equipment and dynamic scheduling. In the pre-equipment stage, the possible output modes of mobile energy storage vehicles under various damage scenarios are calculated in advance to provide a reference for the dynamic scheduling stage; in the dynamic scheduling stage, when the damage scenario is determined, the optimal solution in the pre-equipment stage can be combined to reduce the time to obtain the optimal solution at this time, greatly improving the scheduling speed of mobile energy storage vehicles and shortening the shortest average power outage time of important loads.

[0082] According to one aspect of the present application, based on the principle of multi-source coordination, considering the special requirements of mobile energy storage vehicles and damaged power grid reconstruction, an objective function for the shortest average power outage time of important loads in two different stages before and after the disaster is constructed, specifically:

[0083] The multi-source coordination refers to a mechanism whereby multiple power sources coordinate to restore power outage loads, such as Figure 2 The figure shows a schematic diagram of multi-source coordination. The main implementation method is to mount several mobile energy storage vehicles at the same node, or to mount several mobile energy storage vehicles at different nodes on the same transmission line. The main idea is:

[0084] (1) Use the method of increasing the source node voltage to ensure that the end load has sufficient safety voltage;

[0085] (2) The master-slave coordination method is adopted, with the main power supply supporting most of the load in the grid, and the slave power supply supporting individual loads with larger power requirements, so as to restore as many important loads as possible.

[0086] In one embodiment of the present invention, in step 2, the constraints for grid reconstruction include constructing electrical constraints, topology constraints, power balance constraints and damaged line constraints for mobile energy storage vehicles in coordination; the electrical constraints for mobile energy storage vehicles in coordination include pre-configured constraints and real-time scheduling constraints.

[0087] The pre-configured constraints include:

[0088]

[0089] Wherein, formula (3) represents the upper limit of the capacity of the mobile energy storage vehicle that each standby node can accommodate, formula (4) represents that each mobile energy storage vehicle can only be mounted on one standby node, m represents the mobile energy storage vehicle, M represents the total number of mobile energy storage vehicles, s represents the standby node, S represents the total number of standby nodes, and x sm represents the mobile energy storage vehicle of the standby node s, X s Indicates the upper limit of the number of mobile energy storage vehicles that each standby node can accommodate.

[0090] Real-time scheduling constraints include:

[0091]

[0092] Among them, formula (5) indicates that only the candidate node equipped with a mobile energy storage vehicle can serve as the starting point of the real-time dispatch route of the mobile energy storage vehicle, so that the mobile energy storage vehicle can form a local power grid to restore the nearby important loads. Formula (6) indicates that in any scenario, any load can only be restored by one mobile energy storage vehicle.

[0093] Topology constraints include:

[0094]

[0095]

[0096] Among them, z knIndicates whether the candidate node k is equipped with a power source that can restore the load under a given scenario n; v ikn Indicates whether load i is included in the local power grid composed of power source candidate node k under a given scenario n; c ijn Indicates whether the transmission line (i, j) is included in the local power grid composed of the power source candidate node k under a given scenario n; θ k (i) represents the parent node set of load i in the local power grid composed of power source node k; ζ k (i,j) represents the set of child nodes of transmission line (i,j).

[0097] In each local power grid, all loads are powered by a power source node, and its radial topology determines the constraint relationship between the parent node and the child node. Formula (7) indicates that in a specified scenario n, the condition for any candidate node k to be used as a power source is that there must be a mobile energy storage vehicle m mounted on the candidate node k. Formula (8) indicates that in a given scenario n, the undamaged feeder node k can be used as a power source node to restore the load. Formula (9) indicates that in a given scenario n, the load can only belong to one local power grid. Formulas (10) and (11) respectively indicate that in a given scenario n, in each local power grid, whether load i and candidate node k can be restored depends on whether the candidate node k is mounted with a mobile energy storage vehicle m; Formula (12) indicates that in a given scenario n, whether load i belongs to the local power grid depends on whether its parent node j belongs to the local power grid. Formula (13) indicates that in a given scenario n, the condition for the transmission line (i, j) to belong to the local power grid is that all its child nodes h must belong to the local power grid. Formula (14) indicates that whether load i can be restored under a given scenario n depends not only on whether load i belongs to the local power grid composed of power source node k, but also on whether the switch on load i is closed.

[0098] Power balancing constraints include:

[0099]

[0100] in, and They represent the active power and reactive power flowing out of node i in the local power grid composed of power source node k under a given scenario n, respectively; represents the set of nodes i in the local power grid composed of feeder nodes k supplied by standby nodes s; p i represents the active power demand of load i, q i represents the reactive power demand of load i

[0101] C represents a constant that is large enough to simultaneously meet the requirements of maximum power limitation and narrowing the solution space. represents the maximum transmission power of the transmission line (i, j). V R , V , r ij , x ij They represent the node voltage, reference voltage, voltage upper limit, voltage lower limit, and resistance and reactance of the transmission line (i, j) respectively.

[0102] In order to facilitate the solution and ensure the necessary accuracy, the present invention adopts the classic LinDistFlow power flow equation and omits the quadratic terms that are difficult to express linearly. Formulas (15) and (16) represent the active power and reactive power balance conditions of node i and subordinate node j of the local power grid under a given scenario n. Formulas (17) and (18) represent that under a given scenario n, the active power and reactive power outflow of each node in the local power grid depends on whether the node belongs to the local power grid. Formula (19) indicates that the power flow of the transmission line (i, j) cannot exceed its transmission capacity. Formula (20) indicates that under a given scenario n, the voltage of the undamaged feeder node k is the reference voltage of the local power grid. Formulas (21)-(24) represent the voltage relationship of each node in the local power grid and the acceptable voltage range of each node under a given scenario n.

[0103] Impaired line constraints include:

[0104]

[0105] Among them, LO n It represents the set of damaged lines in the system under a given scenario n.

[0106] Each determined damaged scenario has a determined damaged line, corresponding to the specific realization value of the random variable in the second stage. Formula (25) indicates that the corresponding damage parameter c is set according to the actual damage situation. ijn .

[0107] Step 3: Based on the random integer programming model, the scenario decomposition algorithm is used to optimize the model and obtain the optimal dispatching strategy set for power grid reconstruction.

[0108] According to one aspect of the present application, the random integer programming model is embodied in solving the pre-decision variable x sm and the dynamic decision variable y smkn In the above, the two variables are integers 0 or 1, which are 1 when there is a mobile energy storage vehicle and 0 when there is no mobile energy storage vehicle. The solution of the objective function is an integer.

[0109] The formula for the random integer programming model is as follows:

[0110]

[0111] Where: n represents different damage scenarios, x represents the decision variables in the pre-configuration phase, and y n is the decision variable in the dynamic configuration phase under different damage scenarios, f n represents the objective function of the shortest average power outage time under different damage scenarios, X represents the feasible domain of x determined by the pre-configured constraints, and Y n represents y determined by real-time scheduling constraints, topology constraints, power balance constraints, and damaged line constraints n feasible domain.

[0112] Based on the proposed two-stage random integer programming model, a scenario decomposition algorithm is used to optimize and solve the model to obtain a set of grid reconstruction optimization dispatch strategies, specifically:

[0113]

[0114] in, For all x n is the weighted average of all scenarios n belonging to N in the previous iteration; μ and α are cost coefficient vectors set according to the specific problem; (l) represents the lth element of the vector; ρ is the disturbance parameter, such as Figure 3 Shown is a flowchart based on the scenario decomposition method.

[0115] According to one aspect of the present application, based on the proposed two-stage random integer programming model, a scenario decomposition algorithm is used to optimize and solve the model to obtain a set of grid reconstruction optimization scheduling strategies, including:

[0116] S1: Set the upper bound ub and lower bound lb of the shortest average power outage time objective function to optimize the decision variable x sm ,y smkn and the optimal solution set;

[0117] Among them, the optimal solution set includes the pre-standby position parameters of the mobile energy storage vehicle and the scheduling parameters of the mobile energy storage vehicle after damage. In the scenario decomposition algorithm, each damaged scenario can be regarded as a sub-problem and is independent of each other. The optimal solution of each sub-problem before optimization is obtained by the pre-configuration stage. The optimal solution set before optimization is obtained by adding the optimal solutions of each sub-problem.

[0118] S2: Searching for an upper bound ub and a lower bound lb, wherein searching for the upper bound ub includes: solving the original optimization problem according to all feasible constraints of the second decision variable, and searching for the lower bound lb includes: solving the optimization problem of the second stage according to the decision variables of the first stage; the decision variables of the first stage are the standby position of the mobile energy storage vehicle in the pre-configuration stage, and the decision variables of the second stage are the real-time dispatching position of the mobile energy storage vehicle after damage;

[0119] S3: Calculate the difference between the upper bound ub and the lower bound lb to ensure the accuracy of the algorithm. When the calculated difference is greater than the set difference threshold, return to step 2, where the setting of the difference threshold varies according to different accuracy requirements;

[0120] S4: When the difference meets the minimum accuracy requirement, the algorithm terminates and returns to solve the optimal solution;

[0121] S5: Reconstruct the microgrid based on the optimal solution of real-time dispatch of mobile energy storage vehicles

[0122] According to one aspect of the present application, based on the proposed two-stage random integer programming model, a scenario decomposition algorithm is used to optimize and solve the model to obtain a set of grid reconstruction optimization scheduling strategies, including:

[0123] Step 1: Initialize the upper limit ub = +∞, the lower limit lb = -∞, λ = 0, the standby node set S, the standby node x is a non-empty set, and λ is the Lagrange multiplier;

[0124] Step 2: Obtain the feasible domain X of the first decision variable by pre-configured constraints, and obtain the feasible domain Y of the second decision variable by dynamically configured constraints; each constraint condition can divide the decision variable into two parts: feasible solutions and infeasible solutions. The set of feasible solutions composed of all constraints is the corresponding feasible domain.

[0125] Step 3: Solve α for any scene n belonging to N n , and obtain the first decision variable x n * ;

[0126] α n =min{f n (x n ,y n )+λ T A n x n :x n ∈X\S,y n ∈Y} (28)

[0127] Step 4: If the consistency criterion is not met, update λ and return to step 3; the consistency criterion includes for different first decision variables, α n The value of is the same. When the consistency is not satisfied, it means that the first decision variable at this time is not the optimal solution, and the value of the Lagrange multiplier needs to be updated to find the optimal first decision variable.

[0128] Step 5: Update the lower limit lb and the set of standby nodes S;

[0129]

[0130] Among them, S' is the first decision variable solution set that meets the consistency criterion.

[0131] Step 6: For all x belonging to S, calculate β n =min{f n (x,y n ):y n ∈Y}, if the upper limit Update the upper limit and the first decision variable x * = x;

[0132] Step 7: If the calculation accuracy (ub-lb) / lb≤ε is satisfied, the optimal solution is output; otherwise, λ is updated and the process returns to step 3, where ε is the accuracy of the algorithm required for the specific problem.

[0133] At the same time, the present invention provides a multi-source collaborative power grid reconstruction optimization system based on a mobile energy storage vehicle, the system comprising: a power outage time objective function construction module, a constraint condition construction module and an optimization scheduling strategy set reconstruction module;

[0134] The power outage time objective function construction module builds the load shortest average power outage time objective function based on the principle of multi-source coordination, taking into account the requirements of mobile energy storage vehicles and damaged power grid reconstruction;

[0135] The constraint condition construction module constructs the constraint conditions for power grid reconstruction according to the constructed load shortest average power outage time objective function;

[0136] The module for reconstructing the optimal dispatching strategy set is based on the random integer programming model and adopts the scenario decomposition algorithm to optimize and solve the model and obtain the optimal dispatching strategy set for power grid reconstruction.

[0137] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0138] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0139] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0140] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle, characterized in that: The steps include: Step 1: Based on the principle of multi-source coordination, considering the requirements of mobile energy storage vehicles and damaged power grid reconstruction, the objective function of the shortest average power outage time of the load is constructed; Step 2: Based on the objective function constructed in step 1, construct the constraints for power grid reconstruction; Step 3: Based on the random integer programming model, the scenario decomposition algorithm is used to optimize the model and obtain the optimal dispatching strategy set for power grid reconstruction.

2. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 1, characterized in that: In step 1, based on the principle of multi-source coordination, considering the special requirements of mobile energy storage vehicles and damaged power grid reconstruction, the objective function of the shortest average power outage time of the load is constructed, including: Construct the objective function of the shortest average power outage time of pre-disaster load; And construct the objective function of the shortest average power outage time of load after the disaster.

3. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle as claimed in claim 2, characterized in that: Before the disaster, that is, in the pre-configuration stage, the construction formula of the objective function of the shortest average power outage time of the load is as follows: Among them, u n represents the probability of occurrence of scenario n, w i represents the priority of load i, p i represents the active power demand of load i, γ ikn Indicates whether load i can be restored by the existing power source of feeder node k of load i in scenario n. If yes, γ ikn =1, otherwise, γ ikn =0; T in represents the normal recovery time required for load i in scenario n; y smkn Indicates whether the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s in scenario n. If so, then y smkn =1, otherwise, y smkn =0; t skn It represents the transportation time required for mobile energy storage vehicle m to maneuver from standby node s to node k in scenario n; N is the set of damaged scenarios; B is the set of load nodes; F is the set of feeder nodes; G is the set of candidate nodes for mobile energy storage vehicles; S is the set of standby nodes; and M is the set of the number of mobile energy storage vehicles.

4. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 2 or 3, characterized in that: After the disaster, that is, in the dynamic configuration stage, the construction formula of the objective function of the shortest average power outage time of load is as follows: Among them, u n represents the probability of occurrence of scenario n, w i represents the priority of load i, p i represents the active power demand of load i, γ ikn Indicates whether load i can be restored by the existing power source of feeder node k of load i in scenario n. If yes, γ ikn =1, otherwise, γ ikn =0; T in represents the normal recovery time required for load i in scenario n; y smkn Indicates whether the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s in scenario n. If so, then y smkn =1, otherwise, y smkn =0; t skn It represents the transportation time required for the mobile energy storage vehicle m to maneuver from the standby node s to the feeder node k in scenario n; N is the damaged scenario set; B is the load node set; F is the feeder node set; G is the mobile energy storage vehicle candidate node set; S is the standby node set; M is the number of mobile energy storage vehicles.

5. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 4, characterized in that: In step 2, the constraints for grid reconstruction include electrical constraints, topological constraints, power balance constraints, and damaged line constraints when constructing mobile energy storage vehicles in coordination; The electrical constraints of the mobile energy storage vehicles during coordination include pre-configuration constraints and real-time scheduling constraints.

6. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 1 or 5, characterized in that: The pre-disaster constraints are pre-configured constraints, including: x sm represents the number of mobile energy storage vehicles on the standby node s, X s The upper limit of the number of mobile energy storage vehicles that each standby node can accommodate.

7. The multi-source coordinated power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 5, characterized in that: Post-disaster constraints include real-time scheduling constraints, topology constraints, power balance constraints, and damaged line constraints; Among them, the real-time scheduling constraints are: y smkn Indicates whether, in scenario n, the feeder node k is restored to power supply by the mobile energy storage vehicle m that is mobilized from the standby node s.

8. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 7, characterized in that: Topology constraints include: Among them, z kn Indicates whether the feeder k as a candidate node is equipped with a power source that can restore the load under a given scenario n; v ikn Indicates whether load i is included in the local power grid formed by feeder node k in a given scenario n. If yes, it takes 1, otherwise it takes 0; c ijn Indicates whether the transmission line (i, j) is included in the local grid formed by the feeder node k under a given scenario n; θ k (i) represents the parent node set of load i in the local power grid composed of power source node k; ζ k (i,j) represents the set of child nodes of transmission line (i,j).

9. The multi-source collaborative power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 8, characterized in that: Power balancing constraints include: in, and They represent the active power and reactive power flowing out of node i in the local power grid composed of feeder node k under a given scenario n, respectively; and They represent the active power and reactive power flowing out of node j in the local power grid composed of feeder node k under a given scenario n, respectively; represents the set of nodes i in the local power grid composed of feeder nodes k supplied by standby nodes s; p i represents the active power demand of load i, q i represents the reactive power demand of load i, C represents a constant that can simultaneously meet the requirements of maximum power limit and narrowing the solution space, represents the maximum transmission power of the transmission line (i, j); V R , V , r ij , x ij They represent the node voltage, reference voltage, voltage upper limit, voltage lower limit, and resistance and reactance of the transmission line (i, j) respectively.

10. The multi-source coordinated power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 5, characterized in that: Impaired line constraints include: Among them, LO n represents the set of damaged lines in the power grid under a given scenario n, c ijn is a damage parameter, indicating whether the transmission line (i, j) is connected under a given scenario n; when the transmission line (i, j) is connected, it means that power can be transmitted, and the value is 1; when the transmission line (i, j) is damaged, the value is 0.

11. The multi-source coordinated power grid reconstruction optimization method based on a mobile energy storage vehicle according to claim 1, characterized in that: In step 3, based on the random integer programming model, the scenario decomposition algorithm is used to optimize the model and obtain the grid reconstruction optimization dispatch strategy set including: The model objective function is constructed as follows: in, For all x n is the weighted average of all scenarios n belonging to N in the previous iteration; μ and α are cost coefficient vectors set according to the specific problem; l represents the lth element of the vector; ρ is the disturbance parameter.

12. A multi-source collaborative power grid reconstruction optimization system based on a mobile energy storage vehicle using the method described in any one of claims 1 to 11, characterized in that: The system comprises: a power outage time objective function building module, a constraint condition building module and an optimization scheduling strategy set reconstruction module; The power outage time objective function construction module builds the load shortest average power outage time objective function based on the principle of multi-source coordination, taking into account the requirements of mobile energy storage vehicles and damaged power grid reconstruction; The constraint condition construction module constructs the constraint conditions for power grid reconstruction according to the constructed load shortest average power outage time objective function; The optimized dispatching strategy set reconstruction module is based on the random integer programming model and adopts the scenario decomposition algorithm to optimize and solve the model to obtain the optimized dispatching strategy set for power grid reconstruction.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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