Mobile energy storage vehicle scheduling optimization method and system for power distribution network reconstruction

Abstract

The invention discloses a power distribution network reconstruction-oriented mobile energy storage vehicle scheduling optimization method and system, and the method comprises the steps: carrying out the pre-layout of the configuration number and position of mobile energy storage in a pre-disaster prevention stage; in the post-disaster recovery stage, the influence of disasters on traffic network passing time is considered, dynamic scheduling and time sequence output characteristics of mobile energy storage and electric vehicles are combined, and a multi-source collaborative post-disaster recovery scheduling optimization model matched with post-disaster power distribution network reconstruction is established by taking the minimum load reduction power within fault duration as a target; and solving the post-disaster recovery scheduling optimization model to obtain a scheduling optimization scheme of the output position and the output time of the mobile energy storage and electric vehicle resources in each scheduling period. By setting a two-stage guarantee power supply strategy of pre-disaster mobile energy storage pre-layout and post-disaster dynamic scheduling, the power failure load recovery amount can be increased after large-area power failure of the power distribution network, the transportation time of the mobile resource vehicle is shortened, the resource utilization efficiency is improved, and the toughness of the power distribution network is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of active distribution network reconstruction, and in particular to a mobile energy storage vehicle scheduling optimization method and system for distribution network reconstruction. Background Art

[0002] In recent years, extreme natural disasters have become increasingly frequent, and power outages have shown a trend of increasing frequency, expanding scope, and lasting longer. In addition, the concept and construction of energy Internet are also gradually emerging, further accelerating the deep integration of other energy systems and power systems. The composition of the power system will become increasingly complex, and the number of weak links will further increase. In the foreseeable future, damage scenarios caused by natural disasters will inevitably bring more risky challenges to the stable operation of the power system.

[0003] It is imperative to enhance the disaster resistance of the power grid. However, in the current practice of emergency distribution network reconstruction, the relevant technology only considers the deployment location of mobile energy storage before the disaster, and does not consider the relationship between mobile energy storage and the previous operation risk. In addition, most of the power system recovery measures rely on reliable power sources, and existing research does not consider the transportation status. In the damaged scenario, the number of power sources available for reconstruction is very limited, and the power system cannot implement rapid reconstruction on its own. Therefore, a mobile energy storage vehicle scheduling optimization strategy is urgently needed to provide additional stable power guarantee to implement an effective power service restoration response. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention proposes a mobile energy storage vehicle scheduling optimization method and system for distribution network reconstruction, which can increase the recovery of power outage loads after a large-scale power outage in the distribution network, shorten the transportation time of mobile resource vehicles, reduce the number of switch operations, and enhance the resilience of the distribution network.

[0005] The present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for optimizing the dispatching of a mobile energy storage vehicle for distribution network reconstruction, the method comprising: Step S1: In the pre-disaster prevention stage, pre-layout the number and location of mobile energy storage; Step S2: In the post-disaster recovery phase, based on the number and location of pre-arranged mobile energy storage configurations, considering the impact of disasters on the travel time of the transportation network, combining the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, with the goal of minimizing load reduction power during the fault duration, a multi-source collaborative post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network is established; Step S3: Solve the post-disaster recovery scheduling optimization model to obtain a scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

[0007] Optionally, in step S1, the step of pre-layouting the configuration quantity and location of mobile energy storage includes: S1.1. With the goal of minimizing the mobile energy storage configuration cost and load reduction cost under the scenario of PV output uncertainty, the objective function of the pre-disaster pre-layout robust optimization model is established; S1.2, constructing the constraint conditions of the pre-disaster pre-layout robust optimization model; S1.3. Based on the constraint conditions constructed in step S1.2, the pre-disaster pre-layout robust optimization model is solved to obtain a pre-layout optimization solution for the configuration quantity and location of mobile energy storage.

[0008] Optionally, in step S1.1, the objective function of the pre-disaster pre-layout robust optimization model is as follows: , In the formula, Oh N is the set of load nodes; It is a 0-1 variable, indicating the mobile energy storage and node i The connection status, its value is 1, which means there is only one mobile energy storage connected to the node i Connection, its value is 0 means there is no mobile energy storage and node i connect; α ij For branch ij The disconnection state, its value is 1 to indicate that the circuit is closed, and its value is 0 to indicate that it is disconnected; P i Lshd and Q i Lshd Node i Load active and reactive power reduction; P i DG and Q i DG Node i Active and reactive output of distributed power sources; P ij and Q ij Branch ij Active and reactive power; V i sqr and I ij sqr Node i Voltage, branch ij The square term of the current; CME The cost of mobile energy storage equipment for pre-configured units; For Node i Unit load reduction costs; P i PV For Node i Photovoltaic output.

[0009] Optionally, in step S1.1, when establishing the objective function of the pre-disaster pre-layout robust optimization model, a box uncertainty set U is used to characterize the photovoltaic output prediction error under the uncertainty scenario, and its expression is as follows: , Where: P i PV* For Node i Photovoltaic predicted output; t is the uncertainty, which is used to express the scaling ratio of the uncertainty set.

[0010] Optionally, in step S1.2, the constraint conditions of the pre-disaster pre-layout robust optimization model are as follows: , In the formula, N max ME The upper limit of the number of mobile energy storage in the pre-layout phase, 0-1 variable The value of limits each node to pre-configure at most one mobile energy storage device. Oh B For branch collection; N B is the total number of nodes; d ( i )and c ( i ) are nodes i The child and parent node sets of S i VS It is the virtual source node flag variable; F ij For branch ij Virtual power flow; F i VS For Node i Virtual power emitted; A very large number.

[0011] Optionally, step S1.3 includes: using the C&CG algorithm to solve the pre-disaster pre-layout robust optimization model. During the solution process, the original problem is decomposed into a main problem and sub-problems based on the compact form of its objective function, and the Big-M algorithm is used to convert the non-convex bilinear terms in the sub-problems into linear constrained iterative solutions for the configuration quantity and location scheme of mobile energy storage.

[0012] Optionally, in step S2, the objective function of the post-disaster recovery optimization model is as follows: , In the formula, Oh T is the set of failure periods; Oh N is the set of load nodes; Hewei t Time Node i The load active power reduction at the location; For Node i Unit load reduction cost.

[0013] Optionally, step S2 includes: establishing constraint conditions of the post-disaster recovery optimization model; The constraints include: load reduction constraints, distributed power output constraints, distribution network operation constraints, spatiotemporal dynamic scheduling constraints of mobile energy storage, and electric vehicle charging and discharging constraints.

[0014] Optionally, the spatiotemporal dynamic scheduling constraint is as follows: , In the formula, For mobile energy storage p At the node j With Node k The time spent together, For Node j With Node k The equivalent travel distance between For mobile energy storage p Actual vehicle speed, D j,k,0 For Node j With Node k The road distance between t For mobile energy storage p Arrival Node i The time interval T 0 ME Represents mobile energy storage p At the node k According to the configuration time; v p,0Mobile energy storage under zero traffic conditions p Ideal vehicle speed; c It indicates the degree of congestion of the traffic network under disaster scenarios, which is related to the degree of disaster impact and traffic flow. It can be estimated based on the damage to the traffic network after the disaster. Oh ME A collection of mobile energy storage pre-configured for disaster situations; t 0 is the time when the fault occurs; and Node i exist t The charging and discharging flags of the mobile energy storage at all times, with the value of 1 representing the charging and discharging states respectively; and Node i exist t The charging and discharging active power of mobile energy storage at all times; and Node i exist t Charging and discharging reactive power of mobile energy storage at all times; and Node i The upper limit of charging and discharging active power of mobile energy storage; and Node i The upper limit of charging and discharging reactive power of mobile energy storage; and Node i Charging and discharging efficiency of mobile energy storage; and Node i The upper and lower limits of mobile energy storage capacity.

[0015] Optionally, the output constraint condition of the distributed power source is as follows: , In the formula, M , E , D , P They are the node collections of mobile energy storage, electric vehicle charging piles, diesel generators and photovoltaic units in various types of distributed power sources; α i DG Represents various types of distributed power sources and nodes i The connection status; and Node i Active and reactive output of distributed power sources; and Node i The upper limit of active and reactive output of distributed generation; and Node i The upper and lower limits of the power factor of distributed power generation.

[0016] In a second aspect, the present invention provides a mobile energy storage vehicle scheduling optimization system for distribution network reconstruction using any one of the methods described in the first aspect of the present invention, the system comprising a mobile energy storage pre-layout module, a post-disaster recovery optimization model construction module, and a scheduling optimization module; wherein: Mobile energy storage pre-layout module, used to pre-layout the number and location of mobile energy storage in the pre-disaster prevention stage; The post-disaster recovery optimization model building module is used to establish a multi-source coordinated post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network based on the pre-arranged number and location of mobile energy storage configurations, the impact of disasters on the travel time of the transportation network, the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, and the goal of minimizing load reduction power during the fault duration. The scheduling optimization module is used to solve the post-disaster recovery scheduling optimization model to obtain the scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

[0017] In a third aspect, the present invention provides a terminal, including 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 described in the first aspect of the present invention.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0019] The beneficial effect of the present invention is that, compared with the prior art, The present invention sets up a two-stage power supply guarantee strategy of pre-disaster mobile energy storage pre-layout and post-disaster dynamic scheduling: in the pre-disaster prevention stage, considering the uncertainty of photovoltaic output, the configuration quantity and location of mobile energy storage are pre-layouted with the goal of minimizing the mobile energy storage configuration cost and load reduction risk cost, which can not only enable mobile energy storage to quickly participate in post-disaster load recovery, but also reduce the pre-disaster operation risk, improve resource utilization and the economy of the overall system. In the post-disaster recovery stage, considering the impact of extreme disasters on the travel time of the transportation network, combining the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, a post-disaster recovery scheduling optimization model is established. In the case of relatively scarce reconstruction resources, it is proposed to optimize the reconstruction of the microgrid through multi-source collaboration, that is, in the scenario where it is impossible to restore the power supply of all loads, by adjusting the power supply structure, coordinating the voltage and power relationship between the feeder node and the mobile energy storage, so as to restore as many important loads as possible, and can increase the recovery of the power outage load after a large-scale power outage in the distribution network, shorten the transportation time of the mobile resource vehicle, reduce the number of switch operations, and enhance the resilience of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the mobile energy storage vehicle dispatch optimization method for distribution network reconstruction in the present invention; Figure 2 Schematic diagram of spatiotemporal dynamic scheduling of mobile energy storage in an embodiment of the present invention; Figure 3 This is a structural principle block diagram of the mobile energy storage vehicle scheduling optimization method system for distribution network reconstruction in the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution 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 the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0022] Embodiment 1: Reference Figure 1 The embodiment of the present invention provides a method for optimizing the dispatching of mobile energy storage vehicles for distribution network reconstruction, which specifically includes the following steps: Step S1: In the pre-disaster prevention stage, pre-layout the number and location of mobile energy storage; Step S2: In the post-disaster recovery phase, based on the number and location of pre-arranged mobile energy storage configurations, considering the impact of disasters on the travel time of the transportation network, combining the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, with the goal of minimizing the weighted value of load reduction power during the fault duration, a multi-source collaborative post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network is established; Step S3: Solve the post-disaster recovery scheduling optimization model to obtain a scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

[0023] In this embodiment, in the pre-disaster prevention stage, the uncertainty scenario of photovoltaic output and network reconstruction are taken into account. Under the premise that the time and duration of the disaster are unknown, the configuration quantity and location of mobile energy storage are pre-arranged to ensure that mobile energy storage can respond quickly after the disaster and participate in load recovery; in the post-disaster recovery stage, a multi-source collaborative post-disaster recovery optimization model is established, and the optimal distribution of electricity in time and space is achieved by dispatching resources such as mobile energy storage, electric vehicles and diesel generators, so as to maximize the restoration of power supply to important loads and improve the resilience of the distribution network.

[0024] It is further explained that if Figure 2 The figure shows a schematic diagram of the spatiotemporal dynamic scheduling of mobile energy storage in an embodiment of the present invention. In the figure, mobile energy storage needs to be dispatched from node j Move to Node k contribute, t is the scheduling start time; D j,k(t) For Node j With Node k The road distance between v p ME (t) Represents mobile energy storage in disaster scenarios p Movement speed; T p,j,k ME (t) Represents mobile energy storage p Slave Node j To Node k Time required; T 0 ME Represents mobile energy storage p At the node k Installation and configuration time.

[0025] As an embodiment of the present invention, in step S1, the step of pre-layout of the configuration quantity and position of mobile energy storage includes: S1.1. With the goal of minimizing the mobile energy storage configuration cost and load reduction cost under the scenario of PV output uncertainty, the objective function of the pre-disaster pre-layout robust optimization model is established; Specifically, the objective function of the pre-disaster pre-layout robust optimization model is as follows: , In the formula, Oh N is the set of load nodes; It is a 0-1 variable, indicating the mobile energy storage and node i The connection status, its value is 1, which means there is only one mobile energy storage connected to the node i Connection, its value is 0 means there is no mobile energy storage and node i connect; α ij For branch ij The disconnection state, its value is 1 to indicate that the circuit is closed, and its value is 0 to indicate that it is disconnected; P i Lshd and Q i Lshd Node i Load active and reactive power reduction; P i DG and Q i DG Node i Active and reactive output of distributed power sources; P ij and Q ij Branch ij Active and reactive power; V i sqr and I ij sqr Node i Voltage, branch ij The square term of the current; C ME The cost of mobile energy storage equipment for pre-configured units; For Node i Unit load reduction costs; P i PV For Node i Photovoltaic output.

[0026] Furthermore, considering the real-time volatility of photovoltaic output, in step S1.1, when establishing the objective function of the pre-disaster pre-layout robust optimization model, the box uncertainty set U is used to characterize the photovoltaic output prediction error under the uncertainty scenario, and its expression is as follows: The expression of the box uncertainty set U is as follows: , Where: P i PV* For Node i Photovoltaic predicted output; t is the uncertainty, which is used to express the scaling ratio of the uncertainty set.

[0027] S1.2. Constraints for constructing the pre-disaster pre-layout robust optimization model: , In the formula, N max ME The upper limit of the number of mobile energy storage in the pre-layout phase, 0-1 variable The value of limits each node to pre-configure at most one mobile energy storage device. Oh B For branch collection; N B is the total number of nodes; d ( i )and c ( i ) are nodes i The child and parent node sets of S i VS It is the virtual source node flag variable; F ij For branch ij Virtual power flow; F i VS For Node i Virtual power emitted; A very large number.

[0028] S1.3. Based on the constraint conditions constructed in step S1.2, the pre-disaster pre-layout robust optimization model is solved to obtain a pre-layout optimization solution for the configuration quantity and location of mobile energy storage.

[0029] A preferred but non-restrictive embodiment, step S1.3 of this embodiment adopts the C&CG algorithm (column constraint generation algorithm) to solve the pre-disaster pre-layout robust optimization model. During the solution process, the original problem is decomposed into a main problem and sub-problems based on the compact form of its objective function, and the Big-M algorithm is combined to convert the non-convex bilinear terms in the sub-problem into linear constraints for iterative solution, which greatly reduces the complexity of the calculation and improves the calculation accuracy and efficiency.

[0030] Specifically, when the C&CG algorithm is used to solve the problem, the compact form of the pre-disaster pre-layout robust optimization model in the pre-disaster prevention stage is as follows: , In the formula, X This is the first stage optimization variable. At this time, the location and capacity of the mobile energy storage node are obtained when the mobile energy storage configuration cost and load reduction risk cost before the disaster are minimized; u and Y Optimize variables for the second stage, u is the maximum power of photovoltaic after the disaster, Y The dispatching method of mobile energy storage after a disaster, i.e. the location and output size of the output nodes; U S is the uncertainty set; Ω(X,u) For variables Y The feasible domain of A , B , C , D , Em is a constant coefficient matrix; a , b and f m is the coefficient vector; n is the number of second-order cone constraints.

[0031] Furthermore, based on the compact form of the pre-disaster pre-layout robust optimization model, the original problem can be decomposed into a main problem and sub-problems.

[0032] The main question is: , In the formula, Z is an auxiliary variable, whose value is the given photovoltaic output u* The value of the objective function that minimizes the mobile energy storage configuration cost in this case; K is the maximum number of iterations, which is set according to the algorithm accuracy and is generally 1000; l Represents the variable obtained at the lth iteration.

[0033] The sub-problems are: , The inner min problem of the subproblem is a convex optimization problem. According to the strong duality theory, it is converted into a max form and combined with the outer max problem to form the following single-layer optimization problem: , In the formula, π , f , m m , s m is the dual variable in the constraints contained in the inner minimization problem.

[0034] Due to the non-convex bilinear term u T f , the subproblem is difficult to solve directly. Therefore, we introduce 0-1 variables ϑ i As the upper and lower limit mark of photovoltaic output, when its value is 0, it means that the photovoltaic output is at the lower limit, and when its value is 1, it means that the photovoltaic output is at the upper limit. u T f can be transformed into: , In the formula, u i and f i They are u and f Elements of u i，max and u i，min They are the upper and lower limits of photovoltaic output respectively. ϑ i f i It can be converted into a linear constraint by the Big-M method: , To avoid the model being too conservative, add constraints: , In the formula, h i is the intermediate variable, e is a constant smaller than the number of PV nodes.

[0035] As an embodiment of the present invention, in step S2, the objective function of the post-disaster recovery optimization model is as follows: , In the formula, Oh T is the set of failure periods; OhN is the set of load nodes; Hewei t Time Node i The load active power reduction at the location; For Node i Unit load reduction cost.

[0036] In this embodiment, step S2 also includes the following constraints for establishing a post-disaster recovery optimization model: (1) Load reduction constraints: , In the formula, and Node i The load active and reactive power reduction at and Node i The maximum value of load active power and reactive power.

[0037] (2) Distributed power generation output constraints: , In the formula, M , E , D , P They are the node collections of mobile energy storage, electric vehicle charging piles, diesel generators and photovoltaic units in various types of distributed power sources; α i DG Represents various types of distributed power sources and nodes i The connection status; and Node i Active and reactive output of distributed power sources; and Node i The upper limit of active and reactive output of distributed generation; and Node i The upper and lower limits of the power factor of distributed power generation.

[0038] (3) Distribution network operation constraints: , In the formula, R ij and X ij Branch ij Resistance and Reactance ； and Node i The upper and lower limits of the voltage square term; For branch ij Upper limit of the squared current term.

[0039] (4) Constraints on charging and discharging of electric vehicles: , in, and Node i exist t The charging and discharging flags of the electric vehicle at the moment, with a value of 1 indicating the charging and discharging states respectively; and Node i exist t The charging and discharging power of electric vehicles at all times; and Node i The upper limit of charging and discharging power of electric vehicles; and Node i Charging and discharging efficiency of electric vehicles; and Node i The upper and lower limits of energy storage capacity for electric vehicles.

[0040] (5) The spatiotemporal dynamic scheduling constraints are as follows: , In the formula, For mobile energy storage p At the node j With Node k The time spent together, For Node j With Node k The equivalent travel distance between For mobile energy storage p Actual vehicle speed, D j,k,0 For Node j With Node k The road distance between t For mobile energy storage p Arrival Node i The time interval T 0 ME Represents mobile energy storage p At the node k According to the configuration time; v p,0 Mobile energy storage under zero traffic conditions pIdeal vehicle speed; c It indicates the degree of congestion of the traffic network under disaster scenarios, which is related to the degree of disaster impact and traffic flow. It can be estimated based on the damage to the traffic network after the disaster. Oh ME A collection of mobile energy storage pre-configured for disaster situations; t 0 is the time when the fault occurs; and Node i exist t The charging and discharging flags of the mobile energy storage at all times, with the value of 1 representing the charging and discharging states respectively; and Node i exist t The charging and discharging active power of mobile energy storage at all times; and Node i exist t Charging and discharging reactive power of mobile energy storage at all times; and Node i The upper limit of charging and discharging active power of mobile energy storage; and Node i The upper limit of charging and discharging reactive power of mobile energy storage; and Node i Charging and discharging efficiency of mobile energy storage; and Node i The upper and lower limits of mobile energy storage capacity.

[0041] The beneficial effect of the present invention is that, compared with the prior art, The present invention sets up a two-stage power supply guarantee strategy of pre-disaster mobile energy storage pre-layout and post-disaster dynamic scheduling: in the pre-disaster prevention stage, considering the uncertainty of photovoltaic output, the configuration quantity and location of mobile energy storage are pre-layouted with the goal of minimizing the mobile energy storage configuration cost and load reduction risk cost, which can not only enable mobile energy storage to quickly participate in post-disaster load recovery, but also reduce the pre-disaster operation risk, improve resource utilization and the economy of the overall system. In the post-disaster recovery stage, considering the impact of extreme disasters on the travel time of the transportation network, combining the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, a post-disaster recovery scheduling optimization model is established. In the case of relatively scarce reconstruction resources, it is proposed to optimize the reconstruction of the microgrid through multi-source collaboration, that is, in the scenario where it is impossible to restore the power supply of all loads, by adjusting the power supply structure, coordinating the voltage and power relationship between the feeder node and the mobile energy storage, so as to restore as many important loads as possible, and can increase the recovery of the power outage load after a large-scale power outage in the distribution network, shorten the transportation time of the mobile resource vehicle, reduce the number of switch operations, and enhance the resilience of the distribution network.

[0042] Embodiment 2: like Figure 3 As shown, the present invention provides a mobile energy storage vehicle scheduling optimization system for distribution network reconstruction, the system is used to implement the steps of the method in the above embodiment 1, the system includes a mobile energy storage pre-layout module, a post-disaster recovery optimization model construction module and a scheduling optimization module; wherein: Mobile energy storage pre-layout module, used to pre-layout the number and location of mobile energy storage in the pre-disaster prevention stage; The post-disaster recovery optimization model building module is used to establish a multi-source coordinated post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network based on the pre-arranged number and location of mobile energy storage configurations, the impact of disasters on the travel time of the transportation network, the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, and the goal of minimizing load reduction power during the fault duration. The scheduling optimization module is used to solve the post-disaster recovery scheduling optimization model to obtain the scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

[0043] The mobile energy storage vehicle scheduling optimization system for distribution network reconstruction provided in the embodiment of the present invention and the mobile energy storage vehicle scheduling optimization method for distribution network reconstruction provided in Example 1 are based on the same technical concept, and can produce the beneficial effects described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0044] Embodiment three: A terminal provided by an embodiment of the present invention includes a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0045] Embodiment 4: An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0046] 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.

[0047] 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 above. More specific examples (a non-exhaustive list) of computer-readable storage media 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 above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, 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.

[0048] 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.

[0049] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through 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 through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0050] 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 mobile energy storage vehicle dispatch optimization method for distribution network reconstruction, characterized in that: Methods include: Step S1: In the pre-disaster prevention stage, pre-layout the number and location of mobile energy storage; Step S2: In the post-disaster recovery phase, based on the number and location of pre-arranged mobile energy storage configurations, considering the impact of disasters on the travel time of the transportation network, combining the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, with the goal of minimizing load reduction power during the fault duration, a multi-source collaborative post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network is established; Step S3: Solve the post-disaster recovery scheduling optimization model to obtain a scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

2. The mobile energy storage vehicle scheduling optimization method for distribution network reconstruction according to claim 1 is characterized in that: In step S1, the step of pre-layout of the configuration quantity and location of mobile energy storage includes: S1.

1. With the goal of minimizing the mobile energy storage configuration cost and load reduction cost under the scenario of PV output uncertainty, the objective function of the pre-disaster pre-layout robust optimization model is established; S1.2, constructing the constraint conditions of the pre-disaster pre-layout robust optimization model; S1.

3. Based on the constraint conditions constructed in step S1.2, the pre-disaster pre-layout robust optimization model is solved to obtain a pre-layout optimization solution for the configuration quantity and location of mobile energy storage.

3. The mobile energy storage vehicle scheduling optimization method for distribution network reconstruction according to claim 2 is characterized in that: In step S1.1, the objective function of the pre-disaster pre-layout robust optimization model is as follows: ， In the formula, Ω N is the set of load nodes; It is a 0-1 variable, indicating the mobile energy storage and node i The connection status, its value is 1, which means there is only one mobile energy storage connected to the node i Connection, its value is 0 means there is no mobile energy storage and node i connect; α ij For branch ij The disconnection state, its value is 1 to indicate that the circuit is closed, and its value is 0 to indicate that it is disconnected; P i Lshd and Q i Lshd Node i Load active and reactive power reduction; P i DG and Q i DG Node i Active and reactive output of distributed power sources; P ij and Q ij Branch ij Active and reactive power; V i sqr and I ij sqr Node i Voltage, branch ij The square term of the current; C ME The cost of mobile energy storage equipment for pre-configured units; For Node i Unit load reduction costs; PiPV For Node i Photovoltaic output.

4. The mobile energy storage vehicle dispatch optimization method for distribution network reconstruction according to claim 3 is characterized in that: In step S1.1, when establishing the objective function of the pre-disaster pre-layout robust optimization model, the box uncertainty set U is used to characterize the photovoltaic output prediction error under the uncertainty scenario, and its expression is as follows: ， Where: P i PV* For Node i Photovoltaic predicted output; τ is the uncertainty, which is used to express the scaling ratio of the uncertainty set.

5. The mobile energy storage vehicle dispatch optimization method for distribution network reconstruction according to claim 3, characterized in that: In step S1.2, the constraint conditions of the pre-disaster pre-layout robust optimization model are as follows: ， In the formula, N max ME The upper limit of the number of mobile energy storage in the pre-layout phase, 0-1 variable The value of limits each node to pre-configure at most one mobile energy storage device. Ω B For branch collection; N B is the total number of nodes; δ ( i )and γ ( i ) are nodes i The child and parent node sets of S i VS It is the virtual source node flag variable; F ij For branch ij Virtual power flow; F i VS For Node i Virtual power emitted; A very large number.

6. The method for optimizing the dispatching of mobile energy storage vehicles for distribution network reconstruction according to claim 1, characterized in that: In step S2, the objective function of the post-disaster recovery optimization model is as follows: ， In the formula, Ω T is the set of failure periods; Ω N is the set of load nodes; Hewei t Time Node i The load active power reduction at the location; For Node i Unit load reduction cost.

7. The method for optimizing the dispatching of mobile energy storage vehicles for distribution network reconstruction according to claim 1, characterized in that: Step S2 includes: establishing constraints of the post-disaster recovery optimization model; The constraints include: load reduction constraints, distributed power output constraints, distribution network operation constraints, spatiotemporal dynamic scheduling constraints of mobile energy storage, and electric vehicle charging and discharging constraints.

8. The method for optimizing the dispatching of mobile energy storage vehicles for distribution network reconstruction according to claim 7, characterized in that: The spatiotemporal dynamic scheduling constraint is as follows: ， In the formula, For mobile energy storage p At the node j With Node k The time spent together, For Node j With Node k The equivalent travel distance between For mobile energy storage p Actual vehicle speed, D j,k,0 For Node j With Node k The road distance between t For mobile energy storage p Arrival Node i The time interval T 0 ME Represents mobile energy storage p At the node k According to the configuration time; v p,0 Mobile energy storage under zero traffic conditions p Ideal vehicle speed; c It indicates the degree of congestion of the traffic network under disaster scenarios, which is related to the degree of disaster impact and traffic flow. It can be estimated based on the damage to the traffic network after the disaster. Ω ME A collection of mobile energy storage pre-configured for disaster situations; t 0 is the time when the fault occurs; and Node i exist t The charging and discharging flags of the mobile energy storage at all times, with the value of 1 representing the charging and discharging states respectively; and Node i exist t The charging and discharging active power of mobile energy storage at all times; and Node i exist t Charging and discharging reactive power of mobile energy storage at all times; and Node i The upper limit of charging and discharging active power of mobile energy storage; and Node i The upper limit of charging and discharging reactive power of mobile energy storage; and Node i Charging and discharging efficiency of mobile energy storage; and Node i The upper and lower limits of mobile energy storage capacity.

9. The method for optimizing the dispatching of mobile energy storage vehicles for distribution network reconstruction according to claim 7, characterized in that: The output constraint condition of the distributed power source is as follows: ， In the formula, M , E , D , P They are the node collections of mobile energy storage, electric vehicle charging piles, diesel generators and photovoltaic units in various types of distributed power sources; α i DG Represents various types of distributed power sources and nodes i The connection status; and Node i Active and reactive output of distributed power sources; and Node i The upper limit of active and reactive output of distributed generation; and Node i The upper and lower limits of the power factor of distributed power generation.

10. A mobile energy storage vehicle dispatch optimization system for distribution network reconstruction using the method according to any one of claims 1 to 9, characterized in that: The system includes a mobile energy storage pre-layout module, a post-disaster recovery optimization model building module, and a scheduling optimization module; Mobile energy storage pre-layout module, used to pre-layout the number and location of mobile energy storage in the pre-disaster prevention stage; The post-disaster recovery optimization model building module is used to establish a multi-source coordinated post-disaster recovery scheduling optimization model that cooperates with the reconstruction of the post-disaster distribution network based on the pre-arranged number and location of mobile energy storage configurations, the impact of disasters on the travel time of the transportation network, the dynamic scheduling and timing output characteristics of mobile energy storage and electric vehicles, and the goal of minimizing load reduction power during the fault duration. The scheduling optimization module is used to solve the post-disaster recovery scheduling optimization model to obtain the scheduling optimization plan for the output position and output time of mobile energy storage and electric vehicle resources in each scheduling cycle.

11. 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 1-9.

12. 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 9 are implemented.

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