Mobile energy storage scheduling and micro-grid reconstruction multi-source collaborative optimization method and system

Abstract

The invention provides a mobile energy storage scheduling and micro-grid reconstruction multi-source collaborative optimization method and system, and the method comprises the steps: building a multi-source collaborative optimization model which takes mobile energy storage equipment and micro-grid reconstruction into consideration, and takes the minimum sum of the load loss cost, the micro-grid power generation cost and the transportation cost of the mobile energy storage equipment as a target; and then solving the multi-source collaborative optimization model to obtain an optimal scheduling scheme including mobile energy storage equipment scheduling and a micro-grid reconstruction strategy. According to the method, the toughness and the power supply reliability of the power distribution network under the disaster condition are improved by utilizing the synergistic effect of the mobile energy storage equipment and micro-grid reconstruction.

Description

Technical Field

[0001] The present invention belongs to the field of mobile energy storage scheduling, and specifically relates to a mobile energy storage scheduling and microgrid reconstruction multi-source collaborative optimization method and system. Background Art

[0002] In the case of power grid damage caused by natural disasters (such as earthquakes, typhoons, etc.), traditional power restoration methods are usually difficult to achieve fast and effective power restoration due to their reliance on fixed power sources and limited resource scheduling capabilities. Mobile energy storage devices can provide timely power support in post-disaster power restoration due to their flexible scheduling characteristics. In addition, microgrid reconstruction, as a recovery solution, can effectively improve the recovery speed and power supply reliability by dividing the damaged distribution network into several areas that can operate independently. However, the current research on the scheduling of mobile energy storage devices and the coordinated optimization of microgrid reconstruction and mobile energy storage multi-source is still incomplete, lacking an intelligent and systematic scheduling method. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for multi-source collaborative optimization of mobile energy storage scheduling and microgrid reconstruction in view of the above-mentioned problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] In a first aspect, the present invention proposes a multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction, comprising:

[0006] S1. Construct a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction, with the goal of minimizing the sum of load loss cost, microgrid power generation cost, and transportation cost of mobile energy storage equipment;

[0007] S2. Solve the multi-source collaborative optimization model to obtain the optimal scheduling solution including mobile energy storage equipment scheduling and microgrid reconstruction strategy.

[0008] In S1, the objective function of the multi-source collaborative optimization model includes:

[0009] min(f 1 +f 2 +f 3 )

[0010]

[0011] In the above formula, f 1 、f 2 、f 3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t, and Z is the set of spatiotemporal scheduling trajectories of the mobile energy storage device;

[0012] The constraints include: topological constraints of network reconstruction and spatiotemporal constraints of mobile energy storage devices.

[0013] The topology constraints of the network reconstruction include:

[0014]

[0015] In the above formula, α ij is the connection decision variable of nodes i and j, E is the branch set of the distribution network, β ij is the decision variable of whether node j is the parent of node i, Ψ(l) is a set of buses connected to bus l through branches, and N is the set of distribution network nodes;

[0016] The spatiotemporal constraints of the mobile energy storage device include:

[0017]

[0018]

[0019] In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is the set of virtual microgrid nodes, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

[0020] The constraints of the multi-source collaborative optimization model also include photovoltaic constraints;

[0021] The photovoltaic constraints include:

[0022]

[0023] In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

[0024] The constraints of the multi-source collaborative optimization model also include microgrid operation constraints and distribution network power flow constraints;

[0025] The microgrid operation constraints include:

[0026]

[0027] In the above formula, are the active and reactive power from microgrid m to the distribution system through the bus during time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t, are the maximum active and reactive power of microgrid m, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m, respectively;

[0028] The distribution network power flow constraints include current power balance constraints, line voltage constraints, line power transmission constraints, line capacity constraints, node voltage constraints, load recovery constraints, and network connectivity constraints.

[0029] In a second aspect, the present invention proposes a multi-source collaborative optimization system for mobile energy storage scheduling and microgrid reconstruction, including an optimization model building module and an optimization model solving module;

[0030] The optimization model building module is used to build a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction, with the goal of minimizing the sum of load loss cost, microgrid power generation cost and transportation cost of mobile energy storage equipment;

[0031] The optimization model solving module is used to solve the multi-source collaborative optimization model to obtain an optimization scheduling solution including mobile energy storage device scheduling and microgrid reconstruction strategy.

[0032] The optimization model construction module includes an objective function construction unit, a network reconstruction topology constraint construction unit, and a mobile energy storage device spatiotemporal constraint construction unit;

[0033] The objective function construction unit is used to construct the following objective function:

[0034] min(f 1 +f 2 +f 3 )

[0035]

[0036] In the above formula, f 1 、f 2 、f 3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t, and Z is the set of spatiotemporal scheduling trajectories of the mobile energy storage device.

[0037] The network reconstruction topology constraint construction unit is used to construct the following constraints:

[0038]

[0039] In the above formula, α ij is the connection decision variable of nodes i and j, E is the branch set of the distribution network, β ij is the decision variable of whether node j is the parent of node i, Ψ(l) is a set of buses connected to bus l through branches, and N is the set of distribution network nodes;

[0040] The spatiotemporal scheduling constraint construction unit of the mobile energy storage device is used to construct the following constraints:

[0041]

[0042] In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is the set of virtual microgrid nodes, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

[0043] The optimization model construction module also includes a photovoltaic constraint construction unit;

[0044] The photovoltaic constraint construction unit is used to construct the following constraints:

[0045]

[0046] In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

[0047] The optimization model construction module also includes a microgrid operation constraint construction unit and a distribution network flow constraint construction unit;

[0048] The microgrid operation constraint construction unit is used to construct the following constraints:

[0049]

[0050] In the above formula, are the active and reactive power from microgrid m to the distribution system through the bus during time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t, are the maximum active and reactive power of microgrid m, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m, respectively;

[0051] The distribution network flow constraint construction unit is used to construct current power balance constraints, line voltage constraints, line power transmission constraints, line capacity constraints, node voltage constraints, load recovery constraints and network connectivity constraints.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention provides a multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction. The method first constructs a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction with the goal of minimizing the sum of load loss cost, microgrid power generation cost, and transportation cost of mobile energy storage equipment. Then, the multi-source collaborative optimization model is solved to obtain an optimized scheduling scheme including mobile energy storage equipment scheduling and microgrid reconstruction strategy. The method makes full use of the synergy between mobile energy storage equipment and microgrid reconstruction, uses microgrids as the core node for power supply of key loads, and simultaneously dispatches mobile energy storage equipment to transmit energy between microgrids, alleviates energy imbalance, maximizes the power system's ability to restore system power supply after natural disasters or extreme events, and improves the resilience of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of the method described in Example 1.

[0055] Figure 2 This is a structural diagram of the system described in Example 2. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] After an extreme event causes a distribution network failure (such as substation damage, feeder interruption), the transmission network cannot supply power to the system. At this time, the mobile energy storage device divides the power system into multiple islands to undertake the power supply task of critical loads. It is assumed that the mobile energy storage device can be flexibly dispatched within the time range T, and there is a transportation network connecting all mobile energy storage devices.

[0058] The present invention, on the one hand, reconfigures the power system into multiple islands by controlling the state of the remote control switch. The microgrid serves as the core node for powering critical loads and acts as the interface between the power system and the transportation network. On the other hand, by solving the vehicle scheduling problem, mobile energy storage devices can flexibly transmit energy between microgrids, thereby alleviating energy imbalance in multi-fault scenarios and improving system resilience. Among them, the total cost is used as an indicator to measure the resilience level, including the load loss cost, the microgrid power generation cost, and the transportation cost of the mobile energy storage device.

[0059] Embodiment 1:

[0060] A multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction, such as Figure 1 As shown, the specific steps are as follows:

[0061] 1. Distribution network division and traffic model establishment.

[0062] The topology, line parameters, and node information of the distribution network are obtained, and the distribution network is divided into several sub-areas according to geographical and load distribution.

[0063] Obtain road information such as road distance and traffic capacity, establish traffic connection relationships between nodes, and consider the accessibility of mobile energy storage equipment.

[0064] After a distribution network failure, check network connectivity and identify damaged nodes and lines.

[0065] 2. Build a multi-source collaborative optimization model.

[0066] When a distribution network fails, it is crucial to ensure the power supply of key primary loads. On this basis, the secondary loads should be restored as much as possible, and other loads should be gradually restored. For this purpose, an objective function is proposed to minimize the total cost, including the load loss cost of each node in the distribution network, the microgrid power generation cost, and the transportation cost of mobile energy storage equipment; by combining mobile energy storage scheduling with network reconstruction strategy, it aims to achieve efficient power supply restoration of the distribution network after a fault occurs. The objective function is as follows:

[0067] min(f 1 +f 2 +f 3 )

[0068]

[0069] In the above formula, f 1 、f 2 、f 3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t. It is 1 when the mobile energy storage device ω moves from microgrid m to microgrid u within time period t, otherwise it is 0. Z is the set of time-space scheduling trajectories of the mobile energy storage device;

[0070] (1) Topological constraints of network reconstruction

[0071] Network reconstruction is a key operation to restore power supply capacity to the maximum extent and improve operational reliability by adjusting the topology of the power grid. The power system can be modeled as an undirected graph G = (N, E), where N represents the node set and E represents the edge set. In the event of multiple point failures, some isolated areas may be completely disconnected from the power generation resources, and these completely isolated areas will be deleted. To simplify the symbolic representation, each microgrid m corresponds one-to-one to the node i connected to the distribution network, that is, m = i. Therefore, the microgrid set M is a subset of the node set N, that is When reconfiguring the network, it is necessary to ensure that the grid maintains a radial structure. This means that each island contains only one microgrid and there must be no loops or overlapping areas in the entire network. This constraint can be described as follows:

[0072]

[0073]

[0074] In the above formula, α ij is the connection decision variable of nodes i and j, which is 1 if nodes i and j are connected, otherwise it is 0; β ij is the decision variable for whether node j is the parent of node i. If node j is the parent of node i, it is 1, otherwise it is 0; Ψ(l) is a set of buses connected to bus l through branches.

[0075] The network reconfiguration is achieved through a one-time switching operation. Equation 1 is the necessary condition for spanning the tree. Equation 2 is used to determine whether a line is connected. Equation 3 indicates that each bus has only one parent bus except the microgrid bus. Equation 4 is used to ensure that the microgrid bus as the root bus has no parent bus.

[0076] (2) Temporal and spatial constraints of mobile energy storage equipment

[0077] Temporal and spatial constraints of mobile energy storage:

[0078] The dispatching state of mobile energy storage is jointly determined by its charging and discharging state and transportation state, and has the characteristics of spatiotemporal coupling. Therefore, the present invention considers a spatiotemporal dynamic dispatching model that models the spatiotemporal dynamics of microgrid nodes and transportation paths through mixed integer optimization, and assumes that the transportation process does not consume electrical energy. The device can only switch between charging or transportation at any time. The transportation network between multiple microgrids is fully considered in the model design. In order to accurately represent the dynamic state at each moment, virtual nodes are introduced to handle longer distances. For example, when a mobile energy storage device requires two moments to travel between microgrid 1 and microgrid 2, other adjacent nodes may only require one moment. In order to ensure that the propagation time of each transportation path in the network corresponds accurately to a moment, a virtual node microgrid 3 is added between microgrid 1 and microgrid 2 to refine the path and optimize the accuracy of scheduling.

[0079] Two main states are defined in the model: transport state, It is used to describe the process of the mobile energy storage device ω moving from the microgrid m to the microgrid u within the time period t; parking state, It means that the device stays in the microgrid and interacts with the distribution system for charging and discharging. Based on this model, a dispatch chain for each mobile energy storage device can be constructed to achieve its optimal dispatch among multiple microgrids within the time range T, maximize resource utilization efficiency and improve post-disaster power recovery capabilities.

[0080]

[0081]

[0082] In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is a collection of virtual microgrid nodes.

[0083] Formula 5 ensures that the mobile energy storage device ω can be in the transport state or the parking state; Formulas 6 and 7 reflect the flow conservation of microgrid nodes and virtual microgrid nodes. In Formula 6, the mobile energy storage device ω that has traveled in period t at the end of microgrid m must start from microgrid m in the next period, and Formula 7 declares the initial position; Formula 8 ensures that the mobile energy storage device cannot immediately return in any case, that is, the mobile energy storage device that moves from one microgrid node to another microgrid node is not allowed to return directly to the previous microgrid node.

[0084] Charging and discharging constraints of mobile energy storage:

[0085] When a mobile energy storage device arrives at a microgrid, it is in a parked state and can be charged or discharged from the microgrid while satisfying the following constraints:

[0086]

[0087] In the above formula, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

[0088] Formula 13 indicates that the mobile energy storage device can only enter the charging or discharging mode and exchange power with the microgrid when it is parked on a microgrid. For example, when the mobile energy storage device ω stays on any microgrid, the condition is satisfied. At this time, the mobile energy storage device ω can switch between charging, discharging or idle mode; otherwise, its charging / discharging state will be limited to zero.

[0089] (3) Photovoltaic constraints

[0090]

[0091] In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

[0092] (4) Microgrid operation constraints

[0093] A microgrid is an entity that coordinates distributed energy resources and acts as a single producer or load from the grid's perspective. A microgrid aggregates the entire generation resource, equivalent local loads, and charging / discharging facilities. When integrated with mobile energy storage devices and photovoltaics, the microgrid operation constraints can be expressed as follows, specifically:

[0094] Considering the active power of the mobile energy storage device charged from the microgrid m and the active power discharged to the microgrid m And the active power absorbed by the microgrid m

[0095]

[0096] In the above formula, are the active and reactive power from microgrid m to the distribution system through the bus during time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t respectively.

[0097] The active and reactive power capacity constraints of the dispatchable distributed generation resources in each microgrid are:

[0098]

[0099] In the above formula, are the maximum active and reactive power of microgrid m respectively.

[0100] The energy constraints of each microgrid are as follows:

[0101]

[0102] In the above formula, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m respectively.

[0103] (5) Distribution network flow constraints

[0104] Node current power balance constraints

[0105] For each bus in the distribution network, the balance of active and reactive power must be met in time period t:

[0106]

[0107] In the above formula, are the active and reactive power generated by bus l in time period t, are respectively the active and reactive power consumed by the load of bus l in period t, They are the active and reactive power from bus l to bus p in time period t respectively.

[0108] Line voltage constraints

[0109] In order to consider the on-off state of the line, the large M method is used to deal with the line voltage constraint. For line ij in time period t,

[0110]

[0111] In the above formula, are the voltage amplitudes of nodes i and j in time period t, M M is a large constant, a number large enough to ensure that the constraint is valid, R ij , X ij are the resistance and reactance of line ij respectively, are respectively the active and reactive power of line ij in time period t, V 0 is the voltage of the microgrid bus.

[0112] Line power transfer constraints

[0113] The power transmission of line ij in time period t must meet the following constraints:

[0114]

[0115] In the above formula, is the apparent power capacity of line ij.

[0116] Line capacity constraints

[0117] In order to ensure the thermal stability of the line, the line power needs to meet the second-order cone constraint:

[0118]

[0119] Node Voltage Constraints

[0120] The voltage amplitude of all nodes must be kept within the specified range:

[0121]

[0122] In the above formula, V i max 、V i min are the upper and lower limits of the voltage allowed at node i, is the active power consumed by the load of node i in period t, is the active power consumed by the load of node i in period t.

[0123] Load recovery constraints

[0124] The load recovery of node i in period t must satisfy:

[0125]

[0126] In the above formula, is the power factor of node i.

[0127] Network connectivity constraints

[0128] In order to ensure the connectivity and topology of the network, the following relationships must be met:

[0129]

[0130] In the above formula, Γ is the line set, E′ is the total number of edges in the distribution network, and B is the number of loops in the network, that is, the number of islands formed after network reconstruction.

[0131] In the above multi-source collaborative optimization model, the optimization variables include binary variables: α ij , β ij , Continuous variables:

[0132] 3. Use Gurobi solver to solve the multi-source collaborative optimization model and obtain the optimal scheduling plan, including the mobile energy storage equipment scheduling plan, microgrid reconstruction plan and photovoltaic power generation equipment access plan. The specific solution steps are:

[0133] First, use Gurobi's modeling interface to define variables and constraints and encode the model; then set the solver parameters, including solution accuracy, Gap value, and maximum solution time to prevent the calculation time from being too long. Next, call the Gurobi solver and use its ability to automatically process integers and continuous variables to solve the optimal solution. Finally, analyze the results, extract key variables such as line switch status, equipment scheduling plan, load recovery, etc., and verify whether all constraints are met to ensure the effectiveness and feasibility of the solution.

[0134] 4. Implement optimized scheduling plan

[0135] Network reconstruction: According to the optimization results, operate the distribution network switches to complete the network reconstruction.

[0136] Scheduling of mobile energy storage equipment: Scheduling equipment movement according to the optimal path and time, and performing charging and discharging operations at designated nodes and times.

[0137] Photovoltaic power generation: Based on the optimization results, connect to photovoltaic power generation equipment.

[0138] Through this embodiment, the present invention can effectively guide the recovery and operation of the distribution network under disaster conditions, by giving priority to restoring key primary loads, gradually restoring secondary and other loads, minimizing load losses and controlling the cost of microgrid power generation and the transportation cost of mobile energy storage equipment. Ensure that the microgrid operates safely and stably in island mode, maintain voltage and frequency within normal ranges, and improve the resilience and power supply reliability of the distribution network under disaster conditions.

[0139] Embodiment 2:

[0140] A multi-source collaborative optimization system for mobile energy storage scheduling and microgrid reconstruction, such as Figure 2 As shown, it includes an optimization model building module and an optimization model solving module.

[0141] The optimization model construction module is used to construct a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction, with the goal of minimizing the sum of load loss cost, microgrid power generation cost and transportation cost of mobile energy storage equipment. It includes an objective function construction unit, a network reconstruction topology constraint construction unit, a mobile energy storage equipment spatiotemporal scheduling constraint construction unit, a photovoltaic constraint construction unit, a microgrid operation constraint construction unit, and a distribution network flow constraint construction unit.

[0142] The objective function construction unit is used to construct the following objective function:

[0143] min(f 1 +f 2 +f 3 )

[0144]

[0145] In the above formula, f 1 、f 2 、f 3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t, and Z is the set of spatiotemporal scheduling trajectories of the mobile energy storage device.

[0146] The network reconstruction topology constraint construction unit is used to construct the following constraints:

[0147]

[0148] In the above formula, α ij is the connection decision variable of nodes i and j, E is the branch set of the distribution network, β ij is the decision variable for whether node j is the parent of node i, Ψ(l) is a set of buses connected to bus l through branches, and N is the set of distribution network nodes.

[0149] The spatiotemporal scheduling constraint construction unit of the mobile energy storage device is used to construct the following constraints:

[0150]

[0151]

[0152] In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is the set of virtual microgrid nodes, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

[0153] The photovoltaic constraint construction unit is used to construct the following constraints:

[0154]

[0155] In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

[0156] The microgrid operation constraint construction unit is used to construct the following constraints:

[0157]

[0158] In the above formula, are the active and reactive power from microgrid m to distribution system through bus in time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t, are the maximum active and reactive power of microgrid m, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m respectively.

[0159] The distribution network flow constraint building unit is used to build:

[0160] Node current power balance constraints

[0161]

[0162] In the above formula, are the active and reactive power generated by bus l in time period t, are respectively the active and reactive power consumed by the load of bus l in period t, They are the active and reactive power from bus l to bus p in time period t respectively.

[0163] Line voltage constraints

[0164]

[0165] In the above formula, are the voltage amplitudes of nodes i and j in time period t, M M is a large constant, a number large enough to ensure that the constraint is valid, R ij , X ij are the resistance and reactance of line ij respectively, are respectively the active and reactive power of line ij in time period t, V 0 is the voltage of the microgrid bus.

[0166] Line power transfer constraints

[0167]

[0168] In the above formula, is the apparent power capacity of line ij.

[0169] Line capacity constraints

[0170]

[0171] Node Voltage Constraints

[0172]

[0173] In the above formula, V i max 、V i min are the upper and lower limits of the voltage allowed at node i, is the active power consumed by the load of node i in period t, is the active power consumed by the load of node i in period t;

[0174] Load recovery constraints

[0175]

[0176] In the above formula, is the power factor of node i;

[0177] Network connectivity constraints

[0178]

[0179] In the above formula, Γ is the line set, E′ is the total number of edges in the distribution network, and B is the number of loops in the network, that is, the number of islands formed after network reconstruction.

[0180] The optimization model solving module is used to solve the multi-source collaborative optimization model using the Gurobi solver to obtain an optimization scheduling solution, including a mobile energy storage device scheduling strategy, a microgrid reconstruction strategy, and an access strategy for photovoltaic power generation equipment.

Claims

1. A multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction, characterized in that: The method comprises: S1. Construct a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction, with the goal of minimizing the sum of load loss cost, microgrid power generation cost, and transportation cost of mobile energy storage equipment; S2. Solve the multi-source collaborative optimization model to obtain the optimal scheduling solution including mobile energy storage equipment scheduling and microgrid reconstruction strategy.

2. A multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction according to claim 1, characterized in that: In S1, the objective function of the multi-source collaborative optimization model includes: min(f1+f2+f3) In the above formula, f1, f2, and f3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t, and Z is the set of spatiotemporal scheduling trajectories of the mobile energy storage device; The constraints include: topological constraints of network reconstruction and spatiotemporal constraints of mobile energy storage devices.

3. A multi-source collaborative optimization method for mobile energy storage scheduling and microgrid reconstruction according to claim 2, characterized in that: The topology constraints of the network reconstruction include: In the above formula, α ij is the connection decision variable of nodes i and j, E is the branch set of the distribution network, β ij is the decision variable of whether node j is the parent of node i, Ψ(l) is a set of buses connected to bus l through branches, and N is the set of distribution network nodes; The spatiotemporal constraints of the mobile energy storage device include: In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is the set of virtual microgrid nodes, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

4. A mobile energy storage scheduling and microgrid reconstruction multi-source collaborative optimization method according to claim 2, characterized in that: The constraints of the multi-source collaborative optimization model also include photovoltaic constraints; The photovoltaic constraints include: In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

5. A mobile energy storage scheduling and microgrid reconstruction multi-source collaborative optimization method according to claim 2, characterized in that: The constraints of the multi-source collaborative optimization model also include microgrid operation constraints and distribution network power flow constraints; The microgrid operation constraints include: In the above formula, are the active and reactive power from microgrid m to the distribution system through the bus during time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t, are the maximum active and reactive power of microgrid m, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m, respectively; The distribution network power flow constraints include current power balance constraints, line voltage constraints, line power transmission constraints, line capacity constraints, node voltage constraints, load recovery constraints, and network connectivity constraints.

6. A mobile energy storage dispatching and microgrid reconstruction multi-source collaborative optimization system, characterized in that: The system includes an optimization model building module and an optimization model solving module; The optimization model building module is used to build a multi-source collaborative optimization model that takes into account mobile energy storage equipment and microgrid reconstruction, with the goal of minimizing the sum of load loss cost, microgrid power generation cost and transportation cost of mobile energy storage equipment; The optimization model solving module is used to solve the multi-source collaborative optimization model to obtain an optimization scheduling solution including mobile energy storage device scheduling and microgrid reconstruction strategy.

7. A mobile energy storage dispatching and microgrid reconstruction multi-source collaborative optimization system according to claim 6, characterized in that: The optimization model construction module includes an objective function construction unit, a network reconstruction topology constraint construction unit, and a mobile energy storage device spatiotemporal constraint construction unit; The objective function construction unit is used to construct the following objective function: min(f1+f2+f3) In the above formula, f1, f2, and f3 are the load loss of the distribution network node, the power generation cost of the microgrid, and the transportation cost of the mobile energy storage device, respectively. l is the value coefficient of the load level to which bus l belongs, are the rated load demand and restored load of bus l in time period t, ΔT is the duration of unit time period, T, are the sets of time periods and buses respectively, C gen,m is the unit power generation cost of microgrid m, is the active power generated by microgrid m in time period t, M and Ω are the collection of microgrid and mobile energy storage equipment respectively, and C tran,ω is the unit moving cost of the mobile energy storage device ω, is the decision variable of the mobile energy storage device ω moving from microgrid m to microgrid u within time period t, and Z is the set of spatiotemporal scheduling trajectories of the mobile energy storage device.

8. A mobile energy storage auxiliary dispatching system based on multi-regional electricity demand according to claim 7, characterized in that: The network reconstruction topology constraint construction unit is used to construct the following constraints: In the above formula, α ij is the connection decision variable of nodes i and j, E is the branch set of the distribution network, β ij is the decision variable of whether node j is the parent of node i, Ψ(l) is a set of buses connected to bus l through branches, and N is the set of distribution network nodes; The spatiotemporal scheduling constraint construction unit of the mobile energy storage device is used to construct the following constraints: In the above formula, is the set of spatiotemporal scheduling trajectories starting from microgrid m, is the set of spatiotemporal scheduling trajectories reaching microgrid m, M v is the set of virtual microgrid nodes, are the charging and discharging power of the mobile energy storage device ω in the microgrid m during time period t, are the maximum charging and discharging power of the mobile energy storage device ω in the microgrid m, are the charging and discharging state variables of the mobile energy storage device ω in time period t respectively.

9. A mobile energy storage auxiliary dispatching system based on multi-regional electricity demand according to claim 7, characterized in that: The optimization model construction module also includes a photovoltaic constraint construction unit; The photovoltaic constraint construction unit is used to construct the following constraints: In the above formula, are the active and reactive power absorbed by the distribution network of the photovoltaic installed at node i in time period t, P PVmax is the maximum output active power of photovoltaic, and θ is the power factor angle of photovoltaic.

10. A mobile energy storage auxiliary dispatching system based on multi-regional electricity demand according to claim 7, characterized in that: The optimization model construction module also includes a microgrid operation constraint construction unit and a distribution network flow constraint construction unit; The microgrid operation constraint construction unit is used to construct the following constraints: In the above formula, are the active and reactive power from microgrid m to the distribution system through the bus during time period t, are the active power generated by microgrid m in time period t, is the active power absorbed by the microgrid m during the period t, are the active and reactive loads of microgrid m in time period t, are the maximum active and reactive power of microgrid m, is the energy of microgrid m in time period t, are the upper and lower limits of the energy of microgrid m, respectively; The distribution network flow constraint construction unit is used to construct current power balance constraints, line voltage constraints, line power transmission constraints, line capacity constraints, node voltage constraints, load recovery constraints and network connectivity constraints.

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