Planning method for improving bearing capacity of power distribution network by considering dynamic network reconstruction and mobile energy storage

Abstract

The invention relates to a planning method for improving the bearing capacity of a power distribution network by considering dynamic network reconstruction and mobile energy storage. The planning method comprises the following steps: S1, constructing a power distribution network and traffic network coupling system model; s2, establishing a comprehensive bearing capacity evaluation model of the power distribution network to obtain a bearing capacity scoring formula; and S3, performing equivalent reconstruction on the power distribution network and traffic network coupling system model to obtain an equivalent reconstruction model. S4, constructing a multi-objective planning model based on the equivalent reconstruction model and a bearing capacity scoring formula; and S5, solving the multi-objective planning model to obtain the optimal action time sequence and the optimal mobile energy storage capacity of the interconnection switch, and performing network planning based on the optimal action time sequence and the optimal mobile energy storage capacity. Compared with the prior art, the method has the advantages that the bearing capacity and economical efficiency of the power distribution network are cooperatively improved, and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system distribution network planning and operation, in particular to a planning method for the carrying capacity of an upgraded distribution network considering dynamic network reconfiguration and mobile energy storage. BACKGROUND

[0002] The increase in new energy penetration rate leads to insufficient carrying capacity of the distribution network. Traditional carrying capacity evaluation only focuses on the maximum access capacity of new energy and ignores operation indicators such as network loss and voltage qualification rate. Mobile energy storage has the advantage of flexible spatio-temporal adjustment, but existing research has defects: 1) The dispatching model does not decouple the traffic-grid coupling problem, which does not conform to the actual operation situation and ignores the impact of traffic congestion; 2) It does not optimize jointly with dynamic network reconfiguration, leading to voltage out-of-limit and insufficient new energy consumption.

[0003] In the traffic-grid coupling problem, existing mobile energy storage research has failed to achieve effective decoupling optimization. Mobile energy storage devices, as a special carrier connecting the two key fields of traffic and power, involve movement in the traffic network and charging and discharging operations in the power grid during their operation process. However, previous research often treats the traffic system and the power system in isolation, without fully considering the complex interaction between the two. For example, when planning the dispatching path of mobile energy storage, only the demand of the power grid is considered to determine the moving direction and target node, ignoring the real-time situation in the traffic network that affects the movement process. This leads to the fact that in actual operation, when traffic congestion occurs, mobile energy storage devices cannot arrive at the predetermined power grid node on time, thereby seriously affecting their support and adjustment effect on the power grid. The traffic congestion coefficient is not constant, it will be dynamically adjusted with the real-time change of traffic flow and the congestion threshold at different times. However, existing research generally lacks consideration of this dynamic change factor when constructing the mobile energy storage dispatching model, resulting in a large deviation between the model and the actual operation scenario, and the inability to accurately simulate the operation state of mobile energy storage in a complex traffic environment.

[0004] Existing research on synergistic optimization with dynamic network reconfiguration has significant shortcomings. Dynamic network reconfiguration, as a crucial means of active distribution network management, can effectively improve the absorption rate of renewable energy and the operational economy of the distribution network by optimizing its topology. However, most current studies on mobile energy storage assume a fixed distribution network structure, failing to fully leverage the synergistic advantages between dynamic network reconfiguration and mobile energy storage. When a large number of distributed renewable energy sources are integrated into the distribution network, the intermittent and fluctuating nature of their generation can cause drastic changes in power flow, easily leading to voltage exceedance issues. In such cases, relying solely on mobile energy storage for regulation without combining it with dynamic network reconfiguration to adjust the grid topology in a timely manner cannot fundamentally solve the voltage exceedance problem, and the insufficient absorption of renewable energy remains difficult to improve. While dynamic network reconfiguration offers advantages such as ease of operation and relatively low cost in optimizing distribution network power flow, it cannot add additional capacity to the grid like mobile energy storage. Conversely, although mobile energy storage can flexibly regulate power in time and space, its regulatory effect is significantly reduced without synergy with dynamic network reconfiguration. Therefore, how to organically combine the two and achieve complementary advantages has become a key issue in current research.

[0005] In summary, the current power distribution network planning model deviates significantly from the actual operating scenario, and is prone to problems such as voltage exceeding limits, insufficient absorption of new energy sources, and insufficient network carrying capacity. Summary of the Invention

[0006] This invention provides a planning method for improving the carrying capacity of distribution networks by considering dynamic network reconfiguration and mobile energy storage. By constructing a comprehensive carrying capacity assessment system, optimizing mobile energy storage scheduling strategies and multi-objective planning models, the method achieves a synergistic improvement in the carrying capacity and economy of distribution networks.

[0007] The objective of this invention can be achieved through the following technical solutions: A planning method for upgrading the carrying capacity of distribution networks considering dynamic network reconfiguration and mobile energy storage, the method includes: S1. Construct a coupled system model of the power distribution network and the transportation network; S2. Establish a comprehensive carrying capacity assessment model for the distribution network and obtain the carrying capacity scoring formula; S3. Perform equivalent reconstruction on the coupled system model of the power distribution network and the transportation network to obtain the equivalent reconstruction model; S4. Construct a multi-objective programming model based on the equivalent reconfiguration model and the carrying capacity scoring formula. The constraints of the multi-objective programming model include distributed power output constraints, static var compensator output constraints, power flow constraints, radial distribution network constraints, virtual switch constraints, mobile energy storage constraints, and reconfiguration switch operation number constraints. S5, solving the multi-objective programming model to obtain optimal action timing of the tie switch and optimal mobile energy storage capacity, and performing network planning based on the optimal action timing and the optimal mobile energy storage capacity.

[0008] Further, the carrying capacity scoring formula is: wherein, indicates an index number of the carrying capacity of the power distribution network, indicates a weight of the i th index, indicates a standardized index element.

[0009] Further, the specific steps of S3 are: solving the shortest path between the coupling nodes of the power distribution network and the traffic network coupling system model by using the Floyd algorithm to obtain a shortest distance matrix, and obtaining the number of time intervals based on the shortest distance matrix; adding a virtual node to each node where the mobile energy storage can be connected in the model, and setting a virtual switch between the node where the mobile energy storage can be connected and the virtual node, wherein the closed / open state of the virtual switch represents whether the mobile energy storage is connected to the node; constructing a virtual switch constraint, a mobile energy storage constraint, a power distribution network radial constraint, and a reconstruction switch action frequency constraint.

[0010] Further, the objective function of the multi-objective programming model is: wherein, is a first objective function, is a second objective function, is a carrying capacity score, indicates an annual planning cost, indicates an annual operation cost.

[0011] Further, the annual planning cost is: wherein, in the formula, is a unit capacity planning cost of the mobile energy storage, is a capacity of the mobile energy storage vehicle, that is, a mobile energy storage capacity, is a discount rate, is an equipment operation life.

[0012] Further, the annual operation cost includes a power purchase cost , a transportation cost , and a wind and light abandonment cost . In the formula, For scene set, For the scene The probability of its occurrence, The number of days is set to 365 days. for Electricity price during specific time periods for Electricity purchased from the upper-level power grid during specific time periods. The unit operating cost of mobile energy storage for The energy consumed in time-lapse mobile energy storage transportation The cost of wind and solar power curtailment for power distribution network units, and They are respectively Total amount of wind and solar power curtailment in the power distribution network during a given time period.

[0013] Furthermore, the virtual switch constraint is: ; ; ; ; In the formula, for Time-based virtual switch To virtual switch The switching flag, when At that time, Time-based virtual switch To virtual switch "Switching" indicates that there is at most one switching of the virtual switch within any given time period; e represents the total number of switching operations of the virtual switch during the scheduling process. for Time period The on / off state of a virtual switch takes the value of 0 or 1. The switch closes when the current is active and opens when the current is passive. For mobile energy storage vehicles from nodes To the node The number of time intervals.

[0014] Furthermore, the current flow constraint is: In the formula, , is active power and reactive power at period node , is active power and reactive power at period node purchased from the superior grid, , is active power and reactive power at period node connected to the distribution grid by photovoltaic and wind power, is active power and reactive power at period node connected to the distribution grid by static var compensator, is a binary variable describing the open state of period branch , when branch is closed at period , otherwise branch is open, , is active power and reactive power flowing through period branch , , is the equivalent conductance and susceptance of branch is the voltage amplitude at period node , is the voltage phase angle difference between period node and node ; , is the upper and lower limits of the voltage amplitude at node is the maximum current amplitude that can flow through branch

[0015] Further, the number of times of reconfiguration switch action constraint is: wherein, is the open state of period line , is the maximum number of times of action of the tie switch in a day.

[0016] Further, the distribution grid radial constraint is: wherein, denotes time period node to node a sign of power flow, when denotes the power flow from node node to node .

[0017] Compared with the prior art, the present application has the following beneficial effects: The distributed power output constraint of the present application avoids the impact of new energy output fluctuation on the stability of the distribution network, maximizes the power generation potential of new energy, and provides a reliable output reference for subsequent optimization and dispatching by explicitly limiting the active or reactive power output boundary of photovoltaic, wind turbine and other new energy; The static var compensator output constraint can precisely control the reactive power output range, effectively regulate the node voltage, compensate for the reactive imbalance problem caused by new energy output fluctuation, and ensure that the voltage of the distribution network is stable in the qualified range; The power flow constraint comprehensively considers the topology change and branch breaking state of the distribution network, ensures power balance and safe operation of the power grid equipment by limiting the active and reactive load of the node, branch power flow, voltage amplitude and phase angle difference, prevents line overload and voltage out-of-limit, and provides a key guarantee for reliable power supply of the distribution network; The distribution network radial constraint ensures that the distribution network always maintains a radial topology structure by limiting the direction of node power flow, avoids the circulating current problem that may occur in a ring network, simplifies the grid control logic, reduces the difficulty of fault troubleshooting, and improves the safety and efficiency of grid operation; The virtual switch constraint strictly limits the switching frequency, state and total switching times of the virtual switch, and combines the time interval requirement of the mobile energy storage node to ensure that the mobile energy storage has sufficient time to complete the transfer between nodes, avoid scheduling confusion caused by excessive switching frequency, accurately simulate the space-time scheduling process of the mobile energy storage, decouple the optimization of the transportation network and the distribution network, and make the mobile energy storage scheduling more in line with the actual operation scenario; The mobile energy storage constraint limits the charging and discharging power, state of charge (SOC) and SOC recovery after scheduling, prevents the mobile energy storage from overcharging and discharging to damage the equipment and prolong its service life, ensures the energy regulation capacity of the mobile energy storage within the scheduling period, and ensures that it can effectively play a role in key periods such as load peak and voltage out-of-limit; The reconstruction switch action times constraint limits the maximum action times of the tie switch within a day, reduces the equipment wear caused by frequent operation of the switch, avoids affecting power quality due to excessive switching, reduces operation and maintenance costs, ensures long-term stable operation of the distribution network, and realizes the coordinated improvement of the carrying capacity and economy of the distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 : Distribution network comprehensive carrying capacity evaluation framework; Figure 2 : Mobile energy storage operation schematic diagram at traffic-grid coupling node; Figure 3 : Equivalent reconstruction principle based on virtual switch Figure 4 : IEEE33 node distribution network and 15 node traffic network coupling topology Figure 5 : Comparison of network loss of four schemes Figure 6 : Voltage distribution of scheme one Figure 7 : Voltage distribution of scheme two Figure 8 : Voltage distribution of scheme three Figure 9 : Voltage distribution of scheme four Figure 10 : Comparison of upper grid power purchase Figure 11 : Virtual switch breaking state of scheme three Figure 12 : Virtual switch breaking state of scheme four Figure 13 : Traffic congestion coefficient Figure 14 : Load demand Figure 15 : Time-of-use electricity price Figure 16 : Wind and light output of scenario one Figure 17 : Wind and light output of scenario two Figure 18 : Wind and light output of scenario three DETAILED DESCRIPTION

[0019] The application will be described in detail below in combination with the drawings and specific embodiments. The embodiments are implemented on the premise of the technical scheme of the application, and give detailed implementation modes and specific operation processes, but the protection scope of the application is not limited to the following embodiments.

[0020] Embodiment 1 The application proposes a planning method of upgrade distribution network carrying capacity considering dynamic network reconstruction and mobile energy storage, which comprises the following steps: S1, constructing a distribution network and traffic network coupling system model; S2, establishing a distribution network comprehensive carrying capacity evaluation model to obtain a carrying capacity scoring formula; S3, performing equivalent reconstruction on the distribution network and traffic network coupling system model to obtain an equivalent reconstruction model.

[0021] S4, constructing a multi-objective planning model based on the equivalent reconstruction model and the carrying capacity scoring formula; S5, solve the multi-objective programming model, obtain the optimal action time sequence and optimal mobile energy storage capacity of the tie-in switch, and perform network planning based on the optimal action time sequence and the optimal mobile energy storage capacity.

[0022] Figure 1 It is a power distribution network comprehensive carrying capacity evaluation framework; Figure 2 It is a mobile energy storage operation schematic diagram at a traffic-grid coupling node. Figure 3 It is an equivalent reconstruction principle based on a virtual switch. Figure 4 It is a coupling topological graph of a traffic grid and a power distribution grid.

[0023] The purpose of the present application is to provide a power distribution grid carrying capacity improvement method considering dynamic network reconstruction and mobile energy storage, which realizes the coordinated improvement of power distribution grid carrying capacity and economy by constructing a comprehensive carrying capacity evaluation system, optimizing a mobile energy storage scheduling strategy and a multi-objective programming model. The planning method for improving the carrying capacity of the power distribution grid considering dynamic network reconstruction and mobile energy storage comprises the following steps: (1) Establish a power distribution grid comprehensive carrying capacity evaluation model Four core indicators are selected to reflect the operation state of the power distribution grid: Network loss rate: the network loss rate refers to the ratio of active power loss to feeder segment input power. Renewable energy consumption rate: the renewable energy consumption rate refers to the ratio of distributed power generation power to distributed power predicted power generation power. Voltage qualification rate: the voltage qualification rate refers to the ratio of the number of nodes with qualified voltage to the total number of nodes Line overload rate: the line overload rate represents the ratio of the number of lines with transmission power greater than or equal to 80% of the line rated transmission capacity to the total number of lines. The fuzzy analytic hierarchy process (FAHP) is used to determine the weight (Table 3), and the data is processed by min-max normalization.

[0024] Table 3 FAHP weight determination Forward index, the normalized elements are: Reverse index, the normalized elements are: Carrying capacity comprehensive score: Wherein, The value ranges from [0, 100], with higher values ​​indicating stronger load-bearing capacity.

[0025] Mobile energy storage refers to a modular, portable, and shareable energy regulation device that can move between different geographical locations and store and release electrical energy by connecting to different nodes in the power distribution network. This article describes it as a mobile energy storage vehicle equipped with a large-capacity battery system that can travel within transportation networks. When needed by the power distribution network, the mobile energy storage vehicle can connect to specific nodes for charging or discharging, achieving power regulation.

[0026] Radial structure constraints of power distribution network During the operation of the distribution network, it is necessary to ensure that the distribution network always maintains a radial structure, subject to the following constraints: In the formula, express Time period nodes To the node The symbol of trend, when When, it indicates that the current flows from node to node. Flow to Node .

[0027] Number of times the contact switch is activated Frequent operation of the tie switch will reduce its lifespan and reduce power quality; therefore, it is necessary to limit the number of times the tie switch can be operated. In the formula, for Time-of-day routes The on / off state, This represents the maximum number of times the interconnecting switch can be activated in a day.

[0028] Mobile energy storage model: includes charging and discharging power constraints, SOC constraints and transportation time constraints, with a capacity range of 1MW~2MW and a charging and discharging efficiency of 0.9.

[0029] Dynamic network reconfiguration model: The network topology is changed by the action of the tie switch. Constraints include the limit on the number of switch actions and the radial structure constraint.

[0030] Equivalent reconfiguration model: Virtual nodes and virtual switches are introduced to simulate the time-space temperature of mobile energy storage, and the decoupling of the transportation power grid is achieved by controlling the switch state.

[0031] (2) Construct a mobile energy storage scheduling model based on equivalent reconfiguration By introducing virtual nodes and virtual switches, the time-space scheduling of mobile energy storage is equivalent to the topology reconstruction of the distribution network, achieving decoupling optimization between transportation and the power grid. Mobile energy storage starts from the central station, travels through the transportation network to the target node for charging and discharging, and finally returns to the central station. Its operating path needs to consider road congestion constraints.

[0032] 1. Transportation Network Modeling Construct the equivalent distance matrix of the transportation network: in, Hij For nodes i and j The actual distance τijk for k The time-period congestion coefficient is determined by the ratio of traffic flow to the congestion threshold. The Floyd-Warshall algorithm is used to solve for the shortest paths, resulting in the shortest path matrix between coupled nodes. And normalize the movement time into time intervals: 2. Equivalent Reconstruction Model The constraints of the virtual switch switching model can be expressed as: In the formula, for Time period The on / off state of each virtual switch is either 0 or 1. The switch closes when the time is right and opens when the time is wrong. This represents the maximum time interval required for switching between two virtual switches. The virtual switch switching model constraints, used to represent the minimum time interval required for switching between two virtual switches, ensure that the mobile energy storage vehicle has sufficient time to complete the movement between nodes. The switching flag quantity and total number of switching limits rely on the number of time intervals to calculate the allowable switching frequency.

[0033] Steps for establishing an equivalent reconstruction model: 1. Transportation network modeling and time interval calculation Input data: actual distances between traffic network nodes, road congestion coefficients, traffic flow restrictions, and speeds of mobile energy storage vehicles.

[0034] Processing procedure: The actual distance is corrected based on the congestion coefficient to obtain the equivalent distance matrix.

[0035] The Floyd algorithm is used to find the shortest path between coupled nodes, resulting in the shortest distance matrix. In the formula, for Traffic nodes during time periods To the node The shortest distance, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network,

[0036] Convert the shortest distance into the number of time intervals where, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, 2. Introduction of virtual nodes and virtual switches In the distribution network model, a virtual node is added to each node that the mobile energy storage can access.

[0037] A virtual switch is set between the original node and the virtual node, and its closed / open state represents whether the mobile energy storage is connected to the node.

[0038] Through the state change of the virtual switch, the movement and access of the mobile energy storage between different nodes are simulated.

[0039] 3. Establishment of equivalent reconstruction constraints Virtual switch switching constraints where, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network,

[0040] Switching times and state constraints where, N is the total number of nodes in the transportation network, N is the total number of nodes in the transportation network,

[0041] Virtual switch constraints In the formula, for Time-based virtual switch To virtual switch The switching flag, when At that time, Time-based virtual switch To virtual switch Switching indicates that the virtual switch is switched at most once in any given time period; e is the total number of virtual switch switching during the scheduling process; the last three formulas are the opening and closing constraints of the virtual switch.

[0042] 4. Mobile energy storage operation constraint mapping In the formula, For mobile energy storage The time period is at the node The charging and discharging power, and These represent the maximum and minimum charging and discharging power of the mobile energy storage, respectively. and Mobile energy storage The time period is at the node The discharge power and charging power, For mobile energy storage Nuclear power status during a given period and These are the discharge and charging power of mobile energy storage, respectively. and These represent the minimum and maximum states of charge for mobile energy storage, respectively. This indicates that the mobile energy storage's state of charge is 0.5 after a day's dispatching.

[0043] 5. Distribution network topology and constraints on the number of tripping switches. Radial structure constraints of power distribution network During the operation of the distribution network, it is necessary to ensure that the distribution network always maintains a radial structure, subject to the following constraints: wherein, denotes time period node to node a sign quantity of power flow, when denotes that the power flow from node node flows to node .

[0044] contact switch action times The frequent action of the contact switch can reduce the service life and reduce the power quality, therefore, the action times of the contact switch need to be limited. wherein, is time period line is an open state, is the maximum action times of the contact switch in a day. The contact switch is a reconstruction switch.

[0045] 6. Combined with multi-objective planning model The equivalent reconstruction model is embedded into a mobile energy storage planning model with the maximum carrying capacity and the minimum annual comprehensive cost as targets to obtain: The optimal access position and time of the mobile energy storage; the optimal switching strategy of the virtual switch; and the mobile energy storage capacity configuration scheme.

[0046] (3) Establishing a mobile energy storage multi-objective planning model Objective function: Maximum carrying capacity: Minimum annual comprehensive cost: Among them, the annual planning cost: Annual operation cost: Constraint condition: distributed power output constraint (charge and discharge power, SOC limit, etc.); static var compensator output constraint; distribution network radiation constraint; virtual switch constraint; mobile energy storage constraint; power flow constraint and reconstruction switch action times limit.

[0047] The present application will be further described from the aspects of model establishment, scheduling implementation, model solving and effectiveness verification.

[0048] 1 Model establishment 1.1 Distribution network and traffic network coupled system model The research object of the application is a coupling system of a power distribution network and a traffic network, wherein the power distribution network adopts an IEEE33 node system, the traffic network adopts a 15 node system, and the two are realized through a coupling node (a node containing a charging station) to realize energy interaction. The power distribution network contains distributed photovoltaic, fan, load node and tie switch, the traffic network contains mobile energy storage vehicle, charging station and road network, and the topological structure is as shown in Figure 4 .

[0049] 1.2 Power distribution network comprehensive bearing capacity evaluation model Index selection and calculation Network loss rate: reflects the proportion of active power loss of the power distribution network, the calculation formula is ; Renewable energy consumption rate: reflects the actual utilization proportion of new energy, the calculation formula is ; Voltage qualification rate: describes the proportion of node voltage within the qualified range, the calculation formula is ; Line overload rate: represents the probability that the line transmission power exceeds 80% of the rated capacity, the calculation formula is Weight determination: the fuzzy judgment matrix is constructed by using the FAHP method, and the weight is calculated by formula ; ; ; ; ; Convert to fuzzy consistency matrix and calculate weight, network loss rate 0.0916, renewable energy consumption rate 0.1687, voltage qualification rate 0.4562, line overload rate 0.2835.

[0050] Score calculation: normalize the index by min-max method, and then calculate the bearing capacity score by formula .

[0051] 1.3 Mobile energy storage and dynamic network reconstruction model Mobile energy storage model: contains charging and discharging power constraint, SOC constraint and transportation time constraint, its capacity range is 1MW~2MW, and the charging and discharging efficiency is 0.9.

[0052] Dynamic network reconstruction model: change the network topology through tie switch action, the constraints include switch action times limit and radial structure constraint.

[0053] Equivalent reconfiguration model: Virtual nodes and virtual switches are introduced to simulate the time-space temperature of mobile energy storage, and the decoupling of transportation and power grid is achieved by controlling the switch state.

[0054] 1.4 Multi-objective programming model objective function Maximizing load-bearing capacity: as shown in the formula The comprehensive carrying capacity score is used as the optimization objective; Maximizing economic efficiency: as shown in the formula The annual comprehensive cost includes the investment cost of mobile energy storage. Electricity purchase cost Transportation costs and the cost of wind and solar power curtailment . Constraints: Distributed power generation output constraints: Active / reactive power output limits for photovoltaic and wind turbines; Static Var Compensator Constraints: Reactive Power Output Range Limitation ; Virtual switch constraints: number of switching operations and state constraints ; ; ; ; Power flow constraints ensure power balance and voltage stability, specifically: Power flow constraints take into account the topology of the distribution network after reconfiguration and the on / off states of tie switches in different time periods. Therefore, the power flow constraints of the distribution network can be described as follows: In the formula, , for Time period nodes Active and reactive loads at the location, for Time period nodes Reactive power purchased from the upper-level power grid, , for Time period nodes The reactive power of photovoltaic and wind turbines connected to the power distribution network. for Time period nodes The reactive power of the static var compensator connected to the distribution network. To describe Time-of-day branch A binary variable representing the on / off state. When At that time, the branch road exist Time period closure, otherwise branching disconnect. , for Time-of-day branch The active and reactive power flowing through it. , branch road Equivalent conductance and susceptance, for Time period nodes voltage amplitude, for Time period nodes With nodes The voltage phase angle difference between them. , For nodes Upper and lower limits of voltage amplitude branch road The maximum current amplitude that can flow through it 2. Scheduling Implementation 2.1 Dynamic Network Reconfiguration Scheduling Based on the real-time operating status of the distribution network (such as line load rate and voltage deviation), the optimal operating timing of the tie switches is solved by optimization algorithms (such as genetic algorithms) to achieve power flow optimization.

[0055] 2.2 Mobile Energy Storage Dispatch Status assessment: Based on the new energy output, load demand and voltage status of each node, determine the access node and charging / discharging requirements of mobile energy storage. Path planning: Calculating the shortest path between coupled nodes in a transportation network based on the Floyd algorithm. , Combining congestion coefficient ; Based on travel time, the optimal transportation route for mobile energy storage is generated; Charge / discharge decision-making: During periods of low load, mobile energy storage charges at nodes rich in renewable energy; during periods of high load or voltage exceeding limits, it discharges via a specific mechanism. ; Control charging and discharging power to smooth out power fluctuations.

[0056] 3. Model Solving Solution Algorithm: An improved multi-objective particle swarm optimization algorithm (MOPSO) is adopted, which transforms the carrying capacity and economic objectives into fitness functions, and obtains the Pareto optimal solution through particle iteration optimization.

[0057] Solution steps Initialization: Set the initial range of decision variables such as mobile energy storage capacity and handover switch status; Fitness calculation: For each particle, calculate the load-bearing capacity score and annual comprehensive cost; Iterative optimization: The particle swarm is updated by non-dominated sorting and crowding calculation until the convergence condition is met; Result selection: A compromise solution is selected from the Pareto optimal solution based on the ideal point method.

[0058] The optimal energy storage capacity is obtained through a multi-objective optimization model. Mobile energy storage capacity scheduling determines when and where to charge and discharge given a certain capacity. The optimal mobile energy storage capacity is determined by multi-objective optimization to determine the most suitable total capacity. These are two levels of problems: "time-space scheduling under fixed capacity" and "capacity scale optimization".

[0059] 1. The constraint effect of capacity on scheduling Power constraints: For mobile energy storage The time period is at the node The charging and discharging power, and These represent the maximum and minimum charging and discharging power of the mobile energy storage, respectively. A larger capacity provides higher charging and discharging power and stronger ability to smooth out fluctuations. Energy constraint: E cap The capacity of the mobile energy storage vehicle determines its ability to adjust over time; when capacity is insufficient, dispatching must balance the allocation of limited energy across different time periods. 2. The Reaction of Scheduling to Capacity Assessment Scheduling strategies, by optimizing the timing and location of charging and discharging, affect: Load Capacity Score B c Annual operating cost C (including voltage, network loss, and absorption rate) ope (Electricity purchase, wind and solar curtailment, transportation costs). The differences in dispatch results under different capacities determine the marginal benefit of capacity: Low-capacity areas: Increasing capacity can significantly improve carrying capacity and reduce costs. Saturation zone: Diminishing benefits from increased capacity.

[0060] 3. Collaborative optimization mechanism Simultaneous solution of multi-objective optimization: Upper layer: determine the optimal capacity E cap Lower layer: find the optimal scheduling scheme under given capacity.

[0061] 4 Effectiveness verification 4.1 Parameter setting of calculation example System parameters: the rated voltage of the power distribution network is 12.66 kV, and the line resistance / reactance parameters refer to the standard values of IEEE33 nodes; the speed of the mobile energy storage vehicle in the traffic network is 60 km / h, and the congestion coefficient is as shown in Figure 13 ; Scenario data: historical data of Kaifeng, Henan Province is adopted, including load curve Figure 14 , new energy output curve (Figures 16-18) and time-of-use electricity price Figure 15 ; Comparison schemes Scheme one: no mobile energy storage and no network reconstruction; Scheme two: network reconstruction without mobile energy storage; Scheme three: no network reconstruction with mobile energy storage; Scheme four: network reconstruction + mobile energy storage (the strategy proposed in the present application).

[0062] The network loss comparison chart of the schemes is shown in Figure 5 , the voltage chart of scheme one, the voltage chart of scheme two, the voltage chart of scheme three and the voltage chart of scheme four are shown in Figure 6 , 7 , 8, 9 respectively. The comparison chart of the power purchase amount of the upper grid is shown in Figure 10 . The virtual switch opening state of scheme three is shown in Figure 11 . The virtual switch opening state of scheme four is shown in Figure 12 .

[0063] 4.2 Result analysis Lifting effect of carrying capacity: The index scores of each scheme are shown in Table 1, and the scores of the network loss rate (90.42), the renewable energy consumption rate (95.04), the voltage qualified rate (94.31) and the line overload rate (95.82) of scheme four are all optimal, and the comprehensive carrying capacity score reaches 94.50 (Table 2), which is improved by 15.6% compared with scheme one; Table 1: Score results of each scheme Table 2: Score results of each scheme and annual comprehensive cost The voltage distribution comparison (Figures 6-9) shows that the node voltage deviation of scheme four is the smallest, and there is no over-limit situation, which is better than other schemes.

[0064] Economic promotion effect: The annual comprehensive cost of scheme four is 6,249,300 yuan, which is 8.1% lower than that of scheme one (6,803,500 yuan) and 2.9% lower than that of scheme three (6,434,900 yuan) (Table 2); The amount of abandoned wind and light is significantly reduced: the amount of abandoned wind and light of scheme four is more than 60% lower than that of scheme one, and the amount of electricity purchased by the superior power grid is 15% lower than that of scheme two (Figure 10).

[0065] Dispatching optimization effect: Virtual switch action times: the number of virtual switch switching times of scheme four is less than that of scheme three (Figures 11-12), which reduces the transportation cost of mobile energy storage; Network loss optimization: the network loss rate of scheme four is the lowest (Figure 5), which verifies the loss reduction effect of dynamic network reconstruction and mobile energy storage coordination.

[0066] Embodiment 2: The embodiment provides a system, which comprises a memory and a processor, the memory stores a computer program, and the processor invokes the computer program to execute the steps of the method of embodiment 1.

[0067] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products, which are stored in a storage medium and include a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0068] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code. Embodiments of the present application are also directed to computer program products comprising computer program code to implement aspects of the present application when the computer program code is executed on one or more computing devices.

[0069] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0070] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0072] The preferred embodiments of the present application have been described above in detail. It should be understood that modifications and variations to the present application can be affected by those skilled in the art without departing from the scope of the application. Accordingly, it is intended that all of the subject matter of the above description and the claims be interpreted to encompass all such modifications and changes.

Claims

1. A planning method for upgrading the carrying capacity of a power distribution network considering dynamic network reconfiguration and mobile energy storage, characterized in that the method... include: S1. Construct a coupled system model of the power distribution network and the transportation network; S2. Establish a comprehensive carrying capacity assessment model for the distribution network and obtain the carrying capacity scoring formula; S3. Perform equivalent reconstruction on the coupled system model of the power distribution network and the transportation network to obtain the equivalent reconstruction model; S4. Construct a multi-objective programming model based on the equivalent reconfiguration model and the carrying capacity scoring formula. The constraints of the multi-objective programming model include distributed power output constraints, static var compensator output constraints, power flow constraints, radial distribution network constraints, virtual switch constraints, mobile energy storage constraints, and reconfiguration switch operation number constraints. S5. Solve the multi-objective programming model to obtain the optimal operating timing and optimal mobile energy storage capacity of the tie switch, and perform network planning based on the optimal operating timing and optimal mobile energy storage capacity.

2. The planning method for upgrading the carrying capacity of a power distribution network considering dynamic network reconfiguration and mobile energy storage as described in claim 1, characterized in that, The bearing capacity scoring formula is as follows: in, This is expressed as an index of the power distribution network's carrying capacity. This represents the weight of the i-th indicator. This represents the standardized indicator element.

3. The planning method for upgrading the carrying capacity of a power grid considering dynamic network reconfiguration and mobile energy storage according to claim 1, characterized in that, The specific steps for S3 are as follows: The Floyd algorithm is used to solve for the shortest path between the coupled nodes of the power distribution network and transportation network coupled system model, and the shortest distance matrix is ​​obtained. The time interval is obtained based on the shortest distance matrix. In the model, a virtual node is added for each node that the mobile energy storage may connect to. A virtual switch is set between the node that may connect and the virtual node. The closed / open state of the virtual switch represents whether the mobile energy storage connects to the node. Construct virtual switch constraints, mobile energy storage constraints, distribution network radial constraints, and reconfiguration switch action count constraints.

4. The planning method for upgrading the carrying capacity of a power grid considering dynamic network reconfiguration and mobile energy storage according to claim 1, characterized in that, The objective function of the multi-objective programming model is: in, Let the first objective function be... The second objective function is... To score the load-bearing capacity, Indicates annual planning costs. This indicates the annual operating cost.

5. The planning method for upgrading the carrying capacity of a power grid considering dynamic network reconfiguration and mobile energy storage according to claim 4, characterized in that, Annual planning costs for: Where, in the formula, Planning cost per unit capacity for mobile energy storage The capacity of the mobile energy storage vehicle, also known as the mobile energy storage capacity. For the discount rate, This refers to the equipment's service life.

6. The planning method for upgrading the carrying capacity of a power grid considering dynamic network reconfiguration and mobile energy storage according to claim 5, characterized in that, Annual operating costs include electricity purchase costs. Transportation costs and the cost of wind and solar power curtailment ; In the formula, For scene set, For the scene The probability of its occurrence, The number of days is set to 365 days. for Electricity price during specific time periods for Electricity purchased from the upper-level power grid during specific time periods. The unit operating cost of mobile energy storage for The energy consumed in time-lapse mobile energy storage transportation The cost of wind and solar power curtailment for power distribution network units, and They are respectively Total amount of wind and solar power curtailment in the power distribution network during a given time period.

7. The planning method for upgrading the carrying capacity of a power grid considering dynamic network reconfiguration and mobile energy storage according to claim 1, characterized in that, The virtual switch constraint is: ； ； ； ； In the formula, for Time-based virtual switch To virtual switch The switching flag, when At that time, Time-based virtual switch To virtual switch Switching indicates that the virtual switch will be switched at most once in any given time period; e represents the total number of virtual switch switching operations during the scheduling process. for Time period The on / off state of a virtual switch takes the value of 0 or 1. The switch closes when the current is active and opens when the current is passive. For mobile energy storage vehicles from nodes To the node The number of time intervals.

8. The planning method for upgrading the carrying capacity of a distribution network considering dynamic network reconfiguration and mobile energy storage according to claim 7, characterized in that, Current constraints are: In the formula, , for Time period nodes Active and reactive loads at the location, for Time period nodes Reactive power purchased from the upper-level power grid, , for Time period nodes The reactive power of photovoltaic and wind turbines connected to the power distribution network. for Time period nodes The reactive power of the static var compensator connected to the distribution network. To describe Time-of-day branch A binary variable in an on / off state, when At that time, the branch road exist Time period closure, otherwise branching disconnect, , for Time-of-day branch The active and reactive power flowing upstream, , branch road Equivalent conductance and susceptance, for Time period nodes voltage amplitude, for Time period nodes With nodes The voltage phase angle difference between them; , For nodes Upper and lower limits of voltage amplitude branch road The maximum current amplitude that can flow through it.

9. A planning method for upgrading the carrying capacity of a power distribution network considering dynamic network reconfiguration and mobile energy storage, as described in claim 1, is characterized in that... The constraint on the number of reconfiguration switch actions is: in, for Time-of-day routes The on / off state, This represents the maximum number of times the interconnecting switch can be activated in a day.

10. A planning method for upgrading the carrying capacity of a power distribution network considering dynamic network reconfiguration and mobile energy storage, as described in claim 1, is characterized in that... The radial constraints of the distribution network are: in, express Time period nodes To the node The symbol of trend, when When, it indicates that the current flows from node to node. Flow to Node .

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