Emergency dispatching method and system for charging and swapping stations to improve the elastic recovery capability of distribution network

Abstract

The present invention relates to a charging and swapping station emergency dispatch method and system for improving the elastic recovery capability of a distribution network, the method comprising: constructing a charging and swapping station operation model based on the spatiotemporal transport coupling characteristics of transport vehicles transporting battery packs between charging and swapping stations and the discretized energy transition characteristics of battery packs within the charging and swapping station; constructing a distribution network operation model containing wind-solar-diesel-storage; based on the charging and swapping station operation model and the distribution network operation model, constructing a distribution network-charging and swapping station collaborative emergency dispatch model with the goal of minimizing the overall operating cost of the distribution network and the charging and swapping station; optimizing and solving the distribution network-charging and swapping station collaborative emergency dispatch model to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station. The present invention constructs a streamlined charging and swapping station operation model through the discretization of battery pack energy and the spatiotemporal transport coupling characteristics, which can significantly improve the optimization efficiency of emergency dispatch problems and obtain a practical emergency dispatch strategy within a reasonable time.

Description

Technical Field

[0001] The present invention relates to the field of power distribution, and in particular to an emergency dispatch method and system for a charging and swapping station for improving the elastic recovery capability of a distribution network. Background Art

[0002] The distribution network has to face the problem of blackouts caused by extreme events, that is, how to mobilize a wide range of surviving resources to ensure the restoration of more important loads until the blackout ends. Generally speaking, after a blackout, the distribution network is likely to be divided into multiple isolated networks, which are difficult to effectively support each other, resulting in uneven distribution of sources and loads between the isolated networks, making it difficult to form a joint force and restore more important loads. At this time, the distribution network can only achieve energy mutual assistance between the isolated networks with the help of mobile energy storage resources.

[0003] Charging and swapping stations have sufficient resources of small and micro battery packs. If transport vehicles (special trucks) can be reasonably mobilized to transport these battery packs, energy consumption and release can be achieved through battery pack charging and discharging, forming an efficient energy support channel, it is expected to ensure the recovery of more important loads and improve the elastic recovery margin of the distribution network. Therefore, the battery packs of charging and swapping stations can be used as a high-quality mobile energy storage resource to improve the elastic recovery capacity of the distribution network. However, the coordinated emergency dispatch problem of the distribution network and charging and swapping stations faces two major challenges: 1) There are a large number of battery packs in charging and swapping stations, and their spatiotemporal models may introduce a large number of discrete variables, which brings great difficulties to the solution of the problem. How to construct an accurate and simplified spatiotemporal exchange model of battery packs in charging and swapping stations to reduce the difficulty of problem solving is one of the major challenges. 2) The spatiotemporal exchange of battery packs needs to rely on special trucks. How to construct a spatiotemporal coupling model between special trucks and battery packs is another challenge. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a charging and swapping station emergency dispatch method and system for improving the elastic recovery capability of the distribution network. Through the discretization of battery pack energy and the coupling characteristics of time and space transport, a streamlined charging and swapping station operation model is constructed, which can significantly improve the optimization efficiency of emergency dispatch problems and obtain a practical emergency dispatch strategy within a reasonable time.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: A charging and swapping station emergency dispatching method for improving the elastic recovery capability of the distribution network comprises the following steps:

[0006] S1, based on the spatiotemporal transport coupling characteristics of the battery packs between the charging and swapping stations by the transport vehicles and the discrete energy transition characteristics of the battery packs within the charging and swapping stations, a charging and swapping station operation model is constructed;

[0007] S2, based on the actual operating parameters of the distribution network and taking into account the constraints of the distribution network, a distribution network operation model including wind-solar-diesel-storage is constructed;

[0008] S3, based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station, construct a distribution network-charging and swapping station collaborative emergency dispatch model;

[0009] S4, using a solver to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station.

[0010] Based on the above-mentioned charging and swapping station emergency dispatch method for improving the elastic recovery capability of the distribution network, the present invention also provides a charging and swapping station emergency dispatch system for improving the elastic recovery capability of the distribution network.

[0011] An emergency dispatch system for charging and swapping stations for improving the elastic recovery capability of distribution networks includes the following modules:

[0012] A charging and swapping station operation model construction module, which is used to construct a charging and swapping station operation model based on the spatiotemporal transport coupling characteristics of the transport vehicle transporting the battery pack between the charging and swapping stations and the discrete energy transition characteristics of the battery pack within the charging and swapping station;

[0013] The distribution network operation model construction module is used to construct a distribution network operation model including wind-solar-diesel-storage according to the actual operation parameters of the distribution network and taking into account the constraints of the distribution network;

[0014] A distribution network-charging and swapping station collaborative emergency dispatch model construction module, which is used to construct a distribution network-charging and swapping station collaborative emergency dispatch model based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station;

[0015] The optimization solution module is used to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model using a solver to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station.

[0016] The beneficial effects of the present invention are as follows: in a charging and swapping station emergency dispatch method and system for improving the elastic recovery capability of a distribution network of the present invention, through the coordinated emergency dispatch between the charging and swapping station and the distribution network, the operation arrangement strategy of the charging and swapping station and the diesel generator, energy storage and other components of the distribution network can be obtained to ensure the smooth recovery of more important loads, thereby improving the elastic recovery capability of the distribution network; in addition, the modeling is simplified according to the spatiotemporal transport coupling characteristics of the transport vehicle transporting the battery pack between the charging and swapping stations and the discrete energy transition characteristics of the battery pack in the charging and swapping station, thereby effectively reducing the difficulty of solving the charging and swapping station emergency dispatch model and greatly reducing the solution time. Therefore, the present invention provides a new technical solution for the emergency dispatch of the distribution network and the charging and swapping station after a major power outage, ensuring that the optimal emergency dispatch strategy is obtained in a relatively short period of time, ensuring the safe operation of more important loads, and significantly improving the elastic recovery capability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a charging and swapping station emergency dispatching method for improving the elastic recovery capability of a distribution network according to the present invention;

[0018] Figure 2 To improve the IEEE 33-node distribution network schematic;

[0019] Figure 3 It is the time-varying curve diagram of different types of loads, wind power and photovoltaic power;

[0020] Figure 4 Power curves of charging and swapping stations in different isolated islands;

[0021] Figure 5 The present invention is a structural block diagram of an emergency dispatching system for a charging and swapping station for improving the elastic recovery capability of a distribution network. DETAILED DESCRIPTION

[0022] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0023] like Figure 1 As shown, a charging and swapping station emergency dispatching method for improving the elastic recovery capability of the distribution network includes the following steps:

[0024] S1, based on the spatiotemporal transport coupling characteristics of the battery packs between the charging and swapping stations by the transport vehicles and the discrete energy transition characteristics of the battery packs within the charging and swapping stations, a charging and swapping station operation model is constructed;

[0025] S2, based on the actual operating parameters of the distribution network and taking into account the constraints of the distribution network, a distribution network operation model including wind-solar-diesel-storage is constructed;

[0026] S3, based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station, construct a distribution network-charging and swapping station collaborative emergency dispatch model;

[0027] S4, using a solver to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station.

[0028] S1-S4 are described in detail below.

[0029] In S1, the discrete energy transition characteristics of the battery pack in the charging and swapping station are based on discretizing the battery pack energy. Discretizing the battery pack energy specifically involves evenly dividing the battery pack energy into K energy levels. ( A battery pack with an energy level set (a set of energy levels) that can transition to the previous energy level or the next energy level by charging or discharging.

[0030] The unit energy range of the battery pack is:

[0031]

[0032] Wherein, Δψ is the unit energy range of the battery pack, E b is the capacity of the battery pack, SOC max and SOC min are the maximum state of charge and the minimum state of charge of the battery pack respectively.

[0033] The unit charging active power and unit discharging active power corresponding to the unit energy level transition of the battery pack are:

[0034] Δp c =Δψ / η,Δp d =Δψ·η;

[0035] Among them, Δp c and Δp d are the unit charging active power and unit discharging active power corresponding to the unit energy level transition of the battery pack, and η is the charging and discharging efficiency of the battery pack.

[0036] The charging piles in the charging and swapping station can interact with the distribution network in both directions and have a certain reactive power regulation capability. Therefore, the reactive power of the charging and swapping station also needs to be reasonably considered. In any period of time, the battery pack jumps up or down Δk energy levels, and the corresponding reactive power is,

[0037]

[0038] Among them, S bIt is the maximum apparent power of the charging pile in the charging and swapping station.

[0039] Before building the charging and swapping station operation model, it is necessary to obtain the predicted traffic data of urban roads after the power outage. d,t (where d is the road number and t is the time), the travel time of each road is calculated according to the Bureau of Public Road (BPR) function, which is:

[0040]

[0041] Based on this, the transfer time of the battery pack between the charging and swapping stations is calculated as:

[0042]

[0043] Among them, τ m,m′,t is the transfer time of the battery pack from the m charging and swapping station to the m′ charging and swapping station in time period t, d∈(m,m′) represents the road from the m charging and swapping station to the m′ charging and swapping station, l d,t is the travel time of road d in period t, ΔT swap is the time required for battery packs to be exchanged at the charging and swapping station, Δt is the minimum scheduling time scale, is the ceiling function; x d,t is the predicted traffic data of road d in period t after a power outage occurs, is the free travel time of road d, Q d is the road capacity.

[0044] In S1, the charging and swapping station operation model includes a charging and swapping station operation cost sub-model, a battery pack space-time exchange sub-model, a transport vehicle space-time transfer sub-model, a transport vehicle and battery pack space-time coupling sub-model, and a battery pack active-reactive power sub-model.

[0045] The sub-model of the charging and swapping station operation cost is:

[0046]

[0047] Among them, C bss is the total operating cost of the charging and swapping station, ε bss is the unit charge and discharge loss cost of the battery pack, The expected repair time after a power outage occurs. For charging and swapping station collection, and are the charging active power and discharging active power of the battery pack at the m charging and swapping station in time period t, N is the number of transport vehicles used to transport the battery pack after the power outage, and R is the use cost of each transport vehicle;

[0048] Based on the discretization of battery pack energy, define variables and They represent the number of k-level battery packs in the m-charging and swapping station at the beginning and end of time period t. During time period t, the battery packs can be charged or discharged to change their energy levels, but the total number of battery packs remains unchanged. Indicates the number of battery packs whose energy level transitions from k to k′ in the m charging and swapping station during the t period. In addition, at the end of the t period, the battery packs in the charging and swapping station can be transported away by a transport vehicle (special truck) and arrive at the designated charging and swapping station at the beginning of a certain period in the future. Define the variable It indicates the number of battery packs with energy level k that depart from charging and swapping station m to charging and swapping station m′ at the end of time period t-1.

[0049] Then the battery pack time-space exchange sub-model is:

[0050]

[0051] in, is the battery pack energy level set, τ m,m′,t is the transfer time of the battery pack from the m charging and swapping station to the m′ charging and swapping station during period t;

[0052] Specifically, formula (2) indicates that at the beginning of each time period, the number of battery packs of each energy level at the charging and swapping station is equal to the sum of the number of battery packs transferred from each charging and swapping station. It should be noted that the time required to transfer from m charging and swapping stations to m charging and swapping stations is 0, that is, τ m,m,t = 0. Formulas (3) and (4) indicate that in each time period, the battery packs at the charging and swapping station achieve energy level transitions through charging and discharging behaviors, and the number of battery packs at the corresponding energy levels before and after the energy transition should be conserved. At the end of each time period, the battery packs at the m charging and swapping station can be transferred to different charging and swapping stations (those remaining at the m charging and swapping station can be regarded as transferred to the m charging and swapping station), so there is formula (5).

[0053] Get the number of special trucks N that can be used to transport battery packs after a blackout. These special trucks are used to transport battery packs between charging and swapping stations and have sufficient energy to support their transfer tasks. Define a Boolean variable b m,n,t and Respectively represent the position state and transfer state of transport vehicle n; if b m,n,t =1, it means that the transport vehicle n is located at the m charging and swapping station during the period t, otherwise it means that the transport vehicle n is not at the m charging and swapping station during the period t; if Then it means that the transport vehicle n departs from the m charging and swapping station to the m′ charging and swapping station at the end of the t period, otherwise it means that the transport vehicle n does not have a transfer path from the m charging and swapping station to the m′ charging and swapping station at the end of the t period; then the transport vehicle spatiotemporal transfer sub-model is,

[0054]

[0055] In fact, the battery pack does not have the ability to move, and can only be transferred across time and space with the help of a special truck. Therefore, the transfer state of the battery pack needs to be unified with the transfer state of the special truck. At the same time, considering the maximum number of battery packs that can be loaded on a special truck, the spatiotemporal coupling sub-model of the transport vehicle and the battery pack is:

[0056]

[0057] Among them, O n is the maximum number of battery packs that can be loaded on transport vehicle n;

[0058] The battery pack active-reactive power sub-model is:

[0059]

[0060] Among them, Δp c and Δp d They represent the unit charging active power and unit discharging active power corresponding to the unit energy level of the battery pack transition, It represents the reactive power corresponding to the transition of the battery pack from k to k′ energy level at the m charging and swapping station, represents the reactive power corresponding to the transition of the battery pack from k′ to k energy level at the m charging and swapping station, q m,t Represents the reactive power of the battery pack at m charging and swapping stations during period t.

[0061] According to the energy transition of the battery pack, the active-reactive power model of the battery pack of each charging and swapping station is determined. The active power of the battery pack charging and discharging can be determined according to the number of energy transition levels of the battery pack, that is, according to formula (9). The reactive power of the battery pack can also be determined by the number of its energy transition levels to determine the upper and lower limits, and its upper and lower limit constraints are formulas (10) and (11).

[0062] In S2, the distribution network operation model includes a distribution network operation cost sub-model and the distribution network constraints; the distribution network constraints include diesel generator operation active-reactive power constraints, energy storage system operation constraints, wind and solar power station allowed wind and solar power abandonment constraints, distribution network bus node injection active power and reactive power constraints, and power flow-based distribution network frame constraints.

[0063] The distribution network operation cost sub-model is:

[0064]

[0065] Among them, C ds is the total cost of distribution network operation, For diesel generator set, c g,1 and c g,2 are the fuel efficiency of diesel generator g, p g,t and μ g,t They are respectively the active power and start / stop status of the diesel generator g during period t. g,t =1, the diesel generator runs, otherwise it stops. is the maximum active power of diesel generator g; ε is the energy storage system set, ε e is the unit loss coefficient of charge and discharge of energy storage system e, and are the charging power and discharging power of the energy storage system e during period t respectively; is the set of power nodes, is the unit load shedding loss coefficient at power node i, is the load shedding power at power node i during period t;

[0066] The active-reactive power constraint condition of the diesel generator operation is:

[0067]

[0068] in, is the minimum active power of the diesel generator g, q g,t is the reactive power of diesel generator g in period t; γ is the maximum power factor;

[0069] The energy storage system operation constraints are:

[0070]

[0071] in, is the maximum energy storage power of energy storage system e, q e,t is the reactive power of the energy storage system e during period t, S e is the rated apparent power of the energy storage system e, SOC e,t is the state of charge of the energy storage system e during period t, η e is the charging and discharging efficiency of the energy storage system e, is the rated capacity of the energy storage system e, SOC e,min and SOC e,max are the minimum state of charge and maximum state of charge of the energy storage system e respectively;

[0072] Let the wind turbine and photovoltaic clusters in the distribution network be The constraints for wind and solar power stations to abandon wind and solar are:

[0073]

[0074] in, and are the actual power consumption of wind turbine w and photovoltaic v in period t, and are the maximum absorbed power of wind turbine w and photovoltaic v in period t respectively;

[0075] The constraints of active power and reactive power injected into each bus node of the distribution network are:

[0076]

[0077] Among them, p j,t and q j,t are the active power and reactive power injected into bus node j during period t respectively; and are the active load and reactive load at bus node j in period t respectively; is the load shedding power at bus node j during period t; j is the load power factor at bus node j;

[0078] In addition, the constraints of the distribution network cannot be ignored. The distribution network is usually radial and the Distflow power flow equation model is used. The power flow-based distribution network constraints are:

[0079]

[0080]

[0081] Among them, P ij,t and Q ij,t are the active power and reactive power transmitted by line (i, j) in period t, respectively, ij,t is the square of the current amplitude of line (i, j) during period t, r ij is the resistance of line (i, j), x ij is the reactance of line (i, j); v j,t is the square of the voltage amplitude at bus node j during period t, v i,t is the square of the voltage amplitude at power node i during period t; is the square of the minimum and maximum voltage amplitude at power node i, is the square of the maximum transmission current amplitude of line (i, j); line (i, j) is the transmission line between power node i and bus node j; is the set of lines (i, j).

[0082] In S3, the distribution network-charging and swapping station coordinated emergency dispatch model is specifically:

[0083]

[0084] In S4, for the distribution network-charging and swapping station collaborative emergency dispatch model, the optimal strategy can be solved using an existing solver (such as the cardinality solver, COPT, developed by China Shanshu Technology), that is, the spatiotemporal exchange and charging and discharging plan of the battery packs of the charging and swapping station, the output plan of the diesel generator, the energy storage system, etc. during the expected recovery period, which is used to guide the emergency response behavior of the distribution network and the charging and swapping station after a major power outage, and improve the elastic recovery capability of the distribution network.

[0085] The following are specific embodiments of the present invention:

[0086] This embodiment uses Figure 2 The improved IEEE 33-node distribution network system is used as the basic example, in which the locations of diesel generators, energy storage systems, charging and swapping stations, wind power, photovoltaics and other equipment have been marked in the figure. The two diesel generators have the same specifications, with a rated power of 500kW, a minimum power of 150kW, and a fuel coefficient of 1.875 yuan / kWh and 0.6 yuan / kWh; the two energy storage systems have the same specifications, with a rated capacity of 1000kWh, a maximum charging and discharging power of 500kW, a unit charging and discharging loss cost of 0.2 yuan / kWh, a charging and discharging efficiency of 95%, and a maximum and minimum state of charge of 0.9 and 0.1; the wind power and photovoltaic installed capacity are 200kW, 300kW, 450kW, 500kW, 350kW, 450kW, 400kW, 400kW, 500kW, 450kW, 300kW, and 350kW according to the node number from small to large. The node load can be divided into industrial load (4, 5, 6, 14, 15, 16, 17, 28, 29), commercial load (1, 2, 3, 7, 8, 24, 25, 30, 31, 32, 33) and residential load (all other nodes), and the corresponding unit loss cost of load shedding is 247.53 yuan / kWh, 104.36 yuan / kWh and 5.29 yuan / kWh respectively. The load is based on the IEEE 33 node data. The time-varying curves of various loads and the time-varying curves of wind power and photovoltaic power are as follows: Figure 3 shown.

[0087] There are three charging and swapping stations in the distribution network, each with 20 battery packs. The battery packs have the same specifications, with a capacity of 60kWh, a maximum charging and discharging power (charging pile apparent power) of 120kW, a unit charging and discharging loss cost of 0.4 yuan / kWh, an efficiency of 95%, and a maximum and minimum state of charge of 0.9 and 0.1. The charging and swapping station has three special trucks that can transport battery packs after a major power outage. The maximum number of battery packs that can be transferred is 30, and the rental fee is 200 yuan / vehicle. Assume that when the accident occurs, there are 10 fully charged battery packs (state of charge is 0.9) and 10 zero-charged battery packs (state of charge is 0.1) in each charging and swapping station. The expected recovery time is 6 hours, with 30 minutes as the minimum scheduling time, and the battery pack energy is divided into 31 energy levels. On a personal computer configured with i7-8700CPU and 16GB memory, the scenario is modeled and simulated based on the MATLAB platform and YALMIP modeling software.

[0088] Without considering the participation of charging and swapping stations, the distribution network has to cut off 4421.06 kWh of load, and the expected economic loss is as high as 631,714.28 yuan. Among them, the load shedding mainly comes from Island 2, while there is no load shedding in Islands 1 and 3. However, Island 3 has a large amount of wind power and photovoltaic resources, but due to the lack of transmission channels, it has to abandon 6290.92 kWh of energy. In this case, the operating cost of the distribution network is as high as 642,314.18 yuan. When considering the participation of charging and swapping stations in emergency recovery, the distribution network only needs to cut off 994.62 kWh of residential loads, and the pre-taken economic loss is 61,178.05 yuan, which can ensure the operation of more loads. With the strong support of the three charging and swapping stations, the operating cost of the distribution network has been reduced to 76,276.37 yuan, and a total economic loss of 566,037.81 yuan has been recovered. Figure 4 As shown in the figure, the charging and swapping stations in islands 1 and 3 only charge and absorb surplus renewable energy power generation, while the charging and swapping station in island 2 only discharges and supplies important loads, indicating that the surplus energy in islands 1 and 3 is transmitted to island 2 through battery pack exchange. It can be seen that the reasonable scheduling of charging and swapping stations can effectively improve the elastic recovery capacity of the distribution network, ensure the recovery of more loads, and minimize the economic losses and social harm of the distribution network.

[0089] In addition, the present invention relates to a charging and swapping station emergency dispatch method for improving the elastic recovery capability of the distribution network, which also has significant advantages in solution speed. In this embodiment, the optimal emergency dispatch strategy for the charging and swapping station and the distribution network can be obtained within 600 seconds, and the error gap is 0.0029%. This shows that the present invention can obtain the optimal dispatching scheme within a reasonable time and has good application possibilities in engineering practice. If other methods (such as genetic algorithms, etc.) are adopted, although a feasible dispatching scheme can be obtained within 600 seconds, the optimality of the strategy cannot be guaranteed, and the optimal scheme is related to the initial solution and has a certain randomness, and it is difficult to achieve the effect of the present invention. Therefore, the present invention can not only solve the problem of emergency dispatching of distribution networks and charging and swapping stations, but also reduce the difficulty of solving the problem, and obtain the optimal emergency dispatching strategy for the two in a short time.

[0090] Based on the above-mentioned charging and swapping station emergency dispatch method for improving the elastic recovery capability of the distribution network, the present invention also provides a charging and swapping station emergency dispatch system for improving the elastic recovery capability of the distribution network.

[0091] like Figure 5 As shown, an emergency dispatching system for charging and swapping stations for improving the elastic recovery capability of distribution networks includes the following modules:

[0092] A charging and swapping station operation model construction module, which is used to construct a charging and swapping station operation model based on the spatiotemporal transport coupling characteristics of the transport vehicle transporting the battery pack between the charging and swapping stations and the discrete energy transition characteristics of the battery pack within the charging and swapping station;

[0093] The distribution network operation model construction module is used to construct a distribution network operation model including wind-solar-diesel-storage according to the actual operation parameters of the distribution network and taking into account the constraints of the distribution network;

[0094] A distribution network-charging and swapping station collaborative emergency dispatch model construction module, which is used to construct a distribution network-charging and swapping station collaborative emergency dispatch model based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station;

[0095] The optimization solution module is used to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model using a solver to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station.

[0096] The present invention provides a charging and swapping station emergency dispatch method and system for improving the elastic recovery capability of the distribution network. Through the coordinated emergency dispatch between the charging and swapping station and the distribution network, the operation arrangement strategy of the charging and swapping station and the diesel generator, energy storage and other components of the distribution network can be obtained to ensure the smooth recovery of more important loads, thereby improving the elastic recovery capability of the distribution network; in addition, by discretizing the energy of the battery pack, the spatiotemporal exchange model and power model of the battery pack of the charging and swapping station are simplified and modeled, and a spatiotemporal coupling model between it and the special truck is established, which can effectively reduce the difficulty of solving the charging and swapping station emergency dispatch model and greatly reduce the solution time. Therefore, the present invention provides a new technical solution for the emergency dispatch of the distribution network and the charging and swapping station after a major power outage, ensuring that the optimal emergency dispatch strategy is obtained in a relatively short time, ensuring the safe operation of more important loads, and significantly improving the elastic recovery capability of the distribution network.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging and swapping station emergency dispatch method for improving the elastic recovery capability of a distribution network, characterized in that: The following steps are included: S1, based on the spatiotemporal transport coupling characteristics of the battery packs between the charging and swapping stations by the transport vehicles and the discrete energy transition characteristics of the battery packs within the charging and swapping stations, a charging and swapping station operation model is constructed; S2, based on the actual operating parameters of the distribution network and taking into account the constraints of the distribution network, a distribution network operation model including wind-solar-diesel-storage is constructed; S3, based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station, construct a distribution network-charging and swapping station collaborative emergency dispatch model; S4, using a solver to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station; In S1, the charging and swapping station operation model includes a charging and swapping station operation cost sub-model, a battery pack spatiotemporal exchange sub-model, a transport vehicle spatiotemporal transfer sub-model, a transport vehicle and battery pack spatiotemporal coupling sub-model, and a battery pack active-reactive power sub-model; The sub-model of the charging and swapping station operation cost is: Among them, C bss is the total operating cost of the charging and swapping station, ε bss is the unit charge and discharge loss cost of the battery pack, The expected repair time after a power outage occurs. For charging and swapping station collection, and are the charging active power and discharging active power of the battery pack at the m charging and swapping station in time period t, N is the number of transport vehicles used to transport the battery pack after the power outage, and R is the use cost of each transport vehicle; Defining variables and They represent the number of k-level battery packs in the m-charging and swapping station at the beginning and end of period t, respectively. Define the variable represents the number of battery packs whose energy level transitions from k to k′ in the m charging and swapping station during the t period, and defines the variable represents the number of battery packs with energy level k that depart from the m charging and swapping station to the m′ charging and swapping station at the end of the t-1 period; then the battery pack spatiotemporal exchange submodel is, in, is the battery pack energy level set, τ m,m′,t is the transfer time of the battery pack from the m charging and swapping station to the m′ charging and swapping station during period t; Define a Boolean variable b m,n,t and Respectively represent the position state and transfer state of transport vehicle n; if b m,n,t =1, it means that the transport vehicle n is located at the m charging and swapping station during the t period, otherwise it means that the transport vehicle n is not at the m charging and swapping station during the t period; if Then it means that the transport vehicle n departs from the m charging and swapping station to the m′ charging and swapping station at the end of the t period, otherwise it means that the transport vehicle n does not have a transfer path from the m charging and swapping station to the m′ charging and swapping station at the end of the t period; then the transport vehicle spatiotemporal transfer sub-model is, The spatiotemporal coupling sub-model of the transport vehicle and the battery pack is: Among them, O n is the maximum number of battery packs that can be loaded on transport vehicle n; The battery pack active-reactive power sub-model is: Among them, Δp c and Δp d They represent the unit charging active power and unit discharging active power corresponding to the unit energy level of the battery pack transition, It represents the reactive power corresponding to the transition of the battery pack from k to k′ energy level at the m charging and swapping station, represents the reactive power corresponding to the transition of the battery pack from k′ to k energy level at the m charging and swapping station, q m,t Represents the reactive power of the battery pack at the charging and swapping station m during period t; In S2, the distribution network operation model includes a distribution network operation cost sub-model and the distribution network constraints; the distribution network constraints include diesel generator operation active-reactive power constraints, energy storage system operation constraints, wind and solar power station wind and solar power station allowed abandonment constraints, distribution network bus node injection active power and reactive power constraints, and power flow-based distribution network frame constraints; The distribution network operation cost sub-model is: Among them, C ds is the total cost of distribution network operation, For diesel generator set, c g,1 and c g,2 are the fuel efficiency of diesel generator g, p g,t and μ g,t are the active power and start / stop status of the diesel generator g during period t, is the maximum active power of diesel generator g; ε is the energy storage system set, ε e is the unit loss coefficient of charge and discharge of energy storage system e, and are the charging power and discharging power of the energy storage system e during period t respectively; is the set of power nodes, is the unit load shedding loss coefficient at power node i, is the load shedding power at power node i during period t; The active-reactive power constraint condition of the diesel generator operation is: in, is the minimum active power of the diesel generator g, q g,t is the reactive power of diesel generator g in period t; γ is the maximum power factor; The energy storage system operation constraints are: in, is the maximum energy storage power of energy storage system e, q e,t is the reactive power of the energy storage system e during period t, S e is the rated apparent power of the energy storage system e, SOC e,t is the state of charge of the energy storage system e during period t, η e is the charging and discharging efficiency of the energy storage system e, is the rated capacity of the energy storage system e, SOC e,min and SOC e,max are the minimum state of charge and maximum state of charge of the energy storage system e respectively; Let the wind turbine and photovoltaic clusters in the distribution network be The constraints for wind and solar power stations to abandon wind and solar are: in, and are the actual power consumption of wind turbine w and photovoltaic v in period t, and are the maximum absorbed power of wind turbine w and photovoltaic v in period t respectively; The constraints of active power and reactive power injected into each bus node of the distribution network are: Among them, p j,t and q j,t are the active power and reactive power injected into bus node j during period t respectively; and are the active load and reactive load at bus node j in period t respectively; is the load shedding power at bus node j during period t; j is the load power factor at bus node j; The power flow-based distribution grid constraint condition is: Among them, P ij,t and Q ij,t are the active power and reactive power transmitted by line (i, j) in period t, respectively, ij,t is the square of the current amplitude of line (i, j) during period t, r ij is the resistance of line (i, j), x ij is the reactance of line (i, j); v j,t is the square of the voltage amplitude at bus node j during period t, v i,t is the square of the voltage amplitude at power node i during period t; is the square of the minimum and maximum voltage amplitude at power node i, is the square of the maximum transmission current amplitude of line (i, j); line (i, j) is the transmission line between power node i and bus node j; is the set of lines (i, j); In S3, the distribution network-charging and swapping station coordinated emergency dispatch model is specifically:

2. According to claim 1, a charging and swapping station emergency dispatch method for improving the elastic recovery capability of the distribution network is characterized by: The calculation method of the unit charging active power and the unit discharging active power corresponding to the unit energy level of the battery pack transition is: The energy of the battery pack in the charging and swapping station is evenly discretized into K energy levels; The battery pack is charged or discharged to make the battery pack transition a unit energy level, and the unit energy range difference of the battery pack is calculated; wherein the calculation formula of the unit energy range difference of the battery pack is, Specifically, Δψ is the unit energy range of the battery pack, E b is the capacity of the battery pack, SOC max and SOC min are respectively the maximum state of charge and the minimum state of charge of the battery pack; The unit charging active power and the unit discharging active power corresponding to the unit energy level transition of the battery pack are calculated according to the unit energy range of the battery pack; wherein the calculation formula of the unit charging active power and the unit discharging active power corresponding to the unit energy level transition of the battery pack is, Δp c =Δψ / η,Δp d =Dψ·η; Specifically, Δp c and Δp d are the unit charging active power and unit discharging active power corresponding to the unit energy level transition of the battery pack, and η is the charging and discharging efficiency of the battery pack.

3. According to claim 2, a charging and swapping station emergency dispatch method for improving the elastic recovery capability of the distribution network is characterized by: The calculation formula for the reactive power corresponding to the transition of the battery pack from k to k' energy level and from k' to k energy level at the m charging and swapping station is: Specifically, Δk=|kk′|, and S b It is the maximum apparent power of the charging pile in the charging and swapping station.

4. According to claim 1, a charging and swapping station emergency dispatching method for improving the elastic recovery capability of the distribution network is characterized by: The transfer time calculation formula of the battery pack from the m charging and swapping station to the m′ charging and swapping station is: in, Specifically, τ m,m′,t is the transfer time of the battery pack from the m charging and swapping station to the m′ charging and swapping station in time period t, d∈(m,m′) represents the road from the m charging and swapping station to the m′ charging and swapping station, l d,t is the travel time of road d in period t, ΔT swap is the time required for battery packs to be exchanged at the charging and swapping station, Δt is the minimum scheduling time scale, is the ceiling function; x d,t is the predicted traffic data of road d in period t after a power outage occurs, is the free travel time of road d, Q d is the road capacity.

5. An emergency dispatch system for charging and swapping stations that improves the elastic recovery capability of distribution networks, characterized in that: A charging and swapping station emergency dispatch method for improving the elastic recovery capability of a distribution network as described in any one of claims 1 to 4 comprises the following modules: A charging and swapping station operation model construction module, which is used to construct a charging and swapping station operation model based on the spatiotemporal transport coupling characteristics of the transport vehicle transporting the battery pack between the charging and swapping stations and the discrete energy transition characteristics of the battery pack within the charging and swapping station; The distribution network operation model construction module is used to construct a distribution network operation model including wind-solar-diesel-storage according to the actual operation parameters of the distribution network and taking into account the constraints of the distribution network; A distribution network-charging and swapping station collaborative emergency dispatch model construction module, which is used to construct a distribution network-charging and swapping station collaborative emergency dispatch model based on the charging and swapping station operation model and the distribution network operation model, with the goal of minimizing the overall operation cost of the distribution network and the charging and swapping station; The optimization solution module is used to optimize and solve the distribution network-charging and swapping station collaborative emergency dispatch model using a solver to obtain an emergency dispatch strategy for the distribution network and the charging and swapping station.

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