Three-micro integrated active power distribution network-traffic network planning method considering high power supply reliability

Abstract

The invention discloses a three-micro integrated active power distribution network-traffic network planning method considering high power supply reliability, and relates to the field of power distribution networks. Comprising the steps of establishing a three-micro integrated active power distribution network-traffic network coupling system structure, establishing a three-micro integrated active power distribution network-traffic network coupling system model and establishing a power distribution network and traffic network collaborative planning model. The invention provides a three-micro-integrated active power distribution network-traffic network collaborative planning method considering high power supply reliability, and a three-micro-integrated active power distribution and utilization system planning model with integrated combination of light, vehicles, storage, network and load and power distribution automation equipment is established through overall planning of a distributed power supply access mode, agricultural implement electrification, rural micro traffic charging and battery swapping and the like. The emergency supply protection capability of a rural power distribution system is improved, and a grid foundation is provided for constructing a zero-power-failure high-reliability rural power distribution demonstration area.

Description

Technical Field

[0001] The present invention relates to a distribution network, and in particular to a planning method for a "three-micro" integrated active distribution network-transportation network taking high power supply reliability into consideration. Background Art

[0002] Under the strategic guidance of the "carbon peak and carbon neutrality" goals and the integrated development of energy and transportation, the penetration rate of electric vehicles has increased significantly, the proportion of charging load in the distribution network has continued to increase, and the connection between the distribution network and the transportation network has become increasingly close. The application of fast charging technology and 5G communication technology has broken the limitation that electric vehicles need to charge for a long time at a fixed location, making it possible to guide the path selection and charging decisions of electric vehicles through differentiated incentive signals or traffic congestion information, further enhancing the interactive potential between the distribution network and the transportation network. In order to solve the practical problem that the safety and economy of the independent operation of the distribution network and the transportation network are difficult to guarantee, eliminate the negative impact of high penetration electric vehicle access on the distribution network and the transportation network, and fully tap the positive benefits brought by the interactive potential of the two networks, it is necessary to break the information barriers between the two networks and realize the coordinated planning of the "three micro" integrated active distribution network-transportation network.

[0003] Although the connection between the distribution network and the transportation network is becoming increasingly close, the existing technology still has shortcomings in the integration of the two. For example, in the planning of electric vehicle charging stations, only the power supply capacity and load demand of the distribution network are often considered, while the traffic flow and congestion of the transportation network are ignored. In the collaborative planning process, accurate models and algorithms need to be established to describe the interaction between the distribution network and the transportation network. However, the existing models are often oversimplified and cannot accurately reflect the complexity of the actual situation. At the same time, the distribution network and the transportation network belong to different industries and fields, and there are obstacles in data sharing and communication. This leads to information asymmetry and poor communication between the two networks, affecting the effect and efficiency of collaborative planning. In actual operation, both the distribution network and the transportation network are affected by a variety of uncertain factors, such as weather changes, traffic accidents, and power grid failures. The existing technology has limited ability to deal with these uncertainties and cannot ensure the stable operation and efficient collaboration of the two networks. Summary of the invention

[0004] The purpose of the present invention is to provide a "three-micro" integrated active distribution network-transportation network planning method considering high power supply reliability, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The planning method of the “three micro” integrated active distribution network-transportation network considering high power supply reliability includes the following steps:

[0007] Step 1: Combine the IEEE 54-node standard distribution network and the 12-node transportation network to form a coupling system:

[0008] The nodes of the transportation network represent crossroads or transportation hubs. Charging stations and battery swap stations are built on every road in the transportation network. There are coupling points in the distribution network-transportation network coupling system (PTN), and the load of the transportation network can only be powered by the coupling points. The facilities planned for the distribution network include: RCS, SOP, whether to expand the substation, whether to build a new substation, the location and capacity of photovoltaics, the location of new lines, and the capacity of reactive power compensation devices. The facilities planned for the transportation network include: the location of charging stations, the location of battery swap stations, the number of charging piles at charging stations, the number of batteries at battery swap stations, and the number of charging devices;

[0009] Step 2: Establish a "three-micro" integrated active distribution network-transportation network coupling system model:

[0010] (1) Establishing a transportation network model

[0011] Battery swap station modeling

[0012] A model based on energy demand allocation is used to allocate the energy demand of electric vehicles directly to the roads of the transportation network and the nodes of the distribution network. The model is as follows:

[0013]

[0014] 0≤P c,l,t ≤N c,l n c,l P c (1c)

[0015] E c,l,1 =n huan,l S huan SOC max (1d)

[0016]

[0017] n cp,l P f T+n huan,l S huan SOC max ≤V l η ev E u (1f)

[0018] D c,od,p,l,t， D f,od,p,l,t are the pair od, path p, road l, and time t, respectively, of energy supplied to EV by the battery swap station and charging station, E c,l,t is the maximum energy that the battery swap station on road l can provide at time t, P c,l,tis the charging power of the battery swap station on road l at time t, χ is the battery redundancy parameter, N c,l is the number of battery charging equipment invested in the battery swap station on road l, n c,l The maximum number of batteries that can be charged simultaneously by a single charging device, P c is the maximum charging power of a single battery. (1a) indicates that the energy storage of the battery swap station should not be less than the energy required by the electric vehicles that come to swap batteries during this period. The energy storage of the battery swap station should follow the energy balance constraints of (1b) and (1c). (1f) limits the maximum energy replenishment device installation constraint. (1d) stipulates the maximum energy that the battery swap station can provide during the starting period. The maximum state of charge SOC max =0.95, n huan,l is the number of batteries invested in the battery swap station on road l, S huan is the capacity of a single battery, E g,od,t is the energy gap, i.e., the difference between the battery capacity of the electric vehicle and the actual battery charge at time t for any OD pair;

[0019] Building an Electric Vehicle Energy Distribution Model

[0020]

[0021] 0≤D c,od,p,l,t ≤I c,l M(1j)

[0022] 0≤D f,od,p,l,t ≤I f,l M(1k)

[0023] (1f) indicates that at time t, the energy that can be provided should not be greater than the energy gap of the OD pair, n cp,l is the number of charging piles installed on road l, P f is the charging pile power, T is the unit time interval, V l is the maximum road capacity, η ev is the penetration rate of electric vehicles, Eu is the average energy gap of electric vehicles, η d The proportion of electric vehicles that support battery replacement, (1i) represents the lower limit of energy replenishment through the installation of charging facilities, (1j) and (1k) ensure that energy demand is only allocated on roads where charging stations are installed, and I c,l ,I f,l are binary variables indicating whether a battery swap station and a charging station are installed on road l;

[0024] Establish installation constraints for charging stations and battery swap stations

[0025] 0≤n cp,l ≤I c,l M,0≤n cp,l ≤300(1l)

[0026] 0≤n huan,l ≤I f,l M,0≤n huan,l ≤100(1m)

[0027] (1l) and (1m) stipulate that corresponding energy replenishment devices can only be installed on roads where charging stations and battery swap stations are built, and the maximum number of installations is limited;

[0028] Establishing the “Three Micro” integrated active distribution network model

[0029] Failure recovery

[0030] After a fault occurs on a line, the propagation of the fault current is simulated and a model is established:

[0031] n i / j,Tf,s ≥f ij,s x ij ,ij∈Ω L ,s∈Ω f (2a)

[0032] n i,Tf,s +x ij -1≤n j,Tf,s (2b)

[0033] n j,Tf,s +x ij -1≤n i,Tf,s (2c)

[0034] The RCS is activated after the fault occurs, forming the smallest blackout area and minimizing the losses caused by the fault. The isolation is represented by (2d)-(2g), which represent the connection status after the fault and the fault status at both ends of the line, respectively;

[0035] (1-f ij,s )(x ij -a ij )≤x ij,Tl,s ≤(1-f ij,s )x ij ij∈Ω L s∈Ω f (2d)

[0036] n i / j,Tl,s ≥f ij,s (1-a ij )+x ij -1ij∈Ω L s∈Ω f (2e)

[0037] n i,Tl,s +x ij,Tl,s -1≤n j,Tl,s ij∈ΩL s∈Ω f (2f)

[0038] n j,Tl,,s +x ij,Tl,s -1≤n i,Tl,s ij∈Ω L s∈Ω f (2g)

[0039] f ij,s Indicates whether line ij is faulty in fault scenario s, Ω L is the set of all line branches, Ω f is a set of fault scenarios, x ij is the variable of whether line ij is constructed. Tf is the time when the fault occurs, Tl is the time when the fault is isolated, x ij,t,s is the connection status at time t,ij after the fault, a ij Whether to install RCS variables, n i,t,s is the variable of whether node i is in the fault area under fault scenario s and time t;

[0040] After RCS completes fault isolation, it performs fault repair to restore power supply to the faulty line. When the node fault is caused by a single line fault, the fault can be cleared and normal operation can be restored after the faulty line is repaired. When a node fault is caused by multiple line faults, the fault can be cleared only when all these lines are repaired. Fault repair complies with constraints (2h)-(2l). Constraint (2h) specifies the maximum number of repaired faulty lines. Constraint (2i) indicates that the faulty line will be repaired within the specified repair period. (2j)-(2l) are constraints on the state of the faulty node during the repair process.

[0041]

[0042] n i / j,t,s ≥(f ij,s -a ij )[(1-(x ij,t,s +x ij,t-1,s )]+x ij -1(2j)

[0043] n i,t,s +x ij,t,s -1≤n j,t,s (2k)

[0044] n j,t,s +x ij,t,s -1≤n i,t,s (2l)

[0045] R is the maximum number of lines repaired in each time period, T reThe time required to repair all faults;

[0046] The optimization variables of SOP are active and reactive power:

[0047]

[0048] -(1-n i,t,s )S sop,i ≤P sop,i,t ≤(1-n i,t,s )S sop,i (2n)

[0049] -(1-n i,t,s )Q sop,i,l ≤Q sop,i,t ≤(1-n i,t,s )Q sop,i,u (2o)

[0050]

[0051] P sop,i,t,s , Q sop,i,t,s are the input active power and input reactive power of SOP at node i at time t in scenario s, S sop,i , Q sop,i,l , Q sop,i,u , respectively, the SOP capacity, reactive power lower limit and upper limit of node i, Ω sop It is the collection of lines where SOP is installed. s For normal scene collection;

[0052] Establishing the power supply side model of the distribution network

[0053]

[0054]

[0055] WT i PV i ,ES i SVG i are the installed capacities of wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices at node i, respectively. are the maximum capacity of wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices allowed to be installed at node i, are the variables of whether wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices are installed at node i. are the actual power consumption, curtailment power and output power of wind turbine and photovoltaic respectively, Ω wt ,Ω pv ,Ω svg ,Ωes , are candidate construction nodes for wt, pv, SVG, and ES, respectively. (3a), (3b), (3c), and (3d) stipulate that the output of wt, pv, SVG, and ES cannot be higher than the maximum capacity. (3e) and (3f) represent the power balance of wind turbines and photovoltaics, respectively. (3g) and (3h) represent the power reduction limits of wind turbines and photovoltaics. (3i) and (3j) limit the reactive power compensation output range of SVG.

[0056] Establishing the grid-side model of the distribution network

[0057] Additional constraints (4a)-(4h) are introduced to ensure the radial topology:

[0058]

[0059]

[0060] y ij |≤(nn s0 )x ij ,(i,j)∈Ω L (4e)

[0061] 0≤a ij ≤x ij (i,j)∈Ω L (4f)

[0062] ∑a ij ≤R rcs (i,j)∈Ω L (4g)

[0063] 0≤x ij +x sop,ij ≤1ij∈Ω sop (4h)

[0064] N is the number of new load nodes, y ij is the virtual power flow from node i to node j, n is the number of nodes in the line, n s0 is the number of substations in the network, X sop,ijis the variable for whether SOP is installed on line ij. Constraint (4a) ensures that a new line can be established for each newly added node. Constraint (4b) is the maximum virtual power flow that can be dispatched by the existing substation node. When the substation node is designated as a new substation, constraints (4c)-(4d) ensure the power flow of the node. Constraint (4d) indicates that when the node is designated as a load node, the cumulative virtual flow through the node is equal to -1, which represents the power absorbed from the line. (4e) indicates that the line can only transmit virtual flow after it is built. (4f) indicates that RCS can only be built on the existing line. (4g) indicates the maximum number of RCS to be installed. Constraint (4h) indicates that after line ij is built, it is forbidden to install SOP on this line.

[0065] Establishing a micro energy storage model for distribution networks

[0066] Micro energy storage can capture energy when the micro power station has surplus power generation and discharge it during peak energy consumption. Constraints (5a)-(5f) represent the operation of micro energy storage:

[0067]

[0068]

[0069] ES i is the planned energy storage capacity at distribution network i, is the maximum plannable capacity of energy storage at node i. are the discharge and charging power of the energy storage at location i in scenario s and time t, are the state variables for discharge and charge, respectively, dis , k ch They are the depth of discharge and charge respectively. is the battery charge at time t, constraints (5a)-(5c) limit the maximum charge and discharge power, (5d) represents the charge and discharge state, and (5e)-(5f) represent the energy balance and capacity constraints of the energy storage;

[0070] Building a micro energy grid model

[0071] The power consumption of electric heating furnace is limited to:

[0072] H out,t =η fur P fur,t (6a)

[0073] H out,t , η fur , P fur,t are respectively the heating power, conversion efficiency and electric power consumed by the electric furnace at time t;

[0074] Considering photovoltaic as a distributed generator of micro energy grid, its power balance and output constraints are:

[0075] P pvuse,t +P pvgu,t =PVgP PV.t (6b)

[0076] 0≤PV≤PV rate (6c)

[0077] The generation constraints of energy storage technology are similar to those of energy storage in distribution networks:

[0078] 0≤P esd,t ≤u dis,t P max,dis (6d)

[0079] 0≤P ch,t ≤u ch,t P max,ch (6e)

[0080] u ch,t +u dis,t =1(6f)

[0081]

[0082] E soc,t ≤ES max (6h)

[0083] P esd,t , P ch,t , P max,dis , P max,ch 、u dis,t 、u ch,t 、E soc,t are the discharge power, charging power, maximum discharge power, maximum charging power discharge state variable, charging state variable, and battery remaining capacity of the energy storage in the micro energy grid at time t, respectively. c , η d ,ES max They are charging efficiency, discharging efficiency and energy storage capacity respectively;

[0084] The power balance equation of the micro energy grid is:

[0085] P esd,t +P mg,in,t +P pvuse,t =P ch,t +P load,t +P fur,t (6i)

[0086] -(1-n i,t,s )M≤P mg,in ≤(1-n i,t,s)M(6j)

[0087] P mg,in,t is the power transmitted from the distribution network to the micro energy network, P load,t is the electric power of the micro energy grid at that moment;

[0088] Establishing a power flow model for distribution networks

[0089] The Distflow power flow model is used to model the distribution network:

[0090]

[0091]

[0092] U min ≤U i,t,s ≤U max i∈Ω n (7c)

[0093]

[0094] 0≤P cut,i,t ≤(1-n i,t,s )(P LOAD,i,t,s +P ev,i,t )s∈(Ω s ∪Ω f )(7i)

[0095] Formula (7a) and (7b) are the active and reactive power balance constraints of the node, P in,i,t,s ,Q in,i,t,s ,P LOAD,i,t,s ,Q LOAD,i,t,s ,P ij,t,s ,Q ij,t,s , are respectively the active and reactive power injected into node i at time t under scenario s, the active load and reactive load of the node, and the active and reactive power transmitted from node i to node j. Formula (7e) is the DistFlow power flow model, where U i,t,s ,M,r L,ij 、x L,ij , V0 are the voltage of node i at time t in scenario s, a sufficiently large positive number, the impedance and inductance of the line between nodes ij, and the calibrated voltage of the distribution system. Formula (7c) is a safety constraint to ensure that the voltage of each node is within the allowable range, where U min and U max are the upper and lower voltage limits allowed by the node, and equation (7d) is the capacity constraint of the line, where S ij is the maximum capacity of the transmission line. When the distribution system is difficult to maintain safety and the power supply is insufficient, load shedding is required, as shown in (7f). cut,i,t,sis the active power cut by node i at time t in scenario s, and equations (7g)-(7h) are the active and reactive powers injected into the node, where P ev,i,t,s is the power of the electric vehicle charging station at node i at time t in scenario s;

[0096] Step 3: Establish a coordinated planning model for distribution network and transportation network

[0097] obj=C PN +C DN (8a)

[0098]

[0099] C Pinv =mC f +nC c +m CP C CP +n BC C BC +n B C B (8e)

[0100]

[0101] In the objective function, C PN and C DN are the total cost of the transportation network and the total cost of the distribution network, respectively, time is the time cost, and the average hourly wage H W As a quantification, C gap Fines for electric vehicle energy satisfaction, F n is the unit fine amount, r is the discount rate, x is the planning period, C Pinv is the investment cost of the transportation network, including the charging station fitness cost C f ,Cc,Cc,Cc,C,c, CP , Investment cost of battery charger in battery swap station C BC， Battery investment cost of battery swap station C B。 m,n,m CP ,n BC ,n B are the number of investments in charging stations, battery swap stations, charging piles, chargers, and batteries, respectively. Dinv is the investment cost of the distribution network, c in,wt 、c in,pv 、c in,es 、c in,sop 、c in,l 、c in,svg Represents the unit investment cost of wind power, photovoltaic power, energy storage battery, soft switch, line length and SVG, c esub,i 、c sub,i、c in,rcs Respectively represent the expansion cost, new construction cost and RCS installation cost of the substation; Ω wt ,Ω pv ,Ω es ,Ω sop ,Ω svg ,Ω L ,Ω sub ,Ω esub ,Ω ECS Represents the construction lines and nodes of wind power, photovoltaic power, energy storage battery, soft switch, SVG, line, new substation, expandable substation and RCS respectively; WT i PV i ,ES i SVG i 、x sub，ei 、x sub，i Respectively represent the installed capacity of wind power, photovoltaic power, energy storage, and SVG at the i-th node, and whether a substation is expanded or newly built at the node; SOP ij , l ij 、x ij 、x rcs，ij are binary variables representing the capacity of SOP installed on line ij, the length of the line, whether line ij is built, and whether RCS is installed. wtgu,i,t , P wtpv,i,t , P dis,i,t , P ch,i,t , P sub,i,t , P cut,i,t are the power of wind and solar power abandonment, battery discharge, battery charging, substation power purchase and load shedding at node i at time t; c op,wt 、c op,pv are the annual maintenance costs of wind power and photovoltaic power under unit capacity respectively; f wt 、f pv 、f n 、f op,es are the unit wind curtailment, solar curtailment, load shedding and battery degradation costs respectively; p t and c op,ij is the electricity price of the substation at time t and the annual maintenance cost of each line.

[0102] As a further solution of the present invention:

[0103] The distribution system operator also obtains electricity from the main grid. The expansion plan includes the construction of new substations and the expansion of existing substations. The construction, expansion and operation constraints of substations include:

[0104]

[0105] Respectively represent the output active and reactive power of the substation, are the newly built, initial and expandable substation capacities, respectively, and (3k) and (3l) represent the power output limitations of the substation.

[0106] Compared with the prior art, the present invention has the following beneficial effects:

[0107] 1. A collaborative planning method for the "three micro" integrated active distribution network-transportation network considering high power supply reliability is proposed, and a planning model for the three micro integrated active distribution and utilization system of "light, vehicle, storage, network, load" and distribution automation equipment is established by coordinating distributed power supply access methods, electrification of agricultural tools, and rural micro-transportation charging and swapping. This model will enhance the emergency supply capacity of the rural distribution system and provide a grid foundation for the construction of a "zero power outage" high-reliability rural distribution demonstration area.

[0108] 2. Compared with the traditional single-vehicle energy replenishment model, the traffic flow model based on electric vehicle energy flow is proposed, which helps to understand and optimize the energy replenishment strategy of electric vehicles more efficiently, and bypasses the multiple integer variables involved in the single-vehicle energy replenishment process, and has a higher solution efficiency.

[0109] 3. In order to improve the reliability of the coupling system between the distribution network and the transportation network, the coordinated configuration of SOP and RCS, as well as the coordinated configuration of charging stations and battery swap stations, is proposed, which can fully utilize the rapid energy replenishment advantages of battery swap stations and the economical characteristics of charging stations to meet the energy needs of different user groups. It can also respond quickly when the distribution network encounters a fault, thereby reducing the impact of power outages on the system, which is of great significance to the stable operation and efficiency improvement of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1a This is the topological structure of the distribution network in the present invention.

[0111] Figure 1b This is the topological structure of the transportation network in the present invention.

[0112] Figure 2 This is a planning result diagram of the method proposed in the present invention.

[0113] Figure 3 The figure is a comparison chart of the power situations of the battery swap stations of the two schemes in the embodiment of the present invention.

[0114] Figure 4 Electricity price map. DETAILED DESCRIPTION

[0115] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0116] In an embodiment of the present invention, a "three-micro" integrated active distribution network-transportation network planning method considering high power supply reliability includes the following steps:

[0117] Step 1: Establish the "three micro" integrated active distribution network-transportation network coupling system structure

[0118] This paper takes the IEEE 54-node standard distribution network and the 12-node transportation network as an example to form a coupling system, as shown in Figure 1. The nodes of the transportation network represent crossroads or transportation hubs. There are 20 roads in the transportation network, and charging stations and battery swap stations can be built on each road. There are eight coupling points in the distribution network-transportation network coupling system (PTN), and the traffic network load can only be powered by the coupling points. The relationship is that the 2, 4, 22, 8, 37, 38, 16, and 45 nodes of the distribution network correspond to the 1, 2, 3, 5, 8, 9, 10, and 11 coupling nodes of the transportation network. The facilities to be planned for the distribution network include: RCS, SOP, whether to expand the substation, whether to build a new substation, the location and capacity of photovoltaics, the location of new lines, and the capacity of reactive compensation devices. The facilities to be planned for the transportation network include: the location of the charging station, the location of the battery swap station, the number of charging piles at the charging station, the number of batteries at the battery swap station, and the number of charging devices.

[0119] Step 2: Establish a "three-micro" integrated active distribution network-transportation network coupling system model

[0120] (1) Establishing a transportation network model

[0121] In the field of transportation network planning, the concept of OD (origin-destination) pairs is widely used to describe and model the traffic demand of vehicles. This model directly reflects the traffic flow from the origin to the destination, providing a solid foundation for understanding and predicting traffic flow. It not only helps to deeply analyze and predict the performance of the transportation network, but also guides the formulation of traffic management strategies and fully considers the path selection behavior of travelers when designing the transportation network. When the OD pair information is known, the time and place of vehicle refueling can be further predicted. This paper focuses on the planning of charging and swapping stations for electric vehicles (EVs), without considering the travel path and refueling process of a single electric vehicle. Therefore, we will focus on the modeling of the entire traffic flow in the OD pair in terms of refueling, without in-depth exploration of the refueling model of a single vehicle. This research method helps to understand and optimize the energy refueling strategy of electric vehicles more efficiently, bypasses the multiple integer variables involved in the refueling process of a single vehicle, and has a higher solution efficiency.

[0122] 1) Battery swap station modeling

[0123] This paper adopts a model based on energy demand allocation to directly allocate the energy demand of electric vehicles to the roads of the transportation network and the nodes of the distribution network. The model is as follows:

[0124]

[0125]

[0126] 0≤P c,l,t ≤N c,l n c,l P c (1c)

[0127] E c,l,1 =n huan,l S huan SOC max (1d)

[0128]

[0129] n cp,l P f T+n huan,l S huan SOC max ≤V l η ev E u (1f)

[0130] D c,od,p,l,t， D f,od,p,l,t The energy supplied to EV by the battery swap station and charging station is respectively od pair, path p, road l, and time t. c,l,t The maximum energy that the battery swap station on road l can provide at time t.c,l,t is the charging power of the battery swap station on road l at time t. χ is the battery redundancy parameter. N c,l is the number of battery charging equipment invested in the battery swap station on road l, n c,l The maximum number of batteries that can be charged simultaneously for a single charging device. c is the maximum charging power of a single battery. (1a) indicates that the energy storage of the battery swap station should not be less than the energy required by the electric vehicles that come to swap batteries during this period. The energy storage of the battery swap station should follow the energy balance constraints of (1b) and (1c). (1f) limits the maximum energy replenishment device installation constraint. (1d) stipulates the maximum energy that the battery swap station can provide during the start period. The maximum state of charge SOC max =0.95. huan,l S is the number of batteries invested in the battery swap station on road l. huan is the capacity of a single battery. g,od,t is the energy gap, i.e., the difference between the battery capacity of the electric vehicle at time t and the actual battery charge for any OD pair.

[0131] 2) Electric vehicle energy distribution model

[0132]

[0133] 0≤D c,od,p,l,t ≤I c,l M (1j)

[0134] 0≤D f,od,p,l,t ≤I f,l M (1k)

[0135] (1f) indicates that at time t, the energy that can be provided should not be greater than the energy gap of the OD pair. cp,l is the number of charging piles installed on road l. f is the charging pile power, T is the unit time interval. l is the maximum road capacity, η ev is the penetration rate of electric vehicles, and Eu is the average energy gap of electric vehicles. d The proportion of electric vehicles that support battery replacement. (1i) represents the lower limit of energy replenishment by installing energy replenishment facilities. (1j)-(1k) ensure that energy demand is only allocated on roads with energy replenishment stations installed. M is a sufficiently large number, which is 50000000 in this paper. I c,l ,I f,l are binary variables indicating whether a battery swap station and a charging station are installed on road l.

[0136] 3) Installation constraints of charging stations and battery swap stations

[0137] 0≤n cp,l ≤I c,lM,0≤n cp,l ≤300 (1l)

[0138] 0≤n huan,l ≤I f,l M,0≤n huan,l ≤100 (1m)

[0139] (1l)-(1m) stipulate that corresponding energy replenishment devices can only be installed on roads where charging stations and battery swap stations are built, and the maximum number of installations is limited.

[0140] =

[0141] (2) Establishing a “three-micro” integrated active distribution network model

[0142] 4) Fault recovery

[0143] In order to build a "zero power outage" distribution network and improve power supply reliability, it is necessary to reasonably plan the location and number of soft open point (SOP) and remote control switch (RCS) in the distribution network. Traditional remote control switches can cut off the current after a line fault, preventing the propagation of fault current and the extension of the fault area. SOP can change the flow of power more quickly, and has a faster response speed than remote control switches. In normal circumstances, SOP can also be used as a feeder, enabling the distribution network to achieve "open-loop design and closed-loop operation". Greatly improve power supply reliability.

[0144] After a fault occurs on a line, the propagation of the fault current is simulated and a model is established:

[0145] n i / j,Tf,s ≥f ij,s x ij ,ij∈Ω L ,s∈Ω f (2a)

[0146] n i,Tf,s +x ij -1≤n j,Tf,s (2b)

[0147] n j,Tf,s +x ij -1≤n i,Tf,s (2c)

[0148] The RCS is activated after a fault occurs, forming the smallest blackout area, thereby minimizing the losses caused by the fault. Isolation is represented by (2d)-(2g), which represent the connection state after the fault and the fault state at both ends of the line, respectively.

[0149] (1-f ij,s )(x ij-a ij )≤x ij,Tl,s ≤(1-f ij,s )x ij ij∈Ω L s∈Ω f (2d)

[0150] n i / j,Tl,s ≥f ij,s (1-a ij )+x ij -1ij∈Ω L s∈Ω f (2e)

[0151] n i,Tl,s +x ij,Tl,s -1≤n j,Tl,s ij∈Ω L s∈Ω f (2f)

[0152] n j,Tl,,s +x ij,Tl,s -1≤n i,Tl,s ij∈Ω L s∈Ω f (2g)

[0153] f ij,s Indicates whether line ij is faulty in fault scenario s, Ω L is the set of all line branches, Ω f is a set of fault scenarios, x ij is the variable of whether line ij is constructed. Tf is the time when the fault occurs. Tl is the time when the fault is isolated. ij,t,s is the connection status at time t,ij after the fault, a ij Whether to install RCS variables. i,t,s It is the variable indicating whether node i is in the fault region under fault scenario s and time t.

[0154] After RCS completes fault isolation, fault repair must be performed to restore power to the faulty line. When a node fault is caused by a single line fault, the fault can be cleared and normal operation can be restored after the faulty line is repaired. When a node fault is caused by multiple line faults, the fault can only be cleared when all these lines are repaired. Resource supply limitations such as manpower and equipment will also limit the speed of repair. Fault repair complies with constraints (2h)-(2l). Constraint (2h) specifies the maximum number of repaired fault lines. Constraint (2i) indicates that the faulty line will be repaired within the specified repair period. (2j)-(2l) are constraints on the state of the faulty node during the repair process.

[0155]

[0156] n i / j,t,s ≥(f ij,s -a ij )[(1-(x ij,t,s +x ij,t-1,s )]+x ij -1 (2j)

[0157] n i,t,s +x ij,t,s -1≤n j,t,s (2k)

[0158] n j,t,s +x ij,t,s -1≤n i,t,s (2l)

[0159] R is the maximum number of lines repaired in each time period. re The time required to repair all faults.

[0160] The optimization variables of SOP are active and reactive power:

[0161]

[0162] -(1-n i,t,s )S sop,i ≤P sop,i,t ≤(1-n i,t,s )S sop,i (2n)

[0163] -(1-n i,t,s )Q sop,i,l ≤Q sop,i,t ≤(1-n i,t,s )Q sop,i,u (2o)

[0164]

[0165] P sop,i,t,s , Q sop,i,t,s S are the input active power and input reactive power of SOP at node i at time t in scenario s. sop,i , Q sop,i,l , Q sop,i,u , are the SOP capacity, reactive power lower limit and upper limit of node i. sop It is the set of lines where SOP is installed. s A collection of normal scenes.

[0166] 5) Distribution network power supply side model

[0167] Reasonable planning of power supply is crucial for modern power systems. It can not only improve energy utilization efficiency and reduce energy loss during long-distance power transmission, but also reduce dependence on the central power grid, thereby enhancing the overall stability and disaster resistance of the power grid. This article mainly involves micro power stations (distributed wind turbines, distributed photovoltaic power generation), substations, and reactive power compensation devices. The relevant models and constraints are as follows:

[0168]

[0169]

[0170] WT i PV i ,ES i SVG i are the installed capacities of wind turbine, photovoltaic power generation, energy storage system and reactive power compensation device at node i respectively. are the maximum capacities allowed for installation of wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices at node i, respectively. They are variables for whether wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices are installed at node i. They are the actual power consumption, curtailment power and output power of wind turbine and photovoltaic respectively. Ω wt ,Ω pv ,Ω svg ,Ω es , are candidate construction nodes for wt, pv, SVG, and ES respectively. (3a), (3b), (3c), and (3d) stipulate that the output of wt, pv, SVG, and ES cannot be higher than the maximum capacity. (3e) and (3f) represent the power balance of wind turbines and photovoltaics, respectively. (3g) and (3h) represent the power reduction limits of wind turbines and photovoltaics. (3i) and (3j) limit the reactive power compensation output range of SVG.

[0171] In addition to obtaining electricity from renewable energy sources, distribution system operators also obtain electricity from the main grid. Expansion planning includes the construction of new substations and the expansion of existing substations. The construction, expansion and operation constraints of substations include:

[0172]

[0173] They represent the output active and reactive power of the substation respectively. are the newly constructed, initial and expandable substation capacities respectively. (3k)(3l) represent the power output limit of the substation.

[0174] Distribution network side model

[0175] Distribution system expansion planning usually involves load nodes and additional lines. The integration of renewable energy increases the complexity of network planning and may lead to the emergence of ring topology and isolated nodes. , Additional constraints (4a)-(4h) should also be introduced to ensure the radial topology.

[0176]

[0177] |y ij |≤(nn s0 )x ij ,(i,j)∈Ω L (4e)

[0178] 0≤a ij ≤x ij (i,j)∈Ω L (4f)

[0179] ∑a ij ≤R rcs (i,j)∈Ω L (4g)

[0180] 0≤x ij +x sop,ij ≤1ij∈Ω sop (4h)

[0181] N is the number of new load nodes, y ij is the virtual power flow from node i to node j, n is the number of nodes in the line, n s0 is the number of substations in the network. sop,ij is the variable for whether SOP is installed on line ij. Constraint (4a) ensures that a new line can be established for each newly added node. Constraint (4b) is the maximum virtual power flow that can be dispatched by the existing substation node. When the substation node is designated as a new substation, constraints (4c)-(4d) ensure the power flow of the node. Constraint (4d) indicates that when the node is designated as a load node, the cumulative virtual flow through the node is equal to -1, which represents the power absorbed from the line. (4e) indicates that the line can only transmit virtual flow after it is built. (4f) indicates that RCS can only be built on existing lines. (4g) indicates the maximum number of RCS to be installed. Constraint (4h) indicates that it is prohibited to install SOP on the line after line ij is built.

[0182] Micro energy storage model for distribution network

[0183] Micro energy storage can capture energy when the micro power station has surplus power generation and discharge it during peak energy consumption. Constraints (5a)-(5f) represent the operation of micro energy storage.

[0184]

[0185]

[0186] ES i is the planned energy storage capacity at distribution network i, is the maximum plannable capacity of energy storage at node i. are the discharge and charging powers of the energy storage at location i in scenario s and time t respectively. are the state variables for discharge and charge, respectively. dis , k ch They are the depth of discharge and charge respectively. is the battery charge at time t. Constraints (5a)-(5c) limit the maximum charge and discharge power. (5d) represents the charge and discharge state. (5e)-(5f) represent the energy balance and capacity constraints of energy storage.

[0187] Micro Energy Grid Model

[0188] Assume that the micro energy grid in this paper is connected to a specific node. The micro energy grid is equipped with photovoltaics and energy storage, and has a certain degree of independence and autonomy. With the development of clean energy technology, electric heating furnaces, as a direct way to utilize electric energy, have promoted the use of new energy. At the same time, they play an important role in the national "coal to electricity" policy, promoting building energy conservation and low-peak electricity revenue, and have significant economic and social benefits. The power consumption constraint of the electric heating furnace is:

[0189] H out,t =η fur P fur,t (6a)

[0190] H out,t , η fur , P fur,t They are respectively the heating power, conversion efficiency and electric power consumed by the electric furnace at time t.

[0191] Considering photovoltaic as a distributed generator of micro energy grid, its power balance and output constraints are:

[0192] P pvuse,t +P pvgu,t =PVgP PV.t (6b)

[0193] 0≤PV≤PV rate (6c)

[0194] The development of energy storage technology is of great significance to promoting the application of microgrid technology and the flexibility of power systems, which helps to promote technological progress and strengthen the energy storage industry system, and provides strong support for building an efficient, environmentally friendly and intelligent micro-energy network. Its constraints are similar to those of energy storage in distribution networks:

[0195] 0≤P esd,t ≤u dis,t P max,dis (6d)

[0196] 0≤P ch,t ≤u ch,t P max,ch (6e)

[0197] u ch,t +u dis,t =1 (6f)

[0198]

[0199] E soc,t ≤ES max (6h)

[0200] P esd,t , P ch,t , P max,dis , P max,ch 、u dis,t 、u ch,t 、E soc,t are the discharge power, charging power, maximum discharge power, maximum charging power discharge state variable, charging state variable, and remaining battery capacity of the energy storage in the micro energy grid at time t. c , η d ,ES max They are charging efficiency, discharging efficiency and energy storage capacity respectively.

[0201] On the one hand, the micro energy grid can meet its own electricity demand through distributed generators. When the load increases, it can also obtain additional electricity from the distribution network. The power balance equation of the micro energy grid is:

[0202] P esd,t +P mg,in,t +P pvuse,t =P ch,t +P load,t +P fur,t (6i)

[0203] -(1-n i,t,s )M≤P mg,in ≤(1-n i,t,s )M (6j)

[0204] P mg,in,t is the power transmitted from the distribution network to the micro energy network, P load,t is the electric power of the micro energy grid at that moment.

[0205] Distribution Network Flow Model

[0206] The Distflow power flow model is used to model the distribution network:

[0207]

[0208] U min ≤U i,t,s ≤U max i∈Ω n (7c)

[0209]

[0210] 0≤P cut,i,t ≤(1-n i,t,s )(P LOAD,i,t,s +P ev,i,t )s∈(Ω s ∪Ω f ) (7i)

[0211] Formula (7a) and (7b) are the active and reactive power balance constraints of the node, P in,i,t,s ,Q in,i,t,s ,P LOAD,i,t,s ,Q LOAD,i,t,s ,P ij,t,s ,Q ij,t,s , are respectively the active and reactive power injected into node i at time t under scenario s, the active load and reactive load of the node, and the active and reactive power transmitted from node i to node j. Formula (7e) is the DistFlow power flow model, where U i,t,s ,M,r L,ij 、x L,ij , V0 are the voltage of node i at time t in scenario s, a sufficiently large positive number, the impedance and inductance of the line between nodes ij, and the calibrated voltage of the distribution system. Formula (7c) is a safety constraint to ensure that the voltage of each node is within the allowable range, where U min and U max are the upper and lower voltage limits allowed by the node, and equation (7d) is the capacity constraint of the line, where S ij is the maximum capacity of the transmission line. When the safety of the distribution system is difficult to maintain and the power supply is insufficient, load shedding is required, as shown in (7f). cut,i,t,s is the active power reduced by node i at time t in scenario s, and equations (7g)-(7h) are the active and reactive powers injected into the node, where P ev,i,t,s is the power of the electric vehicle charging station at node i at time t in scenario s;

[0212] Step 3: Establish a coordinated planning model for distribution network and transportation network

[0213] The goal of this paper is to coordinate the resources of the distribution network and the transportation network to minimize the total planning cost and operating cost of the two networks.

[0214] obj=C PN +C DN (8a)

[0215]

[0216] C Pinv =mC f +nC c +m CP C CP +n BC C BC +n B C B (8e)

[0217]

[0218] In the objective function, C PN and C DN are the total cost of the transportation network and the total cost of the distribution network respectively. time is the time cost, with the average hourly wage H W As a quantification. gap Fines for electric vehicle energy satisfaction, F n is the unit fine amount. r is the discount rate, and x is the planning period. Pinv is the investment cost of the transportation network, including the cost of charging stations C f ,Cc,Cc,Cc,C,c, CP , Investment cost of battery charger in battery swap station C BC， Battery investment cost of battery swap station C B .m,n,m CP ,n BC ,n B are the investment numbers of charging stations, battery swap stations, charging piles, chargers, and batteries respectively. Dinv is the investment cost of the distribution network, c in,wt 、c in,pv 、c in,es 、c in,sop 、c in,l 、c in,svg Represents the unit investment cost of wind power, photovoltaic power, energy storage battery, soft switch, line length and SVG, c esub,i 、c sub,i 、c in,rcs Respectively represent the expansion cost, new construction cost and RCS installation cost of the substation; Ω wt ,Ω pv ,Ω es ,Ω sop ,Ω svg ,Ω L,Ω sub ,Ω esub ,Ω ECS Represents the construction lines and nodes of wind power, photovoltaic power, energy storage battery, soft switch, SVG, line, new substation, expandable substation and RCS respectively; WT i PV i ,ES i SVG i 、x sub，ei 、x sub，i Respectively represent the installed capacity of wind power, photovoltaic power, energy storage, and SVG at the i-th node, and whether a substation is expanded or newly built at the node; SOP ij , l ij 、x ij 、x rcs，ij are binary variables representing the capacity of SOP installed on line ij, the length of the line, whether line ij is built, and whether RCS is installed. wtgu,i,t , P wtpv,i,t , P dis,i,t , P ch,i,t , P sub,i,t , P cut,i,t are the power of wind and solar power abandonment, battery discharge, battery charging, substation power purchase and load shedding at node i at time t; c op,wt 、c op,pv are the annual maintenance costs of wind power and photovoltaic power under unit capacity respectively; f wt 、f pv 、f n 、f op,es are the unit wind curtailment, solar curtailment, load shedding and battery degradation costs respectively; p t and c op,ij is the electricity price of the substation at time t and the annual maintenance cost of each line.

[0219] The present invention verifies the effectiveness and superiority of the present invention by comparing three different schemes: Scheme 1 is the distribution network-transportation network collaborative planning scheme proposed by the present invention; Scheme 2 is a step-by-step planning scheme of first planning the transportation network and then planning the distribution network; Scheme 3 is a distribution network-transportation network collaborative planning scheme that does not consider battery swap stations.

[0220] Table 1 Comparison of various costs of Scheme 1-2

[0221]

[0222] Table 1 shows the cost comparison of the two schemes. The total transportation network cost of Scheme 1 is slightly higher than that of Scheme 2, but the total distribution network cost of Scheme 1 is much lower than that of Scheme 2. This generally reflects the economic superiority of collaborative planning.

[0223] The comparison of the power purchase cost of the distribution network in Table 1 and Figure 3 The power of the battery swap station in Scheme 2 shows that the battery swap station has a greater power demand during the peak electricity price period (0.834$ / kwh), which means that the distribution network must purchase more electricity, thereby increasing the cost of purchasing electricity. During the peak electricity consumption period of the transportation network, because the battery swap station in Scheme 1 invested more batteries and used the pre-stored battery resources, it avoided replenishing the battery stock during the peak electricity consumption period, making the charging power at this time relatively small. This strategy effectively reduces the load of the distribution network during peak hours, plays a role in peak load reduction, helps balance the load of the power grid, reduce the operating cost of the power grid, and improve the operating efficiency of the power grid.

[0224] Comparing Plan 1 and Plan 3, Plan 1 has lower transportation network costs. The main reason is that Plan 3 only plans charging stations, which leads to longer charging time and higher time costs.

[0225] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0226] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A "three-micro" integrated active distribution network-transportation network planning method considering high power supply reliability, characterized in that: The steps include: Step 1: A coupling system is formed by using the IEEE 54-node standard distribution network and the 12-node transportation network; Step 2: Establish a "three-micro" integrated active distribution network-transportation network coupling system model; Step 3: Establish a coordinated planning model for distribution network and transportation network.

2. According to claim 1, the "three micro" integrated active distribution network-transportation network planning method considering high power supply reliability is characterized in that: The step 1 is to form a coupling system with an IEEE 54-node standard distribution network and a 12-node transportation network. Specifically, the nodes of the transportation network represent crossroads or transportation hubs, charging stations and battery swap stations are built on each road of the transportation network, there are coupling points in the distribution network-transportation network coupling system, and the transportation network load can only be powered by the coupling points. The facilities planned for the distribution network include: RCS, SOP, whether to expand the substation, whether to build a new substation, the location and capacity of photovoltaics, the location of new lines, and the capacity of reactive compensation devices. The facilities planned for the transportation network include: the location of the charging station, the location of the battery swap station, the number of charging piles at the charging station, the number of batteries at the battery swap station, and the number of charging devices.

3. The "three micro" integrated active distribution network-transportation network planning method considering high power supply reliability according to claim 2 is characterized in that: The step 2 of establishing the "three micro" integrated active distribution network-transportation network coupling system model is specifically as follows: (1) Establishing a transportation network model Battery swap station modeling A model based on energy demand allocation is used to allocate the energy demand of electric vehicles directly to the roads of the transportation network and the nodes of the distribution network. The model is as follows: 0≤P c,l,t ≤N c,l n c,l P c (1c) And c,l,1 =n huan,l S huan SOC max (1d) n cp,l P f T+n huan,l S huan SOC max ≤V l η ev AND u (1f) D c,od,p,l,t， D f,od,p,l,t are the pair od, path p, road l, and time t, respectively, of energy supplied to EV by the battery swap station and charging station, E c,l,t is the maximum energy that the battery swap station on road l can provide at time t, P c,l,t is the charging power of the battery swap station on road l at time t, χ is the battery redundancy parameter, N c,l is the number of battery charging equipment invested in the battery swap station on road l, n c,l The maximum number of batteries that can be charged simultaneously by a single charging device, P c is the maximum charging power of a single battery. (1a) indicates that the energy storage of the battery swap station should not be less than the energy required by the electric vehicles that come to swap batteries during this period. The energy storage of the battery swap station should follow the energy balance constraints of (1b) and (1c). (1d) stipulates the maximum energy that the battery swap station can provide during the starting period, where the maximum state of charge SOC max =0.95, n huan,l is the number of batteries invested in the battery swap station on road l, S huan is the capacity of a single battery, E g,od,t is the energy gap, i.e., the difference between the battery capacity of the electric vehicle and the actual battery charge at time t for any OD pair; (1f) limits the maximum energy replenishment device installation constraint, n cp,l is the number of charging piles installed on road l, P f is the charging pile power, T is the unit time interval, V l is the maximum capacity of the road, Eu is the average energy gap of electric vehicles; Building an Electric Vehicle Energy Distribution Model 0≤D c,od,p,l,t ≤I c,l M(1j) 0≤D f,od,p,l,t ≤I f,l M(1k) 1(g) indicates that the energy supplied to EV should not be greater than the energy gap of OD pair, (1h) indicates the constraint of swappable electric vehicle, η d The proportion of electric vehicles that support battery replacement, (1i) represents the lower limit of energy replenishment through the installation of charging facilities, (1j) and (1k) ensure that energy demand is only allocated on roads where charging stations are installed, and I c,l ,I f,l are binary variables indicating whether a battery swap station and a charging station are installed on road l; Establish installation constraints for charging stations and battery swap stations 0≤n cp,l ≤I c,l M,0≤n cp,l ≤300(1l) 0≤n huan,l ≤I f,l M,0≤n huan,l ≤100(1m) (1l) and (1m) stipulate that corresponding energy replenishment devices can only be installed on roads where charging stations and battery swap stations are built, and the maximum number of installations is limited; (2) Establishing a "three-micro" integrated active distribution network model 1) Fault recovery After a fault occurs on a line, the propagation of the fault current is simulated and a model is established: n i / j,Tf,s ≥f ij,s x ij ,ij∈Ω L ,s∈Ω f (2a) n i,Tf,s +x ij -1≤n j,Tf,s (2b) n j,Tf,s +x ij -1≤n i,Tf,s (2c) The RCS is activated after the fault occurs, forming the smallest blackout area and minimizing the losses caused by the fault. The isolation is represented by (2d)-(2g), which represent the connection status after the fault and the fault status at both ends of the line, respectively; (1-f ij,s )(x ij -a ij )≤x ij,Tl,s ≤(1-f ij,s )x ij ij∈Ω L s∈Ω f (2d) n i / j,Tl,s ≥f ij,s (1-a ij )+x ij -1ij∈Ω L s∈Ω f (2e) n i,Tl,s +x ij,Tl,s -1≤n j,Tl,s ij∈Ω L s∈Ω f (2f) n j,Tl,,s +x ij,Tl,s -1≤n i,Tl,s ij∈Ω L s∈Ω f (2g) f ij,s Indicates whether line ij is faulty in fault scenario s, Ω L is the set of all line branches, Ω f is a set of fault scenarios, x ij is the variable of whether line ij is constructed, Tf is the time when the fault occurs, Tl is the time when the fault is isolated, x ij,t,s is the connection status at time t,ij after the fault, a ij Whether to install RCS variables, n i,t,s is the variable of whether node i is in the fault area under fault scenario s and time t; After RCS completes fault isolation, it will perform fault repair to restore power supply to the faulty line. When the node fault is caused by a single line fault, the fault can be cleared and normal operation can be restored after the faulty line is repaired. When the fault of a node is caused by multiple line faults, the fault can be cleared only when all these lines are repaired; the fault repair complies with the constraints (2h)-(2l). Constraint (2h) stipulates the maximum number of repaired faulty lines, constraint (2i) indicates that the faulty line will be repaired within the specified repair period, and (2j)-(2l) are constraints on the state of the faulty node during the repair process; n i / j,t,s ≥(f ij,s -a ij )[(1-(x ij,t,s +x ij,t-1,s )]+x ij -1(2j) n i,t,s +x ij,t,s -1≤n j,t,s (2k) n j,t,s +x ij,t,s -1≤n i,t,s (2l) R is the maximum number of lines repaired in each time period, T re The time required to repair all faults; The optimization variables of SOP are active and reactive power: -(1-n i,t,s )S sop,i ≤P sop,i,t ≤(1-n i,t,s )S sop,i (2n) -(1-n i,t,s )Q sop,i,l ≤Q sop,i,t ≤(1-n i,t,s )Q sop,i,u (2o) P sop,i,t,s , Q sop,i,t,s are the input active power and input reactive power of SOP at node i at time t in scenario s, S sop,i , Q sop,i,l , Q sop,i,u , respectively, the SOP capacity, reactive power lower limit and upper limit of node i, Ω sop For the line set where SOP is installed, Ω s For normal scene collection; 2) Establish the power supply side model of the distribution network WT i PV i ,ES i SVG i are the installed capacities of wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices at node i, respectively. are the maximum capacity of wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices allowed to be installed at node i, are the variables of whether wind turbines, photovoltaic power generation, energy storage systems, and reactive power compensation devices are installed at node i. are the actual power consumption, curtailment power and output power of wind turbine and photovoltaic respectively, Ω wt ,Ω pv ,Ω svg ,Ω es , are candidate construction nodes for wt, pv, SVG, and ES, respectively. (3a), (3b), (3c), and (3d) stipulate that the output of wt, pv, SVG, and ES cannot be higher than the maximum capacity. (3e) and (3f) represent the power balance of wind turbines and photovoltaics, respectively. (3g) and (3h) represent the power reduction limits of wind turbines and photovoltaics. (3i) and (3j) limit the reactive power compensation output range of SVG. 3) Establish the distribution network side model Additional constraints (4a)-(4h) are introduced to ensure the radial topology: |y ij |≤(nn s0 )x ij ,(i,j)∈Ω L (4e) 0≤a ij ≤x ij (i,j)∈Ω L (4f) ∑a ij ≤R rcs (i,j)∈Ω L (4h) 0≤x ij +x sop,ij ≤1ij∈Ω sop (4h) N is the number of new load nodes, y ij is the virtual power flow from node i to node j, n is the number of nodes in the line, n s0 is the number of substations in the network, X sop,ij is the variable for whether SOP is installed on line ij. Constraint (4a) ensures that a new line can be established for each newly added node. Constraint (4b) is the maximum virtual power flow that can be dispatched by the existing substation node. When the substation node is designated as a new substation, constraints (4c)-(4d) ensure the power flow of the node. Constraint (4d) indicates that when the node is designated as a load node, the cumulative virtual flow through the node is equal to -1, which represents the power absorbed from the line. (4e) indicates that the line can only transmit virtual flow after it is built. (4f) indicates that RCS can only be built on the existing line. (4g) indicates the maximum number of RCS to be installed. Constraint (4h) indicates that after line ij is built, it is forbidden to install SOP on this line. 4) Establish a micro energy storage model for distribution network Micro energy storage can capture energy when the micro power station has surplus power generation and discharge it during peak energy consumption. Constraints (5a)-(5f) represent the operation of micro energy storage: ES i is the planned energy storage capacity at distribution network i, is the maximum plannable capacity of energy storage at node i, are the discharge and charging power of the energy storage at location i in scenario s and time t, are the state variables for discharge and charge, respectively, dis , k ch are the depth of discharge and charge, respectively, is the battery charge at time t, constraints (5a)-(5c) limit the maximum charge and discharge power, (5d) represents the charge and discharge state, and (5e)-(5f) represent the energy balance and capacity constraints of the energy storage; 5) Establish a micro energy network model The power consumption of electric heating furnace is limited to: H out,t =the fur P fur,t (6a) H out,t , η fur , P fur,t are respectively the heating power, conversion efficiency and electric power consumed by the electric furnace at time t; Considering photovoltaic as a distributed generator of micro energy grid, its power balance and output constraints are: P pvuse,t +P pvgu,t =PVgP PV.t (6b) 0≤PV≤PV rate (6c) The generation constraints of energy storage technology are similar to those of energy storage in distribution networks: 0≤P esd,t ≤u dis,t P max,dis (6d) 0≤P ch,t ≤u ch,t P max,ch (6e) u ch,t +u dis,t =1(6f) Yes soc,t ≤ES max (6h) P esd,t , P ch,t , P max,dis , P max,ch 、u dis,t 、u ch,t 、E soc,t are the discharge power, charging power, maximum discharge power, maximum charging power discharge state variable, charging state variable, and battery remaining capacity of the energy storage in the micro energy grid at time t, respectively. c , η d ,ES max They are charging efficiency, discharging efficiency and energy storage capacity respectively; The power balance equation of the micro energy grid is: P esd,t +P mg,in,t +P pvuse,t =P ch,t +P load,t +P fur,t (6i) -(1-n i,t,s )M≤P mg,in ≤(1-n i,t,s )M(6j) P mg,in,t is the power transmitted from the distribution network to the micro energy network, P load,t is the electric power of the micro energy grid at that moment; 6) Establish distribution network power flow model The Distflow power flow model is used to model the distribution network: U min ≤U i,t,s ≤U max i∈Ω n (7c) 0≤P cut,i,t ≤(1-n i,t,s )(P LOAD,i,t,s +P ev,i,t )s∈(Ω s ∪Ω f )(7i) Formula (7a) and (7b) are the active and reactive power balance constraints of the node, P in,i,t,s ,Q in,i,t,s ,P LOAD,i,t,s ,Q LOAD,i,t,s ,P ij,t,s ,Q ij,t,s , are respectively the active and reactive power injected into node i at time t under scenario s, the active load and reactive load of the node, and the active and reactive power transmitted from node i to node j. Formula (7e) is the DistFlow power flow model, where U i,t,s ,M,r L,ij 、x L,ij , V0 are the voltage of node i at time t in scenario s, a sufficiently large positive number, the impedance and inductance of the line between nodes ij, and the calibrated voltage of the distribution system. Formula (7c) is a safety constraint to ensure that the voltage of each node is within the allowable range, where U min and U max are the upper and lower voltage limits allowed by the node, and equation (7d) is the capacity constraint of the line, where S ij is the maximum capacity of the transmission line. When the distribution system is difficult to maintain safety and the power supply is insufficient, load shedding is required, as shown in (7f). cut,i,t,s is the active power cut by node i at time t in scenario s, and equations (7g)-(7h) are the active and reactive powers injected into the node, where P ev,i,t,s is the power of the electric vehicle charging station at node i at time t in scenario s.

4. The "three micro" integrated active distribution network-transportation network planning method considering high power supply reliability according to claim 3 is characterized in that: The step 3 of establishing the distribution network and transportation network collaborative planning model is specifically as follows: obj=C PN +C DN (8a) C Pinv =mC f +nC c +m CP C CP +n BC C BC +n B C B (8e) In the objective function, C PN and C DN are the total cost of the transportation network and the total cost of the distribution network, respectively, time is the time cost, with the average hourly wage H W As a quantification, C gap Fines for electric vehicle energy satisfaction, F n is the unit fine amount, r is the discount rate, x is the planning period, C Pinv is the investment cost of the transportation network, including the cost of charging stations C f ,Cc,Cc,Cc,C,c, CP , Investment cost of battery charger in battery swap station C BC , the battery investment cost of the battery swap station C B ,m,n,m CP ,n BC ,n B are the number of investments in charging stations, battery swap stations, charging piles, chargers, and batteries, respectively. Dinv is the investment cost of the distribution network, c in,wt 、c in,pv 、c in,es 、c in,sop 、c in,l 、c in,svg Represents the unit investment cost of wind power, photovoltaic power, energy storage battery, soft switch, line length and SVG, c esub,i 、c sub,i 、c in,rcs Respectively represent the expansion cost, new construction cost and RCS installation cost of the substation; Ω wt ,Ω pv ,Ω es ,Ω sop ,Ω svg ,Ω L ,Ω sub ,Ω esub ,Ω ECS Represents the construction lines and nodes of wind power, photovoltaic power, energy storage battery, soft switch, SVG, line, new substation, expandable substation and RCS respectively; WT i PV i ,ES i SVG i 、x sub，ei 、x sub，i Respectively represent the installed capacity of wind power, photovoltaic power, energy storage, and SVG at the i-th node, and whether to expand or build a new substation at the node; SOP ij , l ij 、x ij 、x rcs，ij are binary variables representing the capacity of SOP installed on line ij, the length of the line, whether line ij is built, and whether RCS is installed. wtgu,i,t , P wtpv,i,t , P dis,i,t , P ch,i,t , P sub,i,t , P cut,i,t are the power of wind and solar power abandonment, battery discharge, battery charging, substation power purchase and load shedding at node i at time t; c op,wt 、c op,pv are the annual maintenance costs of wind power and photovoltaic power under unit capacity respectively; f wt 、f pv 、f n 、f op,es are the unit wind curtailment, solar curtailment, load shedding and battery degradation costs respectively; p t and c op,ij is the electricity price of the substation at time t and the annual maintenance cost of each line.

5. According to claim 4, the "three micro" integrated active distribution network-transportation network planning method considering high power supply reliability is characterized in that: The distribution system operator also obtains electricity from the main grid. The expansion plan includes the construction of new substations and the expansion of existing substations. The construction, expansion and operation constraints of substations include: Respectively represent the output active and reactive power of the substation, are the newly built, initial and expandable substation capacities, respectively, and (3k) and (3l) represent the power output limitations of the substation.

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