Flexible interconnection power distribution network and charging facility cooperative expansion planning method

Abstract

The invention is suitable for the technical field of power distribution networks, and provides a flexible interconnected power distribution network and charging facility collaborative expansion planning method, which comprises the following steps: constructing a semi-dynamic traffic flow model according to obtained traffic network basic parameters and electric vehicle inventory data; based on the semi-dynamic traffic flow model, an electric vehicle charging and discharging space-time adjustment model is established in combination with electric vehicle charging and discharging adjustment constraints under the influence of the traffic flow; according to the obtained basic parameters of the power distribution network, a semi-dynamic traffic flow model and the electric vehicle charging and discharging space-time adjustment model, constructing a flexible interconnection power distribution network and charging facility collaborative expansion planning model; and converting and solving the collaborative expansion planning model to obtain a final planning scheme of the flexible interconnection power distribution network and the charging facility. The problems that power grid and traffic network coupling operation cannot be finely modeled and the multi-resource flexible adjustment capability cannot be fully excavated in power distribution network extension planning are solved, and the scientificity of a planning scheme and the power grid operation reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network, in particular to a flexible interconnected power distribution network and charging facility collaborative expansion planning method. BACKGROUND

[0002] With the promotion of new energy system and new power system construction, the development of power distribution network puts forward the requirement of building a safe and efficient, clean and low carbon, flexible and intelligent new power distribution system, and promotes the transformation of power distribution network from a single power supply and distribution service subject to a source network load storage resource efficient configuration platform. At the same time, the vehicle network interaction technology is increasingly mature, and electric vehicles, as an important force to promote the construction of new power system, have a significant impact on the operation of power distribution network.

[0003] The existing power distribution network planning method has low calculation efficiency, large communication overhead and other problems in the process of dealing with power and traffic coupling system, especially in the high-dimensional feature and complex tree structure scene, the privacy comparison operation burden is heavy, and the operation simulation cannot be modeled in detail, the charging and discharging adjustment potential of different types of electric vehicles under the influence of traffic flow cannot be scientifically considered, and the collaborative interaction of multiple resources on the source network load storage side of power distribution network cannot be realized.

[0004] In view of this, a flexible interconnected power distribution network and charging facility collaborative expansion planning method is proposed. SUMMARY

[0005] The present application provides a flexible interconnected power distribution network and charging facility collaborative expansion planning method, which is used to solve the problem that the existing method cannot scientifically consider the charging and discharging adjustment potential of different types of electric vehicles under the influence of traffic flow, and is difficult to realize the collaborative interaction of multiple resources on the source network load storage side of power distribution network.

[0006] The present application provides a flexible interconnected power distribution network and charging facility collaborative expansion planning method, which comprises: According to the obtained traffic network basic parameters and electric vehicle ownership data, a semi-dynamic traffic flow model is constructed; Based on the semi-dynamic traffic flow model, combined with the electric vehicle charging and discharging adjustment constraint under the influence of traffic flow, an electric vehicle charging and discharging space-time adjustment model is established; According to the obtained power distribution network basic parameters, the semi-dynamic traffic flow model and the electric vehicle charging and discharging space-time adjustment model, a flexible interconnected power distribution network and charging facility collaborative expansion planning model is constructed; The collaborative expansion planning model is transformed and solved to obtain the final planning scheme of flexible interconnected power distribution network and charging facility.

[0007] Further, the expression of the semi-dynamic traffic flow model is: in: For time period Section Predicted travel time Smoothing coefficient The weighting of historical travel time on current forecasts. For time period Section The actual travel time. For road section The free circulation time, For time period Section Traffic flow, For road section Theoretical accessibility, It is the set of all road segments in the transportation network. The set of all runtime segments considered in the planning. For time period From the starting point To the finish line The travel demand was allocated to the route Traffic, For time period From the starting point To the finish line Overall travel demand A route selection sensitivity parameter characterizes the driver's sensitivity to travel time. For time period path Predicted travel time For the starting point of the connection To the finish line The set of all feasible paths, The set of all pairs of origin-to-end points. For time period Section Predicted traffic flow For path and road segment related variables, if path Included road sections The value is 1 if the value is 1, otherwise it is 0. For time period path Total travel time For the starting point To the finish line of Maximum travel time tolerance factor For path Baseline travel time under free-flow conditions.

[0008] Furthermore, the electric vehicle charging and discharging spatiotemporal adjustment model includes an electric vehicle charging station selection strategy and a charging and discharging power adjustment strategy based on multiple charging modes; the multiple charging modes include a rated power charging mode, a charging power-only adjustable mode, and a charging and discharging power adjustable mode.

[0009] Furthermore, the electric vehicle charging station selection strategy includes: Based on the vehicle spatiotemporal distribution and traffic state information output by the semi-dynamic traffic flow model, the charging and discharging price ranges for each charging station at different times are set. Based on the charging and discharging price range, the distribution of travel demand at the origin and destination points of electric vehicles is adjusted to generate charging location transfer demand; The charging location relocation demand is constrained based on a preset user satisfaction threshold, and a final electric vehicle charging station selection strategy is generated.

[0010] Furthermore, the charging and discharging power regulation strategy includes: Based on the charging station selection results determined by the charging station selection strategy, the quantity distribution of different types of electric vehicles in each charging station is obtained. Based on the safe operating range of the state of charge of electric vehicle batteries, set state of charge constraints for each type of electric vehicle. Based on the capacity limitations of charging station infrastructure, an upper limit constraint is set on the number of electric vehicles that can be charged at each charging station simultaneously. Based on the state of charge constraints and the upper limit constraints on the number of electric vehicles, the charging and discharging power of different types of electric vehicles in each charging station is calculated, and an electric vehicle charging and discharging power adjustment strategy is generated.

[0011] Furthermore, the collaborative expansion planning model takes minimizing the annual comprehensive cost of the distribution network during the planning period as its objective function; the annual comprehensive cost includes the annual planned investment cost of equipment on the source, grid, load and storage sides and the annual operation simulation cost of the distribution network under typical scenarios.

[0012] Furthermore, the expression for the objective function is: in: The annual planned investment cost, The annual operating simulation cost; Present value to annual value factor : in: For the discount rate, For device type Service life, For substation related equipment, For line-related equipment, For photovoltaic equipment, A static var compensator (SVC) is a device used for reactive power regulation in power distribution networks. An energy storage system is a device used for storing and flexibly charging and discharging electrical energy. Intelligent soft switches are devices used to achieve flexible interconnection of power distribution networks. Electric vehicle charging stations refer to various charging facilities that provide power to electric vehicles.

[0013] Furthermore, annual planned investment costs Annual operating simulation cost The expressions are as follows: in: , , , , , , These are the candidate node / line sets for the corresponding equipment to be planned. Number the nodes. For connecting nodes and nodes The route, Indicates whether it is in the candidate node Upgrade / build a new substation To indicate whether a new line has been built. , Indicates at node The number of photovoltaic units built at the site. , These represent the nodes respectively. The rated power and rated capacity of the energy storage system to be built at the site, , These represent the nodes respectively. The number of ordinary charging piles and the number of smart charging piles built in the area. Indicates at node The number of static var compensator units installed at the site For the line The number of intelligent soft-switching units deployed at the site For the node The unit cost of expanding / building a substation. For the construction of new lines unit length cost, For nodes The unit capacity cost of building a photovoltaic unit, , For the node The unit power cost and unit capacity cost of investing in and constructing energy storage systems , For the node The cost of building a regular charging station and a smart charging station For the node The unit capacity cost of deploying a static var compensator unit. For the line The unit capacity cost of constructing a smart soft-switching unit at the site; in: For maintenance costs, , , , Substations Corresponding to the unit operation and maintenance costs of various types of equipment, , The lines are respectively Corresponding to the unit operation and maintenance costs of various types of equipment, It is a collection of typical scenarios. Typical scenario The probability of occurrence The number of days is used to convert daily maintenance costs into annual maintenance costs. For electricity purchase costs, For the scene Time period From node The electricity price purchased from the superior power grid, For the scene Time period From node The active power purchased by the substation, It is the set of all runtime segments throughout the day; To cover the cost of light curtailment, For the scene Time period From node The unit penalty cost for solar power curtailment; For the scene Time period From node The amount of solar power curtailed; For network loss costs, For the scene Time period The unit cost of power grid losses, For the scene Time period line The square value of the current, For the line The resistance.

[0014] Furthermore, the constraints of the collaborative extended planning model include the construction and operation constraints of distribution network-side equipment; the construction and operation constraints include one or more of the following: photovoltaic power generation equipment construction constraints, static var compensator construction constraints, energy storage system construction constraints, charging pile construction constraints, and smart soft switch construction constraints; the energy storage system construction constraints include system-level power and capacity upper limit constraints, node-level power and capacity installation upper limit constraints, and single-point energy storage power and capacity ratio constraints.

[0015] Furthermore, the collaborative expansion programming model is transformed and solved as follows: The original problem is transformed into a two-level optimization model consisting of an upper-level planning problem and a lower-level operational simulation problem by using the decomposition and iteration approach, and then solved iteratively. The second-order cone relaxation method is used to perform convex relaxation on the power flow constraints of the distribution network, and the model is transformed into a mixed integer second-order cone programming problem to be solved.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This invention constructs a semi-dynamic traffic flow model based on acquired basic traffic network parameters and electric vehicle (EV) ownership data. Based on this model, and combined with EV charging and discharging regulation constraints under the influence of traffic flow, a spatiotemporal regulation model for EV charging and discharging is established. Using the acquired distribution network parameters, the semi-dynamic traffic flow model, and the EV charging and discharging spatiotemporal regulation model, a collaborative expansion planning model for flexible interconnected distribution networks and charging facilities is constructed. The collaborative expansion planning model is then transformed and solved to obtain the final planning scheme for the flexible interconnected distribution network and charging facilities. Through this approach, the flexible resources of the power-transport coupled system operation simulation, including intelligent soft switches, energy storage devices, distributed photovoltaics, static var compensators (SVCs), and electric vehicles, are comprehensively considered. This solves the technical problems of traditional distribution network expansion planning, which fails to accurately model the coupled operation of the power grid and transportation network and cannot fully exploit the flexible regulation capabilities of multiple resources. It achieves collaborative optimization planning with the goal of minimizing the annual comprehensive operating cost of the distribution network, significantly improving the scientific nature of the planning scheme and the reliability of power grid operation. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of an embodiment of a collaborative expansion planning method for flexible interconnected power distribution networks and charging facilities in this invention; Figure 2 This is a schematic diagram of an embodiment of the electric vehicle charging station selection strategy in this invention; Figure 3 This is a schematic flowchart of an embodiment of the charging and discharging power regulation strategy in this invention; Figure 4 This is an improved 24-node system topology diagram used in the implementation of this invention. Figure 5 This is a topology diagram of a 29-node traffic system during the implementation of this invention. Figure 6 This is a typical scene diagram of wind and solar load during the implementation of this invention. Figure 7 The diagram shows the planning results for the coordinated expansion of FIDN and charging facilities, taking into account the spatiotemporal adjustability of EV charging and discharging. Figure 8 Here is a diagram showing the charging electricity prices at each charging station in Case 1; Figure 9 Here is a diagram showing the discharge electricity price for each charging station in Case 1; Figure 10 This is a diagram showing the charging and discharging load distribution of each charging station in various typical scenarios of Case 1. Detailed Implementation

[0018] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] Example 1 The implementation method in this embodiment can be implemented in a system, on a server, or on a terminal; no specific limitation is made. The method in this application will be described below from the perspective of system implementation. Please refer to... Figures 1 to 3 The method provided in this application includes the following steps: S1. Based on the obtained basic parameters of the traffic network and the data on the number of electric vehicles, construct a semi-dynamic traffic flow model; Data on basic traffic network parameters and electric vehicle ownership and travel characteristics were obtained from multiple sources, including urban planning departments, traffic management departments, power grid companies, and electric vehicle manufacturers. The basic traffic network parameters include road network topology (nodes, road segments, connections), road segment physical attributes (length, number of lanes, class), free-flow speed, and maximum capacity. These static parameters form the physical basis of traffic flow simulation. Data on electric vehicle ownership and travel characteristics includes the ownership scale, proportion, and average daily number of trips of various types of electric vehicles within the planning area; as well as the origin-destination matrix and travel time distribution characteristics obtained through historical data analysis or travel surveys. This data reflects the travel demand and spatiotemporal distribution patterns of electric vehicles. By integrating the above multi-dimensional and multi-source data, a comprehensive and accurate joint database of traffic and electric vehicles was formed.

[0020] S2. Based on a semi-dynamic traffic flow model and combined with the charging and discharging regulation constraints of electric vehicles under the influence of traffic flow, a spatiotemporal regulation model for charging and discharging of electric vehicles is established. The expression for the semi-dynamic traffic flow model is: in: For time period Section Predicted travel time Smoothing coefficient The weighting of historical travel time on current forecasts. For time period Section The actual travel time. For road section The free circulation time, For time period Section Traffic flow, For road section Theoretical accessibility, It is the set of all road segments in the transportation network. The set of all runtime segments considered in the planning. For time period From the starting point To the finish line The travel demand was allocated to the route Traffic, For time period From the starting point To the finish line Overall travel demand A route selection sensitivity parameter characterizes the driver's sensitivity to travel time. For time period path Predicted travel time For the starting point of the connection To the finish line The set of all feasible paths, The set of all pairs of origin-to-end points. For time period Section Predicted traffic flow For path and road segment related variables, if path Included road sections The value is 1 if the value is 1, otherwise it is 0. For time period path Total travel time For the starting point To the finish line of Maximum travel time tolerance factor For path Baseline travel time under free-flow conditions.

[0021] S3. Based on the obtained basic parameters of the distribution network, the semi-dynamic traffic flow model and the spatiotemporal adjustment model of electric vehicle charging and discharging, construct a collaborative expansion planning model for flexible interconnected distribution networks and charging facilities; Based on the above semi-dynamic traffic flow model, and combined with the electric vehicle charging and discharging adjustment constraints under the influence of traffic flow, a spatiotemporal adjustment model for electric vehicle charging and discharging is established. The spatiotemporal adjustment model for electric vehicle charging and discharging includes electric vehicle charging station selection strategy and charging and discharging power adjustment strategy based on multiple charging modes. Among them, multiple charging modes include rated power charging mode, adjustable charging power mode only, and adjustable charging and discharging power mode.

[0022] In this embodiment, the electric vehicle charging station selection strategy includes the following: S311. Based on the vehicle spatiotemporal distribution and traffic state information output by the multi-period coupled traffic flow model, set the charging and discharging price range for each charging station at different times. S312. Adjust the distribution of travel demand at the origin and destination of electric vehicles according to the charging and discharging price range to generate charging location transfer demand; S313. Constrain the charging location relocation demand based on a preset user satisfaction threshold, and generate the final electric vehicle charging station selection strategy.

[0023] Specifically, based on congestion information and grid status output from traffic flow models, service price ranges are set for different charging stations at different times. Higher prices are used to suppress charging demand in congested areas or during periods of heavy grid load, while lower prices are used to attract users to charge in idle areas or during periods of light grid load. Based on this price signal, the willingness of users to change their preset charging destinations is quantitatively assessed, and the specific number of transfer demands is calculated. User satisfaction is used as a constraint to ensure that only charging transfers where the satisfaction increase due to electricity price discounts exceeds a minimum threshold are actually executed, thus optimizing system operation while ensuring a basic user experience. This strategy is implemented through the following mathematical formula: The pricing mechanism for charging stations is implemented by setting upper and lower limits for their service prices: in: , Time periods area The prices for charging and discharging services, , These represent the lower and upper limits of the price for electric vehicle charging, respectively. , These represent the lower and upper limits of the price for electric vehicle discharge, respectively. For transportation area collection, It is a set of time intervals.

[0024] Guided by price signals, the distribution of travel demand for electric vehicles will dynamically adjust, which can be expressed mathematically as follows: in: For time period ,starting point To the finish line The number of electric vehicle travel demands. For time period ,starting point To the finish line The initial number of travel demands, For time period From the starting point Through the region To the finish line The amount of demand shift, For time period From the starting point To the finish line Then transfer to the region The amount of demand shift, For the region Other transportation areas outside, For the first The set of origin-to-end points for electric vehicles. This is an identifier for electric vehicle types.

[0025] To ensure a good charging experience for users, the aforementioned demand shift is constrained by a satisfaction threshold: in: For time period From the region To the area User satisfaction, This is the minimum threshold for user satisfaction; only when user satisfaction is greater than or equal to this threshold will the shift in charging demand actually occur.

[0026] In this embodiment, the charging and discharging power adjustment strategy includes the following: S321. Based on the charging station selection results determined by the charging station selection strategy, obtain the quantity distribution of different types of electric vehicles in each charging station; S322. Set state-of-charge constraints for each type of electric vehicle based on the safe operating range of the electric vehicle battery's state of charge; S323. Based on the capacity limitations of charging station infrastructure, set an upper limit constraint on the number of electric vehicles that can be charged at each charging station simultaneously; S324. Based on the state of charge constraint and the upper limit constraint on the number of electric vehicles, calculate the charging and discharging power of different types of electric vehicles in each charging station, and generate an electric vehicle charging and discharging power adjustment strategy.

[0027] Specifically, based on the charging station selection results mentioned above, the number of electric vehicles with different access modes at each charging station was counted; safe operating limits were set for the state of charge of all vehicles, and the physical limitations of the charging station infrastructure, i.e., the number of charging piles, determined the upper limit of vehicles that could be served simultaneously; finally, combining the above quantitative information, safety constraints, and capacity constraints, the adjustable charging and discharging power range of each type of electric vehicle group in each time period was calculated in real time using the energy conservation equation, thereby generating a power regulation strategy that both meets user needs and responds to grid dispatch commands. This strategy is implemented through the following set of mathematical formulas: To ensure that the electric vehicle's battery operates within a safe range, its state of charge upon arrival at the charging station must meet the following requirements: in: For the first electric vehicles during the period State of charge upon arrival at the charging station For the first electric vehicles during the period From the starting point State of charge at departure State of charge consumption per unit distance traveled by an electric vehicle. For road section Mileage Representing the Electric vehicles at the starting point To the finish line Did the route pass through this section? , Starting point To the finish line Electric vehicles gathered during the trip A collection of road segments. This represents the minimum safe value for the state of charge of an electric vehicle.

[0028] The capacity constraints for charging station infrastructure are as follows: For the first access mode (rated power charging), the charging power is calculated as follows: in: This indicates the charging power of an electric vehicle in rated power charging mode. For charging stations The maximum power output of the charging stations in the area, and They represent charging stations time The number of electric vehicles charging simultaneously / existing in the first type of charging mode; Improve electric vehicle charging efficiency; The rated capacity of the battery for a single electric vehicle; The average increase in state of charge after charging an electric vehicle; Transportation network nodes With flexible interconnected distribution network nodes There is a coupling relationship between them.

[0029] The second access mode (i.e., the access mode with adjustable charging power only) and the third access mode (i.e., the access mode with adjustable charging and discharging power) are described by a set of complex equations regarding the changes in the energy state of the battery population and the power constraints: in: Indicates the charging mode of electric vehicles. This indicates a charging mode where only the charging power is adjustable. This indicates a charging mode where both charging and discharging power are adjustable; charging station Contains the first Total battery capacity of electric vehicles in charging-like mode The total battery capacity and total charging / discharging power of electric vehicles in the previous period and Total battery power at arrival / departure / Decide, For electric vehicle discharge efficiency; and These represent the maximum / desired state of charge of the electric vehicle, respectively. For time period path The duration of participation in charging (discharging) power regulation is The number of electric vehicles.

[0030] In this embodiment, the collaborative extended planning model takes minimizing the annual comprehensive cost of the distribution network during the planning period as its objective function; the annual comprehensive cost includes the annual planned investment cost of equipment on the source, grid, load and storage sides and the annual operation simulation cost of the distribution network under typical scenarios.

[0031] The expression for the objective function is: in: The annual planned investment cost, The annual operating simulation cost; Present value to annual value factor : in: For the discount rate, For device type Service life, For substation related equipment, For line-related equipment, For photovoltaic equipment, A static var compensator (SVC) is a device used for reactive power regulation in power distribution networks. An energy storage system is a device used for storing and flexibly charging and discharging electrical energy. Intelligent soft switches are devices used to achieve flexible interconnection of power distribution networks. Electric vehicle charging stations refer to various charging facilities that provide power to electric vehicles.

[0032] Annual planned investment cost Annual operating simulation cost The expressions are as follows: in: , , , , , , These are the candidate node / line sets for the corresponding equipment to be planned. Number the nodes. For connecting nodes and nodes The route, Indicates whether it is in the candidate node Upgrade / build a new substation To indicate whether a new line has been built. , Indicates at node The number of photovoltaic units built at the site. , These represent the nodes respectively. The rated power and rated capacity of the energy storage system to be built at the site, , These represent the nodes respectively. The number of ordinary charging piles and the number of smart charging piles built in the area. Indicates at node The number of static var compensator units installed at the site For the line The number of intelligent soft-switching units deployed at the site For the node The unit cost of expanding / building a substation. For the construction of new lines unit length cost, For nodes The unit capacity cost of building a photovoltaic unit, , For the node The unit power cost and unit capacity cost of investing in and constructing energy storage systems , For the node The cost of building a regular charging station and a smart charging station For the node The unit capacity cost of deploying a static var compensator unit. For the line The unit capacity cost of constructing a smart soft-switching unit at the site; in: For maintenance costs, , , , Substations Corresponding to the unit operation and maintenance costs of various types of equipment, , The lines are respectively Corresponding to the unit operation and maintenance costs of various types of equipment, It is a collection of typical scenarios. Typical scenario The probability of occurrence The number of days is used to convert daily maintenance costs into annual maintenance costs. For electricity purchase costs, For the scene Time period From node The electricity price purchased from the superior power grid, For the scene Time period From node The active power purchased by the substation, It is the set of all runtime segments throughout the day; To cover the cost of light curtailment, For the scene Time period From node The unit penalty cost for solar power curtailment; For the scene Time period From node The amount of solar power curtailed; For network loss costs, For the scene Time period The unit cost of power grid losses, For the scene Time period line The square value of the current, For the line The resistance.

[0033] Specifically, the constraints of the collaborative extended planning model include the construction and operation constraints of distribution network-side equipment; equipment construction and operation constraints include one or more of the following: photovoltaic power generation equipment construction constraints, static var compensator construction constraints, energy storage system construction constraints, charging pile construction constraints, and smart soft switch construction constraints; energy storage system construction constraints include system-level total power and capacity upper limit constraints, node-level power and capacity installation upper limit constraints, and single-point energy storage power and capacity ratio constraints. The specific expressions are as follows: Constraints on new equipment construction and expansion: PVG construction constraints: in: , They are nodes The lower and upper limits of the number of PVG projects to be built. For nodes Maximum load at the location, This represents the maximum penetration rate of distributed generation (DG) in a flexible interconnected distribution network (FIND). The unit installation capacity of PVG.

[0034] SVC investment constraints: in: , They are nodes The lower and upper limits of the number of SVCs to be invested in. This represents the upper limit for the number of SVCs that can be built in FIDN.

[0035] ESS Construction Constraints: in: , They are nodes The maximum power and capacity that the ESS allows to be installed, This is the maximum energy storage rate factor. , These represent the maximum power and capacity of the ESS installed in the FIDN.

[0036] Constraints on the construction of charging stations: in: , They are nodes The lower and upper limits for the number of ordinary charging piles to be built. This represents the upper limit for the number of ordinary charging piles that can be deployed in FIDN. , They are nodes The lower and upper limits for the number of smart charging piles to be built. This represents the upper limit for the number of smart charging piles that can be deployed in FIDN.

[0037] SOP (Start of Production) Construction Constraints: in: The decision variable for whether to build a new connecting line at the SOP installation location. , They are nodes The lower and upper limits of the number of SOPs to be constructed. The unit installation capacity for the SOP.

[0038] Network topology constraints: in: For the decision variable of whether or not to build the connecting line, branch road The virtual trend on the internet , These represent the number of load nodes and the number of substation nodes to be expanded, respectively. This is the set of nodes for substations that need to be expanded.

[0039] FIDN operational constraints: Substation node power constraints: in: , They are nodes The rated capacity of transformers in existing substations and substations to be built.

[0040] There are constraints in the voltage regulating transformer: in: For time period node The square of the voltage amplitude, This refers to the tap position of an on-load tap-changing transformer. and These are its upper and lower limits, respectively; This refers to the voltage change value for each tap adjustment of an on-load tap-changing transformer. This is the rated voltage value.

[0041] Node voltage and branch current constraints: in: , These are the lower and upper limits of the node voltage amplitude, respectively; branch road The square of the current amplitude, branch road The upper limit of the current amplitude.

[0042] Current constraints: in: , Branch roads exist Active and reactive power transmitted at all times; , They are nodes exist The active and reactive power injected at all times; For the line Length; , These are the unit resistance and unit reactance of the circuit, respectively.

[0043] SOP execution constraints: in: , They are nodes SOP at The active power and reactive power emitted at all times; For nodes The loss factor of the SOP converter, For nodes Active power loss at SOP.

[0044] PVG operational constraints: in: and These are the upper and lower limits of the PV power factor tangent, respectively.

[0045] SVC operational constraints: in: The installed capacity of the SVC unit.

[0046] ESS operational constraints: in: and These represent the charge / discharge efficiencies of the ESS.

[0047] S4. Transform and solve the collaborative expansion planning model to obtain the final planning scheme for flexible interconnected distribution networks and charging facilities.

[0048] In this embodiment, based on the second-order cone relaxation method, convex relaxation is applied to the nonlinear constraints related to power flow in the distribution network, transforming the original problem into a more easily solvable mixed-integer second-order cone programming problem. Simultaneously, using the decomposition and iteration approach, the flexible interconnected distribution network and charging facility collaborative expansion planning problem with embedded power and communication system operation simulation is decomposed into an upper-level planning problem and a lower-level operation simulation problem. Through alternating iterations of these two layers of problems, as follows: 1. The original problem is transformed into a two-level optimization model consisting of a higher-level planning problem and a lower-level simulation problem by adopting the decomposition and iteration approach, and then iteratively solved. 2. The second-order cone relaxation method is used to perform convex relaxation on the power flow constraints of the distribution network, and the model is transformed into a mixed integer second-order cone programming problem to be solved.

[0049] First, a second-order cone relaxation method is used to transform the power flow constraints of the distribution network into a convex relaxation problem, thus converting the collaborative expansion planning model into a mixed-integer second-order cone programming problem. The upper-level FIDN and charging facility collaborative expansion planning problem, given the known time-space load of electric vehicle charging and discharging, determines an optimized planning scheme and passes it to the lower level. The lower-level operation simulation problem, based on the planning results from the upper level, optimizes the operation simulation cost of the power-transport coupling system under various scenarios, solves for the time-space load distribution of electric vehicle charging and discharging, and feeds it back to the upper level. The two-layer problem is iterated alternately until the relative error of the annual operation simulation cost of the FIDN is less than a set threshold, at which point the iteration stops. Finally, a flexible interconnected distribution network and charging facility collaborative expansion planning scheme is obtained, which enables flexible interaction of multiple resources on the source, grid, load, and storage sides, improves the economic efficiency of distribution network planning, and promotes the rational and orderly grid connection of electric vehicles.

[0050] The above method will be described in detail below with specific examples: 1. The test system is as follows: Improvements to 24-node systems, such as Figure 4 As shown, the topology diagram of the 29-node transportation network is as follows: Figure 5As shown, the relevant planning parameters are set as follows: The initial capacity of the two existing substations is 7.5 MV·A, with an expandable capacity of 6 MV·A. The investment cost is 1 million yuan, and the annual operation and maintenance cost is 10,000 yuan. The rated capacity of the substation to be built is 7.5 MV·A, the investment cost is 1.4 million yuan, and the annual operation and maintenance cost is 15,000 yuan. The resistance and reactance per unit length of the line are 0.307 Ω / km and 0.380 Ω / km, respectively. The line capacity is 3.94 MV·A, the line investment cost is 105,140 yuan / km, and the annual operation and maintenance cost is 3,000 yuan / line. The selected installation lines are listed in the SOP (Standard Operating Procedure). For lines 2-3, 4-7, 5-6, and 7-11, the unit capacity construction cost is 0.1 million yuan / kV·A, the unit installed capacity is 100kV·A, and the maximum allowed installed capacity for a single line is 1MV·A; for PV candidate installation nodes 2, 5, 9, and 17, the unit capacity construction cost is 0.43 million yuan / kV·A, the unit installed capacity is 100kV·A, and the maximum number of nodes that can be installed at a single node is 50; for SVC candidate installation nodes 14 and 19, the unit capacity construction cost is 0.7 million yuan / kV·A, the unit installed capacity is 100kV·A, and the maximum number of nodes that can be installed at a single node is 2. The proposed locations for ESS deployment are nodes 4, 5, 7, and 12. The unit capacity deployment cost is RMB 1,500 / kW·h, and the unit power deployment cost is RMB 1,000 / kW. The maximum capacity / power of a single node is 1MW·h / 0.5MW. The maximum capacity / power of ESS deployment in FIDN is 4MW·h / 1.6MW. The construction cost of a 60kW standard charging pile is 17,800 yuan per unit, and the construction cost of a 60kW intelligent and orderly charging pile is 44,500 yuan per unit. The candidate construction locations are 4, 5, 7, 17, 20, and 26, corresponding to FIDN nodes 2, 5, 10, 11, 17, and 18. The maximum number of standard charging piles that can be installed at a single charging station is 40, and the maximum number of intelligent and orderly charging piles is 60. The FIDN planning period is 15 years, the discount rate is 5%, the baseline value for travel vehicles is set at 500 vehicles, the GV:EV ratio is 7:3, the ratio of electric private cars:electric taxis:electric buses is 6:3:1, and the iteration error threshold is set at 0.8%. The typical scenario shows the distribution of photovoltaic output and load in each functional area as follows: Figure 6 As shown.

[0051] 2. Comparative Analysis of Planning Structures: To verify the effectiveness of the method of this invention, the following three cases are set up for comparative analysis: Case 1: Planning for the coordinated expansion of FIDN and charging facilities with spatiotemporally adjustable EV charging and discharging (using the method of this invention); Case 2: Planning for coordinated expansion of FIDN and charging infrastructure without considering adjustable EV charging and discharging; Case 3: Planning for the coordinated expansion of ordinary power distribution networks and charging facilities, considering the adjustable charging and discharging of EVs but not the SOP access.

[0052] The comprehensive planning costs for each case are shown in Table 1: Table 1. Comprehensive Planning Costs for Each Case Table 1 shows the comprehensive investment cost of the distribution network for each case. It can be seen that the comprehensive cost of Case 1 is the lowest, at only 49.3979 million yuan. Compared with Case 2, which does not consider the charging and discharging regulation capability of EVs, and Case 3, which does not consider the SOP access, it is reduced by 4.8% and 4.9% respectively. This shows that fully considering the flexible regulation capability of multiple resources on the source, grid, load and storage sides in the planning can significantly improve the comprehensive investment efficiency of the distribution network planning scheme.

[0053] Table 2 Planning Results of Case 1 Table 2 shows the FIDN planning results of Case 1. Figure 7 The topology diagram of the planning results for Case 1 is shown. The results indicate that a new substation is needed to meet the FIDN conventional load and EV load demands under scenarios with low PV output. SVC, PV, and SOP can all provide reactive power support, but to improve planning economics, SVC construction is unnecessary. Node 4 is relatively far from power sources such as PV, so the SOP between nodes 4 and 7 is fully deployed. Considering the actual charging demand of EVs, compared to nodes 5 and 10 in the commercial area of ​​the transportation network, nodes 2 and 11 in the office area and nodes 17 and 18 in the residential area have a greater number of charging piles installed.

[0054] Figure 8 and Figure 9 The example shows the EV charging and discharging electricity prices at each charging station in Case 1. Figure 10 The presentation showcases the EV charging and discharging loads at various charging stations under typical scenarios. By adjusting the EV charging location and charging / discharging power, the peak-to-peak phenomenon was mitigated to some extent, promoting the rational and orderly grid connection of EVs. However, Case 2 did not fully utilize the adjustable potential of EV charging and discharging, thus requiring more power regulation equipment to maintain the stable operation of the power grid.

[0055] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0056] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for collaborative expansion planning of a flexible interconnected power distribution network and charging facilities, characterized in that, The application relates to a planning method for a flexible interconnected distribution network and charging facilities. According to the obtained traffic network basic parameters and electric vehicle ownership data, a semi-dynamic traffic flow model is constructed; Based on the semi-dynamic traffic flow model, an electric vehicle charging and discharging space-time regulation model is established in combination with the electric vehicle charging and discharging regulation constraints under the influence of traffic flow; According to the obtained distribution network basic parameters, the semi-dynamic traffic flow model and the electric vehicle charging and discharging space-time regulation model, a flexible interconnected distribution network and charging facility collaborative expansion planning model is constructed; The collaborative expansion planning model is transformed and solved to obtain the final planning scheme of the flexible interconnected distribution network and charging facilities. 2.The method of claim 1, wherein, The expression of the semi-dynamic traffic flow model is as follows: wherein: is the prediction travel time of the link is the actual travel time of the link is the smoothing coefficient is the weight of the influence of the historical travel time on the current prediction is the actual travel time of the link is the free flow travel time of the link is the traffic flow of the link is the theoretical travel capacity of the link is the set of all links in the traffic network is the set of all operation periods considered in the planning is the flow of the trip from the origin to the destination allocated to the path is the total trip demand from the origin to the destination is the path choice sensitivity parameter, representing the sensitivity of the driver to the travel time is the predicted travel time of the path is the set of all feasible paths connecting the origin to the destination is the set of all origin-destination pairs is the predicted traffic flow of the link is the path-link incidence variable, taking the value of 1 if the path contains the link , otherwise 0 is the total travel time of the path is the maximum travel time tolerance coefficient of the for the origin to the destination is the reference travel time of the path in the free flow state.​​​​​​​​​​​​​​​​​​ 3.The method of claim 1, wherein, The electric vehicle charging and discharging space-time regulation model comprises an electric vehicle charging station selection strategy based on multiple charging modes and a charging and discharging power regulation strategy; the multiple charging modes comprise a rated power charging mode, a charging power only adjustable mode and a charging and discharging power adjustable mode. 4.The method of claim 3, wherein, The electric vehicle charging station selection strategy comprises the following steps: Based on the vehicle space-time distribution and traffic state information output by the semi-dynamic traffic flow model, the charging and discharging price intervals of each charging station in different time periods are set; According to the charging and discharging price intervals, the electric vehicle origin-destination travel demand distribution is adjusted to generate charging location transfer demand; Based on a preset user satisfaction threshold, the charging location transfer demand is constrained to generate a final electric vehicle charging station selection strategy.

5. The method of claim 3, wherein, The charging and discharging power regulation strategy comprises the following steps: Based on the charging station selection result determined by the charging station selection strategy, the quantity distribution of different types of electric vehicles in each charging station is obtained; According to the safe operation range of the electric vehicle battery state of charge, the state of charge constraints of each type of electric vehicle are set; Based on the charging station infrastructure capacity limit, the upper limit constraint of the number of electric vehicles simultaneously charged in each charging station is set; According to the state of charge constraints and the upper limit constraints of the number of electric vehicles, the charging and discharging power of different types of electric vehicles in each charging station is calculated to generate an electric vehicle charging and discharging power regulation strategy. 6.The method of claim 1, wherein, The collaborative expansion planning model takes the minimization of the annual comprehensive cost of the distribution network in the planning period as an objective function; the annual comprehensive cost comprises the annual planning investment cost of the source, network, load and storage devices and the annual operation simulation cost of the distribution network under a typical scenario. 7.The method of claim 6, wherein, The expression of the objective function is as follows: wherein: is the annual planning investment cost, is the annual operating simulation cost; Present value to perpetuity factor : wherein: is the discount rate, is the equipment type is the lifetime of the equipment, is the substation related equipment, is the line related equipment, is the photovoltaic equipment, is the static var compensator, equipment for reactive power regulation of distribution network, is the energy storage system, equipment for energy storage and flexible charging and discharging, is the smart soft switch, equipment for realizing flexible interconnection of distribution network, is the electric vehicle charging pile, corresponding to various charging pile facilities for supplying power to electric vehicles. 8.The method of claim 7, wherein, Annual planning investment cost and annual operating simulation cost are expressed by the following equations: wherein: , , , , , , are the candidate node / link sets corresponding to the device to be planned respectively, is the node number, is the line connecting node and node , represents whether to expand / construct a substation at candidate node , is the line , represents the number of photovoltaic units to be built at node , , respectively represent the rated power and rated capacity of the energy storage system to be built at node , , respectively represent the number of ordinary charging piles and the number of intelligent charging piles to be built at node , represents the number of static var compensator units to be built at node , is the number of intelligent soft switch units to be built at line , is the unit cost of expanding / constructing a substation at node , is the unit length cost of constructing line , is the unit capacity cost of building a photovoltaic unit at node , , is the unit power cost and unit capacity cost of building an energy storage system at node , , is the cost of building an ordinary charging pile and an intelligent charging pile at node , is the unit capacity cost of building a static var compensator unit at node , is the unit capacity cost of building an intelligent soft switch unit at line ; wherein: is the operation and maintenance cost, , , , is the substation unit operation and maintenance cost corresponding to each type of equipment, , is the line unit operation and maintenance cost corresponding to each type of equipment, is a set of typical scenarios, is the probability of the occurrence of a typical scenario , is the number of days, used to convert the daily operation and maintenance cost into the annual operation and maintenance cost; is the electricity purchase cost, is the scenario time period the electricity price of the upper grid purchased by the node , is the scenario time period the active power purchased by the substation from the node , is the set of all running time periods in a day; is the light rejection cost, is the scenario time period the unit penalty cost of light rejection of the photovoltaic from the node , is the scenario time period the light rejection power of the photovoltaic from the node , is the network loss cost, is the scenario time period the unit cost of grid loss, is the scenario time period the current square value of the line , is the resistance of the line . 9.The method of claim 6, wherein, The constraint conditions of the collaborative expansion planning model comprise the construction and operation constraints of the distribution network side devices; the device construction and operation constraints comprise one or more of the photovoltaic power generation device construction constraint, the static var compensator construction constraint, the energy storage system construction constraint, the charging pile construction constraint and the intelligent soft switch construction constraint; the energy storage system construction constraint comprises the system-level total power and capacity upper limit constraint, the node-level power and capacity installation upper limit constraint and the single-point energy storage power and capacity ratio constraint. 10.The method of claim 1, wherein, The collaborative expansion planning model is transformed and solved, specifically as follows: The decomposition iteration idea is adopted to transform the original problem into a double-layer optimization model composed of an upper-layer planning problem and a lower-layer operation simulation problem for iterative solving; The second-order cone relaxation method is adopted to convexly relax the distribution network power flow constraints, and the model is transformed into a mixed integer second-order cone programming problem for solving.

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