Method and system for flexibly dividing power supply area of traffic energy consumption system in extreme weather

Abstract

The invention relates to a method and a system for flexibly dividing a power supply area of a traffic energy consumption system in extreme weather, belongs to the field of island operation control of a power system, and solves the problems of low dividing efficiency, insufficient load recovery capability and poor power supply reliability caused by dividing a power supply area of a highway by an existing power consumption area dividing method. The method comprises the following steps: acquiring power grid node and road node information in a to-be-divided region, and constructing a power grid-road network coupling model; on the basis of the power grid and road traffic fault information, updating connection information of the power grid-road network coupling model; a key power grid load node is used as a starting point, and an improved A * algorithm is used to search a nearest connectable distributed power supply node and a mobile power supply vehicle; and constructing a power grid side objective function taking maximum recovery of power supply of the key power grid load nodes as a target, a highway network side objective function taking minimization of power supply recovery time of the key power grid load nodes as a target, and constraint conditions, and performing solving to obtain divided power supply areas.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of island operation control of power systems, and particularly relates to a flexible division method and system for power supply areas of a traffic energy system in extreme weather. BACKGROUND

[0002] With the rapid development of new energy power generation and smart grid technology, the reliability requirements of traffic energy systems are increasing, and the power supply and distribution system of the highway has developed into a complex energy system containing distributed photovoltaic, energy storage power station, charging pile and other types of loads. When facing faults, the distribution network can ensure the power supply of important loads through island division, thereby improving the reliability of the system. In extreme weather and other disaster situations, the distribution network may enter island mode due to the loss of main power support.

[0003] The existing island division technology of the power supply system mainly focuses on the urban distribution network scenario, and focuses on how to maximize the recovery of loads. The goal of island division is usually to prioritize the restoration of power supply. In particular, in urban distribution networks, island division strategies often focus on quickly restoring the power supply system in urban areas. However, the layout of distributed power sources on highways is influenced by road alignment and meteorological conditions. In particular, in extreme disaster situations, the traditional uniform power supply model of urban distribution networks is no longer applicable. At the same time, the linear topology of highway distribution networks is significantly different from the characteristics of ring-shaped urban power grids. The existing static division method based on Voronoi diagrams is difficult to apply to the island division of highway distribution networks. Moreover, the reliability requirements of key loads such as traffic hubs and tunnel lighting on highways far exceed the standard of ordinary civilian loads. SUMMARY

[0004] In view of the above analysis, the embodiments of the present application aim to provide a flexible division method and system for power supply areas of a traffic energy system in extreme weather, to solve the problems of low division efficiency, insufficient load recovery capability and poor power supply reliability caused by the existing power consumption area division method in the division of highway power supply areas.

[0005] The main purpose of the present application is achieved by the following technical solutions:

[0006] The present application provides a flexible division method for power supply areas of a traffic energy system in extreme weather, comprising the following steps:

[0007] Obtain the geographical position and importance of the grid load nodes in the division area, weather information, geographical position of traffic nodes, road traffic information, distributed power source node information and grid fault information;

[0008] obtain a power grid-highway network coupling model based on the connection relationship between power grid load nodes, the connection relationship between traffic nodes, and the geographical positions of the power grid load nodes and the traffic nodes in the region to be divided;

[0009] update the power grid topology connection information and the road topology connection information of the power grid-highway network coupling model based on the power grid fault information and the road traffic information;

[0010] based on the importance of the power grid load nodes and the power grid-highway network coupling model, use an improved A* algorithm to search for the nearest connectable distributed power supply node and mobile power supply vehicle from the key power grid load node as the starting point, respectively construct a power grid side target function aiming to maximize the restoration of power supply of the key power grid load node and a highway network side target function aiming to minimize the power supply restoration time of the key power grid load node and corresponding constraint conditions, and solve them to obtain the divided power supply region.

[0011] Further, the power grid-highway network coupling model includes a power grid topology layer and a road topology layer;

[0012] The nodes of the power grid topology layer include distributed power supply nodes and power grid load nodes, which are connected through power grid lines;

[0013] The nodes of the road topology layer include traffic nodes, which are connected through road lines;

[0014] The power grid load nodes and the traffic nodes are mapped through geographical position information to form the coupling relationship of the power grid topology layer and the road topology layer.

[0015] Further, the key power grid load nodes include special power grid load nodes and first-level power grid load nodes;

[0016] The distributed power supply nodes include photovoltaic power supply nodes, wind power supply nodes, and energy storage power supply nodes.

[0017] Further, the power grid side target function is:

[0018]

[0019] wherein Q represents the total number of the divided power supply regions; I q represents the set of power grid load nodes of the qth power supply region; w i represents the importance weight of the ith power grid load node; θ i represents the restoration state of the ith power grid load node; P i represents the load restoration power value of the ith power grid load node.

[0020] Further, the target function of the highway network side is:

[0021]

[0022] wherein J represents the total number of mobile power supply vehicles; Q represents the total number of divided power supply areas; G jq represents the load node set of the jth mobile power supply vehicle to recover the qth power supply area; ω i represents the importance weight of the ith grid load node; T jq represents the power supply time of the jth mobile power supply vehicle in the qth power supply area; t k,k-1 represents the time of the mobile power supply vehicle moving from the i-1th grid load node to the ith grid load node.

[0023] Further, based on the importance of the grid load node and the grid topology connection information, the nearest connectable distributed power node is searched using the improved A* algorithm starting from the key grid load node; wherein the cost function f grid (n) of the grid side improved A* algorithm is:

[0024] f grid (n)=g grid (n)+h grid (n)

[0025] g grid (n)=P Iloss (n)+V loss (n)+P Lloss (n)

[0026] h grid (n)=α2h elec (n)+α3h load (n)+α4h d (n)

[0027] h elec (n)=αD elec (n)+βL elec (n)

[0028]

[0029] wherein g grid (n) represents the actual cost function of node n on the grid side; h grid (n) represents the heuristic estimation function of the grid side from node n; P Iloss (n) represents the current loss power of the planned path to node n; V loss (n) represents the voltage drop loss to node n of the planned path; P Lloss(n) represents the transmission distance loss from node n to the target node; h elec (n) represents the electrical loss estimate from node n to the target node; D elec (n) represents the power transmission distance from node n to the target node; L elec (n) represents the current loss from node n to the target node; and and represent the adjustment coefficients of the electrical distance and the current loss, respectively; h load (n) represents the load demand estimate from node n to the target node; and represents the adjustment coefficient of the shortest electrical distance estimate. g represents the gth grid load node importance weight; L g represents the gth critical grid load demand power; G q represents the set of grid load nodes in the qth power supply area; h d (n) represents the shortest electrical distance estimate from node n to the target node; and represents the adjustment coefficient of the shortest electrical distance estimate. k represents the capacity priority weight of the kth distributed power supply node; D elec (n, k) represents the power transmission distance from node n to the kth distributed power supply node; K represents the set of available distributed power supply nodes; and, and represent the adjustment coefficients of the electrical loss estimate, the load demand estimate, and the shortest electrical distance estimate, respectively.

[0030] Further, based on the road traffic information, using an improved A* algorithm to search for the nearest connectable mobile power supply vehicle from the traffic node mapped to the critical grid load node in the grid-road network coupling model; wherein the cost function f load (n) of the improved A* algorithm on the road network side is:

[0031] f road (n) = g road (n) + h road (n)

[0032] g road (n) = åT n

[0033] h road (n) = å1h dist (n) + å3h load (n)

[0034]

[0035] wherein g road (n) represents the actual cost function of node n on the road network side; h road (n) represents the heuristic estimate function on the road network side from node n; T n represents the time that has been traveled to node n; h dist(n) represents the estimated road distance from node n; α1 represents the adjustment factor for the road distance estimate.

[0036] Furthermore, the constraints on the grid side include power balance constraints, voltage stability constraints, grid branch capacity constraints, and energy storage node charging and discharging constraints; among which,

[0037] The power balance constraint is that the sum of the power generation of each distributed power source and the power supply of the mobile power vehicle in each power supply area is the same as the sum of the load demand of the corresponding grid load node and the line loss in the power supply area.

[0038] The voltage stability constraint is that the voltage of each grid load node is between the minimum and maximum voltage that the node can withstand.

[0039] The constraints on the highway network side include traversability constraints between power supply areas.

[0040] Furthermore, the power generation capacity of each distributed power source includes the power generation capacity of wind power generation nodes, photovoltaic power generation nodes, and energy storage power generation nodes; among which,

[0041] Based on weather information and the location information of the wind power generation nodes, the power generation capacity of the wind power generation nodes is obtained using the following formula:

[0042]

[0043] Among them, P WT (v t () represents the wind speed v of the wind turbine at time t. t The power generation capacity of the generator; v t V represents the wind speed at time t; cut-in Indicates the cut-in wind speed of the wind turbine; v cut-out P represents the cut-out wind speed of the wind turbine; rated Indicates the rated power of the wind turbine; v rated Indicates the rated wind speed of the wind turbine; D w This represents the power attenuation coefficient of wind power generation;

[0044] Based on weather information and the location information of the photovoltaic power generation nodes, the power generation capacity of the photovoltaic power generation nodes is obtained using the following formula:

[0045]

[0046] Among them, P PV (G t ,T t () represents the amount of light G emanating from the photovoltaic generator at time t. t and temperature T tThe power generation at time t; G t The light intensity at time t; T t The temperature at time t; P STC The photovoltaic rated power under standard test conditions; G STC The light intensity under standard test conditions; γ represents the photovoltaic power temperature coefficient; T STC The temperature under standard test conditions; D pv The photovoltaic power attenuation coefficient;

[0047] Based on the charging and discharging efficiency, self-discharge rate and aging effect, the real-time residual capacity proportion of the energy storage power node is obtained using the following formula:

[0048]

[0049] Wherein, SOC(t) represents the residual capacity proportion of the energy storage power node at time t; η ch The charging efficiency of the energy storage power node; P ch The charging power at time t; Δt represents the time interval; E rated The rated capacity of the energy storage power node; P dis The discharging power at time t; η dis The discharging efficiency of the energy storage power node; γ self The self-discharge rate of the energy storage power node.

[0050] On the other hand, the application provides a flexible division system for power supply area of extreme weather traffic energy system, which comprises a data acquisition module, a power grid-highway network coupling model construction and updating module, and a power supply area division module.

[0051] The data acquisition module M1 is used to acquire the geographical position and importance of the grid load nodes in the region to be divided, weather information, the geographical position of the traffic nodes, road traffic information, grid fault information and distributed power node information.

[0052] The power grid-highway network coupling model construction and updating module M2 is used to obtain the power grid-highway network coupling model based on the power grid connection relationship between the grid load nodes in the region to be divided, the road connection relationship between the traffic nodes, and the geographical position of the grid load nodes and the geographical position of the traffic nodes in the region, and update the power grid topology connection information and road topology connection information of the power grid-highway network coupling model based on the grid fault information and road traffic information, respectively.

[0053] The power supply area division module M3 is used for starting from a key power grid load node, searching a nearest connectable distributed power supply node and a mobile power supply vehicle respectively based on the importance of the power grid load node and the power grid-highway network coupling model, constructing a power grid side target function aiming at maximizing power supply recovery of the important power grid load node, a highway network side target function aiming at minimizing power supply recovery time of the important power grid load node and corresponding constraint conditions, and solving to obtain the divided power supply area.

[0054] Compared with the prior art, the present application can at least realize one of the following beneficial effects:

[0055] 1. The scheme of the present application shortens the road distance between power supply areas and the mobile energy storage scheduling time after the division of the power supply area by constructing a power grid-highway network coupling model, introducing the influence of traffic roads on power grid load on the basis of considering the electrical connection between power grid load nodes, and adopting a shortest path optimization algorithm.

[0056] 2. The scheme of the present application considers the layout and output characteristics of distributed power supplies such as photovoltaic, wind power generation and energy storage systems, ensures the effective use of distributed power supplies under extreme weather, and ensures the rapid recovery of power supply of key loads during power grid failure through the flexible scheduling of mobile power supply vehicles, thereby enhancing the emergency response capability of the system.

[0057] 3. The scheme of the present application realizes the collaborative optimization of the power grid side and the highway network side through an improved A* algorithm, comprehensively considers current loss, voltage drop and transmission distance on the power grid side to plan an optimal power supply path for distributed power supplies to important load nodes, and integrates real-time traffic information and traffic constraints on the highway network side to dynamically plan an optimal scheduling path for mobile power supply vehicles.

[0058] In the present application, the above technical solutions can be combined with each other to realize more preferred combination schemes. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification or be understood through the implementation of the present application. The purpose and other advantages of the present application can be realized and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0059] The accompanying drawings are included to provide a further understanding of the application and are incorporated herein and constitute a part of the application. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 It is a flowchart of a flexible division method for a power supply area of an extreme weather traffic energy system in an embodiment of the present application.

[0061] Figure 2This is a schematic diagram of the power grid-highway network coupling model in an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a flexible power supply area division system for extreme weather transportation energy consumption in an embodiment of the present invention. Detailed Implementation

[0063] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0064] A specific embodiment of the present invention discloses a method for flexibly dividing the power supply area of ​​a transportation energy system in extreme weather, such as... Figure 1 As shown, it includes the following steps S1-S4:

[0065] Step S1: Obtain the geographical location and importance of the power grid load nodes, weather information, geographical location of traffic nodes, road traffic information, distributed power generation node information, and power grid fault information within the area to be divided.

[0066] In this embodiment, the area to be divided is a highway area where the power supply range needs to be planned after the power grid is disconnected from the main power grid due to power grid failure or extreme weather.

[0067] Specifically, the power grid load node is the terminal unit that receives and consumes electrical energy in the power system. In the highway power supply system, it is a facility that needs to obtain power supply from distributed power sources. The geographical location of each power grid load node is obtained through a geographic information system (GIS), and the importance weight of each power grid load node is obtained according to its importance and function.

[0068] In this embodiment, based on predefined tags from the SCADA system of the power grid dispatch center, each power grid load node is classified into four levels according to importance: top-level load nodes, first-level load nodes, second-level load nodes, and third-level load nodes. Top-level load nodes are those associated with critical safety issues in the highway network; power outages directly threaten life safety and require 100% power supply. Examples include tunnel emergency lighting and tunnel fire protection. First-level load nodes are those associated with the operational status of the highway network, affecting highway operation and requiring priority restoration. Examples include tollbooth lane equipment, surveillance cameras, and communication base stations. Second-level load nodes are those associated with the highway network and service areas; their safety importance is not high, and they can be temporarily shut down, but must be restored as soon as possible. Examples include tunnel lighting, office building fire protection, and office building emergency lighting. Third-level load nodes are general loads associated with highways and can be selectively disconnected. Examples include general lighting, office loads, and charging piles.

[0069] Further, in the process of fault recovery, in order to ensure the safety of the highway and the passability of the traffic road, in the embodiment, the special-grade power grid load node and the first-grade power grid load node are taken as the key power grid load node, and the importance weight is increased to maintain the 100% power supply of the special-grade load and the first-grade load, as much as possible power supply of the second-grade load, and part of the third-grade load can be cut off.

[0070] The real-time weather information is obtained through satellite remote sensing data or a weather station arranged in the region to be divided, including but not limited to wind speed, light intensity, and weather information such as precipitation and sand.

[0071] The traffic nodes include toll stations and service areas in the region, the geographic positions of the traffic nodes and the road connection relationship between the traffic nodes are obtained through the highway basic GIS database, and the traffic node adjacency matrix D is constructed based on the road connection relationship and the actual road connection distance, representing the road traffic information between the traffic nodes:

[0072]

[0073] Wherein, N represents the total number of traffic nodes; d mn represents the actual geographic connection relationship and road distance between traffic node m and traffic node n, wherein the reachability distance d mn of each traffic node is represented by the following formula:

[0074]

[0075] Wherein, l mn represents the road distance between traffic node m and traffic node n; when there is no road connection between two traffic nodes, or the road between two traffic nodes is not passable due to extreme weather, the corresponding reachability distance is set to ∞, indicating that it is not reachable.

[0076] The distributed power supply node is a small-scale power generation unit installed along the distribution network or the highway network, which can supply power to local load. In the embodiment, the distributed point source node includes a wind power point source node, a photovoltaic power supply node and an energy storage power supply node.

[0077] The distributed power supply node information includes the positions of the distributed power supply nodes obtained through the geographic information system (GIS) and the power generation power that can be provided by each distributed power supply node according to the weather information.

[0078] Further, the wind turbine of the highway network power supply system establishes a wind turbine spatial distribution model in combination with the terrain and weather factors for long-distance power supply demand, and dynamically represents the wind speed distribution characteristics by using the following formula through the terrain correction coefficient and the turbulence margin component:

[0079]

[0080] where v(x, y, t) represents the wind speed at location (x, y) and time t, v ref (t) represents the reference wind speed, which is the baseline wind speed at flat open terrain; h(x, y) represents the terrain height at the wind power source node; h ref represents the terrain reference height, which is the average elevation of the power supply area; a represents the terrain correction coefficient, which indicates that the wind speed increases when a > 0 (such as the top of a mountain) and the wind speed decreases when a < 0 (such as a valley); Av turb (t) represents the turbulence margin component caused by random wind speed fluctuations such as atmospheric turbulence and instantaneous gusts, which obeys a probability distribution with a mean of 0.

[0081] On the basis of the above, the output model of the wind turbine varying with the wind speed is established, and based on the weather information and the location information of the wind power source node, the power generation of the wind power source node is obtained using the following formula:

[0082]

[0083] where P WT (t) represents the power generation of the wind turbine at time t; v t represents the wind speed at time t; v cut-in represents the cut-in wind speed of the wind turbine; v cut-out represents the cut-out wind speed of the wind turbine; P rated represents the rated power of the wind turbine; v rated represents the rated wind speed of the wind turbine; D w represents the power attenuation coefficient of the wind power.

[0084] Further, considering that the roadside photovoltaic array is affected by the dynamic shadow of the terrain and traffic structures (such as bridges and tunnels), a shadow coefficient matrix based on the solar elevation angle is established, and for the slope photovoltaic system, a slope-irradiance correction factor is introduced to reflect the terrain difference, and the following formula is used to obtain the light intensity at different positions and times:

[0085] G(x, y, t) = G ref (x, y, t) · (1 - β · S(x, y, t)) + AG noise (t)

[0086] where G(x, y, t) represents the light intensity at location (x, y) and time t; G ref(t) represents the reference light intensity, which is the horizontal plane total irradiance at location (x, y) under ideal sunny weather; S(x, y, t) represents the area proportion of the photovoltaic panel that is shielded by the terrain or structure at location (x, y) and time t; β represents a shadow correction coefficient, because the shielding is not completely without light, the shielding area proportion is corrected; ΔG noise (t) represents a margin component, which embodies the uncertainty of photovoltaic output and is subject to a probability distribution with a mean value of 0.

[0087] Specifically, based on the weather information and the location information of the photovoltaic power generation source node, the power generation of the photovoltaic power generation source node is obtained using the following formula:

[0088]

[0089] wherein P PV (t) represents the power generation of the photovoltaic generator at time t; G(x, y, t) represents the light intensity at time t; T t represents the temperature at time t; P STC represents the rated power of the photovoltaic under standard test conditions; G STC represents the light intensity under standard test conditions, which is usually set to 1000 W / m 2 ; γ represents the temperature coefficient of photovoltaic power, which is usually set to -0.004 to -0.005 / ℃; T STC represents the temperature under standard test conditions, which is usually set to 25℃; D pv represents the photovoltaic power attenuation coefficient, which is the annual average attenuation rate of the photovoltaic component output power due to factors such as aging, dust, and hot spots. The photovoltaic power attenuation coefficient is different for different photovoltaic component materials. For example, when the photovoltaic component material is monocrystalline silicon, the attenuation coefficient is set to 0.5% to 0.8%; when the photovoltaic component material is polycrystalline silicon, the attenuation coefficient is set to 0.7% to 1.0%.

[0090] It should be noted that if it is in a sandstorm-prone area, sand coverage may cause a short-term power drop of 5% to 20%, and the photovoltaic power attenuation coefficient needs to be adjusted accordingly.

[0091] Further, considering the dual functions of the energy storage power supply node in island mode in the power supply area (emergency power support and new energy output smoothing), a dynamic SOC model is established considering the attenuation of charging and discharging efficiency, self-discharge rate and cycle aging; by defining the continuous load power / energy margin index, the power supply guarantee capability of the energy storage system to important loads is quantified.

[0092] Specifically, based on the charging and discharging efficiency, self-discharge rate and aging effect, the real-time residual capacity proportion of the energy storage power supply node is obtained using the following formula:

[0093]

[0094] wherein, SOC(t) represents the proportion of the remaining energy of the energy storage power supply node at time t; η ch represents the charging efficiency of the energy storage power supply node, which is a factory calibration value; P ch (t) represents the charging power at time t; Δt represents the time interval; E rated represents the rated capacity of the energy storage power supply node, which is a nameplate parameter of the energy storage device; P dis (t) represents the discharging power at time t; η dis represents the discharging efficiency of the energy storage power supply node, which is a factory calibration value; γ self represents the self-discharge rate of the energy storage power supply node, which is obtained by experiment.

[0095] In the formula of the real-time proportion of the remaining energy (SOC) of the energy storage power supply node, represents the charging increment of the energy storage point source node; represents the discharging decrement of the energy storage point source node; γ self · SOC(t) represents the self-discharge loss of the energy storage point source node, that is, the SOC of the energy storage point source node will slowly decrease due to the internal resistance of the battery even if there is no charging and discharging. Further, the power grid fault information includes power grid line breakage and distributed power supply off-grid.

[0096] Step S2, based on the power grid connection relationship between the power grid load nodes in the to-be-divided region, the road connection relationship between the traffic nodes, and the geographical positions of the power grid load nodes and the geographical positions of the traffic nodes in the region, obtaining a power grid-highway network coupling model.

[0097] Specifically, the power grid-highway network coupling model constructed by the embodiment is a cross-field collaborative optimization framework formed by integrating the topological relationship and spatial position of the power network and the traffic network, which is used to realize efficient division and emergency resource scheduling of the power supply region under extreme weather.

[0098] Further, as Figure 2 shown, the power grid-highway network coupling model includes a power grid topology layer and a road topology layer;

[0099] The nodes of the power grid topology layer include distributed power supply nodes and power grid load nodes, which are connected through power grid lines;

[0100] The nodes of the road topology layer include traffic nodes, which are connected through road lines;

[0101] The power grid load nodes and the traffic nodes are mapped through geographical position information to form a coupling relationship between the power grid topology layer and the road topology layer.

[0102] Specifically, the power grid topology layer is configured to provide a power grid side search space for the key load node by using an improved A* algorithm, and ensure that the path meets the electrical constraints.

[0103] The road topology layer is configured to plan a path for the mobile power supply vehicle, and ensure the feasibility of the dispatch.

[0104] When the key power grid load node is disconnected from the main power supply network due to extreme weather or power grid line failure, it is necessary to search for the nearest distributed power supply to provide the required power. However, due to line reasons, the nearby distributed power supply cannot provide enough power to support the power grid load node to restore power supply, or due to extreme weather, the nearby distributed power supply cannot supply power. Therefore, the mobile power supply vehicle needs to be started for emergency power supply. It should be noted that the driving path of the mobile power supply vehicle does not completely correspond to the power grid line. Therefore, the power grid load node and the traffic node are mapped based on the geographic location information, so that when the important power grid load node needs to be powered by the mobile power supply vehicle, the best driving path of the mobile power supply vehicle can be quickly and accurately determined.

[0105] Step S3, based on the power grid failure information and the road traffic information, updating the power grid topology connection information and the road topology connection information of the power grid-highway network coupling model respectively.

[0106] Specifically, by monitoring the line state in the power grid in real time, the topology structure of the power grid can be dynamically adjusted, thereby optimizing the power supply path. When the power grid fails, the topology connection information is updated in time, so that the affected area can be quickly identified, and the power grid can be reconfigured to reduce the power outage time caused by the failure and improve the overall reliability of the power supply system. By monitoring the road traffic condition in real time, the road topology connection information is updated, so that the driving path of the mobile power supply vehicle can be dynamically adjusted to avoid traffic congestion or the road being destroyed due to extreme weather, so that the mobile power supply vehicle can quickly reach the power grid load node.

[0107] More specifically, for the power grid side, the line state is updated, and the fault line is marked as disconnected; for the road network side, the traffic node adjacency matrix D is updated, and the blocked road is marked as impassable.

[0108] Step S4, based on the importance of the power grid load node and the power grid-highway network coupling model, taking the key power grid load node as the starting point, using an improved A* algorithm to search for the nearest connectable distributed power supply node and the mobile power supply vehicle respectively, constructing a power grid side target function aiming to maximize the restoration of power supply of the key power grid load node and a highway network side target function aiming to minimize the power supply restoration time of the key power grid load node, and solving the corresponding constraint conditions, to obtain a plurality of divided power supply areas.

[0109] The constraint condition is obtained based on distributed power supply node information.

[0110] Specifically, for the extreme working condition after the power supply system of the highway network and the large power grid are disconnected, the target is to maximize the recovery of important loads. At this time, the power supply system loses contact with the large power grid due to faults, and the power supply in the island is mainly provided by the distributed power supply. Therefore, in the process of power supply area division, a distributed power supply radial search method strategy is adopted, the power supply area range is expanded outward from the key fault node, the grid side target function is constructed, the load recovery state is quantified by 0-1 variable, and the importance index of different node loads is weighted calculated. At the same time, when the power supply area is divided, the scheduling time of the mobile power supply vehicle should be shortened as much as possible. Therefore, the minimum power supply system fault recovery time is taken as the highway network side target function.

[0111] Further, the grid side target function is:

[0112]

[0113] Wherein Q represents the total number of the divided power supply areas; I q represents the set of grid load nodes of the qth power supply area; w i represents the importance weight of the ith grid load node; θ i represents the recovery state of the ith grid load node; P i represents the load recovery power value of the ith grid load node.

[0114] Specifically, the grid side target function is used to quantitatively evaluate the effectiveness of the power supply area division scheme. By optimizing the division of the power supply area and the scheduling of the distributed power supply, it is ensured that the key load node can be restored as soon as possible under the condition of extreme weather or power grid fault.

[0115] In solving the grid side target function, an improved A* algorithm is used to generate a plurality of power supply area combinations, and the weighted recovery power sum of each scheme is counted. The maximum power supply area division scheme is the final division strategy.

[0116] Further, based on the importance of the grid load node and the grid topology connection information, the nearest connectable distributed power supply node is searched using the improved A* algorithm with the key grid load node as the starting point. The cost function f grid (n) of the grid side improved A* algorithm is:

[0117] f grid (n)=g grid (n)+h grid (n)

[0118] g grid (n)=PIloss (n) + V loss (n) + P Lloss (n)

[0119] h grid (n) = a2h elec (n) + a3h load (n) + a4h d (n)

[0120] h elec (n) = aD elec (n) + bL elec (n)

[0121]

[0122] wherein g grid (n) represents the actual cost function of node n at the grid side; h grid (n) represents the heuristic estimation function of the grid side from node n; P Iloss (n) represents the current loss power of the planned path to node n; V loss (n) represents the voltage drop loss of the planned path to node n; P Lloss (n) represents the transmission distance loss of the planned path to node n; h elec (n) represents the electrical loss estimation from node n to the target node; D elec (n) represents the power transmission distance from node n to the target node; L elec (n) represents the current loss from node n to the target node; a and b represent the adjustment coefficients of the electrical distance and the current loss, respectively; h load (n) represents the load demand estimation from node n to the target node; g g represents the importance weight of the gth grid load node; L g represents the gth critical grid load demand power; G q represents the set of grid load nodes in the qth power supply area; h d (n) represents the shortest electrical distance estimation from node n to the target node; d k represents the capacity priority weight of the kth distributed power node; D elec (n, k) represents the power transmission distance from node n to the kth distributed power node; K represents the set of available distributed power nodes; a2, a3, a4 represent the adjustment coefficients of the electrical loss estimation, the load demand estimation and the shortest electrical distance estimation, respectively, which are set by simulation optimization under historical disaster scenarios or expert experience, and exemplary values can be a2 = 0.4, a3 = 0.4 and a4 = 0.2.

[0123] Specifically, A* The algorithm is a heuristic path planning algorithm, which finds the optimal path by evaluating the path cost from the start point to the end point, and determines the next node to be explored by the evaluation function f(n), wherein the evaluation function includes the actual cost from the start point to the current node n and the estimated cost from the current node n to the target node.

[0124] More specifically, the actual cost function g grid (n) is related to the actual cost, time cost, power loss and transmission distance of power flow.

[0125] Wherein, P Iloss (n) represents the Joule loss generated by the current flowing through the line resistance, which is calculated by the current I of each node on the planned path and the line resistance value R of each node using the following formula:

[0126] P Iloss (n) = I 2 (n)R(n)

[0127] Wherein, I(n) represents the current to node n; R(n) represents the resistance to node n.

[0128] V loss (n) represents the voltage drop loss of the planned path to node n, which is the voltage deviation caused by line voltage drop, and is calculated using the following formula:

[0129] V loss (n) = I(n)R(n)P Lloss (n) represents the transmission distance loss of the planned path to node n, which is the additional loss related to the length of the line, such as corona loss, and is calculated using the following formula:

[0130] P Lloss (n) = α L L(n)I(n)

[0131] Wherein, L(n) represents the length of the power grid line to node n; α L represents an empirical coefficient set according to the conductor type of the power grid line and the environmental factors of the power grid.

[0132] The heuristic evaluation function of the power grid side is related to the electrical loss estimation, load demand estimation and shortest distance estimation of the distributed power supply of the remaining route.

[0133] Wherein, the electrical loss estimation h elec (n) is used to estimate the electrical loss of the remaining path; the power transmission distance D elec (n) is calculated by the power grid topology graph; the estimated current loss L elec (n) is calculated by the historical current loss.

[0134] the load demand estimation h load (n) represents the weighted sum of the total demand of the unrecovered key grid load nodes on the path, which is calculated based on the load importance weight of each grid load node and the load demand of each grid load node.

[0135] the shortest electrical distance estimation h d (n) is used to select the nearest available distributed power supply to the current node n; the power transmission distance D elec (n, k) represents the electrical distance from node n to available distributed power supply k, which is calculated through the grid topology graph; the capacity priority weight δ of distributed power supply node k k For example, the priority of energy storage point source node > the priority of photovoltaic power supply node > the priority of wind power supply node.

[0136] Further, the target function on the highway network side is:

[0137]

[0138] Wherein, J represents the total number of mobile power supply vehicles; Q represents the total number of divided power supply areas; G jq represents the load node set recovered by the jth mobile power supply vehicle in the qth power supply area; ω i represents the importance weight of the ith grid load node; T jq represents the power supply time of the jth mobile power supply vehicle in the qth power supply area; t k,k-1 represents the time for the mobile power supply vehicle to move from the (i-1)th grid load node to the ith grid load node.

[0139] Specifically, the target function on the highway network side is used to optimize the scheduling strategy of the mobile power supply vehicle, aiming to maximize the recovery efficiency of important loads in each divided power supply area, while minimizing the scheduling time of the mobile power supply vehicle; by using the importance weight of each grid load node for weighting, the power supply time proportion of special and first-class grid load nodes is higher, and the idle time of the power supply vehicle between grid load nodes is reduced.

[0140] Further, in optimizing the scheduling strategy of the mobile power supply vehicle, based on the road traffic information, the traffic node mapped with the key grid load node in the grid-highway network coupling model is taken as the starting point, and the improved A* algorithm is used to search for the nearest connectable mobile power supply vehicle; wherein, the cost function f load (n) of the improved A* algorithm on the highway network side is:

[0141] f road (n) = g road(n) + h road (n)

[0142] g road (n) = ∑T n

[0143] h road (n) = α1h dist (n) + α3h load (n)

[0144]

[0145] wherein, g road (n) represents the actual cost function of node n on the highway network side; h road (n) represents the heuristic estimation function of the highway network side from node n; T n represents the time that has been traveled to node n; h dist (n) represents the road distance estimation from node n; α1represents the adjustment coefficient of the road distance estimation; and α3represents the adjustment coefficient of the load demand estimation, and exemplary, α1may be selected as 0.6 and α3may be selected as 0.4.

[0146] Specifically, the cost function structure on the highway network side is similar to that on the power grid side, but the parameter design focuses on road passability and load recovery timeliness. The actual cost function in the cost function on the highway network side is the actual cost accumulated from the starting point to the current node n, which is the cumulative travel time of the vehicle in the embodiment; the heuristic estimation function in the cost function on the highway network side includes the geographical shortest path and the same load demand estimation as in the heuristic estimation function on the power grid side.

[0147] The road distance estimation h dist (n) is used to represent the remaining actual road distance of node n, and is calculated based on the traffic node adjacency matrix D.

[0148] Further, when solving the power grid side target function and the highway network side target function, corresponding constraint conditions need to be formulated, and the constraint conditions on the power grid side include power balance constraints, voltage stability constraints, power grid branch capacity constraints, and energy storage node charging and discharging constraints; wherein,

[0149] The power balance constraint is that the sum of the power generation of each distributed power supply and the power supply of the mobile power supply vehicle in each power supply area is equal to the sum of the load demand of the corresponding power grid load node and the line loss in the power supply area.

[0150] Specifically, the power balance constraint is used to ensure that in each power supply area, the total power output (distributed power supply + mobile power supply vehicle) is equal to the total load demand plus the line loss, so as to avoid overload or power supply shortage, and the formula is:

[0151] P WT,q (t)+P PV,q (t)+P dis,q (t)=P Load,q (t)+ΔP Loss,q (t)

[0152] wherein, P WT,q (t) represents the sum of power generated by each wind power source node in the power supply area q at time t; P PV,q (t) represents the sum of power generated by each photovoltaic power source node in the power supply area q at time t; P dis,q (t) represents the sum of power emitted by each energy storage power source node in the power supply area q at time t; P Load,q (t) represents the total load demand of each grid load node in the power supply area q at time t; ΔP Loss,q (t) represents the total line loss in the power supply area q at time t.

[0153] The voltage stability constraint is that the voltage of each grid load node is between the minimum and maximum voltages that the node can withstand.

[0154] Specifically, the voltage stability constraint is used to ensure that the voltage of each grid load node is within the allowable range through reactive power compensation, preventing voltage collapse or equipment damage, and its constraint formula is:

[0155]

[0156] wherein, and respectively represent the minimum and maximum voltages that the grid load node i can withstand; U t,i represents the voltage of the grid load node i at time t.

[0157] The grid branch capacity constraint is to prevent the transmission line or distribution equipment from being damaged due to current overload, to ensure the physical safety and stable operation of the grid. Since the current flowing through the conductor generates Joule heat, if the current exceeds the maximum carrying capacity of the conductor, the insulation layer of the line may melt, causing a short circuit, or the mechanical strength of the line may decrease, causing a broken line risk.

[0158] The energy storage node charging and discharging constraint is used to limit the charging and discharging power and capacity of the energy storage, prolong the service life and ensure safe operation, and its constraint formula is:

[0159]

[0160]

[0161] wherein, represents the net power injected into the grid by the energy storage node at time t; represents the discharging power of the energy storage node at time t; represents the charging power of the energy storage node at time t; and respectively represent the minimum and maximum values of the discharging power of the energy storage node at time t; and respectively represent the minimum and maximum values of the charging power of the energy storage node at time t; represents the reactive power emitted by the energy storage node at time t; Q ESS,max represents the upper limit of the energy storage reactive power of the energy storage node; S ESS represents the capacity of the energy storage node.

[0162] Further, the constraint condition of the highway network side includes a passable constraint between power supply areas.

[0163] Specifically, the passable constraint of the power supply area is that there is at least one passable road between the power supply areas to ensure that the mobile power supply vehicle is freely scheduled between different power supply areas.

[0164] It should be noted that, based on the constraint condition, the target function is solved to obtain an optimal route meeting the requirements. In some embodiments, the constraint condition can be increased, and different results can be obtained based on different constraint conditions.

[0165] Further, the nonlinear mixed integer model formed by the power grid side target function and the highway network side target function and the corresponding constraint conditions is converted into a mixed integer second-order cone programming model by using a second-order cone conversion method, and is solved by a convex optimization method to obtain the divided power supply area.

[0166] Specifically, the nonlinear constraint of the power grid side is converted into a convex constraint by second-order cone relaxation (SOCP Relaxation), and the integer variable is retained to form a mixed integer second-order cone programming (MISOCP) model. The passable constraint of the highway network side is directly encoded into a linear constraint through an adjacency matrix D and an integer variable. In the solving stage, a MISOCP solver such as CPLEX / Gurobi is called to process the integer variable by a branch-and-bound method (Branch-and-Bound) to ensure the feasibility of the solution, and a multi-objective weighted balancing load recovery and scheduling time optimization objective is used.

[0167] Another embodiment of the application discloses a flexible division system of a power supply area of an extreme weather traffic energy system, as shown in Figure 3 The system includes a data acquisition module, a power grid-highway network coupling model construction and updating module, and a power supply area division module.

[0168] The data acquisition module M1 is configured to acquire geographical positions and importance of power grid load nodes in a region to be divided, weather information, geographical positions of traffic nodes, road traffic information, power grid fault information, and distributed power supply node information.

[0169] The power grid-highway network coupling model construction and updating module M2 is configured to obtain a power grid-highway network coupling model based on power grid connection relationships between power grid load nodes, road connection relationships between traffic nodes, and geographical positions of the power grid load nodes and geographical positions of the traffic nodes in the region, and update power grid topology connection information and road topology connection information of the power grid-highway network coupling model based on the power grid fault information and the road traffic information.

[0170] The power supply region division M3 is configured to take a key power grid load node as a starting point, search for a nearest connectable distributed power supply node and a mobile power supply vehicle based on the importance of the power grid load nodes and the power grid-highway network coupling model, and construct a power grid side target function with a target of maximizing power supply restoration of important power grid load nodes, a highway network side target function with a target of minimizing power supply restoration time of the important power grid load nodes, and corresponding constraint conditions, and solve the functions to obtain a divided power supply region.

[0171] The system embodiment and the method embodiment described above are based on the same principle, can be mutually referenced, and can achieve the same technical effects. For a specific implementation process, refer to the foregoing embodiments, which will not be described here again.

[0172] In summary, the power supply region flexible division method and system of the extreme weather traffic energy system according to the embodiments of the present application have the following beneficial effects:

[0173] 1. The scheme of the present application shortens the road distance between power supply regions by constructing a power grid-highway network coupling model, introducing the influence of traffic roads on power grid loads on the basis of considering electrical connections between power grid load nodes, and using a shortest path optimization algorithm, thereby shortening the mobile energy storage scheduling time after power supply region division.

[0174] 2. The scheme of the present application considers the layout and output characteristics of distributed power supplies such as photovoltaic power, wind power, and energy storage systems, ensures effective use of the distributed power supplies under extreme weather, and ensures quick restoration of power supply of key loads when the power grid fails through flexible scheduling of mobile power supply vehicles, thereby enhancing the emergency response capability of the system.

[0175] 3. The scheme improves the A* algorithm to realize the collaborative optimization of the power grid side and the highway network side. In the power grid side, the current loss, voltage drop and transmission distance are comprehensively considered to plan an optimal power supply path for the distributed power supply to the important load node. In the highway network side, the real-time road condition information and traffic constraints are integrated to dynamically plan an optimal scheduling path for the mobile power supply vehicle.

[0176] The above merely describes the preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A flexible division method of power supply area of an extreme weather transportation energy system, characterized in that, The method comprises the following steps: obtaining geographical positions and importance of power grid load nodes in a region to be divided, weather information, geographical positions of traffic nodes, road traffic information, distributed power supply node information and power grid fault information; obtaining a power grid-highway network coupling model based on the connection relationship between the power grid load nodes in the region to be divided, the connection relationship between the traffic nodes and the geographical positions of the power grid load nodes and the traffic nodes in the region; updating the power grid topology connection information and the road topology connection information of the power grid-highway network coupling model based on the power grid fault information and the road traffic information; based on the importance of the power grid load nodes and the power grid-highway network coupling model, taking a key power grid load node as a starting point, using an improved A* algorithm to search for a nearest connectable distributed power supply node and a mobile power supply vehicle respectively, constructing a power grid side target function aiming to maximize the power supply recovery of the key power grid load node and a highway network side target function aiming to minimize the power supply recovery time of the key power grid load node and corresponding constraint conditions, and solving them to obtain a divided power supply region.

2. The method of claim 1, wherein, The power grid-highway network coupling model comprises a power grid topology layer and a road topology layer; the nodes of the power grid topology layer comprise distributed power supply nodes and power grid load nodes and are connected through power grid lines; the nodes of the road topology layer comprise traffic nodes and are connected through road lines; the power grid load nodes and the traffic nodes are mapped through geographical position information to form a coupling relationship between the power grid topology layer and the road topology layer.

3. The method of claim 2, wherein, The key power grid load nodes comprise special power grid load nodes and first-level power grid load nodes; The distributed power supply nodes comprise photovoltaic power supply nodes, wind power supply nodes and energy storage power supply nodes.

4. The method of claim 1, wherein, The power grid side target function is: wherein Q represents the total number of divided power supply areas; I q represents the set of grid load nodes of the qth power supply area; w i represents the importance weight of the ith grid load node; θ i represents the recovery state of the ith grid load node; P i represents the load recovery power value of the ith grid load node.

5. The method of claim 4, wherein, The highway network side target function is: wherein, J represents the total number of mobile power supply vehicles; Q represents the total number of divided power supply areas; G jq represents the load node set recovered by the jth mobile power supply vehicle in the qth power supply area; ω i represents the importance weight of the ith grid load node; T jq represents the power supply time of the jth mobile power supply vehicle in the qth power supply area; t k,k-1 represents the time for the mobile power supply vehicle to move from the i-1th grid load node to the ith grid load node.

6. The method of claim 5, wherein, Based on the importance of the grid load node and the grid topology connection information, the nearest connectable distributed power supply node is searched using the improved A* algorithm starting from the key grid load node; wherein the cost function f of the grid side improved A* algorithm is grid (n) is: f grid (n) = g grid (n) + h grid (n) g grid (n) = P Iloss (n) + V loss (n) + P Lloss (n) h grid (n) = a2h elec (n) + a3h load (n) + a4h d (n) h elec (n) = aD elec (n) + βL elec (n) where g grid (n) represents the actual cost function of node n at the grid side; h grid (n) represents the heuristic estimation function of the grid side from node n; P Iloss (n) represents the current loss power of the planned path to node n; V loss (n) represents the voltage drop loss of the planned path to node n; P Lloss (n) represents the transmission distance loss of the planned path to node n; h elec (n) represents the electrical loss estimation from node n to the target node; D elec (n) represents the power transmission distance from node n to the target node; L elec (n) represents the current loss from node n to the target node; a and b represent the adjustment coefficients of the electrical distance and the current loss, respectively; h load (n) represents the load demand estimation from node n to the target node; g g represents the importance weight of the gth grid load node; L g represents the kth critical grid load demand power; G q represents the set of grid load nodes within the qth power supply area; h d (n) represents the shortest electrical distance estimation from node n to the target node; d k represents the capacity priority weight of the kth distributed power node; D elec (n, k) represents the power transmission distance from node n to the kth distributed power node; K represents the set of available distributed power nodes; a2, a3, and a4 represent the adjustment coefficients of the electrical loss estimation, the load demand estimation, and the shortest electrical distance estimation, respectively.

7. The method of claim 6, wherein, Based on the road traffic information, using the improved A* algorithm to search the nearest connectable mobile power supply vehicle from the traffic node mapped with the key power grid load node in the power grid-highway network coupling model; wherein the cost function f of the improved A* algorithm on the highway network side is load (n) is: f road (n) = g road (n) + h road (n) g road (n) =∑T n h road (n) = a1h dist (n) + a3h load (n) where g road (n) represents the actual cost function of node n on the road network side; h road (n) represents the heuristic estimation function from node n on the road network side; T n represents the time that has been traveled to node n; h dist (n) represents the road distance estimation from node n; and a1represents the adjustment coefficient of the road distance estimation.

8. The method according to any one of claims 4 to 7, characterized in that, The constraint conditions of the power grid side include power balance constraints, voltage stability constraints, power grid branch capacity constraints and energy storage node charging and discharging constraints; wherein, The power balance constraints are that the sum of the distributed power supply power and the mobile power supply vehicle power in each power supply region is equal to the sum of the load demand of the corresponding power grid load node and the line loss in the power supply region; The voltage stability constraints are that the voltage of each power grid load node is between the minimum and maximum voltages that the node can withstand; The constraint conditions of the highway network side include passable constraints between power supply regions.

9. The method of claim 8, wherein, The power generation power of each distributed power supply includes the power generation power of the wind power supply node, the power generation power of the photovoltaic power supply node and the power generation power of the energy storage power supply node; wherein, Based on the weather information and the position information of the wind power supply node, the power generation power of the wind power supply node is obtained using the following formula: P (t) = P (v (t) ), wherein P WT (v t ) represents the power generated by the wind turbine at time t for a wind speed v t ; v t represents the wind speed at time t; v cut-in represents the cut-in wind speed of the wind turbine; v cut-out represents the cut-out wind speed of the wind turbine; P rated represents the rated power of the wind turbine; v rated represents the rated wind speed of the wind turbine; D w represents the power decay coefficient of the wind turbine. Based on the weather information and the position information of the photovoltaic power supply node, the power generation power of the photovoltaic power supply node is obtained using the following formula: where P PV represents the power generated by the photovoltaic generator at time t, G t represents the solar irradiance at time t, and T t represents the temperature at time t; P t represents the nominal power of the photovoltaic generator under standard test conditions; G t represents the solar irradiance under standard test conditions; γ represents the temperature coefficient of the photovoltaic power; T t represents the temperature under standard test conditions; and D t represents the attenuation coefficient of the photovoltaic power. STC STC STC pv ​​​​ Based on the charging and discharging efficiency, the self-discharge rate and the aging effect, the real-time residual capacity proportion of the energy storage power supply node is obtained using the following formula: Wherein, SOC(t) represents the proportion of the remaining energy of the energy storage power supply node at time t; η ch represents the charging efficiency of the energy storage power supply node; P ch (t) represents the charging power at time t; Δt represents the time interval; E rated represents the rated capacity of the energy storage power supply node; P dis (t) represents the discharging power at time t; η dis represents the discharging efficiency of the energy storage power supply node; γ self represents the self-discharge rate of the energy storage power supply node.

10. A flexible division system of power supply area of an extreme weather transportation energy system, characterized in that, The system comprises a data acquisition module, a power grid-highway network coupling model construction and updating module and a power supply region division module. The data acquisition module M1 is configured to acquire geographical positions and importance of power grid load nodes, weather information, geographical positions of traffic nodes, road traffic information, power grid fault information, and distributed power supply node information in a region to be divided; The power grid-road network coupling model construction and updating module M2 is configured to obtain a power grid-road network coupling model based on power grid connection relationships between power grid load nodes, road connection relationships between traffic nodes, and geographical positions of the power grid load nodes and geographical positions of the traffic nodes in the region to be divided, and update power grid topology connection information and road topology connection information of the power grid-road network coupling model based on the power grid fault information and the road traffic information, respectively; The power supply region division module M3 is configured to take a key power grid load node as a starting point, search for a nearest connectable distributed power supply node and a mobile power supply vehicle based on the importance of the power grid load nodes and the power grid-road network coupling model, and construct a power grid side target function with a target of maximizing power supply restoration of important power grid load nodes, a road network side target function with a target of minimizing power supply restoration time of the important power grid load nodes, and corresponding constraint conditions, and solve the functions to obtain a divided power supply region.

Images