Main and distribution operation collaborative optimization method and system considering charging load flexibility

Abstract

The invention discloses a main and distribution operation collaborative optimization method and system considering charging load flexibility, and the method comprises the steps: building a random user balance model based on a constructed traffic network model and a path toll model, carrying out the sampling based on the random user balance model, carrying out the regression fitting of a sample, and obtaining a charging load price elastic model, the method comprises the following steps of: establishing an objective function for minimizing maximum load rates of substations and lines at all levels in a power transmission network and lines at all levels in a power distribution network, and establishing a power transmission network direct current power flow constraint, a power distribution network radial topology constraint, a power distribution network direct current power flow constraint, a multi-period switch switching constraint and a charging price adjustment constraint; and a main distribution operation collaborative optimization model is generated based on the objective function, the charging load price elastic model and the constraint, and is solved to obtain a collaborative optimization result, so that the power conversion means at the heavy and overload moment of the equipment are enriched, and the operation flexibility and safety of the urban power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a main distribution and operation collaborative optimization method and system considering charging load flexibility. Background Art

[0002] Urban distribution networks often experience severe overloads on localized main transformers or feeders. Currently, dispatching departments frequently adjust the distribution network's operating mode to shift power. Essentially, this involves adjusting the network topology by reversing some switches, shifting the load at each node of the distribution network between substations in the upstream transmission system to optimize power flow distribution within the network. This is equivalent to a discrete regulation "step-by-step switch." However, due to the relatively limited number of switches that can be flexibly adjusted within the distribution network, the topological combinations for adjusting the distribution network's operating mode are limited, making frequent adjustments inappropriate. Load shifting is also limited to nearby substations. Furthermore, large-scale, one-time load shifts can create the risk of future overloads. Therefore, new flexible regulation resources are urgently needed to enrich the means of shifting power during periods of severe equipment overload and enhance the flexibility and safety of urban distribution network operations. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a main distribution and operation collaborative optimization method and system considering the flexibility of charging load, which can improve the flexibility and safety of urban distribution network operation.

[0004] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0005] A main distribution and operation collaborative optimization method considering charging load flexibility includes the following steps:

[0006] Constructing a traffic network model and a path toll model, and establishing a stochastic user equilibrium model based on the traffic network model and the path toll model;

[0007] Sampling is performed based on the random user equilibrium model to obtain samples, and regression fitting is performed on the samples to obtain a charging load price elasticity model;

[0008] Establish an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establish DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints;

[0009] Based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint, a main distribution operation collaborative optimization model is generated, and the main distribution operation collaborative optimization model is solved to obtain a collaborative optimization result.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] A main distribution and operation collaborative optimization system considering charging load flexibility includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] Constructing a traffic network model and a path toll model, and establishing a stochastic user equilibrium model based on the traffic network model and the path toll model;

[0013] Sampling is performed based on the random user equilibrium model to obtain samples, and regression fitting is performed on the samples to obtain a charging load price elasticity model;

[0014] Establish an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establish DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints;

[0015] Based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint, a main distribution operation collaborative optimization model is generated, and the main distribution operation collaborative optimization model is solved to obtain a collaborative optimization result.

[0016] The present invention has the following beneficial effects: a random user equilibrium model is established based on a constructed traffic network model and a path toll model; sampling is performed based on the random user equilibrium model, and regression fitting is performed on the samples to obtain a charging load price elasticity model; an objective function is established to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network; DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints are established; a main-distribution-operation collaborative optimization model is generated based on the objective function, the charging load price elasticity model, and the constraints, and the model is solved to obtain a collaborative optimization result; thereby, the charging load transfer range is adjusted by adjusting the charging price, and the charging load is used as a continuously adjustable flexible resource. When risks are anticipated, the operating mode of the relevant distribution lines can be adjusted while issuing price signals in advance, guiding vehicles with charging needs in the future to complete charging at locations that are beneficial to the safety of the power grid. As a flexible supplement to the power grid's own control measures, the model enriches the means of power transfer during equipment overload, thereby improving the flexibility and safety of urban distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the steps of a main distribution and operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0018] Figure 2 This is a structural diagram of a main distribution and operation collaborative optimization system considering charging load flexibility according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of a charging station located near an intersection in a main-distribution-operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of vehicles arriving at and leaving a charging station in the main distribution and operation collaborative optimization method considering charging load flexibility in an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of an expanded network that introduces virtual charging roads and bypass roads in the main distribution and operation collaborative optimization method considering charging load flexibility in an embodiment of the present invention;

[0022] Figure 6 A schematic diagram of a power-transportation coupling network taking into account the adjustment of distribution network operation mode in a main distribution and operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0023] Figure 7 A schematic diagram of load rate distribution in scenario 1 of the main distribution and operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0024] Figure 8 A schematic diagram of load rate distribution for scenario 2 in the main distribution and operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0025] Figure 9 A schematic diagram of load rate distribution for scenario 3 in the main distribution and operation collaborative optimization method considering charging load flexibility according to an embodiment of the present invention;

[0026] Figure 10 Schematic diagram of load rate distribution of scenario 4 in the main distribution and operation collaborative optimization method considering charging load flexibility in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0028] Please refer to Figure 1 A main distribution and operation collaborative optimization method considering charging load flexibility includes the following steps:

[0029] Constructing a traffic network model and a path toll model, and establishing a stochastic user equilibrium model based on the traffic network model and the path toll model;

[0030] Sampling is performed based on the random user equilibrium model to obtain samples, and regression fitting is performed on the samples to obtain a charging load price elasticity model;

[0031] Establish an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establish DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints;

[0032] Based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint, a main distribution operation collaborative optimization model is generated, and the main distribution operation collaborative optimization model is solved to obtain a collaborative optimization result.

[0033] As can be seen from the above description, the beneficial effects of the present invention are as follows: a random user equilibrium model is established based on the constructed traffic network model and path toll model; sampling is performed based on the random user equilibrium model, and regression fitting is performed on the samples to obtain a charging load price elasticity model; an objective function is established to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network; DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints are established for the distribution network; a main-distribution-operation collaborative optimization model is generated based on the objective function, the charging load price elasticity model, and the constraints, and the model is solved to obtain a collaborative optimization result. The charging load transfer range is adjusted by adjusting the charging price, and the charging load is used as a continuously adjustable flexible resource. When risks are anticipated, the operating mode of the relevant distribution lines can be adjusted while issuing price signals in advance, guiding vehicles with charging needs in the future to complete charging at locations that are beneficial to the safety of the grid. As a flexible supplement to the grid's own control measures, it enriches the means of power transfer during periods of severe overload, thereby improving the flexibility and safety of urban distribution network operations.

[0034] Furthermore, the construction of the traffic network model and the path toll model includes:

[0035] Determine the travel time function of each road and establish flow conservation constraints, road and path association constraints, and path flow non-negativity constraints;

[0036] Generate a traffic network model based on the travel time function of each road, the flow conservation constraint, the road-path association constraint, and the path flow non-negative constraint;

[0037] The toll of each road is determined, and a path toll model is established based on the toll of each road.

[0038] From the above description, it can be seen that by determining the travel time function of each road, the road congestion and traffic efficiency can be accurately reflected, making the traffic network model closer to the actual operating status. The establishment of the traffic network model and path toll model provides key information on the traffic side for the subsequent main and distribution collaborative optimization model.

[0039] Furthermore, the travel time function of each road is specifically:

[0040]

[0041] Where, t a represents the travel time of road a, t a 0 represents the travel time when the traffic volume of road a is 0, x arepresents the traffic flow on road a, Q a r represents the capacity of conventional roads, represents the set of regular roads, Q a c represents the capacity of the virtual charging road, represents the set of virtual charging roads, represents a set of virtual bypass roads;

[0042] The flow conservation constraint is specifically:

[0043]

[0044] Where, d w represents the total travel demand for the origin-destination pair w, represents the set of feasible paths for the start-end pair w, f rw represents the traffic flow on the path r of the origin-destination pair w, Represents the travel demand set of travel users;

[0045] The road and path association constraints are specifically:

[0046]

[0047] Where, δ ar represents the path-branch correlation coefficient, Represents the set of roads in the transportation network;

[0048] The path flow non-negative constraint is specifically:

[0049]

[0050] From the above description, we can see that the combination of flow conservation constraints, road-path association constraints, and path flow non-negativity constraints ensures that the model can reasonably simulate traffic flow distribution and path selection, providing a reliable foundation for subsequent user equilibrium analysis.

[0051] Furthermore, the path toll model is specifically as follows:

[0052]

[0053] Where c rw represents the toll of the path, c a represents the toll of road a.

[0054] From the above description, we can see that constructing a path toll model and combining it with a traffic network model enables the stochastic user equilibrium model to more accurately simulate the user's behavior under different path choices, taking into account the user's sensitivity to time and cost.

[0055] Furthermore, establishing a stochastic user equilibrium model based on the traffic network model and the path toll model includes:

[0056] An objective function of a random user equilibrium model is established with the goal of minimizing the perceived cost, and the traffic network model and the path toll model are used as constraints of the random user equilibrium model.

[0057] From the above description, we can see that establishing the objective function of the random user equilibrium model with the goal of minimizing the perceived cost, and using the traffic network model and the path toll model as constraints of the random user equilibrium model can more realistically simulate the comprehensive trade-off between time and cost when users choose travel paths, thereby more accurately predicting user behavior and taking into account the randomness and diversity of user choices.

[0058] Furthermore, the objective function of the random user equilibrium model is specifically:

[0059]

[0060] Where, F T represents the perceptual cost, and θ represents the perceptual error parameter.

[0061] From the above description, we can see that, considering the user's perceptual bias and decision randomness, a discrete choice model is used to model their decision, making the constructed random user equilibrium model more accurate.

[0062] Furthermore, the charging load price elasticity model is obtained by performing regression fitting on the sample, specifically:

[0063] P CS =NLR(σ)=SEM T σ☉σ+EM T σ+P b ;

[0064] Where, P CS represents the charging power vector, σ represents the charging price, NLR() represents nonlinear regression, SEM represents the quadratic elastic coefficient matrix obtained by sample regression, EM represents the linear elastic coefficient matrix obtained by sample regression, P b Indicates the basic charging power.

[0065] From the above description, we can see that through the quadratic elasticity coefficient matrix and the linear elasticity coefficient matrix, we can have a deeper understanding of the sensitivity of charging load to price changes and the changing trend of charging load at different price levels.

[0066] Furthermore, the objective function of establishing the minimum maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network is specifically:

[0067]

[0068] Where, Indicates the maximum load rate of substations at all levels in the transmission network, Indicates the maximum load rate of each level of the transmission network. Indicates the maximum load rate of each level of lines in the distribution network.

[0069] From the above description, it can be seen that constructing an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network can reduce the risk of equipment overload. Reducing the load pressure on substations and lines helps to extend their service life and improve the reliability and stability of grid operation.

[0070] Furthermore, the DC power flow constraint of the transmission network is specifically:

[0071]

[0072] 0≤P i ≤P i,max ,i∈E NS ;

[0073] 0≤P ij ≤P ij，max ,i,j∈E NS ;

[0074] Where, P ij Indicates the power of branch ij, E NS represents the set of transmission network nodes, represents the power transfer distribution factor between branch ij and node k, P k represents the power injection of node k, P i represents the power injection of node i, P i,max represents the upper limit of the power injection of node i, P ij,max Indicates the power upper limit of branch ij;

[0075] The radial topology constraints of the distribution network are specifically:

[0076] β ij +β ji =α ij ,i,j∈E ND ;

[0077]

[0078] β kj=0,j∈E ND ,k∈E NS ;

[0079] ∑α ij =|E ND |;

[0080] Where, β ij Indicates the power direction of branch ij, β ji Indicates the power direction of branch ji, α ij Indicates the switch state of branch ij, E ND represents the set of nodes in the distribution network, N(i) represents the set of all nodes connected to node i, β kj Indicates the power direction of branch kj;

[0081] The DC power flow constraints of the distribution network are specifically:

[0082]

[0083] P i =P RLi +R CLi , i∈E ND ;

[0084] 0≤P ij ≤P ij，max β ij ,i,j∈E ND ;

[0085] Where, P ik represents the unidirectional power flow from node i to node k, P ji represents the unidirectional power flow from node j to node i, P RLi Indicates normal load, P CLi Indicates charging load;

[0086] The multi-period switch switching constraint is specifically:

[0087]

[0088] Where, Indicates the switch status before the distribution network operation mode is adjusted, n sw Indicates the maximum number of switching operations;

[0089] The charging price adjustment constraints are specifically:

[0090]

[0091] Where, represents the lower limit of the charging price of the charging station corresponding to the virtual charging road, σ aIndicates the charging price of the charging station corresponding to the virtual charging road, Indicates the upper limit of charging price for the charging station corresponding to the virtual charging road.

[0092] As can be seen from the above description, the construction of the above series of constraints takes into account the respective constraints of the distribution network operation mode and the charging price signal, thereby improving the flexibility and reliability of the urban power grid operation.

[0093] Please refer to Figure 2 Another embodiment of the present invention provides a main distribution and operation collaborative optimization system considering the flexibility of charging load, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the main distribution and operation collaborative optimization method considering the flexibility of charging load is implemented.

[0094] The main distribution and operation collaborative optimization method and system of the present invention considering charging load flexibility can be applied to urban power grids, and are described below through specific implementation methods:

[0095] Please refer to Figure 1 、 Figure 3-Figure 10 , embodiment 1 of the present invention is:

[0096] A main distribution and operation collaborative optimization method considering charging load flexibility includes the following steps:

[0097] S1. Constructing a traffic network model and a path toll model, and establishing a stochastic user equilibrium model based on the traffic network model and the path toll model, specifically including S11-S14:

[0098] S11. Determine the travel time function of each road, and establish flow conservation constraints, road and path association constraints, and path flow non-negativity constraints.

[0099] The urban transportation network can be described as a connected directed graph Node Collection Represents each intersection in the traffic network, a set of directed branches Represents a road in a transportation network.

[0100] The traffic flow on road a in a traffic network is similar to the concept of electric current and is defined as the number of vehicles passing through a certain section per unit time, with the unit being vehicles / hour. The travel demands of users in a traffic network can be clustered into multiple origin-destination (OD) pairs, forming a travel demand set. Each OD pair Contains starting point r, end point s, and traffic flow d w Ternary information. There are many paths between each OD pair. The topology of each path can be determined by the path-branch correlation coefficient δ ar Description: If the path Passing road Then δ ar =1, otherwise, δ ar = 0. Vehicles running in the transportation network can be divided into vehicles with fast charging requirements and vehicles without fast charging requirements.

[0101] Assume that the charging station is located near an intersection, such as Figure 3 As shown, vehicles with charging needs can arrive at and leave the charging station from regular roads in all directions, such as Figure 4 As shown in Figure 2. Due to the limited capacity of urban roads, they exhibit congestion characteristics, that is, the road travel time increases with the increase of traffic volume. Charging stations have a limited number of charging piles, and they exhibit congestion characteristics, that is, the length of vehicle queues increases with the increase of vehicle flow going to the charging station to charge. Therefore, considering the similarities between traffic congestion and charging station queues, a mutually integrated modeling approach can be adopted to establish the following: Figure 5 The extended network shown.

[0102] A virtual charging road is introduced on the basis of the conventional road set to represent the queuing and charging behavior of vehicles at the charging station. At the same time, a virtual bypass road is introduced to represent other vehicles not entering the charging station. Assuming that a vehicle with fast charging requirements needs to charge once during the journey, that is, a route passing through the virtual charging road is selected, then the travel time function of each road is specifically:

[0103]

[0104] Where, t a represents the travel time of road a, t a 0 represents the travel time when the traffic volume of road a is 0, that is, the free flow time of passing the road / completing charging, x a represents the traffic flow on road a, Q a r represents the capacity of conventional roads, represents the set of regular roads, Q a c represents the capacity of the virtual charging road, represents the set of virtual charging roads, Represents a collection of virtual bypass roads.

[0105] The flow conservation constraint is specifically:

[0106]

[0107] Where, d wrepresents the total travel demand for the origin-destination pair w, represents the set of feasible paths for the start-end pair w, f rw represents the traffic flow on the path r of the origin-destination pair w, Represents the travel demand set of travel users.

[0108] The road and path association constraints are specifically:

[0109]

[0110] Where, δ ar represents the path-branch correlation coefficient, Represents a collection of roads in a transportation network.

[0111] The path flow non-negative constraint is specifically:

[0112]

[0113] S12: Generate a traffic network model based on the travel time function of each road, the flow conservation constraint, the road-path association constraint, and the path flow non-negative constraint.

[0114] S13: Determine the toll of each road, and establish a path toll model based on the toll of each road.

[0115] The formation of traffic flow from a macro perspective is the result of the decisions made by various types of travelers from a micro perspective. Assuming that travelers tend to choose the path with the lowest travel cost, the toll c of a path is rw It is represented by the sum of the costs of the roads it passes through. Therefore, the path toll model is specifically:

[0116]

[0117] Where c rw represents the toll of the path, c a represents the toll of road a, γ represents the value of unit commuting time, E ev represents the average charging energy of the vehicle, σ a Indicates the charging price of the charging station corresponding to the virtual charging road.

[0118] As road traffic volume increases, the time and cost of traveling along that road also increase. Therefore, travelers face the interaction of two mechanisms: "shortest path decision" and road "congestion characteristics."

[0119] S14. Establishing an objective function of a random user equilibrium model with the goal of minimizing the perceived cost, and using the traffic network model and the path toll model as constraints of the random user equilibrium model.

[0120] Given network parameters and price signals, all users will choose the path with the highest perceived utility (i.e., the lowest perceived cost). When no user can unilaterally change their path choice to reduce their perceived cost, the traffic flow in the network reaches a stochastic user equilibrium state, which is solved using a stochastic user equilibrium model. The objective function of the stochastic user equilibrium model is specifically:

[0121]

[0122] Where, F T represents the perceived cost, θ represents the perception error parameter, and θ>0 represents the different degrees of perception of travel costs by travelers. The larger the θ value, the smaller the perception error, and therefore the more likely the driver is to correctly choose the route with the lowest actual cost.

[0123] In theory, the pricing model based on model parameter purchases provides the most accurate predictions of user responses. However, directly integrating a detailed user response model into the grid dispatch model would introduce unnecessary computational overhead. In practice, grid dispatch and load management only care about the relationship between user load distribution and prices, not their specific travel needs and routes. Therefore, the following S2 is executed.

[0124] S2. Sampling is performed based on the random user equilibrium model to obtain samples, and regression fitting is performed on the samples to obtain a charging load price elasticity model.

[0125] The sampling based on the random user equilibrium model to obtain the sample includes:

[0126] The random user equilibrium model is solved multiple times to obtain solution results, and sampling is performed based on the solution results to obtain samples.

[0127] Considering that the user's response to price signals may show nonlinear characteristics such as saturation trend, the price-load samples obtained by sampling The charging load price elasticity model is obtained by performing regression fitting on the sample, specifically:

[0128] P CS =NLR(σ)=SEM T σ☉σ+EM T σ+P b ;

[0129] Where, P CSrepresents the charging power vector, σ represents the charging price, NLR() represents nonlinear regression, SEM represents the quadratic elastic coefficient matrix obtained by sample regression, EM represents the linear elastic coefficient matrix obtained by sample regression, P b Indicates the basic charging power.

[0130] Introducing the incidence matrix D CS Describe the association between charging station (CS) and grid node, D CS Elements Defined as:

[0131]

[0132] The charging load P at grid node i can be obtained CLi for:

[0133]

[0134] Where, P CSj P CS The element represents the charging load of charging station i, E ND Represents a collection of distribution network nodes.

[0135] S3. Establish an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establish DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints.

[0136] The objective function of establishing the minimum maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network is specifically:

[0137]

[0138] Where, Indicates the maximum load rate of substations at all levels in the transmission network, Indicates the maximum load rate of each level of the transmission network. Indicates the maximum load rate of each level of lines in the distribution network.

[0139] The ultra-high voltage transmission network usually operates in a ring. Therefore, the DC power flow constraints of the transmission network are specifically:

[0140]

[0141] 0≤P i ≤P i，max , i∈E NS ;

[0142] 0≤P ij ≤Pij，max ,i,j∈E NS ;

[0143] Where, P ij Indicates the power of branch ij, E NS represents the set of transmission network nodes, represents the power transfer distribution factor between branch ij and node k, P k represents the power injection of node k, P i represents the power injection of node i, P i,max represents the upper limit of the power injection of node i, P ij,max Indicates the upper power limit of branch ij.

[0144] The distribution network usually operates in a radial open loop. Therefore, the radial topology constraints of the distribution network are as follows:

[0145] β ij +β ji =α ij ,i,j∈E ND ;

[0146]

[0147] β kj =0,j∈E ND ,k∈E NS ;

[0148] ∑α ij =|E ND |;

[0149] Where, β ij Indicates the power direction of branch ij. A value of 1 indicates that the power flows from node i to node j. That is, in a radial network, node i is the parent node of node j. A value of 0 indicates that the power flows from node j to node i. ji Indicates the power direction of branch ji, α ij Indicates the switch state of branch ij. A value of 1 indicates that branch ij is connected to the closed loop, and a value of 0 indicates that branch ij is not connected to the closed loop. N(i) represents the set of all nodes connected to node i. β kj Indicates the power direction of branch kj.

[0150] The DC power flow constraints of the distribution network are specifically:

[0151]

[0152] P i =P RLi +P CLi , i∈E ND ;

[0153] 0≤P ij ≤P ij，max β ij ,i,j∈E ND ;

[0154] Where, P ik represents the unidirectional power flow from node i to node k, P ji represents the unidirectional power flow from node j to node i, P RLi Indicates normal load, P CLi Indicates charging load.

[0155] Considering the need for safe operation, the number of times the switch can be switched in each period is limited. The multi-period switch switching constraints are specifically as follows:

[0156]

[0157] Where, Indicates the switch status before the distribution network operation mode is adjusted, n sw Indicates the maximum number of switching operations.

[0158] The charging price adjustment constraints are specifically:

[0159]

[0160] Where, Indicates the lower limit of the charging price of the charging station corresponding to the virtual charging road, Indicates the upper limit of charging price for the charging station corresponding to the virtual charging road.

[0161] S4. Based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint, a main distribution operation collaborative optimization model is generated, and the main distribution operation collaborative optimization model is solved to obtain a collaborative optimization result.

[0162] Specifically, the charging load price elasticity model is used as a constraint, and a main distribution and operation collaborative optimization model is generated based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint. The main distribution and operation collaborative optimization model is a mixed integer programming with a small number of quadratic constraints, and the Gurobi solver is used to solve the main distribution and operation collaborative optimization model to obtain a collaborative optimization result.

[0163] The following example uses a reconfigurable power grid to verify the application of the main distribution and operation collaborative optimization model in urban power grid congestion management, focusing on the complementary potential of power grid reconfiguration and electric vehicle load shifting. The selected power grid example is a two-level voltage transmission and distribution system, where the 220kV transmission system includes 8 substations and 8 lines; the 110kV distribution system includes 40 substations and 58 lines, of which 41 lines have switchable switches. The initial network topology is as follows: Figure 6 The selected traffic network includes 12 intersections and 20 roads, with eight fast charging stations connected to eight 110kV substations in the power grid. The original charging price was set at 0.45 yuan / kWh, with adjustments ranging from 75% to 110% of the original price level.

[0164] This example is based on the peak load period. Combined with the two major means of grid reconstruction and electric vehicle fast charging load guidance, power-transportation collaborative congestion management is carried out. To avoid frequent switch actions, the number of switch actions in a single cycle is limited to 16 times. In order to evaluate the benefits of collaborative congestion management of main distribution operations, as shown in Table 1, four scenarios that introduce different congestion management methods are compared in Table 1. Among them, Scenario 1 is the basic situation where no congestion management measures are taken. Scenario 2 only guides the transfer of electric vehicle loads through price signals. Scenario 3 only uses grid reconstruction. Scenario 4 combines the two and introduces collaborative congestion management, which is the above-mentioned method of the present invention.

[0165] Table 1 Comparison of the effects of different congestion management methods

[0166] index Scenario 1 Scenario 2 Scenario 3 Scene 4 Charging load guidance × √ × √ Grid reconstruction × × √ √ 110kV line maximum load rate 0.727 0.667 0.711 0.556 220kV substation maximum load rate 0.836 0.766 0.571 0.646 220kV line maximum load rate 0.854 0.800 0.788 0.747 Number of switching operations 0 0 12 10

[0167] As shown in Table 1, during peak load periods, uneven load distribution resulted in overloaded substations and lines in Scenario 1. In Scenario 2, the maximum load factor was reduced by 5–7% through electric vehicle guidance. In contrast, Scenario 3 altered the connection status of 12 lines. Traditional distribution network operation adjustments significantly alleviated transmission congestion, but had a lesser impact on distribution congestion. In Scenario 4, based on collaborative congestion management, the maximum load factors of 110kV lines, 220kV substations, and 220kV lines were reduced to below 75%, representing reductions of 11%, 12%, and 5.5%, respectively, compared to Scenario 2. Scenario 4 also reduced the number of switching operations compared to Scenario 3.

[0168] In order to further analyze the mechanism of the cooperation between the two regulatory means, Figure 7-10 As shown, the network topology diagrams under four scenarios are further shown, focusing on the comparison Figure 8 Scene 2 and Figure 10Scenario 4 shows that in Scenario 2, while the network topology remains unchanged, charging stations C2, C3, C5, and C7 experience the largest price reductions, aiming to shift EV load away from the heavily loaded 220kV substation S2. In Scenario 4, however, more conventional load can be shifted away from S2 by adjusting the distribution network's operating mode. This, combined with the coordinated adjustment of charging prices, can further alleviate congestion at other substations.

[0169] Please refer to Figure 2 , the second embodiment of the present invention is:

[0170] A main distribution and operation collaborative optimization system considering charging load flexibility includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the main distribution and operation collaborative optimization method considering charging load flexibility in embodiment one is implemented.

[0171] In summary, the present invention provides a main distribution and operation collaborative optimization method and system considering the flexibility of charging load, establishes a random user equilibrium model based on the constructed traffic network model and path toll model, performs sampling based on the random user equilibrium model, and performs regression fitting on the samples to obtain a charging load price elasticity model, establishes an objective function for minimizing the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establishes DC power flow constraints of the transmission network, radial topology constraints of the distribution network, DC power flow constraints of the distribution network, multi-period switch switching constraints and charging price adjustment constraints, generates a main distribution and operation collaborative optimization model based on the objective function, charging load price elasticity model and constraints, and solves it to obtain a collaborative optimization result, thereby utilizing the charging price. Adjustments are made to adjust the transfer range of charging load, and charging load is used as a continuously adjustable flexible resource. When risks are predicted, while adjusting the operation mode of relevant distribution lines, price signals can be issued in advance to guide vehicles with charging needs in the future to complete charging at locations that are beneficial to the safety of the power grid. As a flexible supplement to the power grid's own control measures, it enriches the means of power transfer when equipment is heavily overloaded, thereby improving the flexibility and safety of urban distribution network operation; in addition, by determining the travel time function of each road, it can accurately reflect the congestion situation and traffic efficiency of the road, making the traffic network model closer to the actual operating status. The establishment of the traffic network model and the path toll model provides key information on the traffic side for the subsequent main distribution and operation collaborative optimization model.

[0172] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A main distribution and operation collaborative optimization method considering charging load flexibility, characterized in that: Including steps: Constructing a traffic network model and a path toll model, and establishing a stochastic user equilibrium model based on the traffic network model and the path toll model; Sampling is performed based on the random user equilibrium model to obtain samples, and regression fitting is performed on the samples to obtain a charging load price elasticity model; Establish an objective function to minimize the maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network, and establish DC power flow constraints for the transmission network, radial topology constraints for the distribution network, DC power flow constraints for the distribution network, multi-period switching constraints, and charging price adjustment constraints; Based on the objective function, the charging load price elasticity model, the transmission network DC power flow constraint, the distribution network radial topology constraint, the distribution network DC power flow constraint, the multi-period switch switching constraint and the charging price adjustment constraint, a main distribution operation collaborative optimization model is generated, and the main distribution operation collaborative optimization model is solved to obtain a collaborative optimization result.

2. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 1 is characterized in that: The construction of the traffic network model and the path toll model includes: Determine the travel time function of each road and establish flow conservation constraints, road and path association constraints, and path flow non-negativity constraints; Generate a traffic network model based on the travel time function of each road, the flow conservation constraint, the road-path association constraint, and the path flow non-negative constraint; The toll of each road is determined, and a path toll model is established based on the toll of each road.

3. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 2 is characterized in that: The travel time function of each road is specifically: Where, t a represents the travel time of road a, t a 0 represents the travel time when the traffic volume of road a is 0, x a represents the traffic flow on road a, Q a r represents the capacity of conventional roads, represents the set of regular roads, Q a c represents the capacity of the virtual charging road, represents the set of virtual charging roads, represents a set of virtual bypass roads; The flow conservation constraint is specifically: Where, d w represents the total travel demand for the origin-destination pair w, represents the set of feasible paths for the start-end pair w, f rw represents the traffic flow on the path r of the origin-destination pair w, Represents the travel demand set of travel users; The road and path association constraints are specifically: Where, δ ar represents the path-branch correlation coefficient, Represents the set of roads in the transportation network; The path flow non-negative constraint is specifically:

4. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 3 is characterized in that: The path toll model is specifically: Where c rw represents the toll of the path, c a represents the toll of road a.

5. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 4 is characterized in that: The establishing of a stochastic user equilibrium model based on the traffic network model and the path toll model includes: An objective function of a random user equilibrium model is established with the goal of minimizing the perceived cost, and the traffic network model and the path toll model are used as constraints of the random user equilibrium model.

6. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 5 is characterized in that: The objective function of the random user equilibrium model is specifically: Where, F T represents the perceptual cost, and θ represents the perceptual error parameter.

7. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 1 is characterized in that: The charging load price elasticity model is obtained by performing regression fitting on the sample, specifically: P CS =NLR(σ)=SEM T σ☉σ+EM T σ+P b ; Where, P CS represents the charging power vector, σ represents the charging price, NLR() represents nonlinear regression, SEM represents the quadratic elastic coefficient matrix obtained by sample regression, EM represents the linear elastic coefficient matrix obtained by sample regression, P b Indicates the basic charging power.

8. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 1 is characterized in that: The objective function of establishing the minimum maximum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network is specifically: Where, Indicates the maximum load rate of substations at all levels in the transmission network, Indicates the maximum load rate of each level of the transmission network. Indicates the maximum load rate of each level of lines in the distribution network.

9. The main distribution and operation collaborative optimization method considering charging load flexibility according to claim 1 is characterized in that: The DC power flow constraint of the transmission network is specifically: 0≤P i ≤P i,max ,i∈E NS ; 0≤P ij ≤P ij,max ,i,j∈E NS ; Where, P ij Indicates the power of branch ij, E NS represents the set of transmission network nodes, represents the power transfer distribution factor between branch ij and node k, P k represents the power injection of node k, P i represents the power injection of node i, P i,max represents the upper limit of the power injection of node i, P ij,max Indicates the power upper limit of branch ij; The radial topology constraints of the distribution network are specifically: b ij +b ji =a ij ,j∈E ND ; b kj =0,j∈E ND ,k∈E NS ; You ij =|E ND |; Where, β ij Indicates the power direction of branch ij, β ij Indicates the power direction of branch ji, α ij Indicates the switch state of branch ij, E ND represents the set of nodes in the distribution network, N(i) represents the set of all nodes connected to node i, β kj Indicates the power direction of branch kj; The DC power flow constraints of the distribution network are specifically: P i =P RLi +P CLi ,i∈E ND ; 0≤P ij ≤P ij，max b ij ,i,j∈E ND ; Where, P ik represents the unidirectional power flow from node i to node k, P ji represents the unidirectional power flow from node j to node i, P RLi Indicates normal load, P CLi Indicates charging load; The multi-period switch switching constraint is specifically: Where, Indicates the switch status before the distribution network operation mode is adjusted, n sw Indicates the maximum number of switching operations; The charging price adjustment constraints are specifically: Where, represents the lower limit of the charging price of the charging station corresponding to the virtual charging road, σ a Indicates the charging price of the charging station corresponding to the virtual charging road, Indicates the upper limit of charging price for the charging station corresponding to the virtual charging road.

10. A main distribution and operation collaborative optimization system considering charging load flexibility, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the main distribution and operation collaborative optimization method considering charging load flexibility according to any one of claims 1 to 9 is implemented.

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