A method and system for coordinated optimization of main distribution considering charging load flexibility

Abstract

The application discloses a kind of considering charging load flexibility main distribution camp collaborative optimization method and system, is established random user equilibrium model based on the traffic network model and path traffic expense model established, sampling is carried out based on random user equilibrium model, and sample is fitted to obtain charging load price elasticity model, the objective function of minimizing maximum load rate of each level transformer substation and line in power transmission network and each level line in distribution network is established, and power transmission network direct current flow constraint, distribution network radial topological constraint, distribution network direct current flow constraint, multi-period switch switching constraint and charging price adjustment constraint are established, collaborative optimization model is generated based on objective function, charging load price elasticity model and constraint, and it is solved, and collaborative optimization result is obtained, and the power transfer means of equipment heavy overload time is enriched, so as to improve the flexibility and safety of city distribution network operation.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method and system for coordinating the optimization of main and distribution operations while considering the flexibility of charging load. Background Technology

[0002] Urban distribution networks frequently experience localized overloads of main transformers or feeders. Currently, dispatching departments often adjust the distribution network operation mode to transfer power, which essentially involves changing the network topology by switching some switches, allowing loads at various nodes in the distribution network to be transferred between substations in the upstream transmission system to optimize power flow distribution. This is equivalent to a kind of discrete regulation "tiered switching." However, the number of flexibly adjustable switches in the distribution network is relatively limited, the topology combinations for distribution network operation mode adjustments are limited, and frequent adjustments are not advisable. The target of load transfer is also limited to nearby substations. At the same time, a one-time large-scale load transfer may also create new overload risks in the future. Therefore, there is an urgent need to supplement new flexible regulation resources to enrich the power transfer methods during equipment overload and improve the flexibility and safety of urban distribution network operation. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for coordinating the operation of the main distribution network and the distribution system, which takes into account the flexibility of charging load, and can improve the flexibility and safety of urban power distribution network operation.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A primary-secondary-secondary-operational coordinated optimization method considering charging load flexibility includes the following steps:

[0006] Construct a traffic network model and a route toll model, and establish a stochastic user equilibrium model based on the traffic network model and the route toll model;

[0007] Based on the aforementioned random user equilibrium model, a sample is obtained, and a regression fitting is performed on the sample 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-time period switching constraints, and charging price adjustment constraints.

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

[0010] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0011] A primary-secondary cooperative 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, it performs the following steps:

[0012] Construct a traffic network model and a route toll model, and establish a stochastic user equilibrium model based on the traffic network model and the route toll model;

[0013] Based on the aforementioned random user equilibrium model, a sample is obtained, and a regression fitting is performed on the sample 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-time period switching constraints, and charging price adjustment constraints.

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

[0016] The beneficial effects of this invention are as follows: A stochastic user equilibrium model is established based on the constructed traffic network model and route toll cost model. Sampling is performed based on the stochastic 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. Constraints are also established for DC power flow in the transmission network, radial topology in the distribution network, DC power flow in the distribution network, multi-time-period switching constraints, and charging price adjustment constraints. Based on the objective function, the charging load price elasticity model, and the constraints, a main-distribution-operation collaborative optimization model is generated and solved to obtain the collaborative optimization results. This allows for the adjustment of charging prices to regulate the magnitude of charging load transfer, treating the charging load as a continuously adjustable flexible resource. In the event of anticipated risks, 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 locations favorable to grid safety for charging. This serves as a flexible supplement to the grid's own control measures, enriching the means of power transfer during periods of heavy equipment overload, thereby improving the flexibility and safety of urban distribution network operation. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a main-operational coordination optimization method that considers charging load flexibility according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of a main-distribution cooperative optimization system that considers 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 the main and auxiliary operation coordination optimization method that considers charging load flexibility in an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of vehicle arrival and departure from charging station in the main-distribution-operational collaborative optimization method considering charging load flexibility in an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of an extended network that incorporates virtual charging roads and bypass roads in the main and auxiliary operation coordination optimization method that considers charging load flexibility in an embodiment of the present invention.

[0022] Figure 6 This is a schematic diagram of a power-transportation coupled network that takes into account the adjustment of distribution network operation mode in the main distribution and operation coordination optimization method that considers the flexibility of charging load in an embodiment of the present invention.

[0023] Figure 7 This is a schematic diagram of the load rate distribution in scenario 1 of the main and secondary operation collaborative optimization method considering charging load flexibility in an embodiment of the present invention.

[0024] Figure 8 This is a schematic diagram of the load rate distribution in scenario 2 of the main and secondary operation collaborative optimization method considering charging load flexibility in an embodiment of the present invention.

[0025] Figure 9 This is a schematic diagram of the load rate distribution in scenario 3 of the main and auxiliary cooperative optimization method considering charging load flexibility in an embodiment of the present invention.

[0026] Figure 10 This is a schematic diagram of the load rate distribution in scenario 4 of the main and auxiliary cooperative optimization method that considers charging load flexibility in an embodiment of the present invention. Detailed Implementation

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

[0028] Please refer to Figure 1 A primary-secondary-secondary-operational coordinated optimization method considering charging load flexibility includes the following steps:

[0029] Construct a traffic network model and a route toll model, and establish a stochastic user equilibrium model based on the traffic network model and the route toll model;

[0030] Based on the aforementioned random user equilibrium model, a sample is obtained, and a regression fitting is performed on the sample 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-time period switching constraints, and charging price adjustment constraints.

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

[0033] As can be seen from the above description, the beneficial effects of this invention are as follows: A stochastic user equilibrium model is established based on the constructed traffic network model and path toll cost model. Sampling is performed based on the stochastic 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. Constraints are also established for DC power flow in the transmission network, radial topology in the distribution network, DC power flow in the distribution network, multi-time-period switching constraints, and charging price adjustment constraints. Based on the objective function, the charging load price elasticity model, and the constraints, a main-distribution-operation collaborative optimization model is generated and solved to obtain the collaborative optimization results. This allows for the adjustment of charging prices to regulate the magnitude of charging load transfer, treating the charging load as a continuously adjustable flexible resource. In the event of anticipated risks, 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 locations favorable to grid safety for charging. This serves as a flexible supplement to the grid's own control measures, enriching the means of power transfer during periods of heavy equipment overload, thereby improving the flexibility and safety of urban distribution network operation.

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

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

[0036] A traffic network model is generated based on the travel time function of each road, the flow conservation constraint, the road-path association constraint, and the path flow non-negativity constraint.

[0037] Determine the toll fees for each road and establish a route toll fee model based on the toll fees for each road.

[0038] As described above, by determining the travel time function of each road, the congestion and traffic efficiency of the roads can be accurately reflected, making the traffic network model closer to the actual operating state. The establishment of the traffic network model and the route toll cost model provides key information on the traffic side for the subsequent main-support-operation coordinated optimization model.

[0039] Furthermore, the travel time function for each road is specifically as follows:

[0040]

[0041] In the formula, t a t represents the travel time on road a. a 0 x represents the travel time when the traffic flow on road a is 0. aQ represents the traffic flow on road a. a r Indicates the capacity of conventional roads. Let Q represent the set of regular roads. a c This indicates the capacity of the virtual charging road. Represents a set of virtual charging roads. Represents a set of virtual bypass roads;

[0042] The flow conservation constraint is specifically as follows:

[0043]

[0044] In the formula, d w Let w represent the total travel demand from origin to destination. f represents the set of feasible paths for a start-end pair w. rw This represents the traffic flow on the path r of the origin-end pair w. This represents the set of travel needs of users.

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

[0046]

[0047] In the formula, δ ar Represents the path-branch correlation coefficient. Represents the set of roads in a transportation network;

[0048] The non-negativity constraint on path flow is specifically as follows:

[0049]

[0050] As described above, by combining the constraints of traffic flow conservation, road-path relationship, and non-negativity of path flow, the model can reasonably simulate the distribution of traffic flow and path selection, providing a reliable foundation for subsequent user equilibrium analysis.

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

[0052]

[0053] In the formula, c rw c represents the toll cost of the path. a This represents the toll for road a.

[0054] As described above, constructing a path toll cost model and combining it with a traffic network model enables the stochastic user equilibrium model to more accurately simulate user behavior under different path choices, taking into account users' sensitivity to time and cost.

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

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

[0057] As described above, establishing the objective function of the stochastic user equilibrium model with the goal of minimizing perceived cost, and using the traffic network model and the route toll model as constraints, can more realistically simulate users' comprehensive trade-off between time and cost when choosing a travel route, thereby more accurately predicting user behavior and taking into account the randomness and diversity of user choices.

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

[0059]

[0060] In the formula, F T Let θ represent the perceived cost, and let θ represent the perceived error parameter.

[0061] As can be seen from the above description, considering the user's perceptual bias and the randomness of decision-making, a discrete choice model is used to model their decision-making, making the constructed stochastic user equilibrium model more accurate.

[0062] Furthermore, the regression fitting of the sample to obtain the charging load price elasticity model specifically involves:

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

[0064] In the formula, P CS Let P represent the charging power vector, σ represent the charging price, NLR() represent nonlinear regression, SEM represent the quadratic elasticity coefficient matrix obtained from the sample regression, EM represent the linear elasticity coefficient matrix obtained from the sample regression, and P represent the charging power vector vector. b This indicates the basic charging power.

[0065] As can be seen from the above description, the sensitivity of charging load to price changes and the trend of charging load changes under different price levels can be understood more deeply through the quadratic elasticity coefficient matrix and the linear elasticity coefficient matrix.

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

[0067]

[0068] In the formula, This indicates the maximum load factor of substations at all levels in the power transmission network. This indicates the maximum load rate of each level of the transmission network. This indicates the maximum load rate of each level of lines in the distribution network.

[0069] As described above, constructing an objective function that minimizes 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, reduce the load pressure on substations and lines, help extend their service life, and improve the reliability and stability of power grid operation.

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

[0071]

[0072] 0≤P i ≤P i,max ,i∈E NS ;

[0073] 0≤P ij ≤P ij，max ,i,j∈E NS ;

[0074] In the formula, P ij E represents the power of branch ij. NS Represents the set of nodes in a power transmission network. P represents the power transfer distribution factor between branch ij and node k. k P represents the power injection at node k. i P represents the power injection at node i. i,max P represents the upper limit of power injection at node i. ij,max This indicates the upper limit of power for branch ij;

[0075] The radial topological constraints of the distribution network are specifically as follows:

[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] In the formula, β ij β represents the power direction of branch ij. ji Indicates the power direction of branch ji, α ij E represents the switching state of branch ij. ND Let N(i) represent the set of nodes in the power distribution network, and let β represent the set of all nodes connected to node i. kj Indicates the power direction of branch kj;

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

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

[0086] The multi-time period switching constraint is specifically as follows:

[0087]

[0088] In the formula, Indicates the switch status before the distribution network operation mode adjustment, n sw Indicates the maximum number of switching operations;

[0089] The charging price adjustment constraint is specifically as follows:

[0090]

[0091] In the formula, σ represents the lower limit of the charging price at the charging station corresponding to the virtual charging road. aThis indicates the charging price at the charging station corresponding to the virtual charging road. This indicates the upper limit of the charging price for the charging station corresponding to the virtual charging road.

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

[0093] Please refer to Figure 2 Another embodiment of the present invention provides a main-operational coordination optimization system that considers charging load flexibility, 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, it implements the various steps in the above-described main-operational coordination optimization method that considers charging load flexibility.

[0094] The above-described method and system for main and distribution network coordinated optimization considering charging load flexibility is applicable to urban power grids. The following detailed embodiments illustrate this method:

[0095] Please refer to Figure 1 , Figures 3-10 Embodiment 1 of the present invention is as follows:

[0096] A primary-secondary-secondary-operational coordinated optimization method considering charging load flexibility includes the following steps:

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

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

[0099] An urban transportation network can be described as a connected directed graph. Node set This represents the convergence of directed branch roads at various intersections in the transportation network. It refers to roads in a transportation network.

[0100] Traffic flow on road 'a' in a transportation network is similar to the concept of electric current, defined as the number of vehicles passing through a certain cross-section per unit time, measured in vehicles per hour (v / h). The travel demands of users in a transportation network can be clustered into multiple origin-destination (OD) pairs, forming a travel demand set. Each OD pair Includes origin r, destination s, and traffic flow d w Three-dimensional 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 the road Then δ ar =1, otherwise, δ ar =0. Vehicles operating in the transportation network can be divided into vehicles that require fast charging and vehicles that do not require fast charging.

[0101] Assuming the charging station is located near an intersection, such as Figure 3 As shown, vehicles with charging needs can reach and leave charging stations from various directions via regular road sections, such as... Figure 4 As shown in the diagram. Due to the limited capacity of urban roads, congestion is a common phenomenon, meaning that road travel time increases with traffic flow. Similarly, the limited number of charging stations results in congestion, where queuing time increases with the number of vehicles heading to the station. Therefore, considering the similarities between traffic congestion and charging station queuing, a fusion modeling approach can be adopted to establish a system like... Figure 5 The extended network shown.

[0102] In this system, virtual charging roads are introduced to represent vehicle queuing and charging behavior at charging stations, based on the set of regular roads. Virtual bypass roads are also introduced to represent other vehicles not entering the charging station. Assuming a vehicle with fast charging needs requires one charging trip during its journey (i.e., choosing a path that passes through a virtual charging road), the travel time function for each road is as follows:

[0103]

[0104] In the formula, t a t represents the travel time on road a. a 0 x represents the travel time when the flow rate on road a is 0, i.e., the free-flow time to pass through the road / complete charging. a Q represents the traffic flow on road a. a r Indicates the capacity of conventional roads. Let Q represent the set of regular roads. a c This indicates the capacity of the virtual charging road. Represents a set of virtual charging roads. This represents a set of virtual bypass roads.

[0105] The flow conservation constraint is specifically as follows:

[0106]

[0107] In the formula, d wLet w represent the total travel demand from origin to destination. f represents the set of feasible paths for a start-end pair w. rw This represents the traffic flow on the path r of the origin-end pair w. This represents the set of travel needs of users.

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

[0109]

[0110] In the formula, δ ar Represents the path-branch correlation coefficient. This represents the set of roads in a transportation network.

[0111] The non-negativity constraint on path flow is specifically as follows:

[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-negativity constraint.

[0114] S13. Determine the toll fees for each road and establish a route toll fee model based on the toll fees for each road.

[0115] From a macro perspective, traffic flow is the result of decisions made by various travel users at the micro level. Assuming that travelers tend to choose the route with the lowest travel cost, and the toll for a route is c... rw This represents the sum of the costs of the roads traversed. Therefore, the specific path toll model is as follows:

[0116]

[0117] In the formula, c rw c represents the toll cost of the path. a Let E represent the toll cost for road a, γ represent the value of a unit of commuting time, and E represent the cost of commuting. ev σ represents the average charging energy of a vehicle. a This indicates the charging price at the charging station corresponding to the virtual charging road.

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

[0119] S14. Establish the objective function of the stochastic user equilibrium model with the goal of minimizing perceived cost, and use the traffic network model and the path toll model as constraints of the stochastic user equilibrium model.

[0120] Given network parameters and price signals, all users will choose the path that maximizes perceived utility (i.e., minimizes perceived cost). When no user can unilaterally reduce their perceived cost by changing their path choice, the traffic flow in the network reaches a stochastic user equilibrium state. This equilibrium state is solved using a stochastic user equilibrium model, where the objective function of the stochastic user equilibrium model is specifically:

[0121]

[0122] In the formula, F T Let θ represent the perceived cost, and let θ represent the perception error parameter. θ > 0 indicates that travelers have different levels of perception of travel costs. The larger the value of θ, 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 parameters described above provides the most accurate prediction of user response. However, directly integrating the detailed user response model into the power grid dispatching model would introduce some unnecessary computational burdens. In reality, power grid dispatching and load management only concern themselves with the relationship between user load distribution and price, and not with users' specific travel needs and routes; therefore, the following step S2 is performed.

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

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

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

[0127] Considering that users' responses to price signals may exhibit non-linear characteristics such as saturation trends, the sampled price-load samples... The regression fitting of the sample to obtain the charging load price elasticity model is as follows:

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

[0129] In the formula, P CSLet P represent the charging power vector, σ represent the charging price, NLR() represent nonlinear regression, SEM represent the quadratic elasticity coefficient matrix obtained from the sample regression, EM represent the linear elasticity coefficient matrix obtained from the sample regression, and P represent the charging power vector vector. b This indicates the basic charging power.

[0130] Introducing the correlation matrix D CS Describe the relationship between charging stations (CS) and grid nodes, D CS elements Defined as:

[0131]

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

[0133]

[0134] In the formula, P CSj For P CS The element represents the charging load of charging station i, E. ND This represents the set of nodes in a power distribution network.

[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-time period switching constraints, and charging price adjustment constraints.

[0136] Specifically, the objective function for establishing the minimum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network is as follows:

[0137]

[0138] In the formula, This indicates the maximum load factor of substations at all levels in the power transmission network. This indicates the maximum load rate of each level of the transmission network. This indicates the maximum load rate of each level of lines in the distribution network.

[0139] Ultra-high voltage transmission networks typically operate in a loop; therefore, the DC power flow constraints of the aforementioned transmission network are specifically as follows:

[0140]

[0141] 0≤P i ≤P i，max , i∈E NS ;

[0142] 0≤P ij ≤Pij，max , i, j∈E NS ;

[0143] In the formula, P ij E represents the power of branch ij. NS Represents the set of nodes in a power transmission network. P represents the power transfer distribution factor between branch ij and node k. k P represents the power injection at node k. i P represents the power injection at node i. i,max P represents the upper limit of power injection at node i. ij,max This indicates the upper limit of the power of branch ij.

[0144] Distribution networks typically operate in a radial open-loop configuration; therefore, the radial topology constraints of the distribution network are specifically 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] In the formula, β ij β represents the power direction of branch ij. A value of 1 indicates that the power flow is from node i to node j, meaning that in a radial network, node i is the parent node of node j. A value of 0 indicates that the power flow is from node j to node i. ji Indicates the power direction of branch ji, α ij This represents the switching state of branch ij, where a value of 1 indicates that branch ij is connected and in a loop, and a value of 0 indicates that branch ij is not connected and in a loop. N(i) represents the set of all nodes connected to node i, and β kj This indicates the power direction of branch kj.

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

[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] In the formula, P ik P represents a unidirectional power flow from node i to node k. ji P represents a unidirectional power flow from node j to node i. RLi P represents normal load. CLi This indicates the charging load.

[0155] Considering the need for safe operation, the number of switching operations of the switch within each time period is limited. The specific multi-time period switch switching constraint is as follows:

[0156]

[0157] In the formula, Indicates the switch status before the distribution network operation mode adjustment, n sw This indicates the maximum number of switching operations.

[0158] The charging price adjustment constraint is specifically as follows:

[0159]

[0160] In the formula, This indicates the lower limit of the charging price at the charging station corresponding to the virtual charging road. This indicates the upper limit of the 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 DC power flow constraints of the transmission network, the radial topology constraints of the distribution network, the DC power flow constraints of the distribution network, the multi-time period switching constraints, and the charging price adjustment constraints, a main-distribution-operation collaborative optimization model is generated, and the main-distribution-operation collaborative optimization model is solved to obtain the collaborative optimization results.

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

[0163] The following section validates the application of the main-distribution-operational coordinated optimization model in urban power grid congestion management using a reconfigurable power grid example, focusing on the complementary potential of power grid reconfiguration and electric vehicle load transfer. The selected power grid example is a two-level voltage transmission-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 are switchable. The initial network topology is as follows: Figure 6 As shown. The selected transportation network includes 12 intersections and 20 roads, with 8 fast charging stations connected to 8 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 peak load periods. It employs a combination of grid reconfiguration and electric vehicle fast-charging load redirection to implement power-transport coordinated congestion management. To avoid frequent switching operations, the number of switching operations per cycle is limited to 16. To evaluate the effectiveness of the main distribution and transportation coordinated congestion management, Table 1 compares four scenarios introducing different congestion management methods. Scenario 1 represents the basic case without congestion management measures. Scenario 2 guides the transfer of electric vehicle load solely through price signals. Scenario 3 uses only grid reconfiguration. Scenario 4 combines both methods, introducing coordinated congestion management, i.e., the method described above in this invention.

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

[0166] Charging load guidance × √ × √ Power grid reconfiguration × × √ √ 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 actions 0 0 12 10

[0167] As shown in Table 1, during peak load periods, uneven load distribution leads to some substations and lines being under heavy load in Scenario 1. In Scenario 2, electric vehicle guidance reduces the maximum load rate by 5-7%. In contrast, Scenario 3 alters the connection status of 12 lines. Traditional distribution network operation adjustment measures effectively alleviate transmission system congestion, but have a poor effect on distribution system congestion. In Scenario 4, based on collaborative congestion management, the maximum load rate of 110kV lines, 220kV substations, and 220kV lines is reduced to below 75%, representing reductions of 11%, 12%, and 5.5% respectively compared to Scenario 2. Furthermore, Scenario 4 reduces the number of switching operations compared to Scenario 3.

[0168] To further analyze the mechanism of the combination of the two regulatory methods, such as Figures 7-10 As shown, network topology diagrams for four scenarios are further illustrated, with a focus on comparison. Figure 8 Scene 2 and Figure 10As seen in Scenario 4: In Scenario 2, with the network topology unchanged, charging stations C2, C3, C5, and C7 experienced the largest price reductions, aiming to shift the electric vehicle load away from the heavily loaded 220kV substation S2. In Scenario 4, however, more conventional loads can be shifted away from S2 through adjustments to the distribution network operation mode. Combined with coordinated adjustments to charging prices, this can better alleviate congestion at other substations.

[0169] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0170] A primary-secondary-secondary co-operation optimization system that considers 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, it implements the various steps of the primary-secondary-secondary-secondary co-operation optimization method that considers charging load flexibility in Embodiment 1.

[0171] In summary, this invention provides a primary-distribution-operational coordinated optimization method and system that considers the flexibility of charging load. Based on a constructed traffic network model and a route toll cost model, a stochastic user equilibrium model is established. Sampling is performed based on the stochastic user equilibrium model, and regression fitting is applied to 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. Constraints are also established for the DC power flow of the transmission network, the radial topology of the distribution network, the DC power flow of the distribution network, multi-time-period switching constraints, and charging price adjustment constraints. Based on the objective function, the charging load price elasticity model, and the constraints, a primary-distribution-operational coordinated optimization model is generated and solved to obtain the coordinated optimization result. This allows the utilization of charging prices... Adjusting the charging load transfer range allows for the utilization of a continuously adjustable flexible resource. This enables the system to anticipate risks and, while adjusting the operation of relevant power distribution lines, issue price signals in advance. This guides vehicles with future charging needs to charge at locations favorable to grid safety, serving as a flexible supplement to the grid's own control mechanisms. It enriches the means of power transfer during periods of heavy equipment overload, thereby enhancing the flexibility and safety of urban power distribution network operation. Furthermore, by determining the travel time function for each road, the system accurately reflects road congestion and traffic efficiency, making the traffic network model closer to actual operating conditions. The establishment of the traffic network model and the route cost model provides crucial traffic-side information for subsequent main-distribution-operation coordinated optimization models.

[0172] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A primary-secondary-secondary-operational coordinated optimization method considering charging load flexibility, characterized in that, Including the following steps: Construct a traffic network model and a route toll model, and establish a stochastic user equilibrium model based on the traffic network model and the route toll model; Based on the aforementioned random user equilibrium model, a sample is obtained, and a regression fitting is performed on the sample to obtain a charging load price elasticity model. The regression fitting of the sample to obtain the charging load price elasticity model is as follows: ； In the formula, P CS Represents the charging power vector. This represents the charging price, and NLR() represents nonlinear regression. SEM This represents the matrix of elasticity coefficients of the quadratic term obtained from the sample regression. EM This represents the elasticity coefficient matrix of the first-order term obtained from the sample regression. P b Indicates the basic charging power; 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-time period switching constraints, and charging price adjustment constraints. The DC power flow constraints of the power transmission network are specifically as follows: ； ； ； In the formula, P ij Indicates a branch ij power, E NS Represents the set of nodes in a power transmission network. Indicates a branch ij With nodes k The power transfer distribution factor between them P k Represents a node k Power injection, P i Represents a node i Power injection, P i,max Represents a node i The upper limit of power injection, P ij,max Indicates a branch ij The upper limit of power; The radial topological constraints of the distribution network are specifically as follows: ； ； ； ； In the formula, Indicates a branch ij The direction of power, Indicates a branch ji The direction of power, Indicates a branch ij The on / off state, E ND Represents the set of nodes in a power distribution network. N ( i ) represents all nodes connected to. i The set of nodes, Indicates a branch kj The direction of power; The DC power flow constraints of the distribution network are specifically as follows: ； ； ； In the formula, P ik Indicates that by node i Flow to Node k One-way trend P ji Indicates that by node j Flow to Node i One-way trend P RLi Indicates normal load. P CLi Indicates charging load; The multi-time period switching constraint is specifically as follows: ； In the formula, Indicates the switch status before the distribution network operation mode was adjusted. n sw Indicates the maximum number of switching operations; The charging price adjustment constraint is specifically as follows: ； In the formula, This indicates the lower limit of the charging price at the charging station corresponding to the virtual charging road. This indicates the charging price at the charging station corresponding to the virtual charging road. This indicates the upper limit of the charging price at the charging station corresponding to the virtual charging road; Through the correlation matrix D CS This indicates the association between the charging station CS and the grid node. D CS elements Defined as: ； Obtain power grid nodes i Under charging load for: ； In the formula, P CSj for P CS The element represents the charging station. i The charging load, E ND Represents the set of nodes in a power distribution network; Based on the objective function, the charging load price elasticity model, the DC power flow constraints of the transmission network, the radial topology constraints of the distribution network, the DC power flow constraints of the distribution network, the multi-time period switching constraints, and the charging price adjustment constraints, a main-distribution-operation collaborative optimization model is generated, and the main-distribution-operation collaborative optimization model is solved to obtain the collaborative optimization results.

2. The main and distribution cooperative optimization method considering charging load flexibility according to claim 1, characterized in that, The construction of the traffic network model and route toll cost model includes: Determine the travel time function for each road, and establish flow conservation constraints, road-path relationship constraints, and path flow non-negativity constraints; A traffic network model is generated based on the travel time function of each road, the flow conservation constraint, the road-path association constraint, and the path flow non-negativity constraint. Determine the toll fees for each road and establish a route toll fee model based on the toll fees for each road.

3. The main and distribution cooperative optimization method considering charging load flexibility according to claim 2, characterized in that, The travel time function for each road is as follows: ； In the formula, t a Indicates road a The passage time, t a 0 Indicates road a Passage time when traffic is 0 x a Indicates road a Traffic flow on the road Q a r Indicates the capacity of conventional roads. Represents the set of regular roads. Q a c This indicates the capacity of the virtual charging road. Represents a set of virtual charging roads. Represents a set of virtual bypass roads; The flow conservation constraint is specifically as follows: ； In the formula, d w Indicates a start-end pair w Total travel demand Indicates a start-end pair w The set of feasible paths f rw Indicates a start-end pair w path r Traffic flow on the road This represents the set of travel needs of users. The road and path association constraints are specifically as follows: ； In the formula, Represents the path-branch correlation coefficient. Represents the set of roads in a transportation network; The non-negativity constraint on path flow is specifically as follows: 。 4. The main and distribution cooperative optimization method considering charging load flexibility according to claim 3, characterized in that, The route toll model is as follows: ； In the formula, c rw Indicates the toll cost for the route. c a Indicates road a The toll fees.

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

6. The main and distribution cooperative optimization method considering charging load flexibility according to claim 5, characterized in that, The objective function of the stochastic user equilibrium model is as follows: ； In the formula, F T Indicates perceived cost, This represents the sensing error parameter.

7. The main and distribution cooperative optimization method considering charging load flexibility according to claim 1, characterized in that, The objective function for establishing the minimum load rate of substations and lines at all levels in the transmission network and lines at all levels in the distribution network is as follows: ； In the formula, This indicates the maximum load factor of substations at all levels in the power transmission network. This indicates the maximum load rate of each level of the transmission network. This indicates the maximum load rate of each level of lines in the distribution network.

8. A primary-secondary cooperative 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, it implements each step of the main and auxiliary cooperative optimization method that takes into account the charging load flexibility as described in any one of claims 1 to 7.

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