Space-time coordination optimization scheduling method for mobile pressure regulating device under traffic constraint

Abstract

The application relates to the technical field of power distribution network voltage regulation, in particular to a space-time coordinated optimization scheduling method for a mobile voltage regulating device under traffic constraints, which comprises the following steps: acquiring basic operation data of a power distribution network and traffic network topology data, and solving a shortest passing path matrix among nodes; constructing an optimization function with the minimum system daily operation cost as an objective, wherein a mobile cost item is determined by the shortest passing path matrix and a cross-node mobile decision variable; constructing a constraint condition system, including a traffic network mobile accessibility constraint, a device operation capacity constraint and a power distribution network safe operation constraint; combining the optimization function and the constraint condition to form a mixed integer linear programming model and solving the model, and outputting the access position, mobile path and reactive power output value of each device at each time period. Through power distribution network-traffic network coordinated optimization, the space-time joint scheduling of the device access position and the reactive power output is realized, and the end voltage out-of-limit problem is effectively solved.

Description

Technical Field

[0001] This application relates to the field of distribution network voltage regulation technology, and in particular to a spatiotemporal collaborative optimization scheduling method for mobile voltage regulating devices under traffic constraints. Background Technology

[0002] With the large-scale grid connection of high proportions of renewable energy and the widespread integration of new loads such as electric vehicles, the power distribution network (hereinafter referred to as the distribution network) exhibits significant uncertainties on both the source and load sides. This, coupled with the inherent weaknesses of the distribution network structure—large power supply radius at the end lines, thin conductor cross-sections, and high line impedance—leads to frequent low-voltage exceedances at the distribution network's end and localized voltage rises caused by reverse power flows from distributed photovoltaic systems. These voltage fluctuations are characterized by randomness, intermittency, and localized distribution, severely impacting the stability of user power supply, accelerating the aging of distribution network equipment, and threatening the operational safety of the distribution network.

[0003] Existing voltage regulation technologies still have shortcomings: First, conventional fixed voltage regulating devices are installed in fixed locations, limiting their regulation range and making it impossible to dynamically follow the spatiotemporal changes in load and power supply at the end of the distribution network. This results in insufficient voltage regulation coverage for remote nodes and low effective utilization of the devices. Second, existing mobile resources such as mobile energy storage and generator vehicles focus on active power support and emergency power supply. When used in pure reactive power compensation scenarios, they suffer from equipment redundancy and high operating costs, making it difficult to meet the localized and intermittent reactive power deficit management needs at the end of the distribution network.

[0004] In view of the above problems, there is an urgent need for a reactive power compensation technology solution that can adapt to the voltage fluctuation characteristics at the end of the distribution network and take into account both flexibility and economy, so as to solve the technical problems of insufficient dynamic response, low local compensation accuracy, and high operating costs of existing devices. To this end, this patent proposes a spatiotemporal collaborative optimization scheduling method for mobile voltage regulating devices under distribution network-traffic constraints. By dynamically optimizing the device access nodes and operating strategies, it can achieve rapid local compensation of reactive power, improve line voltage drop problems, and comprehensively enhance voltage management effectiveness. Summary of the Invention

[0005] To address this, the present invention provides a spatiotemporal collaborative optimization scheduling method for mobile voltage regulators under traffic constraints, which overcomes the problems of limited control range of fixed voltage regulators and poor economic efficiency of reactive power compensation for mobile resources in the prior art.

[0006] To achieve the above objectives, this invention provides a spatiotemporal collaborative optimization scheduling method for mobile pressure regulating devices under traffic constraints. It includes:

[0007] Step S1: Obtain basic operation data of the distribution network and traffic network topology data, and solve the shortest travel path matrix between each distribution network node based on the traffic network topology data; the basic operation data of the distribution network includes the distribution network topology, line impedance parameters, active and reactive loads of each node, and power injection boundary of the upper-level power grid.

[0008] Step S2: Construct an optimization function with the objective of minimizing the daily operating cost of the system. The optimization function includes a movement cost term for the mobile voltage regulator. The movement cost term is determined by summing the product of the unit movement cost of the mobile voltage regulator, the distance between nodes in the shortest path matrix, and the cross-node movement decision variable over all mobile voltage regulators, all node pairs, and all time periods. The cross-node movement decision variable is used to characterize whether the mobile voltage regulator moves from one node to another between adjacent time periods.

[0009] Step S3: Construct a constraint system, which includes a transportation network mobility accessibility constraint based on the shortest travel path matrix, a mobile voltage regulator operation capability constraint based on the mobile voltage regulator's own regulation capability boundary, and a distribution network safety operation constraint based on distribution network safety operation standards.

[0010] Step S4: Combine the optimization function with the constraint system to form a mixed integer linear programming model and solve it to output the access node position, movement path between nodes and reactive power output of each mobile voltage regulator in each time period.

[0011] Further, in step S1, solving the shortest travel path matrix between each distribution network node based on the traffic network topology data specifically includes:

[0012] Based on the traffic topology data of the power distribution network nodes, a physical distance matrix between nodes is constructed. The element value between any two nodes in the physical distance matrix is ​​the actual travel distance between the directly connected road segments between the corresponding traffic points of the two nodes. When the two nodes are not directly connected, the corresponding element value is taken as infinity.

[0013] Using the physical distance matrix as input, the Dijkstra algorithm is used to calculate the shortest travel distance between nodes in the distribution network, and the shortest travel path matrix is ​​generated.

[0014] Furthermore, in step S2, the optimization function also includes the operating cost of the mobile voltage regulating device, the distribution network line loss cost, the node voltage over-limit penalty cost, the upstream power grid electricity purchase cost, and the voltage optimization incentive.

[0015] The operating cost item is determined by calculating the reactive power output value of the mobile voltage regulating device and the operating cost coefficient.

[0016] The line loss cost item is determined by the power loss of each line in the distribution network and the line loss cost coefficient.

[0017] The voltage over-limit penalty cost is calculated and determined by the deviation of the node voltage from the preset upper or lower voltage limit. The larger the voltage deviation, the higher the penalty cost.

[0018] The cost of electricity purchased from the upper-level power grid is determined by the active power purchased by the distribution network from the upper-level power grid and the time-of-use electricity price.

[0019] The voltage optimization incentive term is the incentive benefit given when the node voltage is within a preset optimal voltage range, and the voltage optimization incentive term is used as a cost reduction term in the optimization function.

[0020] Furthermore, in step S2, the voltage over-limit penalty cost item specifically includes:

[0021] When the node voltage is lower than the preset lower voltage limit, the voltage over-limit penalty cost is calculated and determined by the absolute value of the voltage lower limit deviation and the first penalty coefficient.

[0022] When the node voltage exceeds the preset upper limit of voltage, the voltage over-limit penalty cost is determined by the absolute value of the upper limit deviation and the second penalty coefficient.

[0023] When the node voltage is between the preset upper and lower voltage limits, the penalty cost for exceeding the voltage limit is zero.

[0024] Furthermore, in step S2, in the voltage optimization excitation term, the preset optimization voltage interval is a true sub-interval of the voltage interval formed by the preset upper and lower voltage limits.

[0025] Furthermore, in step S3, the mobility accessibility constraints of the transportation network include:

[0026] The mobile voltage regulator can only stay at one node position at any given time.

[0027] When a mobile pressure regulating device moves from the node of the current time period to the node of the next time period, the passage distance between the two nodes shall not exceed the maximum passage distance of the mobile pressure regulating device per unit time.

[0028] After the mobile pressure regulating device moves to the target node, it must stay at the target node for a preset minimum dwell time.

[0029] Furthermore, in step S3, the operating capacity constraint of the mobile voltage regulating device is:

[0030] The reactive power output of a mobile voltage regulating device shall not be lower than the lower limit of its rated reactive power capacity and shall not be higher than the upper limit of its rated reactive power capacity.

[0031] Further, in step S3, the distribution network safety operation constraints are constructed based on the distribution network topology, line impedance parameters, active load and reactive load of each node in the basic operation data of the distribution network, including:

[0032] The voltage amplitude at each node does not exceed the preset upper and lower voltage limits;

[0033] The active power transmitted by each line shall not exceed its active power transmission capacity limit.

[0034] The reactive power transmitted by each line shall not exceed its reactive power transmission capacity limit;

[0035] The active power of each node remains balanced, and the difference between the active power injected into the node and the active power outflowed from the node is equal to the active load of that node.

[0036] The reactive power of each node remains balanced, and the difference between the reactive power injected into the node and the reactive power flowing out of the node is equal to the reactive load of that node.

[0037] Furthermore, in step S4, in the mixed integer linear programming model, the cross-node movement decision variable is a binary variable, the reactive power output value of the mobile voltage regulator is a continuous variable, and the access node position of the mobile voltage regulator in each time period is characterized by the dwell state variable of the device at each node.

[0038] Furthermore, in step S1, the traffic network topology data includes traffic intersection nodes corresponding to the geographical locations of each node in the power distribution network, road segment connectivity, and actual travel distance of the road segments.

[0039] Compared with the prior art, the beneficial effects of the present invention are that by constructing a physical distance matrix between nodes and using the Dijkstra algorithm to solve for the shortest travel path, the present invention provides accurate shortest travel distance data between nodes for mobile voltage regulating devices, effectively supporting the accurate quantification of mobile cost calculation and traffic movement constraints in subsequent optimization scheduling, and ensuring the engineering feasibility of device movement paths in the scheduling scheme.

[0040] Furthermore, this invention constructs a multi-dimensional objective function that includes mobile costs, operating costs, line loss costs, voltage over-limit penalty costs, electricity purchase costs, and voltage optimization incentives. It also introduces a voltage deviation penalty mechanism and optimization range incentive measures to achieve comprehensive and accurate quantification of the system's daily operating costs. This guides mobile voltage regulators to actively adjust node voltages to a better operating range while meeting voltage safety constraints, thus balancing voltage management effectiveness with system operating economy.

[0041] Furthermore, this invention constructs a comprehensive constraint system covering the mobility accessibility of the transportation network, the operational capacity of the device, and the safe operation of the distribution network. This system precisely limits the uniqueness of the node location of the mobile voltage regulating device, the cross-time period movement logic, the maximum travel distance per unit time, and the minimum dwell time. At the same time, it comprehensively constrains the reactive power output range of the device, the node voltage safety boundary, the line transmission capacity, and the overall power balance of the network. This ensures that the optimized scheduling scheme meets the actual road capacity limitations at the traffic execution level and meets the safety and stability requirements at the power grid operation level. It achieves global coordination between the spatiotemporal characteristics of voltage regulation demand and the mobility and output characteristics of the device, thus ensuring the engineering feasibility of the scheduling scheme.

[0042] Furthermore, this invention constructs a mixed-integer linear programming model by setting cross-node movement decision variables as binary variables, reactive power output as continuous variables, and access location as a dwell state variable, and calls a commercial solver to solve it. This achieves joint optimization and synchronous output of the access node location, traffic movement path, and reactive power output of the mobile voltage regulating device, ensuring the coordination consistency and engineering feasibility of the scheduling scheme in the spatiotemporal dimensions. Attached Figure Description

[0043] Figure 1 This is a flowchart of the spatiotemporal collaborative optimization scheduling method for mobile pressure regulating devices under traffic constraints, as described in this application embodiment.

[0044] Figure 2 This is a topology diagram of the IEEE 33-node computational example system used in the embodiments of this invention;

[0045] Figure 3 The example shows a comparison curve of the voltage at key nodes with and without a mobile voltage regulator.

[0046] Figure 4 This is a diagram showing the movement trajectory of the mobile pressure regulating device in the embodiment. Detailed Implementation

[0047] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0048] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] Please see Figures 1-4 As shown, Figure 1 This is a flowchart of the spatiotemporal collaborative optimization scheduling method for mobile pressure regulating devices under traffic constraints, as described in this application embodiment. Figure 2This is a topology diagram of the IEEE 33-node computational example system used in the embodiments of this invention; Figure 3 The example shows a comparison curve of the voltage at key nodes with and without a mobile voltage regulator. Figure 4 This is a diagram showing the movement trajectory of the mobile pressure regulating device in the embodiment.

[0050] The spatiotemporal collaborative optimization scheduling method for mobile pressure regulating devices under traffic constraints, as described in this application, includes:

[0051] Step S1: Obtain basic operation data of the distribution network and traffic network topology data, and solve the shortest travel path matrix between each distribution network node based on the traffic network topology data; the basic operation data of the distribution network includes the distribution network topology, line impedance parameters, active and reactive loads of each node, and power injection boundary of the upper-level power grid.

[0052] Step S2: Construct an optimization function with the objective of minimizing the daily operating cost of the system. The optimization function includes a movement cost term for the mobile voltage regulator. The movement cost term is determined by summing the product of the unit movement cost of the mobile voltage regulator, the distance between nodes in the shortest path matrix, and the cross-node movement decision variable over all mobile voltage regulators, all node pairs, and all time periods. The cross-node movement decision variable is used to characterize whether the mobile voltage regulator moves from one node to another between adjacent time periods.

[0053] Specifically, in this embodiment, the optimization function is constructed as follows: Optimization function = moving cost + operating cost + line loss cost + voltage limit violation penalty cost + upstream power grid purchase cost − voltage optimization incentive term;

[0054] The mobility cost is determined by summing the product of unit mobility cost, distance between nodes in the shortest path matrix, and cross-node mobility decision variables over all time periods and all devices.

[0055] The operating cost is determined by summing the product of the operating cost coefficient and the reactive power output value over all time periods and all devices.

[0056] The line loss cost is determined by summing the product of the line loss cost coefficient and the line power loss over the entire time period and the entire line.

[0057] The voltage over-limit penalty cost is determined based on the product of the deviation of the node voltage from the preset upper or lower limit and the corresponding penalty coefficient;

[0058] The cost of purchasing electricity from the upstream power grid is determined by summing the product of the time-period electricity price and the active power of the upstream power grid over the entire time period;

[0059] The voltage optimization incentive term is determined based on whether the node voltage is within a preset optimization range, and is used as a cost reduction term in the optimization function.

[0060] Step S3: Construct a constraint system, which includes a transportation network mobility accessibility constraint based on the shortest travel path matrix, a mobile voltage regulator operation capability constraint based on the mobile voltage regulator's own regulation capability boundary, and a distribution network safety operation constraint based on distribution network safety operation standards.

[0061] Step S4: Combine the optimization function with the constraint system to form a mixed integer linear programming model and solve it to output the access node position, movement path between nodes and reactive power output of each mobile voltage regulator in each time period.

[0062] Specifically, in step S1, solving the shortest travel path matrix between each distribution network node based on the traffic network topology data includes:

[0063] Based on the traffic topology data of the power distribution network nodes, a physical distance matrix between nodes is constructed. The element value between any two nodes in the physical distance matrix is ​​the actual travel distance between the directly connected road segments between the corresponding traffic points of the two nodes. When the two nodes are not directly connected, the corresponding element value is taken as infinity.

[0064] Using the physical distance matrix as input, the Dijkstra algorithm is used to calculate the shortest travel distance between nodes in the distribution network, and the shortest travel path matrix is ​​generated.

[0065] In this embodiment of the invention, basic operation data of the distribution network of the target distribution substation, full-state parameters of the dispatchable mobile voltage regulator, and traffic network topology and node distance data corresponding to the geographical location of the distribution substation are obtained. A mathematical model is constructed based on the traffic network data, and Dijkstra's algorithm is used to calculate the shortest path between nodes.

[0066] Based on traffic topology data from distribution network nodes, the relationship between reachability and distance quantification between nodes is clarified. A physical distance matrix between nodes is defined. ,in Given the total number of distribution network nodes, the mathematical definition of the matrix elements is:

[0067] ;

[0068] In the formula, For nodes With nodes Actual travel distance (km) between direct connecting road sections between traffic points; Represents a node With nodes There is no direct connecting route to the corresponding traffic point; The time distance is 0.

[0069] The Dijkstra algorithm is used to calculate the shortest path between nodes. Dijkstra's algorithm is suitable for networks where the distance between nodes is non-negative, and it can efficiently solve for the shortest travel path from the starting node to all other nodes. In this invention, the algorithm is used to calculate the shortest travel distance between nodes in a distribution network, helping mobile voltage regulators plan efficient travel paths. The core logic of the algorithm is as follows:

[0070] With any node Set the starting node as the starting node, and set the starting node as the starting node. The distance to the first node is 0, and the initial distance to the remaining nodes is set to ∞. Define the set of unvisited nodes. .

[0071] Never visited collection Filter the node closest to the starting node Marked as visited and from Remove from the middle, then calculate the starting node to... shortest distance and to its unvisited adjacent nodes The sum of direct distances, as arrive The new path distance, and Compare the current temporary distances and update with the smaller value. The temporary storage distance.

[0072] When the collection is not visited If empty, complete the shortest path solution from the current starting node to the target node.

[0073] This invention provides accurate data on the shortest travel distance between nodes for mobile voltage regulating devices by constructing a physical distance matrix between nodes and using the Dijkstra algorithm to solve for the shortest travel path. This effectively supports the accurate quantification of travel cost calculation and traffic travel constraints in subsequent optimization scheduling, ensuring the engineering feasibility of device travel paths in the scheduling scheme.

[0074] Specifically, in step S2, the optimization function further includes the operating cost of the mobile voltage regulating device, the distribution network line loss cost, the node voltage over-limit penalty cost, the upstream power grid electricity purchase cost, and the voltage optimization incentive.

[0075] The operating cost item is determined by calculating the reactive power output value of the mobile voltage regulating device and the operating cost coefficient.

[0076] The line loss cost item is determined by the power loss of each line in the distribution network and the line loss cost coefficient.

[0077] The voltage over-limit penalty cost is calculated and determined by the deviation of the node voltage from the preset upper or lower voltage limit. The larger the voltage deviation, the higher the penalty cost.

[0078] The cost of electricity purchased from the upper-level power grid is determined by the active power purchased by the distribution network from the upper-level power grid and the time-of-use electricity price.

[0079] The voltage optimization incentive term is the incentive benefit given when the node voltage is within a preset optimal voltage range, and the voltage optimization incentive term is used as a cost reduction term in the optimization function.

[0080] Specifically, in step S2, the voltage over-limit penalty cost item is as follows:

[0081] When the node voltage is lower than the preset lower voltage limit, the voltage over-limit penalty cost is calculated and determined by the absolute value of the voltage lower limit deviation and the first penalty coefficient.

[0082] When the node voltage exceeds the preset upper limit of voltage, the voltage over-limit penalty cost is determined by the absolute value of the upper limit deviation and the second penalty coefficient.

[0083] When the node voltage is between the preset upper and lower voltage limits, the penalty cost for exceeding the voltage limit is zero.

[0084] Specifically, in step S2, the preset optimal voltage range in the voltage optimization excitation term is a true sub-range of the voltage range formed by the preset upper and lower voltage limits.

[0085] In this embodiment of the invention, the objective function includes the mobile cost and operating cost of the mobile voltage regulating device, the distribution network line loss cost, the voltage over-limit penalty cost, the upstream power grid power purchase cost, the slack term penalty cost, the voltage optimization incentive cost, etc.

[0086] ;

[0087] in: The relocation cost of the mobile voltage regulator:

[0088] ;

[0089] In the formula, This refers to the number of mobile voltage regulating devices (units). This represents the number of system nodes. Unit relocation cost of mobile pressure regulating device (RMB / km). For the number of time periods, For nodes To the node The distance traveled (km) A binary variable, representing Time of the first Is the mobile voltage regulator from the node? Move to node If the element moves, the value is 1; otherwise, the value is 0.

[0090] Operating costs of mobile voltage regulators:

[0091] ;

[0092] In the formula, Operating cost of mobile pressure regulating device (RMB / Mvarh) for Time of the first Mobile voltage regulator at node The reactive power output (Mvar). The time interval is 1 hour.

[0093] Cost of distribution network line loss:

[0094] ;

[0095] In the formula, The line loss cost coefficient (yuan / MWh) for Timetable Line loss (MW). It refers to the number of lines;

[0096] Penalty cost for voltage exceeding limits:

[0097] If the node voltage exceeds the rated voltage constraint range, a corresponding penalty cost will be imposed. The greater the degree of voltage exceeding the limit, the higher the penalty cost.

[0098] ;

[0099] ;

[0100] ;

[0101] In the formula, For nodes exist The absolute value of the voltage difference (pu) exceeding the lower voltage limit during the specified time period. For nodes exist The absolute value of the voltage difference (pu) exceeding the voltage limit during a given period. The lower limit of the node voltage (pu), This represents the upper limit of the node voltage (pu). for Time Node Voltage (pu) The first penalty coefficient (yuan / pu) is used. The second penalty coefficient (yuan / pu);

[0102] Cost of purchasing electricity from the upper-level power grid:

[0103] ;

[0104] In the formula, for Electricity purchase price for the specified time period (RMB / MWh) for Active power (MW) purchased from the upper-level power grid at all times. The reactive power cost of the upstream power grid (RMB / MVarh) for Reactive power (MVar) constantly obtained from the upstream power grid;

[0105] Penalty cost for slack terms:

[0106] ;

[0107] In the formula, The penalty cost coefficient (in yuan) for slack variables. For nodes exist The active slack variable (MW) at time t, For nodes exist The reactive slack variable (MVar) at time t;

[0108] Voltage-optimized excitation:

[0109] To incentivize the model and improve voltage management effectiveness, a voltage optimization incentive term is set: when the adjusted voltage falls within the voltage optimization target range... Internally, appropriate incentives will be provided.

[0110]

[0111] In the formula, The voltage-optimal excitation coefficient (in yuan) is used. A binary variable representing a node. exist The value is set to 1 if the voltage is within the optimal voltage range at any given time, and 0 otherwise.

[0112] This invention constructs a multi-dimensional objective function that includes relocation costs, operating costs, line loss costs, voltage over-limit penalty costs, electricity purchase costs, and voltage optimization incentives. It also introduces a voltage deviation penalty mechanism and optimization range incentive measures to achieve comprehensive and accurate quantification of the system's daily operating costs. This guides mobile voltage regulators to actively adjust node voltages to a better operating range while meeting voltage safety constraints, thus balancing voltage management effectiveness with system operating economy.

[0113] Specifically, in step S3, the mobility accessibility constraints of the transportation network include:

[0114] The mobile voltage regulator can only stay at one node position at any given time.

[0115] When a mobile pressure regulating device moves from the node of the current time period to the node of the next time period, the passage distance between the two nodes shall not exceed the maximum passage distance of the mobile pressure regulating device per unit time.

[0116] After the mobile pressure regulating device moves to the target node, it must stay at the target node for a preset minimum dwell time.

[0117] Specifically, in step S3, the operating capacity constraint of the mobile voltage regulating device is:

[0118] The reactive power output of a mobile voltage regulating device shall not be lower than the lower limit of its rated reactive power capacity and shall not be higher than the upper limit of its rated reactive power capacity.

[0119] Specifically, in step S3, the distribution network safety operation constraints are constructed based on the distribution network topology, line impedance parameters, active and reactive loads of each node in the basic operation data of the distribution network, including:

[0120] The voltage amplitude at each node does not exceed the preset upper and lower voltage limits;

[0121] The active power transmitted by each line shall not exceed its active power transmission capacity limit.

[0122] The reactive power transmitted by each line shall not exceed its reactive power transmission capacity limit;

[0123] The active power of each node remains balanced, and the difference between the active power injected into the node and the active power outflowed from the node is equal to the active load of that node.

[0124] The reactive power of each node remains balanced, and the difference between the reactive power injected into the node and the reactive power flowing out of the node is equal to the reactive load of that node.

[0125] In this embodiment of the invention, a set of mobility accessibility constraints for the transportation network is constructed:

[0126] Position constraints of mobile voltage regulator:

[0127] A mobile voltage regulator can only remain at one node position at a time, as shown below:

[0128] ;

[0129] In the formula, It is a binary variable, representing the first... Mobile voltage regulator at all times Should it stay at the node? If =1 indicates a pause; if =0 indicates no stopping. It is a set of nodes.

[0130] Device movement constraints during time period:

[0131] Mobile voltage regulator from the current node Move to target node The following conditions must be met:

[0132] ,

[0133] ,

[0134] ;

[0135] In the formula, This is a binary variable representing whether the k-th mobile voltage regulator has moved from node t to t+1. Move to node ,like The device remains at node t at time t. ( And remain at node t+1. ( );

[0136] starting node With the target node The distance should not exceed the moving distance of the mobile voltage regulator per unit time, as shown below:

[0137] ;

[0138] In the formula, For the current node With the target node Distance (km) The distance (km) traveled per unit time by the mobile voltage regulator.

[0139] After being moved to the new node, the mobile voltage regulator must remain stationary for a period of time:

[0140] ;

[0141] In the formula, The minimum dwell time (h) for the mobile voltage regulator is when the device moves to the node during the time period from t to t+1. It must be from t+1 to Staying at the node during the time period .

[0142] Constructing the self-operation constraint set of the mobile voltage regulator

[0143] Reactive power output constraint of mobile voltage regulator:

[0144] ;

[0145] In the formula, This represents the maximum reactive power (pu) of the mobile voltage regulator.

[0146] Construct a set of constraints for the safe operation of the distribution network:

[0147] Node voltage constraints:

[0148] Node voltage upper and lower limit constraints:

[0149] ;

[0150] Parent-child node voltage gradient constraints:

[0151] ;

[0152] In the formula, This represents the parent node voltage (pu) at time t. This represents the voltage (pu) of the child node at time t. Let be the active power (pu) transmitted between the parent and child nodes at time t. Let be the reactive power (pu) transmitted between the parent and child nodes at time t. The line resistance (pu) between parent and child nodes. The line reactance (pu) between the parent and child nodes.

[0153] Current constraints:

[0154] Line transmission power constraints:

[0155] ;

[0156] ;

[0157] In the formula, For the line The active power flow lower limit (pu), For the line The upper limit of active power flow (pu). For the line The lower limit of reactive power flow (pu). For the line The upper limit of reactive power flow (pu).

[0158] Line loss constraint:

[0159] ;

[0160] ;

[0161] In the formula, For the line Resistor (pu), For the line Line loss coefficient, For the line exist The absolute value of the active power flow at any given moment (pu), For the line exist The absolute value of reactive power flow (pu) at any given moment. This is a margin for small deviations.

[0162] Active power balance constraints:

[0163] ;

[0164] In the formula, for Time Node The active load (pu), For the node The set of lines from which power flows out. Pointing to a node The set of lines for injected power, for Timetable The active current (pu).

[0165] Reactive power balance constraints:

[0166] ;

[0167] In the formula, for Time Node The reactive load (pu), for Timetable The reactive power flow (pu).

[0168] Power supply constraints from the upstream power grid:

[0169] ,

[0170] ;

[0171] In the formula, The minimum active power output (pu) of the upstream power grid. It is the upper limit of the maximum active power output of the upper-level power grid (pu). The minimum reactive power output (pu) of the upper-level power grid. Maximum reactive power output limit of the upstream power grid (pu).

[0172] This invention constructs a comprehensive constraint system covering the mobility accessibility of the transportation network, the operational capacity of the device, and the safe operation of the distribution network. It precisely limits the uniqueness of the node location, the cross-time period movement logic, the maximum travel distance per unit time, and the minimum dwell time of the mobile voltage regulating device. At the same time, it comprehensively constrains the reactive power output range of the device, the node voltage safety boundary, the line transmission capacity, and the overall power balance of the network. This ensures that the optimized scheduling scheme meets the actual road capacity limitations at the traffic execution level and meets the safety and stability requirements at the power grid operation level. It achieves global coordination between the spatiotemporal characteristics of voltage regulation demand and the mobility and output characteristics of the device, thus ensuring the engineering feasibility of the scheduling scheme.

[0173] Specifically, in step S4, in the mixed integer linear programming model, the cross-node movement decision variable is a binary variable, the reactive power output value of the mobile voltage regulator is a continuous variable, and the access node position of the mobile voltage regulator in each time period is characterized by the dwell state variable of the device at each node.

[0174] In this embodiment of the invention, an optimization model is solved to generate a spatiotemporal coordinated scheduling scheme for mobile voltage regulators. The constructed objective function and the constructed constraint system together form a mixed integer linear programming model. Commercial solvers such as Cplex and Gurobi are called to solve the model, and an integrated spatiotemporal coordinated scheduling scheme for the optimal access node location, traffic movement path planning, and reactive power output strategy of the mobile voltage regulators in the target distribution area is output.

[0175] This invention constructs a mixed-integer linear programming model by setting cross-node movement decision variables as binary variables, reactive power output as continuous variables, and access location as stationary state variables, and calls a commercial solver to solve it. This achieves joint optimization and synchronous output of the access node location, traffic movement path, and reactive power output of the mobile voltage regulator, ensuring the coordination and consistency of the scheduling scheme in the spatiotemporal dimensions and the engineering feasibility.

[0176] Specifically, in step S1, the basic operating data of the distribution network includes the distribution network topology, line impedance parameters, active and reactive loads of each node, and the power injection boundary of the upper-level power grid; the traffic network topology data includes traffic intersection nodes corresponding to the geographical locations of each node of the distribution network, road segment connectivity, and actual travel distance of the road segment.

[0177] In this embodiment of the invention, basic operational data of the distribution network, such as the distribution network topology of the target distribution area, line impedance parameters, active and reactive loads of each node, and power injection boundary of the upper-level power grid, are obtained. At the same time, traffic network topology data, such as traffic intersection nodes, road segment connectivity, and actual travel distance of each road segment, corresponding to the geographical location of each node of the distribution network, are obtained, providing a complete data foundation for subsequent shortest path solving and optimization scheduling model construction.

[0178] Based on the above implementation scheme, in order to verify the beneficial effects of the present invention, a specific calculation example is used for analysis. To verify the effectiveness of the scheduling method proposed in this invention, a specific calculation example is used. Figure 2 The IEEE standard 33-node distribution network area shown is used as the research object for simulation analysis. The example sets up two dispatchable mobile voltage regulating devices, and the Cplex solver is used to complete the solution calculation of the optimization model of this invention.

[0179] Depend on Figure 3 It is known that without a mobile voltage regulator, severe undervoltage exceeding the limit occurs at the end nodes of the distribution network, with the lowest node voltage being only 0.87 pu. After adopting the method proposed in this invention, the voltage of all nodes in the distribution network is stably controlled within the acceptable range of 0.96 pu to 1.02 pu, solving the undervoltage exceeding the limit problem at the end nodes, and the voltage management effect is significant. Figure 4 It can be seen that the method proposed in this invention can output the movement trajectory of the mobile voltage regulating device that meets the traffic accessibility constraints. The two devices can dynamically adjust the stopping nodes according to the voltage management needs of the distribution network nodes, realizing the spatiotemporal coordination between voltage regulation needs and device movement and output, and fully verifying the engineering feasibility of the scheduling scheme.

[0180] Overall verification results show that the proposed distribution network-traffic dual-constraint spatiotemporal collaborative optimization scheduling method can effectively solve the problem of voltage exceeding the limit at the end node of the distribution transformer area, while taking into account the voltage management effect, the feasibility of the solution, and the economic efficiency of operation, providing a technical solution with both flexibility and economy for voltage management at the end of the distribution network.

[0181] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A space-time coordinated optimization scheduling method for mobile pressure regulating devices under traffic constraints, characterized in that, include: Step S1: Obtain basic operation data of the distribution network and traffic network topology data, and solve the shortest travel path matrix between each distribution network node based on the traffic network topology data; the basic operation data of the distribution network includes the distribution network topology, line impedance parameters, active and reactive loads of each node, and power injection boundary of the upper-level power grid. Step S2: Construct an optimization function with the objective of minimizing the daily operating cost of the system. The optimization function includes a movement cost term for the mobile voltage regulator. The movement cost term is determined by summing the product of the unit movement cost of the mobile voltage regulator, the distance between nodes in the shortest path matrix, and the cross-node movement decision variable over all mobile voltage regulators, all node pairs, and all time periods. The cross-node movement decision variable is used to characterize whether the mobile voltage regulator moves from one node to another between adjacent time periods. Step S3: Construct a constraint system, which includes a transportation network mobility accessibility constraint based on the shortest travel path matrix, a mobile voltage regulator operation capability constraint based on the mobile voltage regulator's own regulation capability boundary, and a distribution network safety operation constraint based on distribution network safety operation standards. Step S4: Combine the optimization function with the constraint system to form a mixed integer linear programming model and solve it, outputting the access node position, movement path between nodes and reactive power output of each mobile voltage regulator in each time period.

2. The method according to claim 1, characterized in that, In step S1, solving the shortest travel path matrix between each distribution network node based on the traffic network topology data specifically includes: Based on the traffic topology data of the power distribution network nodes, a physical distance matrix between nodes is constructed. The element value between any two nodes in the physical distance matrix is ​​the actual travel distance between the directly connected road segments between the corresponding traffic points of the two nodes. When the two nodes are not directly connected, the corresponding element value is taken as infinity. Using the physical distance matrix as input, the Dijkstra algorithm is used to calculate the shortest travel distance between nodes in the distribution network, and the shortest travel path matrix is ​​generated.

3. The method according to claim 1, characterized in that, In step S2, the optimization function further includes the operating cost of the mobile voltage regulating device, the distribution network line loss cost, the node voltage over-limit penalty cost, the upstream power grid electricity purchase cost, and the voltage optimization incentive. The operating cost item is determined by calculating the reactive power output value of the mobile voltage regulating device and the operating cost coefficient. The line loss cost item is determined by the power loss of each line in the distribution network and the line loss cost coefficient. The voltage over-limit penalty cost is calculated and determined by the deviation of the node voltage from the preset upper or lower voltage limit. The larger the voltage deviation, the higher the penalty cost. The cost of electricity purchased from the upper-level power grid is determined by the active power purchased by the distribution network from the upper-level power grid and the time-of-use electricity price. The voltage optimization incentive term is the incentive benefit given when the node voltage is within a preset optimal voltage range, and the voltage optimization incentive term is used as a cost reduction term in the optimization function.

4. The method according to claim 3, characterized in that, In step S2, the voltage over-limit penalty cost item specifically includes: When the node voltage is lower than the preset lower voltage limit, the voltage over-limit penalty cost is calculated and determined by the absolute value of the voltage lower limit deviation and the first penalty coefficient. When the node voltage exceeds the preset upper limit of voltage, the voltage over-limit penalty cost is calculated and determined by the absolute value of the upper limit deviation and the second penalty coefficient. When the node voltage is between the preset upper and lower voltage limits, the penalty cost for exceeding the voltage limit is zero.

5. The method according to claim 3, characterized in that, In step S2, the preset optimal voltage range in the voltage optimization excitation term is a true sub-range of the voltage range formed by the preset upper and lower voltage limits.

6. The method according to claim 1, characterized in that, In step S3, the mobility accessibility constraints of the transportation network include: The mobile voltage regulator can only stay at one node position at any given time. When a mobile pressure regulating device moves from the node of the current time period to the node of the next time period, the passage distance between the two nodes shall not exceed the maximum passage distance of the mobile pressure regulating device per unit time. After the mobile pressure regulating device moves to the target node, it must stay at the target node for a preset minimum dwell time.

7. The method according to claim 1, characterized in that, In step S3, the operating capacity constraint of the mobile voltage regulating device is: The reactive power output of a mobile voltage regulating device shall not be lower than the lower limit of its rated reactive power capacity and shall not be higher than the upper limit of its rated reactive power capacity.

8. The method according to claim 1, characterized in that, In step S3, the power distribution network safety operation constraints are constructed based on the power distribution network topology, line impedance parameters, active power load, and reactive power load of each node in the power distribution network basic operation data, including: The voltage amplitude at each node does not exceed the preset upper and lower voltage limits; The active power transmitted by each line shall not exceed its active power transmission capacity limit. The reactive power transmitted by each line shall not exceed its reactive power transmission capacity limit; The active power of each node remains balanced, and the difference between the active power injected into the node and the active power outflowed from the node is equal to the active load of that node. The reactive power of each node remains balanced, and the difference between the reactive power injected into the node and the reactive power flowing out of the node is equal to the reactive load of that node.

9. The method according to claim 1, characterized in that, In step S4, in the mixed integer linear programming model, the cross-node movement decision variable is a binary variable, the reactive power output value of the mobile voltage regulator is a continuous variable, and the access node position of the mobile voltage regulator in each time period is characterized by the dwell state variable of the device at each node.

10. The method according to claim 1, characterized in that, In step S1, the traffic network topology data includes traffic intersection nodes corresponding to the geographical locations of each node in the power distribution network, road segment connectivity, and actual travel distance of the road segments.

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