An optimization scheduling method and system for supporting island operation of a resident transformer area based on off-grid discharge of an electric heavy truck cluster

Abstract

This invention relates to an optimized scheduling method and system for supporting the islanded operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters. The method includes: collecting substation data during the islanded support preparation phase; filtering the spatiotemporal availability of heavy-duty trucks using the substation data to obtain a candidate vehicle set; establishing a dynamic model of the individual vehicle battery in the candidate vehicle set, and setting individual vehicle battery charging and discharging power constraints and available power and energy boundaries of the vehicle set participating in the aggregation to construct an objective function, resulting in a mixed-integer second-order cone programming model; optimizing and solving the mixed-integer second-order cone programming model to obtain a scheduling sequence; and employing a rolling time-domain rescheduling mechanism to implement the target time slot command for each scheduling sequence during the optimization process. This invention can fully utilize the large-capacity battery resources of electric heavy-duty truck clusters during the islanded operation of residential transformer substations, improve the recovery level of critical loads and the operational safety of the substations, while also considering vehicle travel assurance and battery life.

Description

Technical Field

[0001] This invention relates to the field of power system and distributed energy management technology, and in particular to an optimized scheduling method and system for supporting the islanded operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters. Background Technology

[0002] With the increasing proportion of distributed renewable energy access, the risk of localized power distribution areas becoming isolated due to short-term, large-scale faults (such as feeder faults in power distribution areas or grid switching caused by strong winds and sandstorms) is rising. Traditional reliance on distributed generation and fixed energy storage has limitations in terms of time and capacity. However, electric heavy-duty trucks, due to their large-capacity batteries and high discharge / charge power, can serve as mobile, large-capacity energy storage units when parked or dispatched to power distribution areas, providing emergency power support for localized residential loads.

[0003] Existing technical solutions have proven the feasibility and optimization strategies of passenger vehicle fleets in microgrid island scenarios, but their spatiotemporal behavior and energy levels are different from those of heavy trucks, requiring modeling and scheduling tailored to the characteristics of heavy trucks.

[0004] Therefore, there is an urgent need for a systematic and optimized scheduling method that can simultaneously consider spatiotemporal availability, battery charge maintenance, power flow and safety constraints in the transformer area, as well as the uncertainties of wind, solar and load, in order to restore power supply to isolated transformer areas. Summary of the Invention

[0005] To address the challenges of existing technologies in supporting islanded operation of distribution substations, where distributed generation and fixed energy storage cannot simultaneously meet the needs of critical residential load restoration and mobile high-capacity energy storage (electric heavy-duty trucks) operation within a short period, and where existing technologies primarily target passenger vehicle groups and lack comprehensive modeling and scheduling methods for the spatiotemporal regularity, high-capacity energy, and travel energy consumption characteristics of electric heavy-duty truck clusters, this invention aims to provide an optimized scheduling method and system based on off-grid discharge of electric heavy-duty truck clusters to support islanded operation of residential substations. This method comprehensively considers the spatiotemporal availability, travel energy consumption, grid-connected / off-grid time periods, and expected off-grid SOC of the heavy-duty truck cluster. Under the distribution network DistFlow power flow constraint, a solvable optimization model is constructed, thereby maximizing the weighted load restoration value while ensuring substation power flow and safety constraints, and also taking into account the off-grid SOC of heavy-duty trucks.

[0006] To achieve the above objectives, this invention provides an optimized scheduling method for supporting the isolated operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters, comprising: Step S1: Obtain the source-load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load-sensitivity energy consumption model of heavy trucks, calculate the expected arrival time and travel energy consumption of the candidate electric heavy trucks, and screen out the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. Step S2: For the electric heavy trucks in the actual available heavy truck set, construct a single vehicle battery charging and discharging dynamic model, and set rigid constraints for commercial tasks to ensure the vehicle's subsequent operation, so as to ensure that the minimum required power is retained when the vehicle is off-grid. Step S3: Limit the actual available heavy trucks to the designated dedicated line nodes with large-capacity converter capability within the distribution radio area, aggregate and calculate the total charging and discharging power of the heavy truck cluster at the dedicated line nodes, and establish spatial coupling constraints. Step S4: Construct a dynamic model of the fixed energy storage system within the distribution area to integrate distributed power sources, fixed energy storage, aggregated power of heavy truck clusters, and distribution area load to obtain the node active power balance equations for the net active power injected by each node in the distribution area into the distribution network branch. Step S5: Use the DistFlow model to describe the power flow distribution of the radial distribution area. For the nonlinear quadratic equality constraints in the model, use the second-order cone relaxation technique to perform convex relaxation processing. Transform the quadratic equality constraints after convex relaxation processing into the form of second-order norm inequality constraints, and apply safety limit constraints on node voltage and branch current. Step S6: Construct a mixed integer second-order cone programming model with the objective function of maximizing the recovery of important loads in the distribution area and taking into account the power protection of heavy trucks. Call the solver to perform optimization and solve the model, and use a rolling time domain and event-triggered control mechanism to issue scheduling instructions to the distribution area equipment and heavy truck terminals.

[0007] Optionally, based on the load-sensitivity energy consumption model of the heavy-duty truck, the estimated arrival time and travel energy consumption of the candidate electric heavy-duty trucks are calculated, and a set of actually usable heavy-duty trucks that meet the spatiotemporal and energy threshold conditions is selected, including: Calculate candidate heavy trucks Estimated time to reach the access point : ; Calculate heavy trucks Stroke energy consumption for: ; When heavy truck A truck will be included in the actual usable heavy-duty truck set if it meets all of the following conditions: ; ; in, The time when the scheduling instruction is issued. For heavy trucks Driving distance from the access point of the transformer substation This is the projected average vehicle speed; This is the baseline energy consumption for heavy-duty trucks under no-load conditions. The load-bearing energy consumption coefficient, For heavy trucks Current load weight; Heavy truck when the command is issued The initial charge, To ensure a minimum reserve power threshold for safe driving, This is the maximum tolerable period of power outage waiting time for an isolated island in the power distribution area.

[0008] Optionally, a dynamic model for charging and discharging the single-vehicle battery is constructed, and rigid constraints for commercial tasks to ensure subsequent vehicle operation are set, including: heavy trucks Initial battery level upon arrival at the access point: ; in, The amount of electricity at arrival; Any time slot after this moment The energy conservation equation for a single vehicle battery is: ; in, For scheduling step size, and They are time slots and The battery level of the heavy truck. and These are charging power and discharging power, respectively. and These are charging efficiency and discharging efficiency, respectively. The charging / discharging power and capacity constraints are as follows: ; ; ; in, This is a variable indicating the access status of heavy-duty trucks. and These are the maximum charging and discharging power, respectively. This is the maximum capacity of the heavy-duty truck battery; The rigid constraints for commercial tasks are: ; in, The planned offline time for heavy-duty trucks. This refers to the battery level at the time of disconnection from the grid. To meet the minimum state-of-charge ratio requirement for the next phase of logistics tasks.

[0009] Optionally, the nodal active power balance equations for obtaining the net active power injected by each node in the distribution area into the distribution network branches include: definition In time slot Actual access area dedicated line node The available set of heavy-duty trucks, and the total discharge power of the heavy-duty truck cluster at this node. With total charging power They are aggregated separately as follows: ; ; Constructing stationary energy storage systems The energy conservation equation is used, and upper and lower limits of charging and discharging power and capacity are imposed to construct the node. In the time slot The active power balance equation: ; in, It is a collection of distributed power sources. For distributed power sources Those who have made meritorious contributions; A collection of stationary energy storage systems. and Energy storage The discharge and charge power; For nodes The original load, For nodes Load recovery state variables, For nodes The net active power injected into the distribution network branches.

[0010] Optionally, the quadratic equality constraint after convex relaxation is transformed into a second-norm inequality constraint, and safety limit constraints are applied to node voltages and branch currents, including: Introducing intermediate variables of voltage and current: ; ; in, For nodes voltage amplitude The square of, For nodes To node branch road Current amplitude The square of; Construct the active and reactive power balance equations and voltage drop equations for the branch: ; ; ; in, and They are nodes The set of parent and child nodes; and They are the branch roads that flow through. and branch roads The active power; and They are the branch roads that flow through. and branch roads reactive power; For nodes Net injected reactive power; and Branch roads Resistance and reactance; The power flow nonlinear constraint is relaxed using a second-order cone convexity treatment, transforming it into a standard second-order norm inequality: ; in, The L2 norm of a vector; Apply safety limits for node voltages and branch current limits: ; ; in, and These are the minimum and maximum amplitude limits for the node voltage, respectively. This is the maximum allowable current limit for the branch.

[0011] Optionally, the objective function of the mixed-integer second-order cone programming model is: ; The first item is the load recovery benefit. For nodes The first item is the weighting of the load's importance; the second item is the incentive for heavy-duty truck power protection. This is the weighting adjustment factor.

[0012] Optionally, the rolling time-domain and event-triggered control mechanism for issuing scheduling instructions to the distribution area equipment and heavy-duty truck terminals includes: Find the optimal scheduling sequence within the future optimization window at the current scheduling time, and only extract the action command of the first control time slot to send to the equipment in the distribution area and the heavy truck terminal for execution; After the first control time slot is completed, the actual status data of the transformer area and the actual battery charge status of the heavy truck are collected and used as the initial state feedback value to update the model. The optimization time window is then pushed forward for the next round of rolling solution. If, during execution, a communication failure of a heavy truck is detected, a vehicle fails to arrive as expected, or the power flow of the distribution network exceeds the preset safety threshold, an abnormal event is triggered, interrupting the current scheduling sequence and immediately performing an emergency rescheduling solution based on transient data.

[0013] To achieve the above objectives, the present invention also provides an optimized scheduling system for supporting the isolated operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters, comprising: The data acquisition and spatiotemporal availability filtering module is used to acquire source load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load sensitivity energy consumption model of heavy trucks, it calculates the expected arrival time and travel energy consumption of the candidate electric heavy trucks and filters out the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. The vehicle individual and cluster aggregation modeling module is used to obtain the source load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load sensitivity energy consumption model of heavy trucks, it calculates the expected arrival time and travel energy consumption of the candidate electric heavy trucks and selects the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. The actual available heavy trucks are limited to designated dedicated line nodes with large-capacity converter capabilities within the distribution radio area. The total charging and discharging power of the heavy truck cluster at the dedicated line node is aggregated and calculated, and spatial coupling constraints are established. The power flow coupling and security constraint construction module of the distribution network is used to construct a dynamic model of the fixed energy storage system in the distribution area. It integrates the distributed power source, fixed energy storage, aggregated power of heavy truck clusters and the load of the distribution area to obtain the node active power balance equation of each node in the distribution area injecting net active power into the distribution network branch. The DistFlow model is used to describe the power flow distribution of radial distribution substations. For the nonlinear quadratic equality constraints in the model, a second-order cone relaxation technique is used to perform convex relaxation. The quadratic equality constraints after convex relaxation are transformed into second-order norm inequality constraints, and safety limit constraints for node voltage and branch current are applied. The multi-objective optimization solution and scheduling execution module is used to construct a mixed integer second-order cone programming model with the objective function of maximizing the recovery of important loads in the distribution area and taking into account the power protection of heavy trucks. It calls the solver to perform optimization and solves the model, and uses a rolling time domain and event-triggered control mechanism to issue scheduling instructions to the distribution area equipment and heavy truck terminals.

[0014] To achieve the above objectives, the present invention also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the optimized scheduling method.

[0015] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the optimized scheduling method.

[0016] The beneficial effects of this invention are as follows: 1. Introducing load sensitivity assessment to improve the accuracy of available energy prediction: This invention overcomes the shortcomings of existing technologies that ignore the difference between empty and fully loaded heavy trucks, which leads to distorted power prediction. By introducing the load weight to correct the travel energy consumption, the accuracy of energy assessment during the vehicle screening stage is ensured, and the risk of heavy truck breakdown or power outage during emergency support is reduced.

[0017] 2. Establishing rigid constraints for commercial tasks while balancing emergency power supply and commercial operation: This invention breaks through the limitations of traditional V2G, which treats vehicles as purely grid-attached energy storage. By rigidly embedding off-grid minimum power threshold constraints into the single-vehicle model, it effectively balances the rigid demands of restoring critical loads in the distribution area with the subsequent logistics transportation needs of heavy trucks, thus effectively protecting the economic interests of fleet operators.

[0018] 3. Combining spatial node coupling and second-order cone relaxation to ensure power grid physical safety and improve solution efficiency: To address the potential for distribution network over-limit hazards caused by the high-power charging and discharging of heavy trucks, this invention achieves spatial decoupling and risk avoidance by limiting the access of dedicated line nodes; at the same time, SOCP technology is used to accurately transform non-convex AC power flow constraints into convex sets, enabling the MISOCP model to obtain the global optimal solution in polynomial time, taking into account both the voltage / current safety limits of the distribution network and the real-time requirements of fault emergency response.

[0019] 4. The dual closed-loop mechanism of rolling time domain and event triggering significantly enhances the robustness of the system: This invention abandons the traditional static open-loop scheduling and uses rolling updates by collecting real state data at each step, which effectively smooths out random disturbances in wind and solar power output and vehicle access behavior; at the same time, it is supplemented by an abnormal event triggering mechanism to deal with communication interruptions or traffic delays, which greatly improves the engineering practicality and robustness of the islanded control system under extreme conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an optimized scheduling method for supporting the isolated operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters, according to an embodiment of the present invention. Figure 2This is a schematic diagram of an optimized scheduling system architecture based on off-grid discharge of electric heavy-duty truck clusters to support the isolated operation of residential transformer substations, according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, in a scenario where a power outage occurs in a distribution transformer area, leading to islanded operation, this embodiment provides an optimized scheduling method based on off-grid discharge from an electric heavy-duty truck cluster to support islanded operation of a residential distribution transformer area. This method relies on computing devices such as the distribution transformer energy management system (EMS) or edge computing gateways for execution. To accurately describe the optimized scheduling model of this embodiment, the following set of parameters is first defined: This is the set of all dispatchable electric heavy-duty trucks; In time slot The subset of heavy-duty trucks that were actually connected and allowed to discharge; This refers to a collection of distributed power sources within the transformer area. A collection of stationary energy storage systems; The set of all nodes in the transformer area; This is the set of critical load nodes that need to be prioritized for restoration in the transformer area. This is a set of branches in the distribution network topology.

[0024] Specifically, the scheduling method includes the following implementation steps: Step S1: Multidimensional data acquisition and spatiotemporal availability filtering of heavy trucks based on load sensitivity: The system interacts with the vehicle network platform through the edge gateway of the distribution area to obtain load forecast data and distributed power output forecast data of each node in the distribution area, as well as the real-time location, speed, current load weight, expected off-grid time and minimum power requirements of the candidate heavy trucks for subsequent logistics tasks.

[0025] Considering the significant difference in weight between empty and fully loaded commercial electric heavy-duty trucks, their energy consumption per unit mile exhibits a much stronger load sensitivity compared to passenger cars. This embodiment introduces a load factor to accurately estimate the expected arrival time and trip energy consumption: heavy trucks Estimated time to reach the access point for: ; Introducing load-sensitive travel energy consumption for: ; In the formula, The time when the scheduling instruction is issued. For heavy trucks Driving distance from the access point of the transformer substation This is the projected average vehicle speed; The benchmark energy consumption for heavy-duty trucks under no-load conditions. The load-bearing energy consumption coefficient, For heavy trucks Current load weight.

[0026] If heavy truck If the following spatiotemporal and energy availability conditions are met, the candidate will be included in the candidate set; otherwise, it will be eliminated: ; ; In the formula, This refers to the initial battery level of the heavy truck when the command is issued. To ensure a minimum reserve power threshold for safe driving, This is the maximum tolerable period of power outage waiting time for an isolated island in the power distribution area.

[0027] Step S2: Construct a dynamic model of the vehicle battery: For qualified electric heavy-duty trucks, a dynamic model of battery charging and discharging after they connect to the distribution area is constructed. The initial battery level of the heavy-duty truck upon arrival is: ; Any time slot after this moment The energy conservation equation for a single vehicle battery is: ; The upper and lower limits of charge / discharge power and the capacity constraints are as follows: ; ; ; Unlike private cars, commercial heavy-duty trucks have strict logistics mission attributes. This embodiment sets rigid constraints on off-grid missions to ensure that the heavy-duty truck is in operation when it leaves the network. Reserve sufficient power to perform subsequent planned tasks: ; In the formula, For scheduling step size, These are the charging and discharging power, respectively. For the corresponding efficiency; For access status indicator variables; This sets a minimum state-of-charge ratio limit for users to set for the next logistics task.

[0028] Step S3, Heavy Truck Cluster Aggregation: Electric heavy-duty trucks typically have high discharge power, and directly connecting them to ordinary power distribution nodes can easily lead to end-point overload. This embodiment restricts the heavy-duty trucks to connection to high-capacity dedicated line nodes (denoted as nodes) within the distribution area that possess bidirectional converter capabilities. ).definition For time slots Access Node The actual available set of heavy-duty trucks, and the aggregated charging and discharging power of the heavy-duty trucks at this node are: ; ; Step S4: Construction of stationary energy storage model and nodal active power balance: Assuming fixed energy storage in the transformer area The power can be adjusted independently, and its dynamic constraints are as follows: ; ; ; ; Integrating distributed power sources, stationary energy storage, heavy-duty truck clusters, and loads, any node in the distribution network The active power balance equation is: ; In the formula, For nodes The original load, For load recovery state variables, This refers to the net active power injected by this node into the distribution network branch.

[0029] Step S5: Distribution Network DistFlow and Second-Order Cone Relaxation: This embodiment uses the DistFlow model to describe the power flow in a radial distribution area. Intermediate variables are introduced. and After variable substitution, the equations for active and reactive power balance and voltage drop in the branch are as follows: ; ; ; In the formula, and They are nodes The set of parent and child nodes. This is relevant to the original DistFlow model. This non-convex constraint is relaxed to an inequality in this embodiment and transformed into a standard second-order cone (SOCP) form to achieve a polynomial-time efficient solution for the model: ; Simultaneously meets network security constraints: ; ; Step S6: Objective function optimization and instruction issuance: Taking into account both maximizing the recovery of critical loads in the distribution area during islanded operation and ensuring the battery power rights of heavy-duty truck users, the following multi-objective mixed-integer second-order cone programming model (MISOCP) is constructed: ; In the formula, the first term represents the load recovery benefit. For nodes The first item is the weighting of the load's importance; the second item is the incentive for heavy-duty truck power protection. This is the weighting adjustment factor.

[0030] The control center of the distribution area uses a commercial solver (such as CPLEX) to solve the MISOCP model to obtain the optimal heavy-duty truck charging and discharging power sequence and load switching strategy. Subsequently, a rolling time-domain mechanism is used to send the current time slot command to each heavy-duty truck access point and distribution area switching equipment for execution.

[0031] Specifically, considering the fluctuations in wind and solar power output, load changes, and the high degree of randomness in the driving trajectories and access behaviors of heavy trucks, the rolling time-domain mechanism described in this embodiment is implemented as follows: (1) Command interception and issuance: at the current scheduling time The control center of the power grid solved the future. After determining the optimal scheduling sequence for each time slot, the entire sequence is not executed. Instead, only the first control time slot (or the preceding time slot) is selected and issued. The action command (with a shorter time slot) is sent to the switchgear in the distribution area and the V2G terminal of the heavy truck for execution.

[0032] (2) Status Feedback and Data Update: After the first control time slot is completed, the system collects the current real-time status of the distribution area through the communication interface, including the actual source-load power of each node, the actual state of charge (SOC) of the batteries of the heavy trucks on site, and information on newly entering / leaving heavy trucks. These real observations are used as the initial status feedback values ​​for the next optimization window to update the data. .

[0033] (3) Window forwarding and rolling solution: The optimization time window is shifted forward by one control time slot, that is, the new optimization window becomes The system reconstructs and solves the MISOCP model based on the updated initial state and corrected short-term forecast data, repeating the process.

[0034] (4) Event-triggered rescheduling for abnormal events: To improve the system's extreme anti-disturbance capability, a threshold triggering mechanism is introduced between two regular rolling steps. If any heavy truck is detected to be disconnected due to communication failure, fails to arrive as expected, or the actual power flow of the main line in the distribution area exceeds the preset safety threshold, the current execution sequence is immediately interrupted, the system performs scheduling based on the current transient data, and issues correction instructions.

[0035] like Figure 2 As shown, this embodiment also provides an optimized scheduling system for supporting the isolated operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters.

[0036] From a physical hardware architecture perspective, the system primarily relies on an edge computing gateway deployed in the distribution substation. This gateway connects downwards to the substation's smart switches, fixed energy storage PCS, and heavy-duty truck V2G bidirectional charging and discharging terminals via RS485 / Ethernet; and upwards to the fleet management system, vehicle networking platform, and the upper-level distribution network dispatch center via a 5G / fiber optic private network to enable command issuance and data exchange.

[0037] From a logical functional perspective, the system specifically includes a memory, a processor, and multiple virtual functional modules running on the processor. When the processor executes a computer program in the memory, it implements the methods described in steps S1 to S6 above. Specifically, the functional modules of the system include: (1) Data acquisition and spatiotemporal availability filtering module: This module is used to execute step S1, and is mainly responsible for collecting source load prediction data of the transformer area, real-time status of heavy trucks (including current load weight), and subsequent logistics task requirements; and based on the built-in load-sensitive travel energy consumption model, it calculates the expected arrival time and power consumption of heavy trucks, thereby screening out a set of available heavy trucks that meet the spatiotemporal and energy threshold conditions for island support.

[0038] (2) Vehicle Individual and Cluster Aggregation Modeling Module: This module is used to perform steps S2 and S3 above. For the selected available heavy trucks, a single-vehicle dynamic model is established, which includes battery state of charge updates and upper and lower limits of charging and discharging power, and a rigid constraint on the minimum power guarantee for off-grid tasks is strictly applied; at the same time, according to the actual access location of the heavy trucks, they are clustered and aggregated according to the high-capacity dedicated line nodes in the transformer area to generate node-level aggregated charging and discharging power boundaries for heavy trucks.

[0039] (3) Power Flow Coupling and Security Constraint Construction Module for Distribution Networks: This module is used to execute steps S4 and S5 above. It is mainly responsible for coupling the aggregated power of the heavy truck cluster with the fixed energy storage, distributed power sources and important loads in the distribution area to generate active / reactive power balance constraints at the nodes. At the same time, it describes the network voltage drop and branch power flow based on the DistFlow power flow equation of the radial distribution network, and uses the second-order cone (SOCP) technique to perform convex relaxation treatment on the nonlinear power flow terms to ensure that the power grid operates within the voltage and current safety limits.

[0040] (4) Multi-objective optimization solution and scheduling execution module: This module is used to execute step S6 above. It is responsible for constructing a mixed-integer second-order cone programming (MISOCP) model with the objective function of maximizing the recovery benefits of important loads in the distribution area while taking into account the battery power protection of heavy trucks; calling a commercial solver to solve for the optimal load switching strategy and heavy truck charging and discharging power sequence; and using a rolling time-domain control mechanism to send the optimization instructions of the current time slot to the corresponding execution terminals (V2G charging piles, distribution area switches), while monitoring execution feedback to cope with environmental randomness.

[0041] This embodiment also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the optimized scheduling method.

[0042] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the optimized scheduling method.

[0043] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An optimized scheduling method for supporting the isolated operation of residential transformer substations based on off-grid discharge of electric heavy-duty truck clusters, characterized in that, include: Step S1: Obtain the source-load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load-sensitivity energy consumption model of heavy trucks, calculate the expected arrival time and travel energy consumption of the candidate electric heavy trucks, and screen out the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. Step S2: For the electric heavy trucks in the actual available heavy truck set, construct a single vehicle battery charging and discharging dynamic model, and set rigid constraints for commercial tasks to ensure the vehicle's subsequent operation, so as to ensure that the minimum required power is retained when the vehicle is off-grid. Step S3: Limit the actual available heavy trucks to the designated dedicated line nodes with large-capacity converter capability within the distribution radio area, aggregate and calculate the total charging and discharging power of the heavy truck cluster at the dedicated line nodes, and establish spatial coupling constraints. Step S4: Construct a dynamic model of the fixed energy storage system within the distribution area to integrate distributed power sources, fixed energy storage, aggregated power of heavy truck clusters, and distribution area load to obtain the node active power balance equations for the net active power injected by each node in the distribution area into the distribution network branch. Step S5: Use the DistFlow model to describe the power flow distribution of the radial distribution area. For the nonlinear quadratic equality constraints in the model, use the second-order cone relaxation technique to perform convex relaxation processing. Transform the quadratic equality constraints after convex relaxation processing into the form of second-order norm inequality constraints, and apply safety limit constraints on node voltage and branch current. Step S6: Construct a mixed integer second-order cone programming model with the objective function of maximizing the recovery of important loads in the distribution area and taking into account the power protection of heavy trucks. Call the solver to perform optimization and solve the model, and use a rolling time domain and event-triggered control mechanism to issue scheduling instructions to the distribution area equipment and heavy truck terminals.

2. The optimized scheduling method according to claim 1, characterized in that, Based on the load-sensitivity energy consumption model of the heavy-duty truck, the estimated arrival time and travel energy consumption of the candidate electric heavy-duty trucks are calculated, and the set of actually usable heavy-duty trucks that meet the spatiotemporal and energy threshold conditions is selected, including: Calculate candidate heavy trucks Estimated time to reach the access point : ； Calculate heavy trucks Stroke energy consumption for: ； When heavy truck A truck will be included in the actual usable heavy-duty truck set if it meets all of the following conditions: ； ； in, The time when the scheduling instruction is issued. For heavy trucks Driving distance from the access point of the transformer substation This is the estimated average vehicle speed; The benchmark energy consumption for heavy-duty trucks under no-load conditions. The load-bearing energy consumption coefficient, For heavy trucks Current load weight; Heavy truck when the command is issued The initial charge, To ensure a minimum reserve power threshold for safe driving, This is the maximum tolerable period of power outage waiting time for an isolated island in the power distribution area.

3. The optimized scheduling method according to claim 1, characterized in that, The dynamic model of battery charging and discharging for the single vehicle is constructed, and rigid constraints for commercial tasks to ensure subsequent vehicle operation are set, including: heavy trucks Initial battery level upon arrival at the access point: ； in, The amount of electricity at arrival; Any time slot after this moment The energy conservation equation for a single vehicle battery is: ； in, For scheduling step size, and They are time slots and The battery level of the heavy truck. and These are charging power and discharging power, respectively. and These are charging efficiency and discharging efficiency, respectively. The charging / discharging power and capacity constraints are as follows: ； ； ； in, This is a variable indicating the access status of heavy-duty trucks. and These are the maximum charging and discharging power, respectively. This is the maximum capacity of the heavy-duty truck battery; The rigid constraints for commercial tasks are: ； in, The planned offline time for heavy-duty trucks. This refers to the battery level at the time of disconnection from the grid. To meet the minimum state-of-charge ratio requirement for the next phase of logistics tasks.

4. The optimized scheduling method according to claim 1, characterized in that, The nodal active power balance equations for obtaining the net active power injected by each node in the transformer substation into the distribution network branches include: definition In time slot Actual access area dedicated line node The available set of heavy-duty trucks, and the total discharge power of the heavy-duty truck cluster at this node. With total charging power They are aggregated separately as follows: ； ； Constructing stationary energy storage systems The energy conservation equation is used, and upper and lower limits of charging and discharging power and capacity are imposed to construct the node. In the time slot The active power balance equation: ； in, It is a collection of distributed power sources. For distributed power sources Those who have made meritorious contributions; A collection of stationary energy storage systems. and Energy storage The discharge and charge power; For nodes The original load, For nodes Load recovery state variables, For nodes The net active power injected into the distribution network branches.

5. The optimized scheduling method according to claim 4, characterized in that, The quadratic equality constraint after convex relaxation is transformed into a second-norm inequality constraint, and safety limit constraints are applied to node voltages and branch currents, including: Introducing intermediate variables of voltage and current: ； ； in, For nodes voltage amplitude The square of, For nodes To node branch road Current amplitude The square of; Construct the active and reactive power balance equations and voltage drop equations for the branch: ； ； ； in, and They are nodes The set of parent and child nodes; and They are the branch roads that flow through. and branch roads The active power; and They are the branch roads that flow through. and branch roads reactive power; For nodes Net injected reactive power; and Branch roads Resistance and reactance; The power flow nonlinear constraint is relaxed using a second-order cone convexity treatment, transforming it into a standard second-order norm inequality: ； in, The L2 norm of a vector; Apply safety limits for node voltages and branch current limits: ； ； in, and These are the minimum and maximum amplitude limits for the node voltage, respectively. This is the maximum allowable current limit for the branch.

6. The optimized scheduling method according to claim 1, characterized in that, The objective function of the mixed-integer second-order cone programming model is: ； The first item is the load recovery benefit. For nodes The first item is the weighting of the load's importance; the second item is the incentive for heavy-duty truck power protection. This is the weighting adjustment factor.

7. The optimized scheduling method according to claim 1, characterized in that, The rolling time-domain and event-triggered control mechanism is used to issue scheduling instructions to the distribution area equipment and heavy-duty truck terminals, including: Find the optimal scheduling sequence within the future optimization window at the current scheduling time, and only extract the action command of the first control time slot to send to the equipment in the distribution area and the heavy truck terminal for execution; After the first control time slot is completed, the actual status data of the transformer area and the actual battery charge status of the heavy truck are collected and used as the initial state feedback value to update the model. The optimization time window is then pushed forward for the next round of rolling solution. If, during execution, a communication failure of a heavy truck is detected, a vehicle fails to arrive as expected, or the power flow of the distribution network exceeds the preset safety threshold, an abnormal event is triggered, interrupting the current scheduling sequence and immediately performing an emergency rescheduling solution based on transient data.

8. An optimized scheduling system based on off-grid discharge of electric heavy-duty truck clusters to support the isolated operation of residential transformer substations, characterized in that, The system is used to implement the method according to any one of claims 1 to 7, the system comprising: The data acquisition and spatiotemporal availability filtering module is used to acquire source load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load sensitivity energy consumption model of heavy trucks, it calculates the expected arrival time and travel energy consumption of the candidate electric heavy trucks and filters out the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. The vehicle individual and cluster aggregation modeling module is used to obtain the source load prediction data of the transformer area and the real-time status and logistics task requirements of the candidate electric heavy trucks. Based on the load sensitivity energy consumption model of heavy trucks, it calculates the expected arrival time and travel energy consumption of the candidate electric heavy trucks and selects the actual available heavy truck set that meets the spatiotemporal and energy threshold conditions. The actual available heavy trucks are limited to designated dedicated line nodes with large-capacity converter capabilities within the distribution radio area. The total charging and discharging power of the heavy truck cluster at the dedicated line node is aggregated and calculated, and spatial coupling constraints are established. The power flow coupling and security constraint construction module of the distribution network is used to construct a dynamic model of the fixed energy storage system in the distribution area. It integrates the distributed power source, fixed energy storage, aggregated power of heavy truck clusters and the load of the distribution area to obtain the node active power balance equation of each node in the distribution area injecting net active power into the distribution network branch. The DistFlow model is used to describe the power flow distribution of radial distribution substations. For the nonlinear quadratic equality constraints in the model, a second-order cone relaxation technique is used to perform convex relaxation. The quadratic equality constraints after convex relaxation are transformed into second-order norm inequality constraints, and safety limit constraints for node voltage and branch current are applied. The multi-objective optimization solution and scheduling execution module is used to construct a mixed integer second-order cone programming model with the objective function of maximizing the recovery of important loads in the distribution area and taking into account the power protection of heavy trucks. It calls the solver to perform optimization and solves the model, and uses a rolling time domain and event-triggered control mechanism to issue scheduling instructions to the distribution area equipment and heavy truck terminals.

9. A computing device, 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 program, it implements the steps of the optimized scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the optimized scheduling method as described in any one of claims 1 to 7.

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