A hybrid optimization scheduling method for electric transport vehicles and fuel transport vehicles

Abstract

The application provides a hybrid optimization scheduling method for electric transport vehicles and fuel transport vehicles, relates to the technical field of vehicle task scheduling and comprehensive energy operation optimization in rural industrial parks, and describes the transport task demand, vehicle task start and completion, vehicle task occupation, electric transport vehicle power change, charging facility occupation, distributed new energy consumption, fixed energy storage operation and park power grid interaction process in the same optimization framework. On the premise of guaranteeing the completion of the park transport task, the park comprehensive operation cost is reduced, the utilization efficiency of the electric transport vehicle is improved, the new energy consumption level in the park is improved, and a cooperative scheduling plan capable of being directly executed is formed.

Description

Technical Field

[0001] This application relates to the field of vehicle task scheduling and integrated energy operation optimization in rural industrial parks, and in particular to a hybrid optimization scheduling method for electric transport vehicles and fuel-powered backup transport vehicles. Background Technology

[0002] Rural industrial parks typically integrate functions such as agricultural product processing, warehousing and logistics, production material transshipment, cold chain distribution, and agricultural product transportation. There are numerous periodic or time-sensitive transportation tasks within the park and between the park and warehousing, processing, and distribution nodes. With the increasing electrification of transportation vehicles within the parks, electric logistics vehicles, electric agricultural product transport vehicles, electric material transfer vehicles, and electric tractor units are gradually participating in park transportation operations.

[0003] Compared to traditional fuel-powered transport vehicles, electric transport vehicles offer advantages such as high energy efficiency, low operating costs, lower emissions, and the ability to operate in conjunction with distributed renewable energy sources, fixed energy storage, and charging facilities within the park. However, electric transport vehicles are limited by battery capacity, remaining charge, charging time, and the number of charging facilities, resulting in a strict time coupling between task dispatch, task occupancy, task completion and return, and recharging. Vehicles cannot charge within the park while performing transport tasks, and upon returning, they need to be restored to a sufficient charge level to meet the requirements of subsequent tasks within a limited time.

[0004] When transportation tasks occur in concentrated periods, relying solely on electric transport vehicles to handle all the tasks may lead to problems such as insufficient vehicle numbers, insufficient battery power, or conflicts with charging facilities. Therefore, rural industrial parks typically retain a certain number of fuel-powered logistics vehicles, fuel-powered transport vehicles, or fuel-powered tractor units as a backup resource in case of insufficient electric transport vehicles, insufficient battery power, limited charging facilities, or unexpected tasks.

[0005] Electric transport vehicles and fuel-powered backup transport vehicles differ significantly in terms of energy costs, operation and maintenance costs, refueling methods, carbon emission levels, and task availability. Electric transport vehicles have longer charging times but can utilize distributed renewable energy sources and low-cost electricity within the park; fuel-powered backup transport vehicles offer convenient refueling and greater flexibility in task scheduling, but their fuel costs, operation and maintenance costs, and carbon emissions are relatively higher. Therefore, it is necessary to rationally determine the task allocation ratio for the two types of vehicles based on transport task requirements, vehicle status, electricity prices, renewable energy output, and energy storage operation status.

[0006] In existing technologies, the scheduling of park transportation vehicles and the operation of the park's energy system are usually optimized separately. Vehicle scheduling methods mainly determine the dispatch plan based on transportation demand, number of vehicles, and task duration, with little consideration for the impact of electric transportation vehicle charging load on park electricity consumption, distributed renewable energy consumption, and fixed energy storage operation; while park energy scheduling methods usually treat vehicle charging power as a given load or a simple adjustable load, failing to accurately describe the dynamic process of vehicle task start-up, continuous occupation, completion and return, and re-availability.

[0007] The above-mentioned split scheduling method has at least the following shortcomings.

[0008] First, the vehicle task occupancy status is not accurately considered. Some existing methods only determine vehicle availability based on the number of vehicles dispatched or the total number of vehicles in the current time period, without accumulating statistics on vehicles that have already departed but have not yet completed their tasks. This can easily lead to vehicles that are still in a task occupancy status being mistakenly considered as vehicles that can be dispatched again.

[0009] Second, a complete temporal sequence relationship between the start of a task, continuous occupation, return upon completion, and charging of electric transport vehicles has not been established. Without vehicle-level state variables, outcomes that do not conform to actual operating conditions may occur, such as the same electric transport vehicle being scheduled for charging while performing a transport task, or being reassigned a transport task before completing its current task.

[0010] Third, the energy consumption and power recovery process of electric transport vehicles are not adequately considered. Some methods only optimize energy based on the vehicle's charging load, without combining the energy consumption required for the transport task with the start and end times of the specific task and the vehicle's remaining power. This may result in the vehicle being scheduled to perform a task when its power is insufficient, or being unable to recover to the required power level before the next task begins.

[0011] Fourth, transportation task scheduling and the consumption of renewable energy in the park are disconnected. The output of distributed renewable energy in the park is usually concentrated in specific time periods, while the return and charging needs of transportation vehicles have obvious time fluctuations. If vehicle tasks and charging arrangements are not optimized together, problems such as vehicles charging intensively during peak electricity price periods, no vehicles available for charging during periods of high renewable energy output, and fixed energy storage and vehicle charging sharing the park's power distribution capacity are likely to occur.

[0012] Fifth, the economic substitutability between electric transport vehicles and fuel-powered backup transport vehicles was not fully considered. Prioritizing the use of electric transport vehicles alone may lead to congestion of charging facilities, insufficient power, or inability to perform subsequent tasks; while excessive use of fuel-powered backup transport vehicles will increase fuel costs, operation and maintenance costs, and carbon emissions.

[0013] Sixth, some scheduling models do not effectively restrict vehicle tasks that are not completed by the end of the scheduling cycle. If a vehicle starts a task before the end of the scheduling cycle but completes it after the cycle ends, and the model does not perform cross-cycle state transfer, the vehicle occupancy status and task energy consumption may not be correctly included in the current or next scheduling cycle. Summary of the Invention

[0014] This application aims to at least partially address one of the technical problems in the related art.

[0015] To address this, this application proposes a hybrid optimization scheduling method for electric transport vehicles and fuel-powered backup transport vehicles. Within the same optimization framework, it describes the transportation task demand, vehicle task start and completion, vehicle task occupancy, changes in electric transport vehicle battery level, charging facility occupancy, distributed renewable energy consumption, fixed energy storage operation, and the interaction process with the park's power grid. Under the premise of ensuring the completion of transportation tasks in the park, it reduces the overall operating cost of the park, improves the utilization efficiency of electric transport vehicles and the park's renewable energy consumption level, and forms a directly executable collaborative scheduling plan to overcome the problem of the disconnect between the existing rural industrial park's transport vehicle task scheduling and the park's energy system operation.

[0016] Another aspect of this application proposes a hybrid optimized scheduling system for electric transport vehicles and fuel-powered backup transport vehicles.

[0017] To achieve the above objectives, the first aspect of this application proposes a hybrid optimized scheduling method for electric transport vehicles and fuel-powered backup transport vehicles, comprising: Obtain data on park transportation needs, vehicle status, charging facility status, and energy system operation. Based on the aforementioned operational data, a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles, as well as the park's energy, is constructed with the goal of minimizing the overall operating cost of the park. Solve the joint optimization model to output a baseline scheduling scheme that includes vehicle task allocation plan and park energy operation plan; Collect real-time status data of vehicles, tasks, and energy systems after updates, and use this as initial conditions to perform daily rolling corrections to the baseline scheduling scheme.

[0018] In one embodiment of this application, the vehicle status includes the available number of electric transport vehicles and fuel-powered backup transport vehicles, the task occupancy status, and the remaining battery power; The joint optimization model aims to minimize the overall operating cost of the park and includes constraints on transportation task demand, vehicle task occupancy and return status, electric vehicle power and charging facilities, distributed new energy consumption, fixed energy storage operation, and park power balance. The baseline scheduling scheme includes at least two types of time-sharing task allocation for transport vehicles, task start and return times for electric transport vehicles, charging power and charging facility occupancy plan, fixed energy storage charging and discharging plan, distributed new energy consumption plan, and park time-sharing power purchase plan.

[0019] In one embodiment of this application, the joint optimization model includes the following rules for vehicle task occupancy and return status constraints: The total number of electric transport vehicles put into use in the park at each time period is calculated by summing the number of vehicles that started tasks at the corresponding time period; During the first scheduling period, the task occupancy status of electric transport vehicles remains consistent with the vehicle status of the task initiated in that period; Except for the first time period, the vehicle task occupancy status in each other time period is updated incrementally based on the number of newly started tasks and the number of vehicles that have completed their return trips in the current period. The same electric transport vehicle cannot simultaneously complete its return trip and start a new transport mission at the same time. If an electric transport vehicle starts a transport task during a designated time period, the vehicle will remain in a task-occupied state from the start of that time period until the task is completely completed. The vehicle's return time is strictly matched with the task start time and the task's continuous occupation period. The vehicle can only be marked as returning during the period when the corresponding transportation task is fully completed.

[0020] In one embodiment of this application, the joint optimization model includes the following rules regarding the electric vehicle's battery capacity and charging infrastructure constraints: At the beginning of the dispatch cycle, the battery level of each electric transport vehicle is uniformly set to the initial battery level of the vehicle. Before an electric transport vehicle starts a transport mission at any time, its remaining power must cover both the power consumption of the transport mission and the vehicle's minimum safe power level. After an electric transport vehicle completes its transport task, the electricity consumed during the corresponding return trip period will be deducted; during periods when no transport task is performed, the vehicle's electricity can be replenished by charging. At any given time, the remaining battery power of electric transport vehicles is between the vehicle's minimum safe battery power and the battery's maximum capacity; At the end of the entire scheduling cycle, the remaining battery power of all electric transport vehicles shall not be lower than the preset minimum battery power standard at the end of the period. The real-time charging power of a vehicle must not exceed both the maximum charging power of the vehicle itself and the rated charging power of a single charging device at the same time. Electric transport vehicles that are currently occupied for transport tasks are not allowed to use the park's charging facilities for charging. The total number of electric transport vehicles charging at the same time cannot exceed the total number of available charging devices in the park; The total power of all electric transport vehicles charging simultaneously shall not exceed the upper limit of the total power of the park's charging system and supporting power distribution equipment.

[0021] In one embodiment of this application, the joint optimization model includes the following rules for distributed renewable energy consumption constraints, fixed energy storage operation constraints, and park power balance constraints: The sum of the actual power consumption of distributed renewable energy in the current period and the power curtailment is equal to the predicted value of renewable energy output in the current period. The initial and final charge values ​​of the fixed energy storage device remain the same throughout the scheduling cycle. Fixed energy storage devices can only perform one operation at a time, either charging or discharging, and cannot charge or discharge simultaneously. The power of the stationary energy storage device is updated periodically according to the charging and discharging operations in each period, and the power of the energy storage device is always within the minimum and maximum capacity limit range of the energy storage device at any time. The overall power consumption in the park remains in balance between supply and demand. The power purchased from the external grid, the power consumed locally by new energy sources, and the power discharged by energy storage together supply the park's basic load, the total charging load of electric vehicles, and the charging load of energy storage. The real-time power purchased by the park from the external grid shall not exceed the maximum power purchase allowed by the park's grid connection point.

[0022] In one embodiment of this application, the joint optimization model uses the minimum sum of five costs as the optimization objective: the cost of purchasing electricity from the external grid, the cost of transporting goods by fuel-powered backup vehicles, the non-energy operation and maintenance cost of electric transport vehicles, the cycle loss cost of fixed energy storage, and the cost of penalties for abandoning renewable energy. The cost of purchasing electricity from the external grid within the park is calculated by summing the electricity price, the power purchased from the external grid, and the duration of each time period. The cost of fuel-powered vehicle transportation tasks is calculated by summing the total number of fuel-powered vehicles used in each time period and the cost of fuel-powered vehicles per unit time period. The non-energy operation and maintenance cost of electric transport vehicles is calculated by summing the total number of electric vehicles put into use in each time period and the operation and maintenance cost of electric vehicles per unit time period. The fixed energy storage cycle loss cost is calculated by summing the total energy storage charging and discharging power, the unit energy storage cycle loss cost, and the duration of each period. The cost of penalties for curtailing renewable energy is calculated by summing up the amount of renewable energy curtailed in each time period, the unit price of the penalty for curtailment, and the duration of the time period.

[0023] In one embodiment of this application, solving the joint optimization model includes: converting the overall model into a mixed-integer linear programming model, performing calculations using a standard mixed-integer linear programming solver, and outputting a baseline scheduling scheme under normal operating conditions of the park.

[0024] In one embodiment of this application, modifying the baseline scheduling scheme includes: Each time rolling optimization is initiated, multiple types of real-time operational data are collected again, including completed and pending transportation tasks, actual vehicle task occupancy status and remaining power, real-time occupancy status of charging equipment, actual output of distributed new energy and updated output forecast, actual value and updated forecast of park basic load, real-time power of fixed energy storage, and the latest time-of-use electricity price or grid demand response instructions. The real-time actual power of all electric transport vehicles and stationary energy storage devices is set as the initial power condition for this rolling optimization. The vehicle occupancy status and estimated return time for transportation tasks that have been started but not yet completed are locked as fixed actual operating conditions. Only transportation tasks that have not yet started and all subsequent energy system operation plans are re-solved and optimized.

[0025] In one embodiment of this application, the intraday rolling correction adopts a fixed-length rolling optimization window. The window starts from the current rolling calculation time and covers subsequent scheduling periods of a fixed duration. The starting point is the current rolling optimization calculation time, and the total number of window periods is the preset optimization time domain length.

[0026] Furthermore, to achieve the above embodiments, this application proposes a hybrid optimized scheduling system for electric transport vehicles and fuel-powered backup transport vehicles, comprising: The operation data acquisition module is used to acquire operational data on park transportation task requirements, vehicle status, charging facility status, and energy system operation. The joint optimization model construction module is used to construct a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles and the park's energy based on the aforementioned operational data, with the goal of minimizing the overall operating cost of the park. The model solving module is used to solve the joint optimization model and output a baseline scheduling scheme that includes vehicle task allocation plan and park energy operation plan; The scheme correction module is used to collect the actual status data of vehicles, tasks and energy systems after updates, and use this as the initial condition to perform daily rolling corrections to the baseline scheduling scheme.

[0027] The method and system of this application embodiment, for a closed-loop transportation task that starts from a park or a designated energy replenishment base, completes the transportation task, and returns to the park or designated energy replenishment base, jointly determines the task allocation of electric transportation vehicles and fuel-powered backup transportation vehicles, the charging plan of electric transportation vehicles, the charging and discharging plan of fixed energy storage, and the power purchase plan of the park, under the conditions of meeting the transportation task requirements, vehicle task occupancy, electric transportation vehicle power safety, charging facility capacity, park distributed new energy output, fixed energy storage operation, and grid interaction constraints.

[0028] This invention aims to solve the following technical problems: Avoid mistakenly classifying vehicles that have started a mission but have not yet completed it as available vehicles; To avoid the same electric transport vehicle being repeatedly dispatched or scheduled for charging during its transport mission; Ensure that electric transport vehicles have the necessary power and a safety reserve power to complete the corresponding task before performing the task; To avoid the vehicle task occupancy status and task energy consumption not being properly handled at the end of the scheduling cycle; Coordinate the charging load of electric transport vehicles, distributed new energy sources in the park, fixed energy storage, and power purchased from the power grid; The task assignment ratio should be reasonably determined based on the operating costs, vehicle availability, and energy system status of the two types of vehicles. Develop an executable scheduling plan with clear vehicle, task time periods, and equipment power arrangements.

[0029] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a hybrid optimized scheduling method for electric transport vehicles and fuel-powered backup transport vehicles provided in this application embodiment; Figure 2 This is a structural diagram of a hybrid optimized scheduling system for electric transport vehicles and fuel-powered backup transport vehicles provided in an embodiment of this application. Detailed Implementation

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] The following describes, with reference to the accompanying drawings, a method and system for hybrid optimized scheduling of electric transport vehicles and fuel-powered backup transport vehicles, according to embodiments of this application.

[0034] like Figure 1 As shown, this method for optimal scheduling of electric transport vehicles and fuel-powered backup transport vehicles specifically includes the following steps: S1, obtains data on park transportation task requirements, vehicle status, charging facility status, and energy system operation; S2, Based on the aforementioned operational data, a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles and the park's energy is constructed with the goal of minimizing the overall operating cost of the park. S3, Solve the joint optimization model to output a baseline scheduling scheme that includes vehicle task allocation plan and park energy operation plan; S4: Collect the actual status data of vehicles, tasks and energy systems after updates, and use this as the initial condition to perform intraday rolling corrections on the baseline scheduling scheme.

[0035] It is understood that the method is designed for park logistics transportation, agricultural product transportation, production material transfer, warehousing and distribution and other closed-loop transportation tasks. Under the conditions of meeting transportation task requirements, vehicle availability, task occupation time, electric vehicle battery safety, charging facility capacity and park power operation constraints, it jointly optimizes the task allocation of electric transportation vehicles and fuel-powered backup transportation vehicles, the charging arrangement of electric transportation vehicles, the consumption of distributed new energy in the park, the charging and discharging of fixed energy storage and the power grid interaction power. It belongs to the cross-technical field of vehicle scheduling, charging facility operation control and park comprehensive energy synergistic optimization.

[0036] The closed-loop transportation task refers to the complete task cycle in which a vehicle departs from the park or designated energy replenishment base, completes logistics transportation, agricultural product transportation, production material transfer or warehousing and distribution tasks, and then returns to the park or designated energy replenishment base.

[0037] The electric transport vehicles include electric logistics vehicles, electric agricultural product transport vehicles, electric material transfer vehicles, electric tractor vehicles, and other transport vehicles that undertake closed-loop transportation tasks within the park and are replenished with electricity through the park's charging facilities.

[0038] The fuel-powered backup transport vehicles include fuel-powered logistics vehicles, fuel-powered agricultural product transport vehicles, fuel-powered material transfer vehicles, fuel-powered tractor vehicles, and other fuel-powered vehicles capable of completing corresponding closed-loop transport tasks.

[0039] Specifically, this invention addresses closed-loop transportation tasks that depart from a park or designated energy replenishment base, complete a transportation task, and return to the park or designated energy replenishment base. Under the conditions of meeting transportation task requirements, vehicle task occupancy, electric vehicle power safety, charging facility capacity, park distributed new energy output, fixed energy storage operation, and grid interaction constraints, it jointly determines the task allocation of electric transportation vehicles and fuel-powered backup transportation vehicles, the electric transportation vehicle charging plan, the fixed energy storage charging and discharging plan, and the park electricity purchase plan.

[0040] The joint optimization model consists of an objective function and constraints. The objective function is to minimize the overall operating cost of the park. The constraints include transportation task demand, availability of fuel-powered backup transport vehicles, task start, task occupancy and return status of electric transport vehicles, power dynamics, charging facility capacity, distributed new energy power, fixed energy storage operation, and park power balance constraints.

[0041] In one extended implementation, the demand response capacity that the park can provide is further evaluated based on the solution results of the basic joint optimization model. The demand response capacity evaluation does not, in turn, alter the normal vehicle task schedule and baseline power purchase plan formed by the basic joint optimization model.

[0042] In the basic implementation of the present invention, a unified optimization scheduling is performed on closed-loop transportation tasks of the same task category or those with the same task duration and single task energy consumption characteristics after clustering.

[0043] When there are multiple routes or multiple task types in the park at the same time, tasks can be classified according to task route, vehicle type, task duration or single task energy consumption, and corresponding task requirements, task duration and task energy consumption parameters can be set respectively, or task category subscripts can be added to expand.

[0044] This embodiment of the method for hybrid optimization scheduling of electric and fuel-powered backup transport vehicles involves acquiring data on park transport tasks, vehicle status, charging facilities, and energy system operation; constructing a joint optimization model for hybrid scheduling of electric and fuel-powered backup transport vehicles and park energy; solving the joint optimization model to obtain vehicle task allocation plans and park energy operation plans; distributing the task plans and power control plans to the vehicle scheduling system, charging facilities, and fixed energy storage control equipment; and performing daily rolling adjustments based on the actual status of vehicles, tasks, and energy systems. Specifically, it may include the following detailed steps: S101, the basic joint optimization model is constructed as follows: S101-1, Define the meaning of sets, parameters, decision variables, and time series: Let the scheduling period be divided into There are several equal-length scheduling periods, and the set of scheduling periods is:

[0045] The set of state points in time is:

[0046] The scheduling time interval is .

[0047] In one implementation, the scheduling period is one day. , .

[0048] This invention specifies: Indicates electric transport vehicles During the period Accept and begin executing the task at the start; Indicates vehicle Throughout the period The task is currently in use. Indicates vehicle During the period Complete the mission at the start and return to the park or designated refueling base; Indicates vehicle At the state point The remaining electricity, in kWh; Indicates vehicle During the period Average charging power within the range, in kW; This indicates the amount of electricity consumed in a single closed-loop transportation mission, expressed in kWh. In hours It has the same energy unit as the vehicle's remaining battery power.

[0049] Let the set of electric transport vehicles be:

[0050] in, This refers to the number of electric transport vehicles. The number of vehicles used for fuel supply.

[0051] In the basic implementation, each fuel supply vehicle has the same or equivalent task execution capability, and can perform the same type of closed-loop transportation task. Therefore, the vehicle quantity is used as a variable for modeling.

[0052] set up: For time period The number of transportation tasks that need to be started; For time period The number of scheduling periods occupied by the initial single closed-loop transportation task; For time period The amount of electricity required to start a single closed-loop transportation mission; For electric transport vehicles Battery capacity; For vehicles The minimum safe power level; For vehicles The initial charge; For vehicles Minimum electricity consumption requirement at the end of the period; For vehicles Maximum permissible charging power; The rated charging power of a single charging facility; For vehicles The charging efficiency; The number of available charging facilities in the park; The maximum total power of the park's charging system or corresponding power distribution equipment.

[0053] In the basic implementation, the rated charging power of each charging facility is... When the park is equipped with charging facilities of different rated power, the charging facility subscript and vehicle-charging facility connection variables can be further increased.

[0054] Vehicle-side decision variables include: Time period The number of fuel supply vehicles dispatched as backup transport vehicles; Time period The number of electric transport vehicles dispatched; :vehicle Is it during the time period? Start executing the task; :vehicle During the period Is it currently in a task-occupied state? :vehicle Is it during the time period? Complete the task and return; :vehicle The remaining battery power; :vehicle The charging power; :vehicle Does it occupy charging facilities?

[0055] in:

[0056]

[0057] The energy-side decision variables for the industrial park include: The power purchased by the park from the external power grid; Actual absorption capacity of distributed renewable energy in the park; : Distributed renewable energy curtailment power in the industrial park; and Fixed energy storage charging and discharging power; Fixed energy storage capacity; : Fixed energy storage charging and discharging mode variable.

[0058] S101-2, Construct the comprehensive operating cost objective function as follows: The goal is to minimize the sum of the following: the cost of purchasing electricity in the industrial park, the cost of transporting vehicles with fuel as a safety net, the non-energy operation and maintenance costs of electric transport vehicles, the cycle cost of fixed energy storage, and the penalty for abandoning renewable energy. (1) The items are as follows:

[0059]

[0060]

[0061]

[0062]

[0063] In the formula: For time period The electricity purchase price; The cost of a fuel-powered transport vehicle performing a single transport mission; Non-energy maintenance costs for an electric transport vehicle to perform a single transport mission; Cycle loss cost for a fixed unit charge / discharge capacity of energy storage; The penalty coefficient is the amount of electricity wasted from renewable energy sources.

[0064] The cost per mission for a fuel-powered backup transport vehicle can be expressed as:

[0065] in: This refers to the route length corresponding to the closed-loop transportation task; Fuel consumption per unit mileage for fuel-powered transport vehicles; This refers to the unit price of fuel. This covers the unit mileage maintenance cost of transport vehicles for fuel-powered vehicles.

[0066] The non-energy operation and maintenance cost of an electric transport vehicle for a single mission can be expressed as:

[0067] in, Non-energy maintenance cost per unit mileage for electric transport vehicles.

[0068] The electricity consumed by charging electric transport vehicles is included in the cost calculation through the purchase of electricity by the park, the consumption of distributed new energy sources, and the operation of fixed energy storage, and is not calculated repeatedly in the cost of a single task.

[0069] Demand response compensation revenue is not included in the objective function of the basic joint optimization model described in equation (1), so as to avoid the demand response revenue from adversely changing the vehicle task allocation, charging plan, fixed energy storage plan and benchmark power purchase curve under normal operating conditions.

[0070] S101-3, Constructing constraints on transportation task requirements and the availability of fuel backup transport vehicles, as detailed below: The sum of the number of electric transport vehicles and fuel-powered backup transport vehicles dispatched in each scheduling period equals the number of transport tasks that need to be executed in that period: (2) If fuel supply vehicles are in the time period Start executing the task, and Then it is in the time period The vehicle is still in a task-occupied state. Therefore, the number of fuel supply vehicles performing tasks at any given time must not exceed the total number of vehicles. (3) Formula (3) cumulatively counts fuel backup transport vehicles that have been dispatched but have not yet completed their tasks, to avoid mistakenly considering vehicles that have not yet returned as available vehicles.

[0071] S101-4, Construct the constraints for the start, occupancy, and return states of the electric transport vehicle task, as detailed below: The number of electric transport vehicles dispatched is obtained by summing the vehicle-level task start variables: (4) At the start of the scheduling cycle, all electric transport vehicles are located in the park or designated refueling base, and there are no unfinished cross-cycle transport tasks.

[0072] The initial task occupancy status of the electric transport vehicle is as follows: (5) The task occupancy status in subsequent time periods is recursively calculated using the following formula: (6) In one implementation, a scheduling period is reserved after a vehicle completes its task for vehicle inspection, loading and unloading, or task handover. The same vehicle cannot simultaneously return and begin its next task within the same time period. (7) If vehicle During the period Once the transportation task begins, it will start from the time period. To the time period All are currently in a task-occupied state: (8) Equation (8) applies to all conditions that satisfy the following conditions. , and time period .

[0073] The returned variables strictly correspond to the task start time and task duration: (9) Equations (5) to (9) together ensure that electric transport vehicles continuously occupy vehicle resources from the start of the mission to the return, and cannot be dispatched repeatedly.

[0074] In an implementation that allows vehicles to immediately accept the next task after completing their task, formula (7) can be deleted, or a corresponding minimum turnaround time can be set according to the actual loading, unloading and turnaround time.

[0075] S101-5, Construct the task processing constraints at the end of the scheduling cycle, as follows: In a day-ahead scheduling implementation that does not consider cross-cycle state transfer, scheduling electric transport tasks that cannot be completed within the current scheduling cycle is not permitted. (10) Fuel supply vehicles are also prohibited from performing tasks that cannot be completed within the current dispatch cycle: (11) If a rolling scheduling across days is adopted, equations (10) and (11) can be deleted, and the occupancy status of vehicles that have not yet completed their tasks and the power status of electric transport vehicles can be passed to the next scheduling cycle.

[0076] S101-6 establishes constraints on the power consumption of electric transport vehicles and charging infrastructure, as detailed below: The initial battery capacity of the electric transport vehicle meets the following requirements: (12) Vehicles during the time period Before starting a task, its remaining battery power should not be less than the sum of the battery power required to complete the corresponding task and the minimum safe battery power. (13) in, Since it is a large constant, it can be set as:

[0077] when Then, equation (13) becomes:

[0078] When the vehicle begins a task, it needs the electrical power required to complete that task. Reserved power. During the mission, the reserved power shall not be used for other missions, charging capacity assessments, or other vehicle operation decisions; upon completion of the mission and return to the park or designated charging base, the corresponding mission energy consumption will be deducted from the vehicle's power status.

[0079] Defined at state point in time The energy consumption of the returned task is:

[0080] After an electric transport vehicle completes its task, the energy consumption for the task is deducted at the corresponding return time, and the power is restored according to the charging power during periods when no task is performed. (14) Equation (14) adopts a centralized settlement method for task energy instead of simulating the decrease in vehicle battery power during task execution on a time-by-time basis.

[0081] The vehicle has reserved all the power required to complete the task before the task begins by formula (13), and it cannot be charged or accept the task again during the task. Therefore, deducting the task energy consumption when returning from the task will not affect the feasibility of the vehicle's task plan and energy plan.

[0082] If the task performed by the vehicle is at the state time point When completed, the energy consumption of the corresponding task is recorded in the status time. The vehicle's remaining battery power; the vehicle can rejoin the charging process starting from the next unoccupied scheduling period.

[0083] Battery capacity upper and lower limits are met: (15) At the end of the dispatch cycle, the vehicles should meet the minimum battery level requirement for the period: (16) In the implementation method that requires the vehicle to be fully charged at the end of the period, the following settings are made:

[0084] The vehicle's charging power must not exceed the vehicle's maximum allowable charging power. (17a) The charging power of a vehicle must not exceed the rated charging power of a single charging facility at any given time. (17b) Equations (17a) and (17b) together ensure that the actual charging power of the vehicle is limited by both the vehicle's own allowable power and the rated power of a single charging facility.

[0085] Vehicles in a task-occupancy state must not occupy charging facilities: (18) The number of vehicles using charging facilities at the same time shall not exceed the number of available charging facilities in the park: (19) The total charging power of all electric transport vehicles shall not exceed the total power limit of the park's charging system or corresponding power distribution equipment. (20) Equations (17a) to (20) simultaneously limit the vehicle's charging power to the following conditions: the maximum allowable charging power of the vehicle itself; the rated charging power of a single charging facility; the number of available charging facilities in the park; the total power capacity of the park's charging system, transformers, or power distribution lines; and whether the vehicle is in a task-occupied state.

[0086] S101-7, Constructing distributed new energy sources, fixed energy storage, and power balance constraints for industrial parks: set up Contribute to the forecasting of distributed new energy sources in the park. For actual absorption capacity, Let the abandoned power be: (twenty one) And satisfy:

[0087] When the park's distributed renewable energy sources only include photovoltaic power generation, it can and They are respectively denoted as and .

[0088] set up: For fixed energy storage charging power; To maintain a fixed energy storage discharge power; To store a fixed amount of energy; For fixed energy storage charging and discharging mode variables; and These are the maximum charging power and maximum discharging power of fixed energy storage, respectively. and These are the fixed energy storage charging efficiency and discharging efficiency, respectively. and These are the lower and upper limits of the fixed energy storage capacity, respectively. The initial energy capacity for fixed energy storage.

[0089] The initial and final electricity volumes of a fixed energy storage dispatch cycle remain consistent. (twenty two) Stationary energy storage cannot be charged and discharged simultaneously during the same period:

[0090] (twenty three) in:

[0091] Fixed energy storage capacity is calculated using the following formula: (twenty four) Fixed energy storage capacity meets: (25) set up If the basic load of the park does not include the charging load of electric transport vehicles, then the power balance of the park is: (26) The park only considers purchasing electricity from the external power grid and does not consider transmitting electricity back to the external power grid. The park's power purchase capacity meets the following requirements: (27) in, This represents the maximum power purchase allowed at the park's grid connection point.

[0092] S102, Solve the basic joint optimization model. The specific process is as follows: Obtain and input the following data: closed-loop transportation task requirements for each time period; task duration and energy consumption; number of electric and fuel-powered backup transportation vehicles and task costs; initial battery charge, battery capacity, minimum safe charge, end-of-period charge requirement, and maximum charging power of electric transportation vehicles; number of charging facilities, rated power of a single unit, and total power of the charging system; time-of-use electricity price, park base load, and predicted output of distributed renewable energy; fixed energy storage capacity, initial charge, maximum charging and discharging power, and charging and discharging efficiency; and grid connection power limits for the park.

[0093] Among them, the vehicle's remaining battery power, vehicle task status, and vehicle return status are obtained through the vehicle management terminal or battery management system; the charging facility status is obtained through the charging facility controller; and the park's basic load, new energy output, fixed energy storage status, and grid-connected power are obtained through the park's energy management system and metering devices.

[0094] Solve the mixed-integer linear programming model consisting of equations (1) to (27) to obtain the baseline scheduling scheme under normal operating conditions, denoted as .

[0095] The baseline scheduling scheme includes at least: the number of time-sharing tasks undertaken by electric transport vehicles and fuel-powered backup transport vehicles; the task start, task occupancy, and completion return status of each electric transport vehicle; the changes in the power and charging power of each electric transport vehicle; the charging facility occupancy plan; the fixed energy storage charging and discharging plan; the distributed new energy consumption and curtailment plan; and the park's time-sharing power purchase plan.

[0096] The joint optimization model is solved using a mixed-integer linear programming solver.

[0097] For different electric transport vehicles with varying battery capacities, maximum charging power, or mission energy consumption, mission energy consumption can be further set as a vehicle-level parameter. .

[0098] For implementations involving multiple types of transportation tasks, the task category set can be increased, and the task requirements, task duration, task energy consumption, and task start variables can be expanded into parameters and variables with task category subscripts, respectively.

[0099] S103, Demand Response Capability Extended Assessment: The demand response capability assessment is the second stage of processing after the basic joint optimization model is solved. It does not change the normal vehicle task plan and baseline power purchase capacity formed in the first stage. It includes the following sub-steps: S103-1, Determine the demand response baseline: The normal power purchase capacity of the industrial park obtained from the basic joint optimization model is used as the reference power for demand response.

[0100] Let the set of demand response periods be:

[0101] set up and Response direction parameters issued by the power grid or demand response platform:

[0102] (28) when hour: (29) in: For time period The upper limit of the instantaneous power reduction capability; For time period The upper limit of the instantaneous power capability is adjusted upwards; and It is only used for power screening in a single time period and is not directly used as the final reportable continuous response capability of the park.

[0103] S103-2, Calculate the upper limit of the power reduction capability in a single time period: Lowering the response capability satisfies:

[0104] (30) Define the down-regulation power margin that can be formed by fixed energy storage as:

[0105] The park can reduce the power purchased from the grid by decreasing vehicle charging, discontinuing stationary energy storage charging, increasing stationary energy storage discharging, and reducing the curtailment of renewable energy. Therefore: (31) By maximizing Solve the single-time period response screening model for the objective to obtain the upper bound of the instantaneous power reduction capability for each response period.

[0106] S103-3, Calculate the upper limit of the power adjustment capability in a single time period: Upgrading response capability satisfies:

[0107] (32) Define the up-adjustment power margin that can be formed by fixed energy storage as:

[0108] From the perspective of the rated power of charging facilities, the increased capacity is sufficient: (33) From the perspective of the total power of the charging system, the increased capacity meets the following requirements: (34) From the perspective of the actual number of vehicles that can be charged and the maximum charging power at the vehicle level, the increased capacity is to meet the following requirements: (35) Equations (33) to (35) should be satisfied simultaneously.

[0109] By maximizing Solve the single-time period response screening model for the objective to obtain the upper bound of the instantaneous power adjustment capability for each response period.

[0110] The single-period capability obtained by equations (30) to (35) can only be used as the upper limit of power for the response scenario, the initial screening result or the solution boundary, and cannot be directly used as the final deliverable capability for multiple consecutive scheduling periods.

[0111] S103-4, Verification of multi-time period response continuity capability: Let the first The time period set of the demand response events is:

[0112] in, In response to the start time of the event, The number of continuous scheduling periods in response.

[0113] In the demand response execution scenario, the vehicle task start variable, task occupancy variable, and return variable are fixed to the first-stage normal operation plan:

[0114]

[0115]

[0116] in, superscript Indicates the first phase of the normal operation baseline plan, symbol This represents a variable in the demand response execution scenario.

[0117] While keeping the vehicle task plan unchanged, re-solve the following in the response scenario: electric transport vehicle charging power; electric transport vehicle remaining power; charging facility occupancy status; fixed energy storage charging and discharging power; fixed energy storage power; new energy consumption and abandoned power; and park power purchase.

[0118] The response execution scenario should still satisfy the vehicle power, charging facilities, fixed energy storage, distributed new energy and park power balance constraints described in equations (13) to (27).

[0119] The scenario of reducing the response time satisfies: (36) The scenario of increasing the response level satisfies: (37) respectively or Solve the demand response execution scenario for the objective.

[0120] in: For the first Downgraded deliverability after a response event has been validated for its full duration; For the first Increase deliverability after a response event has been validated for its full duration.

[0121] Only when equation (36) or equation (37) is satisfied for all periods during the response duration, and the vehicle tasks, lower limit of power, end-of-period power, charging facility capacity, fixed energy storage status, and park power balance are still feasible after the response is executed, will the [response] be [implemented]. or This is determined to be the park's ability to report and implement continuous demand response capabilities to external parties.

[0122] S104, the steps to execute and output the scheduling plan are as follows: The park's vehicle dispatching system, based on the solution results of the first-stage basic joint optimization model, determines the number of transportation tasks undertaken by electric transport vehicles and fuel-powered backup transport vehicles respectively; designates the specific electric transport vehicles to perform the tasks; issues task start instructions to vehicles or vehicle management terminals; updates the vehicle task occupancy status and estimated return time; and prohibits vehicles in task occupancy status from participating in charging or being repeatedly dispatched.

[0123] After electric transport vehicles return to the park or designated energy replenishment base, they will be charged or wait for charging based on their remaining battery power, subsequent tasks, and the status of charging facilities, according to the optimization results.

[0124] Based on the solution results, the park's energy management system: issues charging power commands to each electric transport vehicle at the charging facilities; issues charging or discharging power commands to the fixed energy storage control equipment; adjusts the park's purchased power based on the park's base load and vehicle charging load; prioritizes the consumption of distributed renewable energy within the park; and, under the extended demand response implementation method, reports and executes data verified for a complete duration. or .

[0125] The model output should include at least: time-sharing dispatch plans for the two types of transport vehicles; task start, task occupancy, and return plans for each electric transport vehicle; remaining battery power change curves for each electric transport vehicle; charging time periods and charging power plans for each electric transport vehicle; park charging facility occupancy plans; park distributed renewable energy consumption and curtailment plans; fixed energy storage charging and discharging plans; park time-sharing power purchase plans; and optional park continuously adjusting demand response capabilities.

[0126] S105, the intraday rolling correction steps are as follows: In actual operation, transportation task demand, vehicle return time, actual vehicle energy consumption, distributed new energy output, and park infrastructure load may deviate from the previous day's forecast results.

[0127] Therefore, this invention adopts a combination of day-ahead optimization and intraday rolling correction.

[0128] At each rolling optimization moment, the following data is retrieved again: completed and incomplete transportation tasks; actual vehicle task occupancy status, return status, and remaining power; actual charging facility occupancy status; actual output and update forecast of distributed new energy; actual base load and update forecast of the park; actual power of fixed energy storage; and the latest electricity price or demand response instructions.

[0129] The scrolling optimization window is represented as:

[0130] in, For the current time of scrolling optimization, Optimize the time domain length for scrolling.

[0131] The actual electricity generated by vehicles and stationary energy storage is used as the initial condition for rolling optimization: (38) (39) in, To optimize vehicle timings for rolling The actual remaining power, This refers to the actual amount of electricity stored in fixed energy storage.

[0132] For transportation tasks that have started but not yet been completed, fix the vehicle occupancy status and expected return time to the actual status, and resolve for tasks that have not yet started and subsequent energy operation plans.

[0133] By making daily rolling corrections, the impact of task occupancy time, task energy consumption, renewable energy output, and basic load forecasting errors on the feasibility of scheduling plans can be reduced.

[0134] The hybrid optimization scheduling method for electric transport vehicles and fuel-powered backup transport vehicles in this application describes the transportation task demand, vehicle task start and completion, vehicle task occupancy, electric transport vehicle power changes, charging facility occupancy, distributed new energy consumption, fixed energy storage operation, and park power grid interaction process within the same optimization framework. Under the premise of ensuring the completion of park transportation tasks, it reduces the overall operating cost of the park, improves the utilization efficiency of electric transport vehicles and the park's new energy consumption level, and forms a collaborative scheduling plan that can be directly executed.

[0135] In yet another embodiment, such as Figure 2 As shown, this application provides a hybrid optimized scheduling system 10 for electric transport vehicles and fuel-powered backup transport vehicles, comprising: The operation data acquisition module 100 is used to acquire operational data of park transportation task requirements, vehicle status, charging facility status and energy system. The joint optimization model construction module 200 is used to construct a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles and the park's energy based on the aforementioned operational data, with the goal of minimizing the overall operating cost of the park. The model solving module 300 is used to solve the joint optimization model and output a baseline scheduling scheme that includes a vehicle task allocation plan and a park energy operation plan. The scheme correction module 400 is used to collect the actual status data of vehicles, tasks and energy systems after updates, and use this as the initial condition to perform intraday rolling corrections on the baseline scheduling scheme.

[0136] This application provides an embodiment of a hybrid optimization scheduling system for electric transport vehicles and fuel-powered backup transport vehicles. Within the same optimization framework, it describes the processes of transport task demand, vehicle task start and completion, vehicle task occupancy, electric transport vehicle power changes, charging facility occupancy, distributed new energy consumption, fixed energy storage operation, and park power grid interaction. Under the premise of ensuring the completion of park transport tasks, it reduces the overall operating cost of the park, improves the utilization efficiency of electric transport vehicles and the park's new energy consumption level, and forms a collaborative scheduling plan that can be directly executed.

[0137] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0138] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for optimal scheduling of electric transport vehicles and fuel-powered backup transport vehicles, characterized in that, include: Obtain data on park transportation needs, vehicle status, charging facility status, and energy system operation. Based on the aforementioned operational data, a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles, as well as the park's energy, is constructed with the goal of minimizing the overall operating cost of the park. Solve the joint optimization model to output a baseline scheduling scheme that includes vehicle task allocation plan and park energy operation plan; Collect real-time status data of vehicles, tasks, and energy systems after updates, and use this as initial conditions to perform daily rolling corrections to the baseline scheduling scheme.

2. The method according to claim 1, characterized in that, The vehicle status includes the available number of electric transport vehicles and fuel-powered backup transport vehicles, their task occupancy status, and remaining battery power. The joint optimization model aims to minimize the overall operating cost of the park and includes constraints on transportation task demand, vehicle task occupancy and return status, electric vehicle power and charging facilities, distributed new energy consumption, fixed energy storage operation, and park power balance. The baseline scheduling scheme includes at least two types of time-sharing task allocation for transport vehicles, task start and return times for electric transport vehicles, charging power and charging facility occupancy plan, fixed energy storage charging and discharging plan, distributed new energy consumption plan, and park time-sharing power purchase plan.

3. The method according to claim 2, characterized in that, Within the established joint optimization model, the vehicle task occupancy and return state constraints include the following rules: The total number of electric transport vehicles put into use in the park at each time period is calculated by summing the number of vehicles that started tasks at the corresponding time period; During the first scheduling period, the task occupancy status of electric transport vehicles remains consistent with the vehicle status of the task initiated in that period; Except for the first time period, the vehicle task occupancy status in each other time period is updated incrementally based on the number of newly started tasks and the number of vehicles that have completed their return trips in the current period. The same electric transport vehicle cannot simultaneously complete its return trip and start a new transport mission at the same time. If an electric transport vehicle starts a transport task during a designated time period, the vehicle will remain in a task-occupied state from the start of that time period until the task is completely completed. The vehicle's return time is strictly matched with the task start time and the task's continuous occupation period. The vehicle can only be marked as returning during the period when the corresponding transportation task is fully completed.

4. The method according to claim 3, characterized in that, Within the established joint optimization model, the constraints on the electric vehicle's power capacity and charging infrastructure include the following rules: At the beginning of the dispatch cycle, the battery level of each electric transport vehicle is uniformly set to the initial battery level of the vehicle. Before an electric transport vehicle starts a transport mission at any time, its remaining power must cover both the power consumption of the transport mission and the vehicle's minimum safe power level. After an electric transport vehicle completes its transport task, the electricity consumed during the corresponding return trip period will be deducted; during periods when no transport task is performed, the vehicle's electricity can be replenished by charging. At any given time, the remaining battery power of electric transport vehicles is between the vehicle's minimum safe battery power and the battery's maximum capacity; At the end of the entire scheduling cycle, the remaining battery power of all electric transport vehicles shall not be lower than the preset minimum battery power standard at the end of the period. The real-time charging power of a vehicle must not exceed both the maximum charging power of the vehicle itself and the rated charging power of a single charging device at the same time. Electric transport vehicles that are currently occupied for transport tasks are not allowed to use the park's charging facilities for charging. The total number of electric transport vehicles charging at the same time cannot exceed the total number of available charging devices in the park; The total power of all electric transport vehicles charging simultaneously shall not exceed the upper limit of the total power of the park's charging system and supporting power distribution equipment.

5. The method according to claim 4, characterized in that, Within the established joint optimization model, the constraints on distributed renewable energy consumption, fixed energy storage operation, and park power balance include the following rules: The sum of the actual power consumption of distributed renewable energy in the current period and the power curtailment is equal to the predicted value of renewable energy output in the current period. The initial and final charge values ​​of the fixed energy storage device remain the same throughout the scheduling cycle. Fixed energy storage devices can only perform one operation at a time, either charging or discharging, and cannot charge or discharge simultaneously. The power of the stationary energy storage device is updated periodically according to the charging and discharging operations in each period, and the power of the energy storage device is always within the minimum and maximum capacity limit range of the energy storage device at any time. The overall power consumption in the park remains in balance between supply and demand. The power purchased from the external grid, the power consumed locally by new energy sources, and the power discharged by energy storage together supply the park's basic load, the total charging load of electric vehicles, and the charging load of energy storage. The real-time power purchased by the park from the external grid shall not exceed the maximum power purchase allowed by the park's grid connection point.

6. The method according to claim 5, characterized in that, The established joint optimization model uses minimizing the sum of five costs as its optimization objective: the cost of purchasing electricity from the external grid for the industrial park, the cost of transporting goods by fuel-powered backup vehicles, the non-energy operation and maintenance cost of electric transport vehicles, the cycle loss cost of fixed energy storage, and the penalty cost for abandoning renewable energy. The cost of purchasing electricity from the external grid within the park is calculated by summing the electricity price, the power purchased from the external grid, and the duration of each time period. The cost of fuel-powered vehicle transportation tasks is calculated by summing the total number of fuel-powered vehicles used in each time period and the cost of fuel-powered vehicles per unit time period. The non-energy operation and maintenance cost of electric transport vehicles is calculated by summing the total number of electric vehicles put into use in each time period and the operation and maintenance cost of electric vehicles per unit time period. The fixed energy storage cycle loss cost is calculated by summing the total energy storage charging and discharging power, the unit energy storage cycle loss cost, and the duration of each period. The cost of penalties for curtailing renewable energy is calculated by summing up the amount of renewable energy curtailed in each time period, the unit price of the penalty for curtailment, and the duration of the time period.

7. The method according to claim 6, characterized in that, Solving the joint optimization model includes: converting the overall model into a mixed-integer linear programming model, completing the calculation using a standard mixed-integer linear programming solver, and outputting a baseline scheduling scheme under normal operating conditions of the park.

8. The method according to claim 7, characterized in that, The baseline scheduling scheme is modified as follows: Each time rolling optimization is initiated, multiple types of real-time operational data are collected again, including completed and pending transportation tasks, actual vehicle task occupancy status and remaining power, real-time occupancy status of charging equipment, actual output of distributed new energy and updated output forecast, actual value and updated forecast of park basic load, real-time power of fixed energy storage, and the latest time-of-use electricity price or grid demand response instructions. The real-time actual power of all electric transport vehicles and stationary energy storage devices is set as the initial power condition for this rolling optimization. The vehicle occupancy status and estimated return time for transportation tasks that have been started but not yet completed are locked as fixed actual operating conditions. Only transportation tasks that have not yet started and all subsequent energy system operation plans are re-solved and optimized.

9. The method according to claim 8, characterized in that, Intraday rolling correction uses a fixed-length rolling optimization window, which starts from the current rolling calculation time and covers subsequent scheduling periods of a fixed duration. The starting point is the time of this rolling optimization calculation, and the total number of window periods is the preset optimization time domain length.

10. A hybrid optimized scheduling system for electric transport vehicles and fuel-powered backup transport vehicles, characterized in that, include: The operation data acquisition module is used to acquire operational data on park transportation task requirements, vehicle status, charging facility status, and energy system operation. The joint optimization model construction module is used to construct a joint optimization model for the mixed scheduling of electric transport vehicles and fuel-powered backup transport vehicles and the park's energy based on the aforementioned operational data, with the goal of minimizing the overall operating cost of the park. The model solving module is used to solve the joint optimization model and output a baseline scheduling scheme that includes vehicle task allocation plan and park energy operation plan; The scheme correction module is used to collect the actual status data of vehicles, tasks and energy systems after updates, and use this as the initial condition to perform daily rolling corrections to the baseline scheduling scheme.

Images