A method for multi-gun charging scheduling of electric buses considering power dynamic allocation

Abstract

The application discloses a kind of electric bus multi-gun charging scheduling methods considering power dynamic allocation, it is related to electric bus charging technical field, solve the technical problem that existing electric bus single-gun charging mode cannot effectively reduce the total energy cost and power grid load pressure of system under the premise of satisfying bus strict operation timetable;The application includes collecting basic data for pretreatment;Build electric bus multi-gun charging scheduling optimization model considering dynamic power allocation, the model introduces continuous variable to represent charging power;The model is solved by using solver, and the optimal decision variable is obtained;Based on optimal decision variable, extract electric bus charging plan and evaluate the effect of charging plan;The application can intelligently suppress the instantaneous peak when multiple vehicles are charging simultaneously, thereby significantly reducing the peak power demand of power grid under the premise of strictly meeting the bus operation timetable, significantly reducing the demand charge expenditure.

Description

Technical Field

[0001] This invention relates to the field of electric bus charging technology, and more specifically to a multi-gun charging scheduling method for electric buses that considers dynamic power allocation. Background Technology

[0002] With the full electrification of urban public transportation systems, the charging and scheduling of electric buses has become a key link in ensuring bus operation and reducing corporate costs.

[0003] Most existing electric bus charging scheduling technologies are based on traditional single-gun charging facilities, assuming a strict one-to-one correspondence between charging piles and buses, and that the output power of the charging piles is fixed during the charging process.

[0004] This single-gun charging mode makes electric bus charging inflexible, resulting in low utilization of charging facilities during vehicle downtime, and often requires a large investment in charging facility construction to meet peak charging demand.

[0005] On the other hand, this model cannot dynamically and precisely adjust the charging power output of the charging pile according to the grid's time-of-use pricing and real-time load conditions, resulting in high instantaneous peak power load when multiple vehicles are charging at the same time, leading to higher electricity demand costs.

[0006] Therefore, the existing single-gun charging mode for electric buses cannot effectively reduce the total energy cost of the system and the load pressure on the power grid while meeting the strict operating schedule of buses. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a multi-gun charging scheduling method for electric buses that considers dynamic power allocation, solving the technical problem that the existing single-gun charging mode for electric buses cannot effectively reduce the total energy cost of the system and the load pressure on the power grid while meeting the strict operating schedule of buses.

[0008] A multi-gun charging scheduling method for electric buses considering dynamic power allocation includes:

[0009] Step 1: Collect basic data and preprocess it. The basic data includes bus timetable information, vehicle battery parameters, charging facility parameters, grid electricity price parameters, and time discretization parameters. The preprocessing includes compressing the charging bus set and filtering the charging time window.

[0010] Step 2: Construct an optimization model for multi-gun charging scheduling of electric buses that considers dynamic power allocation. This model introduces continuous variables to represent charging power, and the constraints involved include: multi-gun charging pile allocation constraints; dynamic power allocation constraints.

[0011] Step 3: Solve the model constructed in Step 2 using the solver to obtain the optimal decision variables;

[0012] Step 4: Extract electric bus charging plans based on optimal decision variables and evaluate the effectiveness of the charging plans.

[0013] Furthermore, the preprocessing in step 1 includes:

[0014] Charging bus set compression: Calculate the remaining power of each bus without daytime charging. If the remaining power is higher than the minimum allowable power, remove it from the optimization set and only schedule nighttime charging. This method can effectively reduce the scale of variables and improve computational efficiency.

[0015] Charging time window filtering: Iterate through all bus parking periods. If the total duration of a parking period is less than the minimum effective charging time, remove the charging decision variable for that period to avoid invalid calculations.

[0016] Furthermore, in step 2, the model refers to a mixed-integer linear programming model constructed with the objective of minimizing the total electricity cost of the system. The objective function is expressed as:

[0017]

[0018] In the formula, the first term is the average daily electricity demand cost, the second term is the daytime time-of-use electricity cost during a day's operation, and the third term is the nighttime supplementary electricity cost. The number of days in the current month. For continuous variables, it represents the maximum peak power (kW) of the charging system. This refers to the demand-based electricity price parameter; As a continuous variable, representing public transportation In the During the second parking period Always from charging station Actual received dynamic charging power (kW) Time-of-use pricing; The price is for off-peak electricity at night. Maximum allowable power; For vehicles Battery charge level after operation ends.

[0019] Furthermore, the multi-gun charging pile allocation constraints in step 2 include:

[0020] bus In the During each stop, charging can only be done at one charging station at most;

[0021] A bus can only be in a charging state when it is assigned to a charging station.

[0022] At any given time, the number of buses charging at a charging station shall not exceed the number of its charging guns.

[0023] Furthermore, the dynamic power allocation constraints in step 2 include:

[0024] When a bus is charging, its charging power is not lower than the minimum power.

[0025] The sum of the charging power of all vehicles connected to the charging station shall not exceed the total power of the charging station.

[0026] Furthermore, the model in step 2 also includes battery energy balance and state constraints:

[0027] The bus starts with a full charge; the electric bus leaves the depot with the sum of its charge at the destination and the charge level; ensuring that the bus's battery level is always within a safe range.

[0028] Furthermore, the model in step 2 also includes charging continuity and uniqueness constraints: the vehicle can only perform one plug-in / plug-out operation during a single stop, to avoid frequent start-stop operations damaging the battery or increasing operational complexity.

[0029] Furthermore, step 4, which involves extracting an electric bus charging plan based on optimal decision variables, includes:

[0030] Extracting optimal decision variables from the solver This indicates that it refers to public transportation vehicles. In the Whether to charge during the next stop; extracting the optimal decision variables. This indicates that the bus is at Is it currently charging? Iterate through all. In this situation, a list of charging times for electric buses can be obtained. The list is used to obtain the start and end charging times for electric buses; finally, the optimal decision variables are extracted. This is to provide specific power values ​​for each vehicle, at each time, and on each charging gun, to guide the actual charging equipment in its execution.

[0031] Furthermore, evaluating the effectiveness of the charging plan in step 4 includes: calculating the optimal total electricity cost based on the optimal decision variables. Peak power Electricity demand Then, the results are compared with the benchmark value to calculate the key evaluation indicators. The benchmark value refers to the corresponding type of value calculated using the traditional multi-gun charging strategy as a control group.

[0032] The beneficial effects of this invention include:

[0033] First, the present invention adopts a joint optimization technology solution based on dynamic power management, which breaks through the rigid limitations of traditional single-gun charging or multi-gun power sharing mode. Through the algorithm, the output power of each gun port is dynamically adjusted according to the time-of-use electricity price and real-time load. When multiple vehicles are charging at the same time, the instantaneous peak value can be intelligently reduced. Thus, under the premise of strictly meeting the bus operation timetable, the peak power demand of the power grid is greatly reduced, and the demand electricity cost is significantly reduced.

[0034] Secondly, by comprehensively optimizing the charging start-stop time, the vehicle-gun assignment relationship and real-time power allocation, this invention makes full use of the parking gaps of buses during off-peak hours for low-power supplementary charging, effectively avoiding the high electricity prices and load superposition during peak hours, and achieving optimal control of the total system operating cost (including electricity cost and demand cost).

[0035] Third, this invention enables pooled sharing of power within the charging pile. In dual-gun or multi-gun scenarios, the power is no longer rigidly divided, but flows in real time according to the actual needs of the vehicle, ensuring that the power capacity of the charging pile is fully utilized and improving the utilization efficiency of multi-gun facilities. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a multi-gun charging scheduling method for electric buses that considers dynamic power allocation, as described in an embodiment of this application.

[0037] Figure 2 The peak charging loads for multi-gun and single-gun electric buses involved in the embodiments of this application are defined. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0039] Example 1

[0040] The following is in conjunction with the appendix Figure 1 Specific embodiments of the present invention will be described in detail;

[0041] A multi-gun charging scheduling method for electric buses considering dynamic power allocation includes:

[0042] Step 1: Basic Data Acquisition and Preprocessing

[0043] Step 11: Obtain basic data from the public transport company and the power grid, including:

[0044] (1) Bus timetable information: including each bus (by serial number) (Indicates) the first (All shifts are grouped together) Arrival time at the station Departure time And the energy consumption for executing train schedule tasks ;

[0045] (2) Vehicle battery parameters: Total capacity of vehicle battery Maximum allowable power Minimum allowable power Initial state of charge and minimum charging time for the vehicle. ;

[0046] (3) Charging facility parameters: collection of charging piles Charging piles Rated total output power Charging efficiency Number of charging guns (Supports dual-gun and multi-gun scenarios), minimum charging power per gun ;

[0047] (4) Grid electricity price parameters: Time-of-use pricing (Including peak, flat, and off-peak electricity prices) and demand-based electricity rates ;

[0048] (5) Time discretization parameter: scheduling period (Usually 24 hours), divided into There are three discrete time intervals of equal length, each with a step size of . .

[0049] Step 12: Data Preprocessing

[0050] To improve computational efficiency, data preprocessing is performed before building the optimization model:

[0051] (1) Charging bus aggregation compression: Calculate the remaining power of each bus without daytime charging. If the remaining power is higher than the minimum allowable power... If the variable is removed from the optimization set and only nighttime charging is scheduled, this method can effectively reduce the size of the variable and improve computational efficiency.

[0052] (2) Charging time window filtering: Iterate through all bus parking periods (Layover), if the total duration of a certain parking period is less than the minimum effective charging time. If so, the charging decision variables for that period are removed to avoid invalid calculations.

[0053] Step 2: Construct an optimization model for multi-gun charging scheduling of electric buses that considers dynamic power allocation.

[0054] To minimize the total electricity cost of the system, a mixed-integer linear programming model is constructed. The objective function is:

[0055]

[0056] The first term represents the average daily electricity demand cost (assuming 30 days per month), the second term represents the daytime time-of-use electricity cost during a day's operation, and the third term represents the nighttime supplementary electricity cost. The mathematical symbols in the formula have the following meanings: For continuous variables, it represents the maximum peak power (kW) of the charging system. This refers to the demand-based electricity price parameter; As a continuous variable, representing public transportation In the During the second parking period Always from charging station Actual received dynamic charging power (kW) Time-of-use pricing; The price is for off-peak electricity at night; For vehicles Battery charge level after operation ends;

[0057] The constraints of the model include:

[0058] (1) Battery energy balance and state constraints:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] The above constraints ensure the conservation of vehicle battery power flow, conforming to the actual battery power variation patterns of public buses in operation. Equation 1 indicates that the bus initially has a full charge, where... Indicates bus Battery charge upon leaving the depot; Formula 2 represents the bus's charge level. Battery level upon arrival at the station The amount of electricity when leaving the depot Subtract the electricity consumed during the trip Formula 3 indicates that the electric bus's battery level when leaving the depot is the sum of the battery level at the destination and the charging battery level; Formulas 4 and 5 ensure that the bus's battery level is always within a safe range.

[0065] (2) Constraints on the allocation of multi-gun charging piles:

[0066]

[0067]

[0068]

[0069] Formula 1 represents public transportation In the During each stop, charging can only be done at one charging station at most. Let be the decision variable, representing public transportation. In the Was the vehicle at the charging station during the second stop? On-board charging; Equation 2 indicates that charging is only available when the bus is assigned to a charging station. It can only be in a charging state when it is being charged. Let be the decision variable, representing public transportation. In the The next stop Always charging Charging; Equation 3 indicates at any given time Charging piles The number of buses charging at any one time cannot exceed the number of their charging guns. This constraint reflects the physical limitation of multiple charging stations, meaning that at any given moment, only a limited number of buses can be charged at a single charging station. The number of vehicles charging at the charging station cannot exceed the number of charging ports on that station. (For example, a dual-gun charging station cannot charge three vehicles simultaneously), but less than [number] vehicles are allowed. (For example, only one vehicle is charging at a dual-gun charging station).

[0070] (3) Charging continuity and uniqueness constraints:

[0071]

[0072]

[0073]

[0074] Equations 1 and 2 calculate the time when the electric bus begins charging, where the decision variables are... Indicates public transportation In the The next stop Did the moment just begin at the charging station? The charging process begins on the first charge. The third type of charging system limits the vehicle to only one plug-in / plug-out operation during each stop, in order to avoid frequent start-stop cycles that could damage the battery or increase operational complexity.

[0075] (4) Minimum charging time constraint for buses

[0076]

[0077] Formula 1 indicates that the charging time for buses shall not be less than the minimum charging time. ;

[0078] (5) Dynamic power allocation constraints

[0079]

[0080]

[0081] This part is the key technology of this invention. In conventional technology, if two vehicles share a single charging station, the vehicles are usually forced to each use 50% of the power. However, in the formula of this invention... It is a continuous decision variable, representing the vehicle exist The dynamic charging power at any given time; in the above constraints, Equation 1 indicates that when the bus is charging, its charging power is not lower than the minimum power. Equation 2 indicates that the sum of the charging power of all vehicles connected to the charging pile does not exceed the total power of the charging pile. This constraint also means that the model can freely optimize the charging power of multiple vehicles based on the urgency of each vehicle (whether it is dispatched) and the current electricity price / total load, thereby reducing the total electricity cost of the system.

[0082] Step 3: Solving the model

[0083] The constructed model is a mixed-integer linear programming model, which can be solved directly using commercial solvers such as Gurobi and CPLEX, or open-source solvers such as Or-tools.

[0084] Step 4: Extraction and Effectiveness Evaluation of Electric Bus Charging Plan

[0085] (1) Electric bus charging plan extraction: Extracting the optimal decision variables from the solver This indicates that bus b is in the [number]th [location]. Whether to charge during the next stop; extracting the optimal decision variables. This indicates that the bus is at Is it currently charging? Iterate through all. In this situation, a list of charging times for electric buses can be obtained. The list is used to obtain the start and end charging times for electric buses; finally, the optimal decision variables are extracted. This is to provide specific power values ​​for each vehicle, at each time, and on each charging gun, to guide the actual charging equipment in its execution.

[0086] (2) Evaluation of the effectiveness of the charging plan: In order to verify the effectiveness of this method, a traditional strategy was established as a control group and evaluated from the two dimensions of economic benefits and grid friendliness.

[0087] First, a control group was constructed, using a plug-and-charge combined with a power-sharing strategy that aligns with current operational practices as the benchmark. In this benchmark strategy, buses charge immediately upon returning to the depot. If multiple buses share a single charging station (multi-gun scenario), the power is allocated evenly based on the number of currently connected vehicles. For example, when two buses are connected to a dual-gun charging station, each bus is forced to receive 50% of the power, without considering time-of-use pricing or demand control. The total cost under the benchmark scenario was then calculated. Peak power and demand electricity costs .

[0088] Secondly, the optimal objective function value calculated based on this invention Peak power Electricity demand Compared to the benchmark, calculate the following key evaluation metrics, including:

[0089] Indicator 1: Total cost savings rate

[0090]

[0091] This indicator reflects the economic value of this method in reducing total operating expenses.

[0092] Indicator 2: Demand-based electricity price reduction rate

[0093]

[0094] This indicator reflects how this method reduces the contribution to grid capacity utilization by flattening the load curve.

[0095] Indicator 3: Peak load reduction rate

[0096]

[0097] This indicator assesses the extent to which the method reduces the impact on the power grid from a physical perspective, reflecting the technological advancement.

[0098] Specifically, compared with traditional strategies, the method proposed in this embodiment significantly reduces peak load, and the charging cost is correspondingly reduced after the peak load is reduced. Figure 2As shown, when using multi-gun charging piles for dynamic charging, the peak charging load decreased from 39.9kW in the traditional strategy to 34.2kW, a decrease of 14.3%, and the peak load was reduced by a maximum of 14.3%.

[0099] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A multi-gun charging scheduling method for electric buses considering dynamic power allocation, characterized in that, Includes the following steps: Step 1: Collect basic data and preprocess it. The basic data includes bus timetable information, vehicle battery parameters, charging facility parameters, grid electricity price parameters, and time discretization parameters. The preprocessing includes compressing the charging bus set and filtering the charging time window to reduce the scale of variables. Step 2: Construct an optimization model for multi-gun charging scheduling of electric buses that considers dynamic power allocation. This model introduces continuous variables to represent charging power, and the constraints involved include: multi-gun charging pile allocation constraints; dynamic power allocation constraints. Step 3: Solve the model constructed in Step 2 using the solver to obtain the optimal decision variables; Step 4: Extract electric bus charging plans based on optimal decision variables and evaluate the effectiveness of the charging plans.

2. The method for scheduling multi-gun charging of electric buses considering dynamic power allocation according to claim 1, characterized in that, The preprocessing in step 1 includes: Charging bus set compression: Calculate the remaining power of each bus without daytime charging. If the remaining power is higher than the minimum allowable power, remove it from the optimization set and only schedule nighttime charging. Charging time window filtering: Iterate through all bus parking periods. If the total duration of a parking period is less than the minimum effective charging time, remove the charging decision variable for that period to avoid invalid calculations.

3. The method for scheduling multi-gun charging of electric buses considering dynamic power allocation according to claim 1, characterized in that, In step 2, the model refers to a mixed-integer linear programming model constructed with the objective of minimizing the total electricity cost of the system. The objective function is expressed as: ； In the formula, the first term is the average daily electricity demand cost, the second term is the daytime time-of-use electricity cost during a day's operation, and the third term is the nighttime supplementary electricity cost. The number of days in the current month. For continuous variables, it represents the maximum peak power (kW) of the charging system. This refers to the demand-based electricity price parameter; As a continuous variable, representing public transportation In the During the second parking period Always from charging station Actual received dynamic charging power (kW) Time-of-use pricing; The price is for off-peak electricity at night. Maximum allowable power; For vehicles Battery charge level after operation ends.

4. The method for scheduling multi-gun charging of electric buses considering dynamic power allocation according to claim 1, characterized in that, The multi-gun charging pile allocation constraints in step 2 include: bus In the During each stop, charging can only be done at one charging station at most; A bus can only be in a charging state when it is assigned to a charging station. At any given time, the number of buses charging at a charging station shall not exceed the number of its charging guns.

5. A multi-gun charging scheduling method for electric buses considering dynamic power allocation according to claim 1, characterized in that, The dynamic power allocation constraints in step 2 include: When a bus is charging, its charging power is not lower than the minimum power. The sum of the charging power of all vehicles connected to the charging station shall not exceed the total power of the charging station.

6. A multi-gun charging scheduling method for electric buses considering dynamic power allocation according to claim 1, characterized in that, The model in step 2 also includes battery energy balance and state constraints: The bus starts with a full charge; the electric bus leaves the depot with the sum of its charge at the destination and the charge level; ensuring that the bus's battery level is always within a safe range.

7. A multi-gun charging scheduling method for electric buses considering dynamic power allocation according to claim 1, characterized in that, The model in step 2 also includes charging continuity and uniqueness constraints: the vehicle can only perform one plug-in / plug-out operation during a single stop, to avoid frequent start-stop operations damaging the battery or increasing operational complexity.

8. A multi-gun charging scheduling method for electric buses considering dynamic power allocation according to claim 1, characterized in that, Step 4, which involves extracting an electric bus charging plan based on optimal decision variables, includes: Extracting optimal decision variables from the solver This indicates that it is a public bus. In the Whether to charge during the next stop; extracting the optimal decision variables. This indicates that the bus is at Is it currently charging? Iterate through all. In this situation, a list of charging times for electric buses can be obtained. The list is used to obtain the start and end charging times for electric buses; finally, the optimal decision variables are extracted. This is to provide specific power values ​​for each vehicle, at each time, and on each charging gun, to guide the actual charging equipment in its execution.

9. A multi-gun charging scheduling method for electric buses considering dynamic power allocation according to claim 1, characterized in that, Step 4, evaluating the effectiveness of the charging plan, includes: calculating the optimal total electricity cost based on the optimal decision variables. Peak power Electricity demand Then, the results are compared with the benchmark value to calculate the key evaluation indicators; the benchmark value refers to the corresponding type of value calculated using the traditional multi-gun charging strategy as a control group.

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