Charging scheduling method for electric bus fleet considering photovoltaic energy storage and battery degradation

Abstract

The application discloses a charging scheduling method for electric buses considering photovoltaic energy storage and battery degradation, and belongs to the cross technical field of urban public transportation and smart grid. The method collects bus operation, charging infrastructure, electricity price and photovoltaic energy storage data, defines charging, energy state, photovoltaic utilization, demand electricity cost and battery degradation related decision variables, constructs a mixed integer linear programming model with the minimum sum of demand electricity cost, time-of-use electricity cost and battery degradation cost as the target, introduces multiple constraints such as trip departure / arrival energy, trip energy consumption, parking charging, charging pile number, battery state, energy storage charging and discharging and segmented charging degradation, and uses Gurobi solver to solve the optimal charging plan and energy storage scheduling strategy. The application realizes the cross space-time collaborative scheduling of station fixed photovoltaic and vehicle mounted mobile photovoltaic, quantifies the battery cycle degradation cost, effectively improves the photovoltaic utilization rate, and prolongs the service life of the battery.

Description

Technical Field

[0001] This invention relates to the field of urban public transportation and smart grid intersection technology, and in particular to a charging scheduling method for electric bus fleets that takes into account photovoltaic energy storage and battery degradation. Background Technology

[0002] The electrification of urban public transportation has become an inevitable trend. The large-scale deployment of electric bus fleets, while reducing carbon emissions and improving air quality, also poses unprecedented challenges to the dynamic operation and scheduling of public transportation and the safe and stable operation of urban power grids.

[0003] As a mature and cost-decreasing clean energy source, photovoltaic power generation has shown broad application prospects in the public transportation sector, mainly in two forms: First, building integrated photovoltaic, energy storage, and charging facilities at bus stations and hubs to form a station-level fixed photovoltaic power supply network, using energy storage systems to smooth out fluctuations in photovoltaic output and achieve off-peak energy storage and peak-peak discharge; Second, integrating lightweight solar panels on the roof of buses as on-board mobile photovoltaic auxiliary power sources to directly replenish low-voltage loads or power batteries, reducing the vehicle's dependence on grid charging.

[0004] However, existing technologies have the following shortcomings in the charging and dispatching of electric bus fleets:

[0005] 1. Lack of coordination in photovoltaic utilization: Most existing studies remain at the level of energy efficiency analysis in a single scenario. They either only consider the energy storage and allocation of fixed photovoltaics at stations or only focus on the independent consumption of mobile photovoltaics on vehicles. They fail to take a holistic view of the public transportation network and achieve cross-temporal and spatial systematic coordinated scheduling of fixed photovoltaics at stations and mobile photovoltaics on vehicles, resulting in low utilization of photovoltaic resources.

[0006] 2. Ignoring Battery Degradation Effect: Most charging scheduling models only aim to minimize grid electricity costs, generally ignoring the impact of charging and discharging strategies on the cycle life of the power battery. As the core cost component of electric buses, the irreversible degradation of the power battery significantly increases the total life cycle operating cost. Ignoring this factor will lead to serious biases in the economic evaluation of the optimization results.

[0007] 3. High risk of grid impact: The lack of detailed consideration of demand electricity costs can easily lead to multiple buses charging at high power during peak grid hours, forming peak loads, exacerbating the impact on local urban distribution networks, and increasing the demand electricity cost expenditure of operators. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a charging scheduling method for electric bus fleets that considers photovoltaic energy storage and battery degradation. By constructing a mixed integer linear programming model that includes the cost of battery health state degradation, the invention achieves cross-temporal and spatial coordinated scheduling of station fixed photovoltaic and vehicle-mounted mobile photovoltaic, quantifies the cost of battery cycle degradation, effectively reduces the total life cycle operating cost, improves photovoltaic utilization, and reduces the dependence on grid peak power.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A charging scheduling method for electric bus fleets considering photovoltaic energy storage and battery degradation includes the following steps:

[0011] S1. Collect bus operation data, charging infrastructure data, electricity price data, and photovoltaic and energy storage data;

[0012] S2. Define decision variables, including charging-related variables, energy state variables, photovoltaic utilization variables, demand electricity cost auxiliary variables, and battery degradation cost variables;

[0013] S3: Construct an objective function, which includes demand electricity cost, time-of-use electricity cost, and battery degradation cost;

[0014] S4: Establish constraints, including trip departure energy constraints, trip arrival energy constraints, trip energy consumption constraints, stop charging constraints, power-state coupling constraints, charging pile quantity constraints, continuous charging constraints, battery state constraints, demand electricity cost constraints, energy storage facility charging and discharging constraints, maximum power constraints, and segmented charging degradation constraints.

[0015] S5: The model is solved using the mixed-integer linear programming solver gurobi, which outputs the optimal charging plan and energy storage scheduling strategy.

[0016] In some embodiments of the present invention, step S1 specifically includes the following steps:

[0017] S101. Collect bus operation data, including route sets. Meet at the final stop Bus assembly , Time at the terminal station Belongs to the route bus collection Set of discrete points in time Time interval duration The route collection for each bus Departure time of the trip Arrival time of the trip Complete the trip Energy required Set of demand electricity price ranges Peak / off-peak set , Demand price measurement duration Photovoltaic terminal station collection Photovoltaic route collection ;

[0018] S102. Collect charging infrastructure data, including the number of charging piles at the terminal station. Maximum charging power Minimum charging time Battery SOC maximum boundary Battery SOC minimum boundary Bus battery capacity Battery capacity segment set , No. Slope of unit decay cost , No. Maximum energy range ;

[0019] S103. Collect electricity price data, including time-of-use pricing. Peak electricity price Low electricity price per unit during off-peak hours ;

[0020] S104. Collect photovoltaic and energy storage data, including the available photovoltaic panel area at the terminal station. Photovoltaic conversion efficiency Sunlight intensity at different times Rated capacity of energy storage system Area of ​​photovoltaic panels available for buses ;

[0021] In some embodiments of the present invention, step S2 specifically includes the following steps:

[0022] S201. Before defining variables, make the following definitions:

[0023] (1) Assume that for a photovoltaic bus, the energy of the photovoltaic panels is insufficient to cover the energy required for the entire journey;

[0024] (2) Assume that the cost of battery degradation is only related to the amount of charge and discharge;

[0025] S202. Define charging-related variables, continuous variables Indicate route Get on the bus In time Charging power obtained from the power grid; continuous variable Indicate route Get on the bus In time Charging power obtained from energy storage facilities; binary variables A value of 1 indicates the bus is charging, and 0 indicates it is not charging; auxiliary variable Used to detect the start of a charging event;

[0026] S203. Define energy state variables, continuous variables. Indicates bus In time Battery state of energy at the end; continuous variable Indicates the final stop Energy storage systems in time Remaining energy at the end;

[0027] S204. Define photovoltaic utilization variables, binary variables. Indicate route The convoy in time Whether to utilize photovoltaic charging;

[0028] S205. Define the demand-based electricity cost auxiliary variable, a continuous variable. Indicates the final stop During demand periods Average power; continuous variable , Representing terminals respectively Billing power during peak and off-peak hours.

[0029] S206. Define the degradation cost variable, a continuous variable. Indicates bus In time At the end, the charge level falls to the first A segment; a binary variable Ensure that the data is filled gradually from the lowest to the highest segment; binary variables Used to record buses Whether the time falls within a certain travel period; Indicates bus journey The average power consumed at each time step; Indicates bus In time The power consumed at the end;

[0030] In some embodiments of the present invention, step S3 specifically includes the following steps:

[0031] S301. Demand electricity cost is defined as follows:

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[0033] S302. Time-of-use electricity costs are defined as follows:

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[0035] S303. The battery degradation cost is defined as follows:

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[0037] S304. Define the objective function as follows:

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[0039] In the formula, 30 represents the number of days in a month, and λ represents the duration of the time interval.

[0040] In some embodiments of the present invention, step S4 specifically includes the following steps:

[0041] S401. Set the constraints as follows:

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[0068] in, It is a very small positive number. For a large positive number, Formula (5) is the energy constraint for departure, Formula (6) is the energy constraint for arrival, Formula (7) is the energy consumption constraint for departure, Formula (8) is the charging constraint for stop, Formula (9) is the power-state coupling constraint, Formula (10) is the number of charging piles constraint, Formulas (11)-(12) are the continuous charging constraint, Formulas (13)-(14) are the battery state constraint, Formulas (15)-(17) are the demand electricity cost constraint, Formulas (18)-(20) are the charging and discharging constraints for energy storage facilities, Formula (21) is the maximum power constraint, Formula (22) indicates that non-photovoltaic vehicle fleets cannot use photovoltaic panels for charging, Formula (23) indicates that non-photovoltaic terminal stations cannot use photovoltaic panels for charging, Formulas (24)-(27) are used to calculate the discharge amount, and Formulas (27)-(31) are used to calculate the battery degradation cost.

[0069] In some embodiments of the present invention, step S5 specifically includes the following steps:

[0070] S501. The model is solved using the mixed-integer linear programming solver gurobi, which outputs the optimal charging plan and energy storage scheduling strategy.

[0071] This invention proposes a charging scheduling method for electric bus fleets that considers photovoltaic energy storage and battery degradation, which has the following advantages and beneficial effects:

[0072] 1. Achieve cross-temporal and spatial photovoltaic collaborative scheduling: For the first time, from the perspective of the entire public transport network, the system integrates station-based fixed photovoltaic energy storage systems with vehicle-mounted mobile photovoltaic power sources. Through optimized scheduling, it achieves cross-temporal and spatial distribution of photovoltaic energy, significantly improving the renewable energy absorption capacity of the entire system and reducing dependence on the power grid.

[0073] 2. Quantifying the cost of battery cycle degradation: A piecewise linearization method is used to accurately model the nonlinear relationship between battery charge and discharge capacity and degradation cost. The battery degradation cost is incorporated into the objective function, avoiding decision distortion caused by ignoring battery loss in traditional economic assessments. This effectively extends battery life and reduces the total life cycle operating cost.

[0074] 3. Refined demand-based electricity cost management: By optimizing the distribution of charging time periods, the peak charging power of the fleet is reduced, demand-based electricity costs are lowered, and the impact of large-scale charging of electric bus fleets on local urban power distribution networks is mitigated, thereby improving the safety margin of power distribution network operation.

[0075] 4. Strong model versatility: The constructed mixed-integer linear programming model considers multiple constraints such as public transport operation, charging facilities, electricity prices, and photovoltaic energy storage. It is applicable to the charging scheduling of urban electric bus fleets of different sizes and configurations, and has broad application prospects. Attached Figure Description

[0076] Figure 1 A flowchart of the electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to the present invention;

[0077] Figure 2 This is a diagram of the overall system architecture of the present invention. Detailed Implementation

[0078] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. The described embodiments are only some embodiments of the present invention, and not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0079] Unless otherwise defined, the technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this invention is for describing specific embodiments only and is not intended to limit the invention.

[0080] like Figure 1 As shown, this embodiment provides a charging scheduling method for an electric bus fleet that considers photovoltaic energy storage and battery degradation. The implementation process includes the following steps:

[0081] S1. Collect bus operation data, charging infrastructure data, electricity price data, and photovoltaic and energy storage data.

[0082] S101. Collect bus operation data, including route sets. Meet at the final stop Bus assembly , Time at the terminal station Belongs to the route bus collection Set of discrete points in time Time interval duration The route collection for each bus Departure time of the trip Arrival time of the trip Complete the trip Energy required Set of demand electricity price ranges Peak / off-peak set , Demand price measurement duration Photovoltaic terminal station collection Photovoltaic route collection ;

[0083] S102. Collect charging infrastructure data, including the number of charging piles at the terminal station. Maximum charging power Minimum charging time Battery SOC maximum boundary Battery SOC minimum boundary Bus battery capacity Battery capacity segment set , No. Slope of unit decay cost , No. Maximum energy range ;

[0084] S103. Collect electricity price data, including time-of-use pricing. Peak electricity price Low electricity price per unit during off-peak hours ;

[0085] S104. Collect photovoltaic and energy storage data, including the available photovoltaic panel area at the terminal station. Photovoltaic conversion efficiency Sunlight intensity at different times Rated capacity of energy storage system Area of ​​photovoltaic panels available for buses ;

[0086] S2. Define decision variables, including charging-related variables, energy state variables, photovoltaic utilization variables, demand electricity cost auxiliary variables, and battery degradation cost variables.

[0087] S201. Before defining variables, make the following definitions:

[0088] (1) Assume that for a photovoltaic bus, the energy of the photovoltaic panels is insufficient to cover the energy required for the entire journey;

[0089] (2) Assume that the cost of battery degradation is only related to the amount of charge and discharge;

[0090] S202. Define charging-related variables, continuous variables Indicate route Get on the bus In time Charging power obtained from the power grid; continuous variable Indicate route Get on the bus In time Charging power obtained from energy storage facilities; binary variables A value of 1 indicates the bus is charging, and 0 indicates it is not charging; auxiliary variable Used to detect the start of a charging event;

[0091] S203. Define energy state variables, continuous variables. Indicates bus In time Battery state of energy at the end; continuous variable Indicates the final stop Energy storage systems in time Remaining energy at the end;

[0092] S204. Define photovoltaic utilization variables, binary variables. Indicate route The convoy in time Whether to utilize photovoltaic charging;

[0093] S205. Define the demand-based electricity cost auxiliary variable, a continuous variable. Indicates the final stop During demand periods Average power; continuous variable , Representing terminals respectively Billing power during peak and off-peak hours.

[0094] S206. Define the degradation cost variable, a continuous variable. Indicates bus In time At the end, the charge level falls to the first A segment; a binary variable Ensure that the data is filled gradually from the lowest to the highest segment; binary variables Used to record buses Whether the time falls within a certain travel period; Indicates bus journey The average power consumed at each time step; Indicates bus In time The power consumed at the end;

[0095] S3: Construct an objective function, which includes demand electricity cost, time-of-use electricity cost, and battery degradation cost.

[0096] S301. Demand electricity cost is defined as follows:

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[0098] S302. Time-of-use electricity costs are defined as follows:

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[0100] S303. The battery degradation cost is defined as follows:

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[0102] S304. Define the objective function as follows:

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[0104] In the formula, 30 represents the number of days in a month, and λ represents the duration of the time interval.

[0105] S4: Establish constraints, including trip departure energy constraints, trip arrival energy constraints, trip energy consumption constraints, stop charging constraints, power-state coupling constraints, charging pile quantity constraints, continuous charging constraints, battery state constraints, demand electricity cost constraints, energy storage facility charging and discharging constraints, maximum power constraints, and segmented charging degradation constraints.

[0106] S401. Set the constraints as follows:

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[0134] in, It is a very small positive number. For a large positive number, Formula (5) is the energy constraint for departure, Formula (6) is the energy constraint for arrival, Formula (7) is the energy consumption constraint for departure, Formula (8) is the charging constraint for stop, Formula (9) is the power-state coupling constraint, Formula (10) is the number of charging piles constraint, Formulas (11)-(12) are the continuous charging constraint, Formulas (13)-(14) are the battery state constraint, Formulas (15)-(17) are the demand electricity cost constraint, Formulas (18)-(20) are the charging and discharging constraints for energy storage facilities, Formula (21) is the maximum power constraint, Formula (22) indicates that non-photovoltaic vehicle fleets cannot use photovoltaic panels for charging, Formula (23) indicates that non-photovoltaic terminal stations cannot use photovoltaic panels for charging, Formulas (24)-(27) are used to calculate the discharge amount, and Formulas (28)-(31) are used to calculate the battery degradation cost.

[0135] S5: The model is solved using the mixed-integer linear programming solver gurobi, which outputs the optimal charging plan and energy storage scheduling strategy.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, and combinations made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A charging scheduling method for electric bus fleets considering photovoltaic energy storage and battery degradation, characterized in that, Includes the following steps: S1. Collect bus operation data, charging infrastructure data, electricity price data, and photovoltaic and energy storage data; S2. Define decision variables, including charging-related variables, energy state variables, photovoltaic utilization variables, demand electricity cost auxiliary variables, and battery degradation cost variables; S3. Construct an objective function, which includes demand electricity cost, time-of-use electricity cost, and battery degradation cost; S4. Establish constraints, including travel departure energy constraints, travel arrival energy constraints, travel energy consumption constraints, stop charging constraints, power-state coupling constraints, charging pile quantity constraints, continuous charging constraints, battery state constraints, demand electricity cost constraints, energy storage facility charging and discharging constraints, maximum power constraints, and segmented charging degradation constraints. S5. The model is solved using a mixed-integer linear programming solver, and the optimal charging plan and energy storage scheduling strategy are output.

2. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, The bus operation data collected in step S1 includes: a set of routes. Meet at the final stop Bus assembly , Time at the terminal station Belongs to the route bus collection Set of discrete points in time Time interval duration The route collection for each bus Departure time of the trip Arrival time of the trip Complete the trip Energy required Set of demand electricity price ranges Peak / off-peak set , Demand price measurement duration Photovoltaic terminal station collection Photovoltaic route collection .

3. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, The charging infrastructure data collected in step S1 includes: the number of charging piles at the terminal station. Maximum charging power Minimum charging time Battery SOC maximum boundary Battery SOC minimum boundary Bus battery capacity Battery capacity segment set , No. Slope of unit decay cost , No. Maximum energy range .

4. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, The electricity price data collected in step S1 includes: time-of-use electricity price. Peak electricity price Low electricity price per unit during off-peak hours .

5. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, The photovoltaic and energy storage data collected in step S1 includes: the area of ​​available photovoltaic panels at the terminal station. Photovoltaic conversion efficiency Sunlight intensity at different times Rated capacity of energy storage system Area of ​​photovoltaic panels available for buses .

6. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, Before defining the decision variables in step S2, the following assumptions are made: (1) The energy generated by the vehicle-mounted photovoltaic system is insufficient to cover the total energy consumption of a single trip; (2) Battery degradation costs are only related to charge and discharge energy throughput; The charging-related variables include: Continuous variables , indicating route Get on the bus In time Charging power obtained from the power grid; Continuous variables , indicating route Get on the bus In time Charging power obtained from energy storage facilities; binary variables A value of 1 indicates that the bus is charging, and 0 indicates that it is not charging. Auxiliary variables It is used to detect charging events and initiate the process.

7. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 6, characterized in that, The energy state variables defined in step S2 include: the remaining energy of the bus battery. Residual energy in energy storage systems The photovoltaic utilization variable is a binary variable representing the activation status of the vehicle-mounted photovoltaic system. Demand-based electricity pricing ancillary variables include average power output at the terminal station during the designated time period. Peak / off-peak billing power , Battery degradation cost variables include segmented charge / discharge energy. Segmented filling of binary variables binary variables Power Variable and power Power Variable Indicates bus journey Average power consumed at each time step; power Indicates bus In time The power consumed at the end.

8. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, Step S3 specifically includes the following steps: S301. Demand electricity cost is defined as follows: S302. Time-of-use electricity costs are defined as follows: S303. The battery degradation cost is defined as follows: S304. Define the objective function as follows: In the formula, 30 represents the number of days in a month, and λ represents the duration of the time interval.

9. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, Step S4 specifically includes the following steps: S401. Set the constraints as follows: in, It is a very small positive number. For a large positive number, Formula (5) is the energy constraint for departure, Formula (6) is the energy constraint for arrival, Formula (7) is the energy consumption constraint for departure, Formula (8) is the charging constraint for stop, Formula (9) is the power-state coupling constraint, Formula (10) is the number of charging piles constraint, Formulas (11)-(12) are the continuous charging constraint, Formulas (13)-(14) are the battery state constraint, Formulas (15)-(17) are the demand electricity cost constraint, Formulas (18)-(20) are the charging and discharging constraints for energy storage facilities, Formula (21) is the maximum power constraint, Formula (22) indicates that non-photovoltaic vehicle fleets cannot use photovoltaic panels for charging, Formula (23) indicates that non-photovoltaic terminal stations cannot use photovoltaic panels for charging, Formulas (24)-(27) are used to calculate the discharge amount, and Formulas (28)-(31) are used to calculate the battery degradation cost.

10. The electric bus fleet charging scheduling method considering photovoltaic energy storage and battery degradation according to claim 1, characterized in that, The mixed-integer linear programming solver used in step S5 is specifically the Gurobi solver.

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