Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems
Abstract
:1. Introduction
- A clear definition of the purpose of the analysis or optimization, which implies clarifying the use-case, its system boundaries, the stakeholder perspective, and the stakeholder objective.
- A careful a-priori selection of a reduced number of important influence factors—variables or parameters—depending on the use-case definition, the perspective, and the ultimate objective.
1.1. Literature Overview of Stakeholder Perspectives and Objective Formulation
- converting different objectives to one via weighted sum of objectives,
- a hierarchical optimization model with layers, and
- a cross-entropy method.
1.2. Literature Overview of Modeling Methodologies
Methodology | Remarks | Reference |
---|---|---|
Facility Location Problem | Generic problem in transportation research | |
Estimation of Stationary Demand Density at System Nodes | Estimates charging demand at homes, stores, working places | [3] |
Estimation of Spatial Demand and Mobility of BEVs | Estimation is based on traffic flow models; demand can be covered along the routes | [3] |
Estimation of Spatial-temporal Demand | Real-world GPS data or fleet schedules extend demand estimation to the time domain | [3] |
Flow-Capturing Location Model | Captures as many routes as possible by placing charging points along them | [3] |
Multipath-Refueling Location Model | Allows drivers to deviate from their original path and to refuel more than once along the way | [3] |
Spatial-Temporal Model: Multistage Infrastructure Planning | Budgeted multistage planning | [7] |
Queuing Model | Implemented for a taxi fleet with waiting areas | [19] |
Bi Level Stochastic Queuing Models | ||
Graph Theoretic Model |
1.3. Impact Factors for the Planning of Charging Infrastructure
- infrastructure,
- technology related,
- operational planning,
- bus network, and
- energy consumption.
- In their comprehensive comparative investigation of heavy-duty vehicles performance, Giakoumis et al. found that vehicle velocity was the most influential parameter affecting performance and the whole operation of it. Further, the indicators stops-per-kilometer and relative positive acceleration correlate very well with fuel or energy consumption [21] (p. 16). Eßer et al. confirmed that vehicle consumption profiles strongly depend on the driving profiles [22].
- Impact factors on the consumption were also sorted in a graph by Gallet et al. in their study regarding a bus fleet [23] (p. 14). The three most influential parameters in decreasing order are: curb mass, auxiliary power, and rolling resistance.
- Regarding the fleet charging strategy, if overnight charging is chosen, this requires bigger battery capacities for the electric vehicles, according to Gallet et al. [23].
- Short distances between bus stops favor the use of electric buses because they are better suited for driving profiles with frequent start and stop situations than conventional buses [24] (p. 191).
- Kunith et al. stated that, in general, as concluded by previous studies, maximum charging power has a significant influence on the number of charging stations needed [4] (p. 9).
- Kunith et al. found that the extension of dwell time requires the adaptation of the operational schedule or the increase of the number of vehicles but relaxes the infrastructure requirements [4] (p. 9).
- Battery aging over time becomes a more restrictive constraint for the fleet management because with it the range of the vehicles decreases. These stricter constraints result in a higher fleet management effort for the fleet operator. However, it is seldom considered.
1.4. The Fixed-Route Transportation System Problem of Fleet Operators
2. Materials and Methods
2.1. Optimization Framework Structure
2.2. Preparing the Optimization: Pre-Processing
2.3. Core Optimization Process
2.4. Evaluation of Optimization Results: Post Processing
- at what point in time does there have to be an infrastructure expansion,
- with what maximum charging capacity should charging points be equipped, and
- what is the fleet management like on every single representative day?
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Sector | Stakeholder | Perspective | Objective | Reference |
---|---|---|---|---|
Energy | Electricity Producers | Economical | Minimize electricity production costs | Authors. |
Electricity Producers | Environmental | Minimize greenhouse-gas emissions | Authors. | |
Electricity Producers | Environmental | Minimize resource utilization | Authors. | |
Grid Operators | Economical | Maximize voltage stability | [3] | |
Grid Operators | Economical | Minimize cost of infrastructure | Authors. | |
Grid Operators | Economical | Minimize transmission losses | [3] | |
Grid Operators | Economical | Minimize resource utilization | Authors. | |
Transportation | End-user/Client | Social Welfare | Minimize individual agent’s travel time | Authors. |
End-user/Client | Social Welfare | Minimize cumulative travel time | Authors. | |
End-user/Client | Social Welfare | Minimize delivery times | Authors. | |
End-user/Client | Social Welfare | Minimize discomfort of drivers | [8] | |
End-user/Client | Social Welfare | Maximize social welfare | [9] | |
End-user/Client | Social Welfare | Maximize number of reached households | [10] | |
End-user/Client | Environmental | Minimize energy consumption | Authors. | |
End-user/Client | Environmental | Minimize greenhouse-gas emissions | Authors. | |
End-user/Client | Environmental | Maximize share of electric vehicle-kilometers traveled | [11] (p. 166) | |
Fleet Operator | Economical | Minimize infrastructure cost | Authors. | |
Fleet Operator | Economical | Minimize investment cost including vehicles | Authors. | |
Fleet Operator | Economical | Minimize number of charging stations | Authors. | |
Fleet Operator | Economical | Maximize schedule length of buses | [12] | |
Fleet Operator | Economical | Maximize the share of distance traveled electrically | [13] | |
Fleet Operator | Economical | Minimize operation cost | Authors. | |
Fleet Operator | Economical | Minimize total cost of ownership | [12] | |
Fleet Operator | Environmental | Minimize greenhouse-gas emissions | Authors. | |
Fleet Operator | Environmental | Minimize noise pollution | Authors. | |
Fleet Operator | Environmental | Maximize electrically traveled range | [11] | |
Vehicle Manufacturer | Economical | Minimize production costs | Authors. | |
Vehicle Manufacturer | Environmental | Minimize resource utilization | Authors. |
Description | Unit | Value |
---|---|---|
Time horizon | Years | 2 |
Number of reference days | - | 4 |
Total number of vehicles | - | 76 |
Number of unique trips | - | 240 |
Underlying time discretization for the optimization | Minutes | 30 |
Cost for electricity | € per kWh | 0.12 |
Cost for diesel fuel | € per liter | 1.05 |
Cost for activation of a CPC with 50 kW maximum charging power | € | 7500 |
Cost for activation of a CPC with 150 kW maximum charging power | € | 11,500 |
Annual cost for a power contract with 1 MW aggregate power | € | 10,000 |
Annual cost for a power contract with 2 MW aggregate power | € | 20,000 |
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Blat Belmonte, B.D.; Rinderknecht, S. Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems. World Electr. Veh. J. 2021, 12, 258. https://doi.org/10.3390/wevj12040258
Blat Belmonte BD, Rinderknecht S. Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems. World Electric Vehicle Journal. 2021; 12(4):258. https://doi.org/10.3390/wevj12040258
Chicago/Turabian StyleBlat Belmonte, Benjamin Daniel, and Stephan Rinderknecht. 2021. "Optimization Approach for Long-Term Planning of Charging Infrastructure for Fixed-Route Transportation Systems" World Electric Vehicle Journal 12, no. 4: 258. https://doi.org/10.3390/wevj12040258