Optimal Deployment of Dynamic Wireless Charging Facilities for an Electric Bus System

2017 ◽  
Vol 2647 (1) ◽  
pp. 100-108 ◽  
Author(s):  
Zhaocai Liu ◽  
Ziqi Song ◽  
Yi He
Keyword(s):  
The United States ◽  
Base Station ◽  
Mixed Integer ◽  
Wireless Charging ◽  
Mixed Integer Linear Program ◽  
System A ◽  
Electric Bus ◽  
Design Variables ◽  
Optimal Deployment ◽  
Bus System

Diesel engine buses still make up the majority of the bus fleet in the United States, even with the problem of diesel exhaust and greenhouse gas emissions. Electric buses, which generate no emissions, are a promising green alternative for bus fleets. However, electric buses have a limited travel range and a time-consuming recharging process. Dynamic wireless charging, which allows electric buses to charge while traveling, could alleviate the drawbacks of electric buses. With dynamic wireless charging technology, electric buses can operate with smaller batteries, and the stationary recharging time at the base station can be shortened. The key design variables in deploying dynamic wireless charging facilities for an electric bus system are battery size and the location of the wireless charging facilities. This paper addresses the problem of simultaneously selecting the optimal locations for the wireless charging facilities and designing the battery size for an electric bus system. A mixed integer linear program was developed to minimize the total implementation cost. The model was demonstrated with a real-world bus system. The results demonstrate that the proposed model can solve the optimal deployment problem of dynamic wireless charging facilities for an electric bus system.

scholarly journals Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1229 ◽  
Author(s):  
Hyukjoon Lee ◽  
Dongjin Ji ◽  
Dong-Ho Cho
Keyword(s):  
Reinforcement Learning ◽  
Optimal Design ◽  
Electric Vehicle ◽  
Optimal Number ◽  
Mixed Integer ◽  
Power Cables ◽  
Wireless Charging ◽  
Electric Bus ◽  
Battery Capacity ◽  
Bus System

The design of conventional electric vehicles (EVs) is affected by numerous limitations, such as a short travel distance and long charging time. As one of the first wireless charging systems, the Online Electric Vehicle (OLEV) was developed to overcome the limitations of the current generation of EVs. Using wireless charging, an electric vehicle can be charged by power cables embedded in the road. In this paper, a model and algorithm for the optimal design of a wireless charging electric bus system is proposed. The model is built using a Markov decision process and is used to verify the optimal number of power cables, as well as optimal pickup capacity and battery capacity. Using reinforcement learning, the optimization problem of a wireless charging electric bus system in a diverse traffic environment is then solved. The numerical results show that the proposed algorithm maximizes average reward and minimizes total cost. We show the effectiveness of the proposed algorithm compared with obtaining the exact solution via mixed integer programming (MIP).

scholarly journals Plug-in vs. wireless charging: Life cycle energy and greenhouse gas emissions for an electric bus system

2015 ◽  
Vol 146 ◽  
pp. 11-19 ◽  
Author(s):  
Zicheng Bi ◽  
Lingjun Song ◽  
Robert De Kleine ◽  
Chunting Chris Mi ◽  
Gregory A. Keoleian
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Wireless Charging ◽  
Gas Emissions ◽  
Electric Bus ◽  
Bus System

scholarly journals Multi-objective optimization of a power-to-hydrogen system for mobility via two-stage stochastic programming

2021 ◽  
Vol 2042 (1) ◽  
pp. 012034
Author(s):  
Marta Fochesato ◽  
Philipp Heer ◽  
John Lygeros
Keyword(s):  
Stochastic Programming ◽  
Large Scale ◽  
Mixed Integer ◽  
Programming Approach ◽  
Total Annual Cost ◽  
Multi Objective Optimization ◽  
Two Stage ◽  
Mixed Integer Linear Program ◽  
Multi Objective ◽  
Design Variables

Abstract A systematic way for the optimal design of renewable-based hydrogen refuelling stations in the presence of uncertainty in the hydrogen demand is presented. A two-stage stochastic programming approach is used to simultaneously minimize the total annual cost and the CO2 footprint due to the electricity generation sources. The first-stage (design) variables correspond to the sizing of the devices, while the second-stage (operation) variables correspond to the scheduling of the installed system that is affected by uncertainties. The demand of a fleet of fuel cell vehicles is synthesized by means of a Poisson distribution and different scenarios are generated by random sampling. We formulate our problem as a large-scale mixed-integer linear program and we rely on a two-level approximation scheme to keep the problem computationally tractable. A solely deterministic setting which does not take into account uncertainties leads to underestimated device sizes, resulting in a significant fraction of demand remaining unserved with a consequent loss in revenue. The multi-objective optimization produces a convex Pareto front, showing that a reduction in carbon footprint comes with increasing costs and thus diminishing profit.

scholarly journals Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

2017 ◽  
Author(s):  
Shyang-Chyuan Fang ◽  
Bwo-Ren Ke ◽  
Chen-Yuan Chung
Keyword(s):  
Energy Use ◽  
Transportation System ◽  
Construction Costs ◽  
System A ◽  
Electric Bus ◽  
Battery Capacity ◽  
The Hours ◽  
Two Parameters ◽  
Bus System ◽  
Bus Transportation

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. An electric-bus (e-bus) transportation system favors reducing pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was lower than that of the battery-swapping e-bus system.

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

Political Competition, Partisanship and Interpersonal Trust in Electoral Democracies

2016 ◽  
Vol 48 (1) ◽  
pp. 115-139 ◽  
Author(s):  
Ryan E. Carlin ◽  
Gregory J. Love
Keyword(s):  
Party System ◽  
Social Preferences ◽  
Political Competition ◽  
Interpersonal Trust ◽  
The United States ◽  
Behavioral Experiments ◽  
Minimal Group ◽  
System A ◽  
The Social ◽  
Osama Bin Laden

How does democratic politics inform the interdisciplinary debate on the evolution of human co-operation and the social preferences (for example, trust, altruism and reciprocity) that support it? This article advances a theory of partisan trust discrimination in electoral democracies based on social identity, cognitive heuristics and interparty competition. Evidence from behavioral experiments in eight democracies show ‘trust gaps’ between co- and rival partisans are ubiquitous, and larger than trust gaps based on the social identities that undergird the party system. A natural experiment found that partisan trust gaps in the United States disappeared immediately following the killing of Osama bin Laden. But observational data indicate that partisan trust gaps track with perceptions of party polarization in all eight cases. Finally, the effects of partisanship on trust outstrip minimal group treatments, yet minimal-group effects are on par with the effects of most treatments for ascriptive characteristics in the literature. In sum, these findings suggest political competition dramatically shapes the salience of partisanship in interpersonal trust, the foundation of co-operation.

Sign in / Sign up

Export Citation Format

Share Document