Deploying Public Charging Stations for Electric Taxis: A Charging Demand Simulation Embedded Approach

Abstract

Properly deployed public charging stations are important foundations for the large-scale operation of electric taxis. This paper proposes a novel framework for the deployment of public charging stations, which takes into consideration the effects of passengers, taxi drivers, electricity retailers, transportation network, distribution network, and power consumers. First, on the premise that public charging stations have already been deployed, an agent-based model is constructed to simulate the charging demands of each station, considering passengers' travel demands and retailers' mutual competition. Second, to obtain candidate sites for public charging stations, the critical node index is put forward based on the massive trajectory data of taxis. Finally, a multi-objective optimizing model for public charging station deployment is proposed with charging demand simulation embedded. By traversing candidate sites and quantities of charging spots at each station using a modified genetic algorithm, the optimal deployment results are obtained. The framework and models are demonstrated and verified by a test case. The results indicate that the proposed framework could minimize the costs of charging stations, electric utilities, electric taxi drivers, and passengers while lowering the load heterogeneity in the distribution network at the same time. INDEX TERMS Electric taxi, load heterogeneity, multi-agent simulation, pricing strategy, public charging station planning, trajectory data mining.

Charging Strategy Unifying Spatial-Temporal Coordination of Electric Vehicles

“…In order to model the spatial-temporal characteristics of EV charging load, some approaches have been presented, e.g., the origin-destination (OD) approach [8], [15], trip chain [9], [10], agent-based approach [11], Markov chain approach [12], [13], stochastic approach [14], queuing theory [15], multiple agents approach [16], and mobile crowdsensing data approach [17]- [19]. In [8], the OD analysis was used to model the EV mobility, i.e., the travel distance of each EV within a day and the time (or the location) that EVs start charging.…”

“…Unlike the aforementioned works, some studies only focus on the spatial-temporal distribution of the charging load of plug-in electric taxis (PETs) rather than that of all types of EVs. In [16], a multiple-agent framework was proposed to simulate the operation of related players. Reference [17] presented a mobile crowdsensing system to forecast the charging behavior of PET based on both the historical and real-time data of PET, rather than the historical data of PET (e.g., from the global positioning system (GPS) [18], [19]) or the historical data of the inner-combustion-engine vehicles (e.g., from NHTS).…”

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

“…Likewise, the work of [32] provides an optimal location for charging stations using a multi-agent systems simulation framework to simulate the PETs' daily operation in real life such as cruising, picking up passengers, and charging. Pan et al [33] authors discuss the installation of charging stations that take the impact of travelers, taxi drivers, electricity distributors, transport networks, distributors, and electricity users into account. Multi-target optimization is proposed to solve the proposed model.…”

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

“…However, this methodology included a reward function that depended on the time for finding passengers, which could be feasible for this particular case study, but the behavior of ETs in the real world is significantly different and depends mostly on local laws. The authors of [67] considered the deployment of charging stations, considering the effects of passengers, taxi drivers, electricity retailers, the transportation and distribution networks, and power consumers. To solve the proposed model, a multi-objective optimization was proposed.…”