Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

Abstract

This study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon-neutral transition of transportation. This paper illustrates a twostage stochastic programming model capturing the uncertainty of PV power outputs and designs a step-wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L-shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step-wise solution approach can provide high-quality solutions within acceptable computational time. A case study is performed using a real-world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems.

Analysis on Braess paradox and network design considering parking in the autonomous vehicle environment

“…On the algorithmic side, various innovations have been introduced for solution methodologies, for instance, genetic algorithm (GA; Chakroborty, 2003), intelligent decision-support tool (Karim & Adeli, 2003), scatter search algorithm (Gallo et al, 2010), column generation method (S. Wang & Meng, 2014), clustering algorithm (Rashidi et al, 2016), simulation-based algorithm (Chong & Osorio, 2018), reinforcement learning (Tong et al, 2021), L-shaped algorithm (X. Liu et al, 2023), and column and constraint generation algorithm (Hajibabai et al, 2023). However, none of the aforementioned studies has investigated the NDP considering road capacity expansion strategy for the autonomous transportation system (ATS).…”

“…(2019) proposed the NDPs that minimized the total system travel time by link capacity expansion and meanwhile incorporated various equity measures into transport modeling. On the algorithmic side, various innovations have been introduced for solution methodologies, for instance, genetic algorithm (GA; Chakroborty, 2003), intelligent decision‐support tool (Karim & Adeli, 2003), scatter search algorithm (Gallo et al., 2010), column generation method (S. Wang & Meng, 2014), clustering algorithm (Rashidi et al., 2016), simulation‐based algorithm (Chong & Osorio, 2018), reinforcement learning (Tong et al., 2021), L‐shaped algorithm (X. Liu et al., 2023), and column and constraint generation algorithm (Hajibabai et al., 2023).…”

An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

“…To reduce the difficulty of solving, another approach is to decompose the complex ELRP problem into several sub-problems to be solved separately, i.e., the ELRP can be divided into the EFLP [29] and EVRP [30]. The EFLP is one of the sub-problems in the ELRP and is to decide the optimal location in the network, especially the electric charging stations [2,3,[15][16][17] or the battery swapping stations [12,29]. In the EVRP, a single vehicle is required to achieve the maximum profit and minimum cost with constraints that the route duration is not more than a given threshold.…”

“…Recently, researchers generally believe that the ELRP problem is a more complex task than a traditional location-routing problem (LRP). And the application of the ELRP will become increasingly widespread with the popularity of electric vehicles, such as charging station locations [2,11], battery swapping stations [12], smart cities [6], urban logistics [9,13], home health care [12], waste collection [14], electric trucks [7], electric aircraft [15], and electric buses [16].…”

On the role of time‐of‐use electricity price in charge scheduling for electric bus fleets

“…Meanwhile, battery degradation can lead to reduced capacity, necessitating recharging during daily operations. Thus, when determining a BEB fleet's service schedules, the BEBs’ charging activities must be jointly considered to ensure that they can fulfill their assigned service runs without exhausting the batteries, commonly known as charge scheduling (X. Liu et al., 2023; Q. Zhang et al., 2023). Compared to the conventional bus scheduling problem for diesel buses, jointly optimizing the charge scheduling renders the BEB scheduling problem much more complicated (Ji et al., 2022; Zhao et al., 2021).…”