2020
DOI: 10.1016/j.seta.2020.100741
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Battery charging and discharging scheduling with demand response for an electric bus public transportation system

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Cited by 28 publications
(24 citation statements)
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“…Teng et al (2019) introduced a multi-objective particle swarm optimization algorithm to optimize the single-line bus timetabling and VSP that smoothes the headway and minimizes the number of vehicles and charging cost [26]. Ke et al (2020) optimized the battery recharging scheduling at different times during the timeframe, in an effort to minimize the single-day total cost of the public transport system [27]. In addition, this study took solar and wind power generation and different feeder loads of the main transformer into consideration, integrating demand response and the resale of battery electricity to the power company.…”
Section: Related Workmentioning
confidence: 99%
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“…Teng et al (2019) introduced a multi-objective particle swarm optimization algorithm to optimize the single-line bus timetabling and VSP that smoothes the headway and minimizes the number of vehicles and charging cost [26]. Ke et al (2020) optimized the battery recharging scheduling at different times during the timeframe, in an effort to minimize the single-day total cost of the public transport system [27]. In addition, this study took solar and wind power generation and different feeder loads of the main transformer into consideration, integrating demand response and the resale of battery electricity to the power company.…”
Section: Related Workmentioning
confidence: 99%
“…denote respectively the passenger arrival rates at stop n in Type 1 and Type 2, pax/min; t r q,n denotes the waiting times of residual passengers at stop n, min. The first term in Equation (27) indicates the waiting times of passengers in Type 2 when the bus is all-stop scheduling and its leading vehicle is short-turning strategy. The second term refers to the waiting times of residual passengers from the previous bus, and they can take the following bus regardless of scheduling strategy.…”
Section: Passenger-related Travel Time Costsmentioning
confidence: 99%
“…The phenomenon of self-discharge voltage has proven to be an important subject of study for any electrochemical device, particularly for batteries and supercapacitors. For batteries, the self-discharge voltage is the main limitation to storing energy for a long time, so the efficiency and autonomy of systems are poor [ 20 ], which is a challenging issue when using batteries in electric vehicles, industries, and residences [ 21 , 22 ]. To reduce battery self-discharge voltage, many strategies have been developed, such as optimizing materials for battery construction [ 22 , 23 ] and developing different models with a high degree of reliability (i.e., by taking into account several variables or assimilating the battery like an electronic circuit) [ 24 , 25 ].…”
Section: Introductionmentioning
confidence: 99%
“…For batteries, the self-discharge voltage is the main limitation to storing energy for a long time, so the efficiency and autonomy of systems are poor [ 20 ], which is a challenging issue when using batteries in electric vehicles, industries, and residences [ 21 , 22 ]. To reduce battery self-discharge voltage, many strategies have been developed, such as optimizing materials for battery construction [ 22 , 23 ] and developing different models with a high degree of reliability (i.e., by taking into account several variables or assimilating the battery like an electronic circuit) [ 24 , 25 ]. On the other hand, for supercapacitors, besides being determinant for the duration of energy storage (i.e., rest phases), self-discharge voltage is an important indicator to quantify performance [ 26 , 27 ].…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, the modes also need to ensure the battery storages can perform better and improve the utilization of renewable energy systems [9,[13][14][15]. Looking at the importance of battery storage charging -discharging, methods or mechanisms such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) [6,16,17], backtracking search algorithm [10,18,19], non-simultaneous charging and discharging [20], genetic algorithm [4,21], particle swarm optimized fuzzy controller [22][23][24][25][26][27], model predictive control [28][29][30][31][32], dynamic optimal power flow [33][34][35][36], and grey model and genetic algorithms [37] are commonly used to perform the battery storage charging and discharging. The used methods or mechanisms also conduct system optimization to reduce the stress on the battery storages as well as protect the batteries from being damaged.…”
mentioning
confidence: 99%