A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

Umetani, Shunji; Fukushima, Yuta; Morita, Hiroshi

doi:10.1016/j.omega.2016.04.005

Cited by 81 publications

(29 citation statements)

References 25 publications

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“…Due to the complexity of these problems, many existing optimization techniques have been applied to solve them-for example, linear programming [35], ant-based swarm algorithm [12], genetic algorithm [27], particle swarm optimization [21,36], multi-agent based system [37], estimation of distribution algorithm [30], moving window optimization [29], tabu search and GRASP [38], among others.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

et al. 2018

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Scheduling the charging times of a large fleet of Electric Vehicles (EVs) may be a hard problem due to the physical structure and conditions of the charging station. In this paper, we tackle an EV’s charging scheduling problem derived from a charging station designed to be installed in community parking where each EV has its own parking lot. The main goals are to satisfy the user demands and at the same time to make the best use of the available power. To solve the problem, we propose an artificial bee colony (ABC) algorithm enhanced with local search and some mating strategies borrowed from genetic algorithms. The proposal is analyzed experimentally by simulation and compared with other methods previously proposed for the same problem. The results of the experimental study provided interesting insights about the problem and showed that the proposed algorithm is quite competitive with previous methods.

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Section: Literature Reviewmentioning

confidence: 99%

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

et al. 2018

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“…The former and the latter were solved by a state‐independent four‐threshold feedback policy via DP and a reminiscent water‐filling solution, respectively. Umetani et al 12 introduced a fast heuristic algorithm for charge and discharge scheduling of EVs in a building energy management system to reduce the peak load. Uncertainty in demands and departure times of EVs were handled with a two‐stage heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Although related works in the literature have investigated the unidirectional V2G from EV owner's point of view, the energy cost difference of bidirectional V2G capability is not studied. Other works have concentrated on either one charging method, 11,12 on the profit of CSs and aggregators 4,5,7 or on peak load reduction or load shifting 9,10,12 . In addition, a careful cost‐aware analysis taking account the battery degradation is missing 11,15,21 in EVs with and without V2G.…”

Section: Introductionmentioning

confidence: 99%

Performance of different optimal charging schemes in a solar charging station using dynamic programming

Hajidavalloo

Shirazi

Mahjoob

2020

Optim Control Appl Methods

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Summary Electric Vehicles (EVs) are gradually replacing conventional vehicles as they are environmentally friendly and cause less pollution problems. Unregulated charging has severe impacts on the distribution grid and may incur EV owners higher charging costs. Therefore, controlled charging infrastructures to supply the charging needs of large numbers of EVs are of vital importance. In this article, an optimal control scenario is presented to formulate the charge scheduling problem of EVs in a solar charging station (CS). Two different objective functions are considered. The first objective function holds for minimizing the total charging cost of EVs. In this case, the benefits of Vehicle‐to‐Grid (V2G) are investigated by comparing the charging costs of EVs with and without this capability. The total EV charging costs and grid benefits are also investigated in the second objective function which holds for minimizing the extracted power from the grid. A modified version of Dynamic Programming is used to solve the large state‐space model defined for the optimal control problem with extremely shorter computation time and minimal loss of optimality. Extensive simulations are done in two representative summer and winter climates to determine the role of solar energy in the CS performance. The results show that in the cost minimization algorithms, significant savings for EV owners and a smooth load shape for the grid are achieved. For the minimized power from the grid algorithm, a total near Photovoltaic (PV)‐curve charging power is obtained to exploit the PV power as much as possible to minimize the impacts on the grid.

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“…In order to meet these requirements, electricity markets need to become more flexible. While traditionally, flexibility has been mainly provided by the supply side, demand side flexibility has gained increasing interest over the last decade and is expected to become increasingly important in the future [2,3,4]. Kirby and Hirst [5] as well as Chen et al [6], for instance, describe the system benefits (mainly efficiency gains and cost reductions) of an increased demand side flexibility.…”

Section: Introductionmentioning

confidence: 99%

Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model

Devine

Bertsch

2018

European Journal of Operational Research

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A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

Cited by 81 publications

References 25 publications

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Performance of different optimal charging schemes in a solar charging station using dynamic programming

Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model

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