Design of an Energy Consumption Scheduler Based on Genetic Algorithms in the Smart Grid

Lee, Junghoon; Park, Gyung-Leen; Kwak, Ho‐Young; Jeon, Hongbeom

doi:10.1007/978-3-642-23935-9_43

Cited by 7 publications

(1 citation statement)

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“…The design of a simulation environment, which produces charging schedules using a multi-objective evolutionary optimisation algorithm is presented by Ramezani et al (2011). Lee et al (2011) expose an energy consumption scheduler that is able to reduce peak power load in smart places based on genetic algorithms. A concept of real-time scheduling techniques for EV charging that minimises the impact on the power grid and guarantees the satisfaction of consumer's charging requirements is suggested by Kang, Duncan, and Mavris (2013).…”

Section: Related Workmentioning

confidence: 99%

Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches

Sassi

Oulamara

2016

International Journal of Production Research

103

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This paper deals with the Electric Vehicle (EV) Scheduling and Optimal Charging Problem. More precisely, given a fleet of EVs and Combustion Engine Vehicles (CVs), a set of tours to be processed by vehicles and a charging infrastructure, the problem aims to optimise the assignment of vehicles to tours and minimise the charging cost of EVs while considering several operational constraints mainly related to chargers, electricity grid and EVs driving range. We prove that the Electric Vehicle Scheduling and Charging Problem (EVSCP) is NP-hard in the ordinary sense. We provide a mixed-integer linear programming formulation to model the EVSCP and use CPLEX to solve small and medium instances. To solve large instances, we propose two heuristics: a Sequential Heuristic (SH) and a Global Heuristic (GH). The SH considers the EVs sequentially. To each EV, it assigns a set of tours and guarantees the feasibility of a charging schedule. Then, it generates an optimal charging schedule for this EV. However, the GH computes, in the first step, a feasible assignment of tours to all EVs. In the second step, it applies a global Min-Cost-Flow-based charging algorithm to minimise the charging cost of the EVs fleet. To evaluate the efficiency of our solving approaches, computational results on a large set of real and randomly generated test instances are reported and compared.

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Section: Related Workmentioning

confidence: 99%