Probabilistic Electric Vehicle Charging Optimized With Genetic Algorithms and a Two-Stage Sampling Scheme

Introduction

Optimal integration of electric vehicles into modern power grids plays an essential role in future power system operation and control. Numerous investigations have been performed in order to identify optimal charging policies for meeting objectives like peak-shaving, optimization of power quality metrics or efficient usage of power from renewable sources. Especially this interaction of zero-emission supply plants and electrified vehicles is seen as central concern, since the usage of energy from renewables directly influences the reachable environmental benefit of electric vehicles (EV). Here, both the supply as well as the demand side show nondeterministic behavior which has to be tackled in some way. Therefore, a simulation-based optimization approach will be demonstrated, that uses metaheuristic algorithms for finding optimal charging schedules of an EV fleet within a given system. This approach therefore considers both, the physical power grid as well as the individual electrified traffic through probabilistic simulation models, where all nondeterministic influences can be incorporated dynamically into the heuristic search process. Each solution candidate will be evaluated a sufficient number of times through simulation in order to increase the accuracy of the performance estimation within an uncertain environment. A fitness-based sampling approach will be introduced for decreasing computational effort of solution evaluation.

The rest of the paper is organized as follows: next, basics of optimization issues within power grid operation will be discussed, introducing the problem of optimal electric vehicle charging control and its formulation. After showing the simulation-based optimization approach as being capable of handling the formulated problem, necessary simulation-models will be considered in detail. Beside modeling and simulation, optimization of the given problem will represent the core of this work. Thus, parameterization and adaptation of a suitable optimization algorithm will be discussed, introducing a novel approach for adaptive fitness-based sampling when evaluating a solution candidate in an uncertain environment. In the end, final conclusions can be drawn for rounding up the paper.