Abstract
In the literature, optimization-based approaches are frequently proposed for the control of electric vehicle charging. However, they are usually evaluated under simplifying assumptions and are not compared to more simple approaches. The present work compares optimization-based approaches with rule-based ones in a simple but realistic scenario, in which a certain limit for the total load has to be satisfied. The scenario is based on the situation at an office building in Germany. In simulation experiments, different control approaches are evaluated not only in terms of pure performance but also from an economic perspective. The results indicate that, although the optimization-based approaches outperform the rule-based approaches, they are not always the right choice from an economic point of view.
Highlights
Public electric vehicle (EV) charging stations can result in a high electrical load in times of high utilization
Optimization-based approaches for EV charging control were compared with rule-based approaches in a realistic scenario based on simulation experiments
The optimization-based approaches outperformed the rule-based approaches, and the mixed integer quadratic programming (MIQP)-based approach yielded a higher user utility compared to the mixed integer linear programming (MILP)-based approach
Summary
Public electric vehicle (EV) charging stations can result in a high electrical load in times of high utilization. A certain base load, for example, of a shopping mall, an office building, or a public building comes on top of the charging load. This imposes technical problems, like potential transformer overloads, and economic problems since industrial consumers usually have to pay a fee per kW of the peak of the electrical load in a billing period—the so called peak demand charge. The first is to minimize the peak under the constraint that all charging requirements have to be fulfilled This can be realized in different ways, like the use of an intelligent charging control [1,2], of stationary batteries [3,4], of vehicle-to-grid (V2G) technology [5,6,7], or of dynamic pricing strategies [8,9,10]. A drawback of such methods is that, even though the peak load is minimized, it can still be in an undesirably high range
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