Optimization of charging strategies for electric vehicles in PowerMatcher-driven smart energy grids

A crucial challenge in future smart energy grids is the large-scale coordination of distributed energy demand and generation. The well-known PowerMatcher is a promising approach that integrates demand and supply exibility in the operation of the electricity system through dynamic pricing and a hierarchical bidding coordination scheme. However, as the PowerMatcher focuses on short-term coordination of demand and supply, it cannot fully exploit the exibility of e.g. electric vehicles over longer periods of time. In this paper, we propose an extension of the PowerMatcher comprising a planning module, which provides coordinated predictions of demand/price over longer times as input to the users for determining their short-term bids. The optimal short-term bidding strategy minimizing a user's costs is then formulated as a Stochastic Dynamic Programming (SDP) problem. We derive an analytic solution for this SDP problem leading to a simple short-term bidding strategy. Numerical results using real-world data show a substantial performance improvement compared to the standard PowerMatcher, without significant additional complexity

A game theoretic software framework for optimizing demand response

Abstract—Demand response (DR) is a crucial and necessary aspect of the smart grid, particularly when considering the optimization of both, power consumption and generation. While many benefits of DR are currently under study, an issue of particular concern is optimizing end-users ’ power consumption profiles at various levels. This study proposes a fundamental, game theoretic software framework for DR simulation that is capable of investigating the effect of optimizing multiple electric appliances by utilizing game theoretic algorithms. Initial results show that by shifting the switch-on time of three household appliances provides a savings of up to 6%. I