Optimal battery charging in smart grids with price forecasts

Abstract

In this paper, we consider a residential cluster in which some of the households own home batteries. The battery owners have forecasts of future prices for optimally utilizing the long-term flexibility of the battery. These forecasts become increasingly uncertain the further we look into the future. The home batteries are individually too small to influence prices, collectively however, they have enough capacity to have an influence. We study three possible scenarios: (i) Each household controls its own battery to maximize its own profits; (ii) The battery owners coordinate their strategies to maximize the collective battery profits; (iii) The battery owners coordinate their strategies to maximize the overall cluster profits. For (i) we formulate an algorithm for a single price taker battery based on Stochastic Dynamic Programming. Through simulation with realistic data we find that this solution performs well for one isolated home battery and remains stable when used by every battery in the cluster. Additionally, we formulate an algorithm based on Stochastic Dynamic Programming for scenarios (ii) and (iii). Using simulation with realistic data we find that scenarios (ii) and (iii) outperform scenario (i), and that from a cluster perspective, scenario (iii) is more beneficial than scenario (ii). We conclude that incentives have to be put in place to promote the right use of storage in the future grid.