Optimal Scheduling of Distributed Energy Storage Systems by Means of ACO Algorithm

Energy Storage is the best candidate to improve renewable energy penetration and moderate the intermittent generation problems supporting the match between energy demand and production. This paper addresses the optimal storage operations scheduling based on load and renewable production forecast. Stored energy is controlled to minimize the energy input from the grid and maximizing the revenue from selling renewable energy. This work proposes an optimal scheduling solution based on the Ant Colony Optimization (ACO) algorithm enabling the battery to respond to external signals, e.g. the energy price or on the basis of energy trades. This feature is highly demanded in scenarios with a high share of intermittent renewable energy sources. Four different ACO implementations, customized with respect to the specific problem, are compared. The developed algorithms have been tested by using real load consumption data along a week.