A Memetic Computing Approach for Unit Commitment with Energy Storage Systems

The intermittency and unpredictability of renewable energy source availability cause relevant problems in terms of grid balance and stability. Such problems can be partially solved by introducing energy storage systems (ESS), although capital costs required could be prohibitive. To explore the potential achievable benefits, a generalized unit commitment algorithm has been developed. The adopted approach integrates original memetic operators into a genetic algorithm. To evaluate the optimal amount of energy storage, the memetic computing approach has been coupled with a recursive quadratic programming optimizer. The computational code has been applied to a test case available in literature. Results show a good capability of the proposed algorithm to find really satisfactory solutions in reduced computational time.