Self-adaptive polyclonal selection algorithm-based multi-objective kW scheduling considering renewables

Abstract

This paper proposes a novel method to solve short-term kW scheduling in a standalone power system that is an independent system consisting of diesel generators, wind farms, solar photovoltaic (PV) arrays and/or energy storages, etc. The fuel cost of diesel units and green gas emission are minimized while all operation constraints are satisfied. Uncertainties in both wind and PV powers are modeled by the fuzzy set. The self-adaptive polyclonal selection algorithm is used to solve this multi-objective problem. Various preferred references, degrees of fuzziness, and priority list for diesel generators are discussed. Simulation results show that the proposed method is efficient to deal with the interactive multi-objective kW scheduling problem.