Optimum Generation Scheduling of Thermal and Hydro Power System with Renewable Power Sources Using Enhanced Grey Wolf Optimization Algorithm

Abstract

In this work, an algorithm of enhanced grey wolf optimization (EGWO) is developed to resolve the scheduling problem of a power system, which is having thermal, hydro, wind, and solar power plants with a battery energy storing system (BESS). The planning period covers 24 equal intervals of a day. In this scheduling problem, the impact on valve point loading of thermal plants, the time coupling effect in cascaded reservoirs of hydro units, and various constraints imposed on the power network are taken into consideration. The grey wolf optimization (GWO) technique is used, which is framed on the group attitude of grey wolves such as governance ranking and hunting activity. In this proposed algorithm, GWO algorithm is enhanced using three techniques. First, the quasi-oppositional learning approach is exploited to obtain the optimal solution quickly. Second, the elite mutation operator is applied to maximize the diversity of the swarm. Finally, by implementing the elastic-ball strategy, infeasible solutions are modified into feasible one. The potential of the EGWO technique is ascertained by employing it in two experimental settings. From the simulation outcome, it is inferred that the implemented technique bestows less operating cost with minimal computation time as compared to other evolutionary techniques.