Renewable energy incorporating short-term optimal operation using oppositional grasshopper optimization

Renewable energy-based hydro-thermal scheduling is a new assignment in solar-wind-hydro power structures including thermal plants with non-convex fuel costs, a time delay of the multi-reservoir cascaded hydro unit, generating units for wind power, and photo-voltaic plant of the solar system. Renewable energy resources are used in immense quantity as they are naturally accessible and charge-free. In this regard, this article presents a single-objective economic replica of short-term hydro-thermal scheduling (HTS) problems having renewable solar and wind units. To speed up the convergence swiftness, of OBL is incorporated with the fundamental grasshopper optimization algorithm (GOA) method which is actively associated with the social communication of the grasshopper in the environment. Furthermore, HTS and hydro thermal scheduling incorporating solar and wind energy are considered for the benchmark test systems. Results presented by a few recent techniques (like fuzzy based evolutionary programming, teaching learning-based optimization, etc.) have been compared with those obtained by the oppositional GOA (OGOA) to set up its effectiveness. Simulation results of OGOA technique clearly show that the renewable solar and wind units can significantly reduce the fuel cost of the power systems.