Oppositional Chemical Reaction Optimization Applied to Renewable-Hydro-Thermal Energy Systems

Renewable wind energy has always been extremely helpful to minimizing generation costs as well as reducing emissions to develop green/clean environments, in response to the environmental challenges. The high-proportional integration of renewable power presents challenges to the safe and stable operation of power networks because of its randomness, intermittency, and volatility. However, renewable wind power integrated hydro-thermal power plant is a complex optimization problem due to the water discharge rate, variable wind speed, water transport delay, valve point effect, scheduling time linkage and constraints related with hydraulic continuity and generational limits. The stochastic wind speed is evaluated using Weibull probability density function (W-pdf). For resolving complex economic problems, an effective chemical reaction optimization (CRO) is introduced, for the Hydro-Thermal-Wind (HTW) scheduling problem. Additionally, opposition-based learning (OBL) is integrated with the primary CRO algorithm to increase the solution superiority and convergence speed. The proposed optimization (OCRO) is applied to four hydro, three thermal, and two wind power plants systems. Moreover, proposed technique is compared to other current approaches and the suggested method is determined to be more effective than other methods described in the literature. Lastly, the output of cascade hydropower plants is stable, highly adjustable, and responds quickly.