Wind and Solar Based Multi-Objective Hydro-Thermal Scheduling Using Chaotic-Oppositional Whale Optimization Algorithm

In multi-objective hydro-thermal scheduling (HTS), the hydro and thermal units are arranged to reduce the cost of generation and emission simultaneously. The wind and solar are incorporated with hydro-thermal to get reliable electricity generation at the lowest price with low emission. Whale optimization algorithm (WOA) has been developed as an optimization technique which works on whales' hunting behavior. However, WOA has slow convergence rate and experiences premature convergence, just like other optimization methods. Thus, chaotic-oppositional learning is combined with WOA in the suggested chaotic oppositional WOA (COWOA) technique for boosting the performance and convergence speed of the basic WOA. The first test system consists of four hydro and three thermal units, whereas for the second test system, one wind unit, and one solar unit are incorporated with four hydro and one thermal generating units. In the cost model, the improbability of wind and solar power generation is considered. It includes power imbalance terms like overestimation and underestimation cost. The suggested COWOA method is applied to handle the nonlinearity of the cost function due to valve-point loading and improbability aspect as both solar radiation and wind speed are unpredictable. The simulation results demonstrate that COWOA provides superior results in terms of minimum cost of fuel, least emission, and least convergence time. Moreover, it is observed that after incorporating wind and solar units with hydro-thermal, the total cost and emission get reduced significantly as compared to conventional HTS.