Electricity generation scheduling of thermal- wind-solar energy systems

The paper presents a solution methodology for a dynamic electricity generation scheduling model to meet hourly load demand by combining power from large-wind farms, solar power using photovoltaic (PV) systems, and thermal generating units. Renewable energy sources reduce the coal consumption and hence reduce the pollutants' emissions. Because of uncertain wind velocity and solar radiation, the share of solar and wind units is restricted to certain limits of demand, considering ramping requirements. The solar and wind units start quickly, while the thermal units start slowly. The commitment to renewable energy units is made through a proposed optimistic optimizer to restrict the power generation share. Economic and environmental objectives of thermal units are unified using a price penalty factor, and the hybrid sine-cosine algorithm (HSCA) optimization method solves the problem. A forward approach is applied to handle the dynamic electricity generation scheduling optimization problem. HSCA incorporates local mutation and hill-climbing heuristics to reduce global operating costs and emission pollutants' while allocating generation to the devoted units. To handle the constraints, replacement and heuristic techniques are applied. The proposed method is verified on a system having solar PV units and wind power plants connected to 6, 13, and 40 thermal generating units, respectively.