A virus-evolutionary differentiated-PSO approach for short-term generation scheduling with uncertainties

This paper presents a virus-evolutionary differentiated-particle swarm optimization approach for short-term generation scheduling with uncertainties consideration. Different from the traditional power generation scheduling problems, the paper considers the characteristics of the change of atmospheric flow at day and night. This paper also considers the uncertainties in the load demand, available water in the reservoir, wind speed, and radiation. This paper studies on the best dispatch both economic and emission, which include two objective functions of generation cost and emission cost at different time periods. The proposed method that combines differentiated-particle swarm optimization with virus-evolutionary technique can expand the scope and increase the diversity of search. The study system comprises 10 thermal units, seven hydro units (including one pumped-storage hydro unit), one equivalent wind energy system, and one equivalent solar energy system. The results from the proposed method are compared with those from other intelligent algorithms. The test results show that the proposed method can indeed obtain a better solution. Copyright (c) 2016 John Wiley & Sons, Ltd.