Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

This paper proposes an optimal scheduling/allocation of energy and spinning reserves for a wind-thermal power system. There is a considerable need for the renewable energy resources in the modern power system; therefore, in this paper, wind energy generators are used. Here, two different market clearing models are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators, and demand/consumers. The stochastic behavior of wind speed and wind power generation is represented by the Weibull probability density function. The objective function considered in this paper includes cost of energy provided by conventional thermal and wind generators, cost of reserves provided by conventional thermal generators and load demands. It also includes costs due to under-estimation and over-estimation of available wind power generation. Clustered adaptive teaching learning based optimization algorithm is used to solve the proposed optimal scheduling problem for both conventional and wind-thermal power systems considering the provision for spinning reserves. To show the effectiveness and feasibility of the proposed frame work, various case studies are presented for two different test systems.