Multiobjective augmented Eps-constraint optimization for economic/environmental stochastic unit commitment in power systems with high penetration of wind power

To regard environmental protection renewable energy sources especially wind, has been applied to achieve emission reduction goals. While wind generation does not directly produce air pollutants emission, it causes some changes on thermal power generation scheduling which may lead them to produce more air pollutants emission especially during low and medium energy demand periods. So it seems necessary to consider air pollutants emission level in wind-thermal scheduling problems. This paper proposes a methodology for wind-thermal scheduling in a power system with high penetration of wind power subject to consider air pollutants emission reduction. Because of simultaneous minimizing total operating cost and air pollutants emission, a Multiobjective Mathematical Programming (MMP) is introduced. The computation of the required reserve levels and their costs is attained through a stochastic programming marketclearing model. Also, the network constraints and the costs of both the load shedding and the wind spillage are considered. The MMP approach based on Augmented Eps-constraint algorithm has been suggested for generating Pareto-optimal solutions of wind-thermal units scheduling problem. Additionally, Fuzzy decision making process is employed to choose one of the Paretooptimal solutions as the best compromise nondominated solution. The usefulness of the proposed approach were demonstrated through an IEEE 30-bus test system.