Two-Stage Stochastic Programming Model for Optimal Scheduling of RES-Based Virtual Power Plants in Electricity Markets

To promote investment in the electricity sector, the deregulated electricity market regime has created an enabling environment to accelerate the all-around development of power generation, transmission and distribution systems. RE-based power generation is proliferating in the power sectors worldwide. Participation of large numbers of market players, and massive penetration of RE-generation have created enough complexities and has made fundamental changes in the deregulated electricity market conditions. Small scale RE generating units have limited participation in the electricity markets due to the uncertainties. These units integrate with other fossil fuel plants and forms as Virtual Power Plants (VPPs). Increasing participation of RE based VPPs in the competitive electricity market, has brought out further complexity in market operation primarily in terms of its generation scheduling, economic profitability, etc. In this paper a two-stage stochastic programming approach for optimal scheduling of VPPs in the electricity markets is presented, along with modeling of uncertainties in the electricity market price, available level of stochastic renewable generation and the request for reverse deployment. These uncertainties are modeled using scenario bounds and are formulated using stochastic programming approach. Simulation results are carried out on 4-h planning horizon.