Optimization of Market Based Energy Bidding of a Virtual Power Plant Using Genetic Algorithm

Few years ago, only a small number of power stations used to feed national grid of a country (specially developed countries) which will be thousands of them shortly due to the huge implementation of renewable plants and deregulation concept of markets. Variability and uncertainty are intrinsic characteristics of power systems which becomes more complicated when renewable energy penetrates. Virtual Power Plant (VPP) concept tries to deal with the fast growing penetration of Distributed Energy Resource (DER) challenge forcing it towards a more liberalized electricity market. Participation of VPP in a spot energy market can be an impressive solution for handling peak hour loads or other energy demands. In a day-ahead market, the producers of power must decide their offer curve based on the optimal dispatch of generators considering profit maximization concept and the constraints like price uncertainty, limits of generating units, ramping rates and many more depending on the existing scenario. In this paper, it is revealed a Genetic Algorithm (GA) based technique for optimal dispatch of the generators included in a VPP to participate in a day-ahead market with profit maximization scheme. The paper has considered the forecasted price and generation as uncertain parameters and used GA to model the uncertainties.