Generation expansion under risk using stochastic programming

In this work, the problem of power plant expansion for electricity generation under risk from demand uncertainty and supply is addressed. We begin with a deterministic model. Then, this model is expanded to a stochastic model by means of considering the various demands for different operation modes as random. After this model is analyzed, a way of quantifying risk using the value at risk methodology (VaR) is proposed. The last model presented is such that randomness in the availability factors is considered. The concepts of expected value of perfect information (EVPI) and value of stochastic solution (VSS) are also studied. The models presented are based on an extended form of the well known stochastic programming and chance constrained programming.