Backward reduction application for minimizing wind power scenarios in stochastic programming

Abstract

In order to make informed decisions in the presence of uncertainties, risk management problems of power utilities may be modelled by multistage stochastic programs. These programs use a set of scenarios (or plausible realizations) and corresponding probabilities to model the multivariate random data process, e.g. electrical load, stream flows to hydro units, generation output of intermittent renewable sources as well as fuel and electricity prices. The number of scenarios needed to accurately represent the uncertainty involved is generally large, thus due to computational complexity and time limitation, scenario reduction techniques are often utilized. The paper proposes a new application for recursive backward scenario reduction to establish possible next-day scenarios for wind power generation at Mersing Johor, Malaysia. The algorithm determines a subset from the initial scenario set and assigns new probabilities to the preserved scenarios. The output is intended to assist generation scheduling of power system employing intermittent type renewable sources.