Stochastic NPV Based vs Stochastic LCOE Based Power Portfolio Selection Under Uncertainty

Abstract

This paper investigates the problem of power portfolio selection under uncertainty using two different metrics, namely the stochastic Net Present Value (NPV) and the stochastic Levelized Cost of Electricity (LCOE). In the first metric, stochastic revenues, as well as stochastic costs incurred during the whole lifetime of power plants, are taken into account. The second metric is based on stochastic costs only. This means that revenues deriving from selling electricity in power markets over long-term horizons play an important role in determining optimal portfolios under the stochastic NPV metric, but they have no impact on optimal portfolios under the stochastic LCOE metric. Uncertainty arising from unpredictable movements of electricity market prices, fossil fuels, and nuclear fuel prices is considered. Moreover, stochastic CO 2 costs are included into the analysis. The aim of this study was to examine in what circumstances efficient NPV-based portfolios differ in a significant way from efficient LCOE-based portfolios. The portfolio selection is performed using two different risk measures, namely the standard deviation and the Conditional Value at Risk Deviation (CVaR) deviation. The proposed methodology can be used as a powerful tool of analysis for planning profitable investments in new generating technologies paying attention to risk reducing strategies through power sources diversification.