José M. Chamorro

Valuing uncertain cash flows from investments that enhance energy efficiency

There is a broad consensus that investments to enhance energy efficiency quickly pay for themselves in lower energy bills and spared emission allowances. However, investments that at first glance seem worthwhile usually are not undertaken. One of the plausible, non-excluding explanations is the numerous uncertainties that these investments face. This paper deals with the optimal time to invest in an energy efficiency enhancement at a facility already in place that consumes huge amounts of a fossil fuel (coal) and operates under carbon constraints. We follow the Real Options approach. Our model comprises three sources of uncertainty following different stochastic processes which allows for application in a broad range of settings. We assess the investment option by means of a three-dimensional binomial lattice. We compute the trigger investment cost, i.e., the threshold level below which immediate investment would be optimal. We analyze the major drivers of this decision thus aiming at the most promising policies in this regard.

Measuring Performance of Long-Term Power Generating Portfolios

We propose a model for assessing the performance of generation mixes in a mean-variance context. In particular, we focus on the expected price of electricity and the price volatility that result from different generating portfolios that change over time (because of investments and retirements). Our valuation model rests on solving an optimization problem. At any time it minimizes the total costs of electricity generation and delivery. A distinctive feature of our model is that the optimization process is subject to the behavior of stochastic variables (e.g. load, wind generation, fuel prices). Thus we deal with a problem of stochastic optimal control. The model combines optimization techniques, Monte Carlo simulation over the decades-long planning horizon, and market data from futures contracts on commodities. It accounts for uncertain dynamics on both the demand side and the supply side. The aim is to assist decision makers in trying to assess electricity portfolios or supply strategies regarding generation infrastructures. To demonstrate the model by example we consider the case of Great Britain’s generation mix over the next 20 years. In particular, we compare three future energy scenarios and the contracted background, i.e. four time-varying generating portfolios. Major British power producers are covered by the EU Emissions Trading Scheme (ETS), so they operate under binding greenhouse gas (GHG) emission constraints. Further, the UK Government has announced a floor price for carbon in the power sector from 1 April 2013. The generation mix is optimally managed every period by changing input fuel and electricity output as required.