A model for optimizing energy investments and policy under uncertainty
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Copyright 2016, Society of Petroleum Engineers. An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal energy investment strategies is challenging. One of the main challenges is the large uncertainty in many of the parameters involved in the optimization process. Most existing large-scale energy models are deterministic and so have limited capability for assessing uncertainty. Modelers usually use scenario analysis to address model input uncertainty. In this paper, we describe a coarse probabilistic model developed for optimizing energy investments and policies from an energy producer's perspective. The model uses a top-down approach to probabilistically forecast primary energy demand. Distributions rather than static values are used to model uncertainty in the input variables. The model can be applied to a country-level energy system. It maximizes portfolio expected net present value (ENPV) while ensuring energy sustainability. The model is built in MS Excel using the @RISK add-in, which is capable of modeling uncertain parameters and performing stochastic simulation optimization. The model was applied to synthetic data for a typical fossil-fuel-dependent country to determine its optimum energy strategy. For this synthetic case, the model suggests that the subject country should increase its oil production capacity slightly higher than its current level, increase its gas production, and meet most of its future power generation (electricity) demand using alternative energy sources - nuclear, solar, and wind. A primary contribution of this work is rigorously addressing uncertainty quantification in energy modeling. The model could be applied, with minor modification, by either companies or countries to assist in determining optimal energy investment strategies.
