A review of approaches to uncertainty assessment in energy system optimization models. (August 2018) - Yue, Xiufeng; Pye, Steve; DeCarolis, Joseph; Li, Francis G.N.; Rogan, Fionn; Gallachóir, Brian Ó.
Abstract: Energy system optimization models (ESOMs) have been used extensively in providing insights to decision makers on issues related to climate and energy policy. However, there is a concern that the uncertainties inherent in the model structures and input parameters are at best underplayed and at worst ignored. Compared to other types of energy models, ESOMs tend to use scenarios to handle uncertainties or treat them as a marginal issue. Without adequately addressing uncertainties, the model insights may be limited, lack robustness, and may mislead decision makers. This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs. We have identified four prevailing uncertainty approaches that have been applied to ESOM type models: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives. For each method, we review the principles, techniques, and how they are utilized to improve the robustness of the model results to provide extra policy insights. In the end, we provide a critical appraisal on the use of these methods. Highlights: Systematic review of treatment of uncertainty in over 2000 papers that use energy systems optimization models (ESOMs). Importance of uncertainty widely acknowledged, yet only a minority of papers use a formal uncertainty analysis methodology. Methods identified: Monte Carlo analysis, stochastic programming, robust optimization, modelling to generate alternatives. All four methods have their own focus, advantages and limitations, and inform different aspects of decision-making. We provide a critical appraisal and technique selection flowchart on the use of these four methods.