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.
Energy models can be categorized in various ways . A comprehensive review by Jebaraj and Iniyan on existing energy models in 2006 classifies energy models into energy planning models, energy supply–demand models, forecasting models, renewable energy models, emission reduction models, and optimization models. Gargiulo and Ó Gallachóir classify long term energy models based on underlying methodology (simulation, optimisation, economic equilibrium), analytical approach (top-down, bottom-up, hybrid [, and sectoral coverage (energy system , power system. As an important branch of energy models, energy system optimization models (ESOMs) can be characterised as technology-rich, optimization models covering an entire energy system. ESOMs have been widely used to offer critical climate and energy policy insights at national, global, and regional scales . These models provide an integrated, technology-rich representation of the whole energy system for analysing energy dynamics over a long-term, multi-period time horizon. Optimal solutions are computed using linear programming techniques.
Publisher : ELSEVIER
By : Xiufeng Yue, Steve Pye, Joseph DeCarolis, Francis G.N. Lic, Fionn Rogan,Brian Ó. Gallachóir
File Information: English Language/ 14 Page / size: 440 KB
سال : ۱۳۹۶
ناشر : ELSEVIER
کاری از : Xiufeng یو، استیو Pye، جوزف دکارلیس، فرانسیس G.N. Lic، Fionn Rogan، Brian Ó. گالاچویر
اطلاعات فایل : زبان انگلیسی / 14 صفحه / حجم : KB 440
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