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An interval type-2 fuzzy fractional programming (IT2FFP) method is developed for planning the renewable energy in electric power system for supporting sustainable development under uncertainty. IT2FFP can tackle output/input ratio problems where complex uncertainties are expressed as type-2 fuzzy intervals (T2FI) with uncertain membership functions. The IT2FFP method is then applied to planning Beijing electric power system, where issues of renewable energy utilization, electricity supply security, and pollutant/greenhouse gas (GHG) emissions mitigation are incorporated within the modeling formulation. The obtained results suggest that the coal-fired power would continue to decrease and the share of renewable energy in gross electricity supply would maintain an increasing trend. Results also reveal that imported electricity plays a significant role in the city’s energy supply. A number of decision alternatives are also analyzed based on the interval solutions as well as the projected applicable conditions, which represent multiple options with sustainable and economic considerations. The optimal alternative that can give rise to the desirable sustainable option under the maximization of the share of renewable power generation has been suggested. The findings can help decision makers identify desired alternatives for managing such a mixed energy system in association with sustainable development. Compared with the conventional optimization methods that optimize single criterion, it is proved that IT2FFP is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors as well as in tackling various subjective judgments of decision makers with different interests and preferences. [ABSTRACT FROM AUTHOR]

In this study, a copula-based flexible-stochastic programming (CFSP) method is developed for planning regional energy system (RES). CFSP can deal with multiple uncertainties expressed as interval values, random variables and fuzzy sets as well as their combinations employed to objective function and soft constraints. It can also reflect uncertain interactions among random variables through using copula functions even having different probability distributions and previously unknown correlations. Then, based on the developed CFSP approach, a CFSP-RES model is formulated for planning RES of the urban agglomeration of Beijing and Tianjin (China). Results disclose that uncertainties existed in the system components have significant effects on the outputs of decision variables and system cost, and the variation of system cost is reached 16.3%. Results also reveal that air pollutant emissions can be mitigated if the urban agglomeration can co-implement renewable energy development plans (REDP) over the planning horizon, with the reductive rates of [3.3, 7.6] % of sulfur dioxide (SO 2 ), [2.7, 4.1] % of nitrogen oxides (NO x ) and [7.0, 11.5] % of particulate matter (PM 10 ). Compared to joint-probabilistic chance-constrained programming (JCP), the CFSP method is more effective for handling multiple random parameters associated with different probability distributions in which their correlations are unknown. Thus, it is not limited to some unjustified assumptions and can be applied to a wider range of problems than previous studies. The findings are helpful to explore the influence of interaction among random variables on modeling outputs and provide in-depth analysis for identifying desired decision schemes for planning RES. [ABSTRACT FROM AUTHOR]