Main content area

Inexact mixed-integer programming with interval-valued membership function for sustainable power-generation capacity planning

Jin, S.W., Li, Y.P., Huang, G.H., Zhang, K.
Journal of cleaner production 2016 v.122 pp. 52-66
air pollution, case studies, electric power, electricity, energy, issues and policy, linear programming, planning, pollution control, power generation, renewable energy sources, resource allocation, sustainable development, uncertainty
In this study, inexact mixed-integer linear programming with interval-valued membership function (IMILP-IMF) is developed for power-generation capacity planning. IMILP-IMF can deal with uncertainties described as fuzzy sets with interval-valued membership function, and allows uncertainties to be directly communicated into the optimization process and the resulting solution. IMILP-IMF is applied to a case study of supporting long-term planning for an electric power system (EPS). It can facilitate dynamic analysis for decisions of capacity expansion planning within a multi-facility, multi-option and multi-period context. Solutions of energy resources allocation, capacity expansion, air pollution control, and electricity generation with a minimized system cost are obtained. Results reveal that, from a long-term planning point of view, more capacities of renewable energy generation need to be installed to replace the outdated facilities to transfer the EPS to a clean, sustainable and reliable one. Results of power-generation capacity planning in association with economic consideration and environmental requirement can also help decision makers to formulate the relevant policies, optimize energy supply structure, as well as facilitate the sustainable development of electric power systems. The findings will help generate decision alternatives under multiple scenarios, and thus offer insight into the tradeoffs between economic and environmental objectives.