An inexact stochastic–fuzzy jointed chance-constrained programming for regional energy system management under uncertainty

Abstract

Environmental problems associated with socio-economic development have been a growing concern facing many regional and/or national authorities. However, effective planning may encounter difficulties since uncertainties existing in a number of impact factors and pollution-related processes are often not well acknowledged and reflected. Combining chance-constrained programming and fuzzy credibility-constrained programming with interval parameters and stochastic programming, this study advances an inexact stochastic–fuzzy jointed chance-constrained programming method for planning regional economic and environmental systems under multiple uncertainties presented as intervals, fuzzy sets and probability distributions. The developed method has been applied to a case of long-term energy management system with multiple energy resources and three communities. Emissions of sulphur dioxide and nitrogen oxides are controlled and capacity expansion is scheduled. The results can help to identify desired alternatives for planning regional development strategies, where compromised schemes are provided under an integrated consideration of economic efficiency and environmental protection under multiple uncertainties.

Keywords:

• decision making
• energy system
• environment
• chance-constrained programming
• fuzzy credibility-constrained programming
• uncertainty

Notes

Mr. Guohe Huang is also currently affiliated with MOE Key Laboratory of Regional Energy Systems Optimization, S&C Resources and Environmental Research Academy, North China Electric Power University, Beijing, PR China.