A hybrid fuzzy-stochastic technique for planning peak electricity management under multiple uncertainties. (June 2017)

• Record Type: Journal Article
• Authors: Yu, L.; Li, Y.P.; Huang, G.H.; Shan, B.G.
• Is Part Of: Engineering applications of artificial intelligence. Volume 62(2017:Feb.)
• Page Start: 252
• Page End: 264
• Publication Date: 2017-06
• Subjects: Chance-constrained -- Decision making -- Electric power systems -- Fuzzy probability -- Monte Carlo -- Peak electricity; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.04.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this study, an interval-fuzzy chance-constrained programming (IFCCP) method is developed for reflecting multiple uncertainties expressed as interval-fuzzy-random (integration of interval values, fuzzy sets, and probability distributions). IFCCP has advantages in uncertainty reflection and policy analysis as well as avoiding complicated intermediate models with high computational efficiency. The developed IFCCP method is applied for planning a regional-scale electric power system (EPS) with consideration of peak-electricity demand issue. Results reveal that different peak demands in different seasons lead to changed electricity-generation pattern, pollutant emission and system cost. IFCCP is more reliable for the risk-aversive planners in handling high-variability conditions by considering peak-electricity demand. Results also disclose that fossil-fuels consumption should be cut down in future (i.e. the energy-supply structure would tend to the transition from fossil-dominated into renewable-energy dominated) in order to meet the increased power demand and mitigate the pollutant emissions. Results can help decision makers improve energy supply patterns, facilitate dynamic analysis for capacity expansion, as well as coordinate conflict interactions among system cost, pollutant mitigation and energy-supply security. Highlights: Interval-fuzzy chance-constrained method (IFCCP) is developed for planning EPS. It can address uncertainties as probability distributions and fuzzy sets. IFCCP can satisfy peak-electricity demand and optimize energy allocation. Solutions under various α-cut levels and fuzzy dominance indices are analyzed. Results create tradeoff among system cost and peak-electricity demand violation risk.