Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty. (1st December 2016)

• Record Type: Journal Article
• Authors: Huang, Yun-Hsun; Wu, Jung-Hua; Hsu, Yu-Ju
• Is Part Of: Energy. Volume 116:Part 1(2016)
• Page Start: 1145
• Page End: 1157
• Publication Date: 2016-12-01
• Subjects: Multi-region optimization model -- Two-stage stochastic programming -- Demand uncertainty -- Monte Carlo simulation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.09.112
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results. Highlights: A multi-region, two-stage stochastic programming model has been developed. The decision tree and Monte Carlo simulation are integrated into the framework. Taiwan's electricity sector is used to illustrate the applicability of the model. The results under deterministic and stochastic cases are shown for comparison. Optimal portfolios of regional generation technologies can be identified.