Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada

In this study, a chance-constrained two-stage fractional optimization (CTFO) method is proposed for planning regional energy systems in the province of British Columbia, Canada. Through simultaneously integrating two-stage stochastic programming (TSP), chance-constrained programming (CCP), and mixed...

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Bibliographic Details
Published in:Applied energy Vol. 154; pp. 663 - 677
Main Authors: Zhou, Xiong, Huang, Guohe, Zhu, Hua, Chen, Jiapei, Xu, Jinliang
Format: Journal Article
Language:English
Published: 15.09.2015
Subjects:
British Columbia
economic costs
economic development
energy
environmental protection
issues and policy
linear programming
planning
probability distribution
renewable energy sources
uncertainty
British Columbia
ISSN:0306-2619
Online Access:Get full text
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Summary:In this study, a chance-constrained two-stage fractional optimization (CTFO) method is proposed for planning regional energy systems in the province of British Columbia, Canada. Through simultaneously integrating two-stage stochastic programming (TSP), chance-constrained programming (CCP), and mixed-integer linear programming (MILP) techniques into a linear fractional programming (LFP) framework, CTFO can effectively tackle multiobjective and capacity-expansion issues, as well as uncertainties described as probability distributions in the constraints and objectives. Based on the developed CTFO method, a chance-constrained two-stage fractional regional energy model (CTFO-REM) is developed in this study for supporting energy management in the province of British Columbia. Conflicts between environmental protection that maximizes the renewable energy resource utilization and economic development that minimizes the system cost can be effectively addressed through the CTFO-REM model without setting a factor for each objective. The results also indicate that the CTFO-REM model can facilitate dynamic analysis of the interactions among efficiency, policy scenarios, economic cost, and system reliability.
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ISSN:0306-2619
DOI:10.1016/j.apenergy.2015.05.013