A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies
- 주제(키워드) Electricity , Capacity expansion planning , System reliability , Renewable energy technology , Stochastic programming
- 주제(기타) Computer Science, Interdisciplinary Applications; Engineering, Industrial; Operations Research & Management Science
- 설명문(일반) [Min, Daiki] Ewha Womans Univ, Sch Business, 52 Ewhayeodae Gil, Seoul 03760, South Korea; [Ryu, Jong-hyun] Hongik Univ, Coll Business Management, 2639 Sejong Ro, Jochiwon Eup 30016, Sejong Si, South Korea; [Choi, Dong Gu] Pohang Univ Sci & Technol, Dept Ind & Management Engn, 77 Cheongam Ro, Pohang, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 PERGAMON-ELSEVIER SCIENCE LTD
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000151716
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.cor.2017.10.006
초록/요약
The recent interest in reducing greenhouse gas emissions and the recent technical evolution of energy networks to smart grids have facilitated the integration of renewable energy technologies (RETs) into the electricity sector around the world. Although renewable energy provides substantial benefits for the climate and the economy, the large-size deployment of RETs could possibly hurt the level of power system reliability because of the RETs' technical limitations, intermittency, and non-dispatchability. Many power system planners and operators consider this a critical problem. This paper proposes a possible solution to this problem by designing a new stochastic optimization model for the long-term capacity expansion planning of a power system explicitly incorporating the uncertainty associated with RETs, and develops its solution by using the sample average approximation method. A numerical analysis then shows the effects of the large-scale integration of RETs on not only the power system's reliability level but also, and consequentially, its long-term capacity expansion planning. From the results of the numerical analysis, we show that our proposed model can develop a long-term capacity expansion plan that is more robust with respect to uncertain RETs and quantify the capacity the system requires to be reliable. (C) 2017 Elsevier Ltd. All rights reserved.
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