A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning. (3rd August 2018)

• Record Type: Journal Article
• Authors: Zhan, Yiduo; Zheng, Qipeng P.
• Is Part Of: IISE transactions. Volume 50:Number 8(2018)
• Page Start: 720
• Page End: 734
• Publication Date: 2018-08-03
• Subjects: Multistage stochastic programming -- bilevel optimization -- generation expansion -- decision-dependent probability -- decomposition algorithms; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2018.1442032
• Languages: English
• ISSNs: 2472-5854

Abstract

ABSTRACT: In this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.