A Multistage Decision-Dependent Stochastic Bilevel Programming Approach For Power Generation Investment Expansion Planning

Keywords

bilevel optimization; decision-dependent probability; decomposition algorithms; generation expansion; Multistage stochastic programming

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.

Publication Date

8-3-2018

Publication Title

IISE Transactions

Volume

50

Issue

8

Number of Pages

720-734

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/24725854.2018.1442032

Socpus ID

85046707078 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046707078

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