LAPSE:2023.30995
Published Article
LAPSE:2023.30995
A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power
April 17, 2023
Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants.
Keywords
bilevel programming, KKT conditions, market power, mixed-integer linear/quadratic programming, stochastic programming, transmission expansion planning
Suggested Citation
Alnowibet KA, Alshamrani AM, Alrasheedi AF. A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power. (2023). LAPSE:2023.30995
Author Affiliations
Alnowibet KA: Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia [ORCID]
Alshamrani AM: Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia [ORCID]
Alrasheedi AF: Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia [ORCID]
Journal Name
Energies
Volume
16
Issue
7
First Page
3256
Year
2023
Publication Date
2023-04-05
Published Version
ISSN
1996-1073
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Original Submission
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PII: en16073256, Publication Type: Journal Article
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LAPSE:2023.30995
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doi:10.3390/en16073256
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Apr 17, 2023
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