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Economic impact of congestion risk and optimal portfolio for siting renewable assets in Texas

Chung, Anthony
(2024)

Files

Chung_25881800_2024.pdf
  • Open access
  • Adobe PDF
  • 9.78 MB

Details

Supervisors
de Maere d'Aertrycke, Gauthier ; Hendrickx, Julien
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil en mathématiques appliquées, à finalité spécialisée
Abstract
As the need to invest in renewable energy grows, driven by the need to reduce carbon emissions, this thesis addresses congestion risk in the Texas electricity market with the aim of identifying optimal locations for new renewable assets. In collaboration with ENGIE Impact, the study first utilizes a security-constrained optimal power flow model to replicate market outcomes, accounting for generation capacity, demand, network, and security constraints. The impact of congestion is assessed through the so-called congestion rent, and renewable generation curtailment. Secondly, a methodology is proposed to help business developers identify geographic opportunities and build efficient portfolios in a nodal market, based on revenue and downside risk. The results, supported by data from academic sources (e.g. Texas A&M University) and industrial sources (e.g. ERCOT and ENGIE Impact), highlight the growing risk of congestion and its impact on nodal market prices, particularly around major cities. A list of efficient portfolios is provided, maximizing expected revenues while minimizing downside risk. Higher expected revenues were noted in northern Texas, but efficient portfolios included both northern and western assets to balance revenue and risk. While the findings are promising, they should be interpreted with caution due to assumptions made during the research. Future improvements could involve incorporating batteries and optimizing yearly outcomes in a single run, removing the need for an iterative method. Additionally, the assumption that future installed capacities do not affect nodal market prices should be re-evaluated. Lastly, future analyses should consider varying asset weights and including more than two assets in portfolio construction.