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Analyzing the impact of renewable generation on the locational marginal price (LMP) forecast for California ISO

Abstract Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages ... (more)
Created Date 2019
Contributor Vad, Chinmay (Author) / Honsberg, Christiana B. (Advisor) / King, Richard R. (Committee member) / Kurtz, Sarah (Committee member) / Arizona State University (Publisher)
Subject Engineering / Energy / California ISO / forecasting / generation outages / locational marginal price / machine learning / Renewable Energy
Type Masters Thesis
Extent 86 pages
Language English
Note Masters Thesis Electrical Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis