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) |
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| 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 |
| Copyright |
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| Note | Masters Thesis Electrical Engineering 2019 |
| Collaborating Institutions | Graduate College / ASU Library |
| Additional Formats | MODS / OAI Dublin Core / RIS |
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