Probabilistic analysis of grid reliability with renewables and storage
Abstract/Contents
- Abstract
- Can the California grid support 100% renewables and 100% EVs? Rigorous analysis in this area says the answer is yes, but this would require large energy storage and charging EVs during the day. Doubling green energy imports, such as Canadian hydro, will be also required. This thesis presents new probabilistic analysis tools that allow evaluating risk and cost of the grid with storage, variable generation, and EVs at scales beyond anything seen before. First, we demonstrate a new method of probabilistic analysis of grid reliability based on Machine Learning models built using convex optimization from historical data. To analyze the impact of large-scale storage and high penetration of EVs on the grid, we introduce a probabilistic energy balancing analysis. The developed rigorous methods allow to analyze capacity allocation scenarios quickly and accurately. Second, this thesis presents the analysis of capacity planning scenarios for a Green California grid using the developed methods. Third, the developed methods are applied to practical reliability analysis of major power grids including California ISO, ISO New England, and Horizon (Western Australia). Grid reliability analysis is to assess the grid outage risk forgiven the recourse capacity portfolios. North American Electric Reliability Corporation (NERC) defined '1-in-10' reliability rule, which is no more than one-day outage in 10 years. This rule is currently used in the capacity analysis for major regional grids in North America. However, the rapidly increasing growth of renewable generation challenges traditional grid structure and started to threaten grid reliability gradually as the penetration increases. Further, the fast development of EVs boosts the increase in demand. Both supply and demand of electricity grid become complicated. Storage can be a tool to balance the risk. Analyzing the grid reliability with these variable components is complicated and calls for the development of new analysis. Many regions and countries have very ambitious plans to reduce carbon emissions. California plans to go carbon-free by 2045, Massachusetts considers similar legislation. This requires all generations to be renewable and most cars EV near future. This thesis established the new methods of reliability analysis for the grid with extremely high penetration of renewables, EVs, and storage. A New Machine Learning method is proposed to capture the randomness and uncertainty among grid components based on historical data. This question is whether the grid can have both 100% renewables and 100% EVs without losing the reliability of the power supply. This thesis uses California ISO data to explore the path to achieve a completely green scenario.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Gao, Weixuan |
|---|---|
| Degree supervisor | Lepech, Michael |
| Thesis advisor | Lepech, Michael |
| Thesis advisor | Gorinevsky, Dimitry |
| Thesis advisor | Lall, Sanjay |
| Degree committee member | Gorinevsky, Dimitry |
| Degree committee member | Lall, Sanjay |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Weixuan Gao. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kf620rp0138 |
Access conditions
- Copyright
- © 2021 by Weixuan Gao
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