Optimizing the integration of renewable energy in the United States
Abstract/Contents
- Abstract
- The effects of aggregating 1) electric load alone and 2) electric load with dispersed renewable generators through an enhanced transmission infrastructure in the United State (U.S.) were evaluated. First, a transmission network topology was created to estimate the additional transmission capacity needed for aggregating electric load. Aggregation benefits (generator capacity capital cost savings, load energy shift operating cost savings, and reserve requirement cost savings) were found to be significantly outweighed by the transmission costs required to achieve these benefits for nearly all cases. To evaluate the effects of aggregating electric load with dispersed renewables, a large-scale linear programming model was built to deterministically find the least-cost portfolio of generators (baseload, dispatchable, variable), storage, and transmission that met the electric load and reserve requirements (exogenously determined) each hour while attaining a given renewable portfolio standard (RPS) target. Various scenarios and sensitivity tests were completed of regional and U.S. spatial extents to evaluate the effect of flexibility mechanisms (aggregation, overgeneration, storage, and electric vehicle charging) and tradeoffs in modeling "levers" (temporal and spatial resolution and extent). For the set-up examined, a 100% RPS system for CA and the full U.S. was found to be technically feasible, but with significantly higher costs and overgeneration (curtailment) levels than lower RPS targets. Of the flexibility mechanisms considered, geographic aggregation had the greatest total system cost benefit, especially at high RPS levels. In interconnected scenarios, transmission was found to occupy a very small percentage of the total cost, and the contribution of each region to the aggregate RPS was significantly disproportionate, highlighting the need for regional-and-resource-specific RPS targets. Electric vehicle charging, which adds new load to the system, some of which was assumed to be flexible, always resulted in a larger total system cost but less expensive total system levelized cost (cost per unit of load served). This revealed the growing need for demand-side flexibility as the penetration of renewables increases; however results indicated that demand response price structures may need to be adjusted to encourage ideal flexible load in highly renewable systems. The cost tradeoffs of temporal and spatial modeling levers revealed small changes in accuracy and computational load as each lever was adjusted. Storage was found to be particularly sensitive to the temporal treatment in the model, highlighting the need for proper daily/weekly/seasonal storage balancing constraints. The findings presented here reflect technically feasible scenarios for a simplified U.S. electricity system and ignore many social, environmental, and political barriers, which may slow or prevent actual implementation.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Frew, Bethany Ann |
|---|---|
| Associated with | Stanford University, Department of Civil and Environmental Engineering. |
| Primary advisor | Jacobson, Mark Z. (Mark Zachary) |
| Thesis advisor | Jacobson, Mark Z. (Mark Zachary) |
| Thesis advisor | Brandt, Adam (Adam R.) |
| Thesis advisor | Weyant, John P. (John Peter) |
| Advisor | Brandt, Adam (Adam R.) |
| Advisor | Weyant, John P. (John Peter) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bethany Ann Frew. |
|---|---|
| Note | Submitted to the Department of Civil and Environmental Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Bethany Ann Frew
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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