Andoni, M. and Robu, V. (2016) Using Stackelberg Games to Model Electric Power Grid Investments in Renewable Energy Settings. In: Osman, N. and Sierra, C. (eds.) Autonomous Agents and Multiagent Systems: AAMAS 2016 Workshops, Best Papers, Singapore, Singapore, May 9-10, 2016, Revised Selected Papers. Series: Lecture notes in Computer Science, 10002. Springer: Cham, pp. 127-146. ISBN 9783319468822 (doi: 10.1007/978-3-319-46882-2_8)
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Abstract
Often renewable generators cluster in remote regions (such as windy islands) located away from demand centres. Suitability of these locations in terms of renewable resources, is often coupled with insufficient grid capacity, which leads to the application of generation curtailment, when power generated exceeds local aggregate demand. This work studies the effect of curtailments schemes on the strategic interaction of different investors. Our work uses a game-theoretic approach to study the profitability and decision making on future renewable investment, for a variety of different schemes. Next, we study the effect of curtailment and line access rules in power grid expansion. We model the interplay between a private line investor and local generators as a Stackelberg game and determine the generation capacity and profits at equilibrium. Finally, we examine a UK-based network upgrade case-study and show how results can be utilised to set a grid access payment mechanism, ensuring both the implementation of transmission and local generation investments.
| Item Type: | Book Sections |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Andoni, Dr Merlinda |
| Authors: | Andoni, M., and Robu, V. |
| College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer |
| ISSN: | 16113349 03029743 |
| ISBN: | 9783319468822 |
| Related URLs: |
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