A Novel Zero-Sum Polymatrix Game Theory Bidding Strategy for Power Supply Market
Saurabh Kumar1, Bharti Dwivedi2
1Saurabh Kumar, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India.
2Bharti Dwivedi, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India.
Manuscript received on 12 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 5669-5675 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2889078219/19©BEIESP | DOI: 10.35940/ijrte.B2889.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The competitive power system market involves very high financial risk due to the essential requirements of real-time bidding decision making. Decisions once taken cannot be altered easily because multiple generators participate in bidding process while simultaneously dispatching to meet the load demand most economically. In order to avoid such risks it becomes pertinent to re-structure the bidding strategies from time to time to meet upcoming techno-economical challenges. In this paper, three generating units are studied using Matrix Laboratory software with a novel approach for deciding the best strategy from the most economical strategy viewpoint. A scenario of different formulations is created for muti-player game, which then is solved with the help of zero-sum polymatrix game theory. A systematic tabular layout of revenues pertaining to each formulation in terms of mixed strategies is developed. The minimax and maximin revenues, identified using Game theoretic approach, gave the most economical strategy. Thus exact and self-enforcing generalized method for best bidding strategies of all three generators are logically derived for the most optimal solution.
Index Terms: Game Theory, Bidding Strategy,
Scope of the Article: Algorithmic Game Theory