Abstract
Utility companies can organize e-auctions to procure electricity from other suppliers during peak load periods. For this purpose, we develop an efficient Combinatorial Reverse Auction (CRA) to purchase power from diverse sources, residents and plants. Our auction is different from what has been implemented in the electricity markets. In our CRA, which is subject to trading constraints, an item denotes a time slot that has two conflicting attributes, energy volume and its price. To ensure the security of energy, we design our auction with two bidding rounds: the first one is for variable-energy suppliers and the second one for other sources, like controllable load and renewable energy. Determining the winner of CRAs is a computational hard problem. We view this problem as an optimization of resource allocation that we solve with multi-objective genetic algorithms to find the best solution. The latter represents the best combination of suppliers that lowers the price and increases the energy.
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Shil, S.K., Sadaoui, S. (2017). Combinatorial Reverse Electricity Auctions. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_21
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DOI: https://doi.org/10.1007/978-3-319-57351-9_21
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