Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

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In this paper, we propose a periodic energy trading system in microgrids based on day-ahead forecasting of energy generation and consumption. In the proposed model, each noncooperative prosumer calculates her reward function under her energy change forecasting based on Gaussian process regression and determines her optimal action. Then, the system establishes the equilibrium trading price when all prosumers execute their optimal actions simultaneously. We prove the existence of the equilibrium trading price and establish an algorithm that leads to the equilibrium. Our numerical example shows that the proposed system outperforms its previous model.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-04
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.63885 - 63900

ISSN
2169-3536
DOI
10.1109/ACCESS.2020.2985258
URI
http://hdl.handle.net/10203/274342
Appears in Collection
MA-Journal Papers(저널논문)
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