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

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 an...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.63885-63900
Main Authors: Jeong, Gyohun, Park, Sangdon, Hwang, Ganguk
Format: Article
Language:English
Subjects:
Algorithms
Distributed generation
Energy states
energy trading system
Forecasting
future forecasting
Games
Gaussian process
Gaussian process regression
Mathematical analysis
Mathematical model
Mathematical models
Microgrids
Predictive models
Regression analysis
Renewable energy sources
smart grids
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2985258