Risk-averse energy trading in multienergy microgrids: A two-stage stochastic game approach

Li, C, Xu, Y, Yu, X, Ryan, C and Huang, T 2017, 'Risk-averse energy trading in multienergy microgrids: A two-stage stochastic game approach', IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2620-2630.

Document type: Journal Article
Collection: Journal Articles

Title Risk-averse energy trading in multienergy microgrids: A two-stage stochastic game approach
Author(s) Li, C
Xu, Y
Yu, X
Ryan, C
Huang, T
Year 2017
Journal name IEEE Transactions on Industrial Informatics
Volume number 13
Issue number 5
Start page 2620
End page 2630
Total pages 11
Publisher IEEE
Abstract Multienergy microgrids are a promising solution to improve overall energy (electricity, cooling, heating, etc.) efficiency. In this paper, a new optimal energy trading strategy is developed considering the risk from uncertain energy supply and demand in a set of individual multienergy microgrids. According to the historical data about energy supply of each microgrid, an aggregator aims to maximize each microgrid's profit while minimizing the risk of overbidding for renewable energy resources trading based microgrids. A novel two-stage stochastic game model with Cournot Nash pricing mechanism and the conditional value-at-risk criterion is proposed to characterize the payoff function of each microgrid. The sample average approximation (SAA) technique is employed to approximate the stochastic Nash equilibrium of the game model. The existence of the SAA Nash equilibrium is investigated and the corresponding Nash equilibrium seeking algorithm is also realized in a distributed manner. The proposed method is validated by numerical simulations on real-world data collected in A ustralia, and the results show that the SAA Nash equilibrium based strategy can effectively reduce the risk of not meeting the demand and improve the economic benefits for each microgrid.
Subject Distributed and Grid Systems
Power and Energy Systems Engineering (excl. Renewable Power)
Keyword(s) Conditional value-at-risk (CVaR)
energy trading
multienergy microgrids
pricing mechanism
stochastic game
DOI - identifier 10.1109/TII.2017.2739339
Copyright notice © 2017 IEEE.
ISSN 1551-3203
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