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Title: Pricing in Prosumer Aggregations using Reinforcement Learning
Prosumers with generation and storage capabilities can supply en- ergy back to the grid, or trade their surplus with other prosumers for their mutual benefit. A prosumer aggregation that facilitates such trades will price the energy being traded to achieve an objective such as profit maximization, social welfare, or market equilibrium. We propose the use of reinforcement learning to design a trans- active controller to price energy in a prosumer aggregation. This has an advantage over other decentralized pricing mechanisms as it does not rely on iterative price settlement or load estimation by prosumers, and estimates the price in a day ahead manner. We present numerical case studies to evaluate our controller, and dis- cuss extensions to implement this in real prosumer aggregations.  more » « less
Award ID(s):
1646612
PAR ID:
10317189
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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