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Model:Multi-agent reinforcement learning-based energy hub model
Accessible to Public +
Authors Andrew Bollinger +
Description This is a multi-agent-based implementation
This is a multi-agent-based implementation of an energy hub model, loosely based on the generic energy hub model configuration. The model identifies an optimal dispatch schedule for technologies in a distributed multi-carrier system. The model was developed to test the implementation of a multi-agent reinforcement learning-based approach to energy hub modeling as a possible alternative to MILP under certain conditions. The model implements two different reinforcement learning algorithms, a Q-learning algorithm and a Continuous Actor Critic Learning Automaton (CACLA) algorithm. Both the Q-learning and CACLA implementations of the model are able to identify near-optimal dispatch solutions for the case without storage. For the case with storage, neither approach is able to identify a near-optimal solution.
able to identify a near-optimal solution.  +
Documentation file File:EGICE Bollinger Evins.pdf +
Download URL  +
License type MIT License  +
Related publications L.A. Bollinger and R. Evins. 2016. Multi-agent reinforcement learning for optimizing technology deployment in distributed multi-energy systems. EG-ICE Workshop. Krakow, Poland.  +
Required software Python 3  +
Tags Energy hub model + , Agent-based model + , Reinforcement learning +
Categories Models , Modules2
Modification date
This property is a special property in this wiki.
6 January 2017 09:23:54  +
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