Model:Multi-agent reinforcement learning-based energy hub model

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General information

Description 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.
Download URL https://bitbucket.org/hues/multi-agent-reinforcement-learning-based-energy-hub-model/get/HEAD.zip
Authors Andrew Bollinger
Required software Python 3
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.
Licence type MIT License
Tags Energy hub model, Agent-based model, Reinforcement learning
Accessible to Public

Documentation

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Documentation URL(s):
Documentation file: File:EGICE Bollinger Evins.pdf
Documentation page: