Open AccessReinforcement learning for control of flexibility providers in a residential microgrid
Author(s) -
Mbuwir Brida V.,
Geysen Davy,
Spiessens Fred,
Deconinck Geert
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0196
Subject(s) - microgrid , flexibility (engineering) , computer science , reinforcement learning , smart grid , controller (irrigation) , demand response , electricity , photovoltaic system , control engineering , scheduling (production processes) , control (management) , distributed computing , artificial intelligence , engineering , operations management , agronomy , statistics , mathematics , electrical engineering , biology
The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model‐free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q‐iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data‐driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi‐agent collaborative and single‐agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self‐consumption in the multi‐agent setting and a 3.7% increase in the single‐agent setting. Both RL algorithms perform better than a rule‐based controller, and compete with a model‐based optimal controller, and are thus, a valuable alternative to model‐ and rule‐based controllers.