| Summary: | 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.
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