Energy Management in Solar Microgrid via Reinforcement Learning

This paper proposes a single agent system towards solving energy management issues in solar microgrids. The system considered consists of a Photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local consumer. The trade-offs and complexities involved in the operation of the different units, and the quality of services' demanded from energy consumer units (e.g. the desalination unit), makes the energy management a challenging task. The goal of the agent is to satisfy the energy demand in the solar microgrid, optimizing the battery usage, in conjunction to satisfying the quality of services provided. It is assumed that the solar microgrid operates in island-mode. Thus, no connection to the electrical grid is considered. The agent collects data from the elements of the system and learns the suitable policy towards optimizing system performance. Simulation results provided, show the performance of the agent.

[1]  Anastasios I. Dounis,et al.  Artificial intelligence for energy conservation in buildings , 2010 .

[2]  Hak-Man Kim,et al.  An Intelligent Multiagent System for Autonomous Microgrid Operation , 2012 .

[3]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[4]  Georgios Chalkiadakis,et al.  Factored MDPs for Optimal Prosumer Decision-Making in Continuous State Spaces , 2015, EUMAS/AT.

[5]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Hamid R. Berenji,et al.  Learning and tuning fuzzy logic controllers through reinforcements , 1992, IEEE Trans. Neural Networks.

[7]  R. S. Milton,et al.  Distributed Optimization of Solar Micro-grid Using Multi Agent Reinforcement Learning☆ , 2015 .

[8]  R. S. Milton,et al.  Reinforcement learning for optimal energy management of a solar microgrid , 2014, 2014 IEEE Global Humanitarian Technology Conference - South Asia Satellite (GHTC-SAS).

[9]  George Papadakis,et al.  An Intelligent MPPT controller based on direct neural control for partially shaded PV system , 2015 .

[10]  S. X. Chen,et al.  Multi-Agent System for Distributed Management of Microgrids , 2015, IEEE Transactions on Power Systems.

[11]  George Papadakis,et al.  A direct adaptive neural control for maximum power point tracking of photovoltaic system , 2015 .

[12]  Anastasios I. Dounis,et al.  Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system , 2013 .

[13]  Enrico Zio,et al.  Reinforcement learning for microgrid energy management , 2013 .

[14]  Derong Liu,et al.  A self-learning scheme for residential energy system control and management , 2013, Neural Computing and Applications.

[15]  Antoine Jouglet,et al.  DC microgrid power flow optimization by multi-layer supervision control. Design and experimental validation , 2014 .

[16]  Il-Yop Chung,et al.  Distributed Intelligent Microgrid Control Using Multi-Agent Systems , 2013 .