Lezama, FernandoMunoz de Cote, EnriqueFarinelli, AlessandroSoares, JoãoPinto, TiagoVale, Zita2021-09-222021-09-222019-08http://hdl.handle.net/10400.22/18478The current energy scenario requires actions towards the reduction of energy consumption and the use of renewable resources. In this context, a microgrid is a self-sustained network that can operate connected to the smart grid or in isolation. The long-term scheduling of on/off cycles of devices is a critical problem that has been commonly addressed by centralized approaches. In this work, we propose a novel agent-based method to solve the long-term scheduling problem as a distributed constraint optimization problem (DCOP) by modelling future system configurations rather than reacting to changes. Moreover, with respect to approaches based on decentralised reinforcement learning, we can directly encode system-wide hard constraints (such as for example the Kirchhoff law) which are not easy to represent in a factored representation of the problem. We compare different multi-agent DCOP algorithms showing that the proposed method can find optimal/near-optimal solutions for a specific case study.engDecentralizedDistributed optimizationMicrogridMulti- agent systemsSmart gridEnergy resource managemenconference object10.1007/978-3-030-30241-2_37