Publicação

Distributed Constrained Optimization Towards Effective Agent-Based Microgrid Energy Resource Management

Detalhes bibliográficos
Resumo:	The 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.
Autores principais:	Lezama, Fernando
Outros Autores:	Munoz de Cote, Enrique; Farinelli, Alessandro; Soares, João; Pinto, Tiago; Vale, Zita
Assunto:	Decentralized Distributed optimization Microgrid Multi- agent systems Smart grid Energy resource managemen
Ano:	2019
País:	Portugal
Tipo de documento:	documento de conferência
Tipo de acesso:	acesso aberto
Instituição associada:	Instituto Politécnico do Porto
Idioma:	inglês
Origem:	Repositório Científico do Instituto Politécnico do Porto

Descrição
Resumo:	The 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.

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