Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures. (15th March 2022)

• Record Type: Journal Article
• Authors: Pinto, Giuseppe; Kathirgamanathan, Anjukan; Mangina, Eleni; Finn, Donal P.; Capozzoli, Alfonso
• Is Part Of: Applied energy. Volume 310(2022)
• Publication Date: 2022-03-15
• Subjects: Deep Reinforcement Learning (DRL) -- Building energy flexibility -- Soft Actor Critic (SAC) -- Multi Agent Reinforcement Learning (MARL) -- Grid-interactive buildings; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118497
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: The increasing penetration of renewable energy sources has the potential to contribute towards the decarbonisation of the building energy sector. However, this transition brings its own challenges including that of energy integration and potential grid instability issues arising due the stochastic nature of variable renewable energy sources. One potential approach to address these issues is demand side management, which is increasingly seen as a promising solution to improve grid stability. This is achieved by exploiting demand flexibility and shifting peak demand towards periods of peak renewable energy generation. However, the energy flexibility of a single building needs to be coordinated with other buildings to be used in a flexibility market. In this context, multi-agent systems represent a promising tool for improving the energy management of buildings at the district and grid scale. The present research formulates the energy management of four buildings equipped with thermal energy storage and PV systems as a multi-agent problem. Two multi-agent reinforcement learning methods are explored: a centralised (coordinated) controller and a decentralised (cooperative) controller, which are benchmarked against a rule-based controller. The two controllers were tested for three different climates, outperforming the rule-based controller by 3% and 7% respectively for cost, and 10% and 14% respectively for peak demand. The study shows that the multi-agent cooperative approach may be more suitable for districts with heterogeneous objectives within the individual buildings. Highlights: Two DRL architectures for buildings energy management are compared to a RBC. Daily and seasonal KPIs are used to assess controllers stability and adaptability. DRL controllers optimise district load reducing peaks and increasing self-consumption. Cooperative DRL architecture performs better with heterogeneous energy systems.