Distributed reinforcement learning energy management approach in multiple residential energy hubs

Energy management optimization in residential buildings plays an essential role in addressing the problem of energy crisis in the world. This paper introduces a novel method to optimize the energy scheduling for multiple residential buildings in an interconnected framework through transferring energy concepts. To this end, a distributed reinforcement learning energy management (DRLEM) approach is proposed to manage the energy scheduling in multi-carrier energy buildings. In such facilities, equipped with the micro-combined heat and power (micro-CHP) and the gas boiler, the possibility of heat and electrical energy transfer among energy hubs is provided. The effectiveness of the proposed method is verified in a test residential interconnected energy hubs (EHs). Results show a noticeable improvement in energy costs while transferring energy concept is available. In a test frame consisting of three residential EHs, the proposed DRLEM approach in an interconnected mode leads to a daily cost reduction of 3.3% and wasted heat energy decrement of about 18.3% in a typical day compared to the independent mode. Furthermore, in peak tariff energy hours, EHs tend to share their produced excess energy about 23% more than low tariff energy hours to reduce overall energy price.(C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Energy management optimization in residential buildings is crucial for addressing the global energy crisis. This paper introduces a novel method that optimizes energy scheduling for multiple residential buildings through the transfer of energy concepts. The proposed method demonstrates effectiveness in improving energy costs.