Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

Electricity consumers are often faced with challenges relating to the choice of an optimal energy saving plan. Increasing integration of transient renewable energy sources promises tantalizing solutions but also poses emerging stability challenges for the electricity grid. Demand side management using battery energy storage systems (BESSs) is crucial towards extending the physical limits of existing electricity grid. However, problems related to consumer behavior towards adoption of energy/battery efficiency measures and consumer comfort feedback exist. In this study, we present BESS technologies that are embedded into the grid and further enhanced with the use of reinforcement learning control and recommendation system technologies for improving the grid reliability, attaining self-consumption and demand response goals. The novelty of the proposed work is highlighted by using a separate class of active controller for BESS technologies, thereby separating it from loads which determine user comfort. Similarly, an adaptive demand side recommender scheme was used to provide recommendations targeting various microgrid entities. The result of the study shows that operating BESS using the multi-agent reinforcement learning control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. The linear reduction in peak load was further enhanced by the BESS efficiency-related recommendations when compared to the baseline scenario.