Predictive demand side management of a residential house under intermittent primary energy source conditions

This paper targets the process of optimizing the operation of a PV-battery backup system under intermittent grid electricity supply. A predictive scheduling layer is developed as a part of a complete load management process. The main objective of the study is to ensure a permanent power supply for a high energy consuming residential application. The control algorithm plans the activation of predictable loads 24 h ahead through compromising between a decrease in the resulting discomfort levels and the conservation of a high autonomy of the system. The strength of the developed control lies in ensuring the complete coordination between all the components of the installation: the grid, PV panels, battery storage, and the load demand. No loss of power supply is allowed during the day and realistic and technical constraints are applied. The demand side management program is formulated as a multi-objective optimization algorithm solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II) technique. A fuzzy logic decision maker is developed for an automatic trade-off process implementing the residents' preferences. The simulation results show excellent performance and flexibility of the proposed algorithm. The benefits of the load management are proved to have a great impact on the backup installation sizing, which leads to notable reduction of its price.