A data-driven approach with dynamic load control for efficient demand-side management in residential household across multiple devices

Increasing PV penetration in the residential sector has led to supply demand mismatch in PV in the electricity market, specially during the peak demand hours and peak PV generation hours. Smart grid and smart meters have opened up avenues for designing data driven methodologies to optimize the generation and consumption of energy. In this paper, a dynamic load control mechanism is designed which optimizes the operation of individual appliances (heat pump, electric boiler, battery storage, solar PV and electric car). The optimization algorithm utilizes rolling horizon approach to consider the real time load control. A case of an individual house in Helsinki, Finland is considered to test the developed method. The results of dynamic load control mechanism were compared with operational optimization, wherein dynamic control is not implemented with different building classification and electricity contracts. From the results, it is observed that the optimization with a longer duration offers more benefits as compared to real time control mechanism, but does not reflect a real world scenario. Additionally, consumers having electricity contracts which are variable had the most savings and provides the highest flexibility to the electricity system.