Power consumption scheduling for future connected smart homes using bi-level cost-wise optimization approach

Abstract

Future smart-home functionalities enable users to manage their home appliances through a single application by connecting home appliances through an integrated platform and server. In the smart home, a Home Energy Management System (HEMS) is necessary to monitor, control and optimize electrical generation and consumption. On the other hand Demand Response (DR) provides an opportunity for consumers to play a significant role in the operation of the electrical grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. In this paper we propose an autonomous Demand-Side Management (DSM) model to control the residential load of customers equipped with local power storage facilities as an auxiliary source of energy. In our proposed model the power consumption level of local devices, the amount of power being demanded from both local storage facilities and local utility companies are scheduled using a bi-level quadratic optimization approach of a well-defined convex cost function. Therefore we show that this goal can be fulfilled with a bi-level scheduler unit installed inside the smart meters. In addition our proposed model can also achieve the global optimal performance in terms of energy minimization cost at the Nash equilibrium of a formulated non-cooperative game. We also extend our DSM model to a two tiers cloud computing environment in which both customers and utility companies participate on it.