A hybrid IGDT-robust optimization model for optimal self-scheduling of a smart home

Abstract

This paper proposes a novel hybrid information gap decision theory (IGDT)-robust optimization (RO) model to solve the robust self-scheduling problem of a smart home (SH). This strategy gives a capability to the SH to manage its domestic energy production and consumption autonomously. The SH is fed by a wind turbine, a local market, and a battery. It is also allowed to sell/buy to/from a local market to reduce its cost or increase its profit. The SH supplies different types of load including controllable load, shiftable and non-shiftable loads. The electricity market prices and wind turbine generations are subject to uncertainty. Hence, the hybrid IGDT-RO framework is deployed to reach the worst-case realization of the electricity prices and wind turbine generations in the robust self-scheduling of the smart home. The results demonstrate that the optimal robust solutions are obtained with the proposed hybrid model and it makes sure the operator about the profitability of energy management.