Variational quantum circuit based demand response in buildings leveraging a hybrid quantum-classical strategy

To counter the significant contribution of buildings to global energy consumption and greenhouse gas emissions, participation in demand response programs incentivizes grid-interactive buildings to curtail their load demand and promote energy efficiency with environmental sustainability. Quantum computing has the potential to impact problems at various scales, including demand response in buildings, by addressing the limitations of conventional demand response techniques implemented with classical computers. In this work, we propose a variational quantum circuit (VQC)-based hybrid control strategy for demand response that leverages the complementary strengths of quantum and classical computing paradigms. The hybrid VQC-based demand response technique exploits the expressive power of parameterized quantum circuits trained under a reinforcement learning setting, while a classical optimization solver computes controls for the demand response problem formulated as a sequential decision-making problem. The applicability and efficiency of the proposed hybrid quantum-classical control strategy are demonstrated by performing computational experiments involving energy management in grid-interactive buildings equipped with various energy storage devices. The hybrid VQC-based strategy exhibits improvement in load reduction and carbon emissions reduction of over 13.6% compared to classical baselines like deep deterministic policy gradient and model predictive control while ensuring scalability as the size of the building microgrids increases.