Integrating quantum computing into building-to-grid control framework: Application of benders decomposition in mixed-integer nonlinear programming

Buildings use a large amount of energy in the United States. It is important to optimally manage and coordinate the resources across building and power distribution networks to improve overall efficiency. Optimizing the power grid with discrete variables was very challenging for traditional computers and algorithms, as it is an NP-hard problem. In this study, we developed a new optimization solution based on quantum computing for BTG integration. We first used MPC for building loads connected with a commercial distribution grid for cost reduction. Then we used discretization and Benders Decomposition methods to reformulate the problem and decompose the continuous and discrete variables, respectively. We used D-Wave quantum computer to solve dual problems and used conventional algorithm for primal problems. We applied the proposed method to an IEEE 9-bus network with 3 commercial buildings and over 300 residential buildings to evaluate the feasibility and effectiveness. Compared with traditional optimization methods, we obtained similar solutions with some fluctuations and improved computational speed from hours to seconds. The time of quantum computing was greatly reduced to less than 1% of traditional optimization algorithm and software such as MATLAB. Quantum computing has proved the potential to solve large-scale discrete optimization problems for urban energy systems.