Discrete optimal control of building to grid integration based on quantum computing

Buildings use a large amount of energy in the US. It is imperative to optimally manage and coordinate the resources across building and power distribution networks to improve the overall system’s efficiency. Optimizing the power grid with discrete variables for energy saving was very challenging for traditional computers and algorithms, as a large number of discrete variables need to be optimized. The traditional optimization process typically used a searching algorithm and heuristic techniques. In this study, we developed a new optimization solution based on quantum computing for building to grid integration. We first used model predictive control for building loads connected with a commercial distribution grid for cost reduction. Then we formulated the problem to quadratic unconstrained binary optimization problem. By minor embedding that mapped them to the node and edge weight of the chimeric graph architecture of qubits, D-Wave quantum computer can solve such optimization problems and find the global optimum. We applied the proposed method to a 9-bus network with 31 commercial buildings to evaluate the feasibility and effectiveness. Compared with traditional optimization methods, we obtained similar solutions with some fluctuatoins less than 6% differences and improved computational speed from days to seconds. The time of quantum computing was greatly reduced to less than 1.1% of traditional optimization algorithm and software such as MATLAB. Quantum computing has proved potential to solve large-scale discrete optimization problems for urban energy systems.