Discrete optimal control of building to grid integration based on quantum computing
Zhipeng Deng, Bing Dong · Building Simulation Conference proceedings · 2023
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. Aggregated control of electrical loads in a large cluster of buildings has been a challenge due to the complexity of the system involving generators, grid constraints, load serving entities complex load models, and occupant behavior. 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. With the recent development of quantum computers, it has become feasible.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. We converted the continuous and discrete variables that to be optimized into binary variables {0,1} for qubits. 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. Hence, we can obtain the results of the original urban energy system control optimization by analyzing the Hamiltonian energy of annealer. We applied the proposed method to a 9-bus network with 31 commercial buildings to evaluate the feasibility and effectiveness. We also compared the results and computing time with traditional optimization methods.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.