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Abstract Across the U.S., the increasing demand for electric vehicle (EV) charging infrastructure is placing new demands on the power grid, challenging its stability and efficiency. To address these challenges, this study proposes a Vehicle-to-Building-to-Grid (V2B2G) framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging. Using anonymized GPS traces from the study community, we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user. The framework was tested in a mixed-use community in Phoenix, Arizona that includes both residential and commercial buildings. A comprehensive decentralized model predictive control (MPC) framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort. Four different control strategies were designed and evaluated, the strategy which balances both user and grid benefits demonstrated the best performance, achieving: (1) a flattened grid net load curve, with a 56% reduction in on-peak demand and a 56% decrease in peak load rebound; (2) a 37.96% reduction in grid net load compared to the baseline control; and (3) a 68.05% performance improvement when considering six flexibility factors: cost savings, total-energy reduction, on-peak demand reduction, off-peak demand reduction, load shifting from peak to non-peak hours, and peak-load-rebound reduction. These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management. 

Abstract 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.