LAUSR

The ability to control commercial buildings and electric vehicles (EVs) is a promising source of demand flexibility. In some cases, buildings and EVs share common infrastructure (e.g., a transformer) or interact with each other to accomplish a goal (e.g., reduce local peak demand). In such cases, the building and EV demand scheduling problems are effectively a single demand scheduling problem. Ideally, it would be solved as a single optimization problem. However, doing so might not be possible due to a number of concerns (e.g., data privacy). This paper proposes the use of a mixed-integer adaptation of the Dantzig–Wolfe decomposition to solve the building-EV demand scheduling problem in a decentralized fashion. The effectiveness of the proposed methodology is demonstrated in three case studies, where the building and EV problems are coupled by either: 1) demand limits; 2) a peak demand charge; or 3) an itemized billing tariff. Results show that the optimal solution can be reached while sharing a minimal amount of information. Furthermore, we show that the proposed methodology is scalable.