Distributed Optimization of Multi-building Energy Systems with Spatially and Temporally Coupled Constraints

In this paper, we formulate the distributed optimization of multi-building energy systems as a cost minimization problem with spatially and temporally coupled constraints. The problem is divided into subproblems of each building at each time slot via Lagrangian dual decomposition. The strong duality of the primal and dual problem is proved, and distributed algorithms are proposed based on the subgradient projection method to converge to the global optimum. Specifically, the local temperature control signals are designed for handling the temporally coupled constraints of each building, while an information sharing platform is designed to update and broadcast the congestion price to autonomous buildings to handle the spatially coupled constraints. Numerical results are provided to illustrate the convergence and effectiveness of the proposed method.