Distributed Optimization for Scheduling Electrical Demand in Complex City Districts

This paper analyzes and compares one central and one distributed optimization method applied to complex city districts embedded in a hierarchical architecture. We define a complex city district by the coexistence of residential, nonresidential, and industry. The proposed methods are for offline, day-ahead optimization for operation planning purposes of, e.g., electrothermal heating systems. This paper analyzes how this way city districts may provide flexibility for demand response purposes. The optimization objective is the matching of supply and demand on system level by maximizing the usage of renewable energy generation. We provide quantitative and qualitative results to compare the performance of the algorithms and their potential for providing flexibility.