PhD Student Listowell Appiah with Advisor K. Haran

Modern power systems face significant challenges from the integration of distributed energy resources, necessitating a shift from centralized control towards distributed energy management frameworks. This work addresses this by developing a distributed economic dispatch algorithm grounded in alternating direction method of multipliers and dynamic average consensus algorithm. To achieve this, the optimization problem is decomposed into optimizations at local agents, where each agent iteratively estimates the solution of the optimization problem by communicating only with its immediate neighbors. The algorithm’s primary advantage is preserving data confidentiality; local agents are not required to share private information, such as generator incremental costs. As a corollary, computational and communication costs are reduced which facilitates the plug-and-play property required by smart grids. Compared with centralized solutions, the distributed algorithm not only achieves comparable solutions but can also respond timely when the system experiences sudden change in operating conditions. Results of a case study based on the IEEE 39-bus test system illustrate the feasibility of the proposed approach.