| The focus of the present thesis is to develop cooperative distributed algorithms for scheduling of electric energy in power grids consisting of responsive demands, dispatchable generators and storage devices within an enabled communications infrastructure for information exchange. The objective is to provide fully distributed solutions for energy management problems without using any control center, coordinator or leader. While most of the conventional scheduling approaches are based on centralized concept, distributed algorithms are more suitable for large scale systems such as the smart-grid because of their scalability, robustness to single point of failure, and resilience to communication failures. Five algorithms are developed each attacking a specific energy management problem. Cooperative Distributed Plug in Electric Vehicle Demand Management (CDPDM) algorithm was developed for distributed demand side management of large scale Plug in Hybrid /Plug in Electric Vehicles (PHEV/PEV) charging considering limit on the total available power. Incremental Welfare Consensus (IWC) algorithm was proposed for distributed energy management of a gird populated with distributed generators and responsive demands. Asynchronous Incremental Welfare Consensus (AIWC) algorithm was developed to relieve the synchronous communication/update requirement from IWC algorithm. Based on the concept of IWC algorithm, Distributed Real-Time Pricing for Charging and Generation Control (DRPCG) was proposed to manage PHEV/PEV charging considering multiple resources on the generation side. Finally, Cooperative Distributed Energy Scheduling for Storage Devices (CoDES) algorithm was proposed to do a multi-time step scheduling in microgrids consisting of storage devices, dispatchable generation units, and renewables. |
