Dinh Hoa Nguyen

By Dinh Hoa Nguyen, Huynh Ngoc Tran, Tatsuo Narikiyo and Michihiro Kawanishi

This chapter presents a distributed optimization method named sequential distributed consensus-based ADMM for solving nonlinear constrained convex optimization problems arising in smart grids in order to derive optimal energy management strategies. To develop such distributed optimization method, multi-agent system and consensus theory are employed. Next, two smart grid problems are investigated and solved by the proposed distributed algorithm. The first problem is called the dynamic social welfare maximization problem where the objective is to simultaneously minimize the generation costs of conventional power plants and maximize the satisfaction of consumers. In this case, there are renewable energy sources connected to the grid, but energy storage systems are not considered. On the other hand, in the second problem, plug-in electric vehicles are served as energy storage systems, and their charging or discharging profiles are optimized to minimize the overall system operation cost. It is then shown that the proposed distributed optimization algorithm gives an efficient way of energy management for both problems above. Simulation results are provided to illustrate the proposed theoretical approach.

Part of the book: Research Trends and Challenges in Smart Grids

By Javad Khazaei and Dinh Hoa Nguyen

One of the major challenges of existing highly distributed smart grid system is the centralized supervisory control and data acquisition (SCADA) system, which suffers from single point of failure. This chapter introduces a novel distributed control algorithm for distributed energy storage devices in smart grids that can communicate with the neighboring storage units and share information in order to achieve a global objective. These global objectives include voltage regulation, frequency restoration, and active/reactive power sharing (demand response). Consensus theory is used to develop controllers for multiple energy storage devices in a cyber-physical environment, where the cyber layer includes the communication system between the storage devices and the physical layer includes the actual control and closed-loop system. Detailed proof of designs is introduced to ensure the stability and convergence of the proposed designs. Finally, the designed algorithms are validated using time-domain simulations in IEEE 14-bus system using MATLAB software.

Part of the book: AI and Learning Systems