Course Description
In the Network Control Systems course, students delve into the intricate world of controlling and optimizing complex networks, with a specific focus on the operation of DC micro grids. DC micro grids represent a cutting-edge approach to decentralized power distribution, offering efficiency and flexibility in energy management. The course analyzes the theoretical foundations of networked systems control and applies them to real-world scenarios, focusing on the challenges and strategies involved in managing DC micro grid operations. By the course's end, students will have a comprehensive understanding of network control principles and their practical implications, equipping them to address the evolving demands of sustainable energy systems.
Course References:
Bullo, Francesco. Lectures on network systems. Kindle Direct Publishing, 2019.
Kia, Solmaz S., Bryan Van Scoy, Jorge Cortes, Randy A. Freeman, Kevin M. Lynch, and Sonia Martinez. "Tutorial on dynamic average consensus: The problem, its applications, and the algorithms." IEEE Control Systems Magazine 39, no. 3 (2019): 40-72.
Tucci, Michele, Stefano Riverso, Juan C. Vasquez, Josep M. Guerrero, and Giancarlo Ferrari-Trecate. "A decentralized scalable approach to voltage control of DC islanded microgrids." IEEE Transactions on Control Systems Technology 24, no. 6 (2016): 1965-1979.
Cucuzzella Michele, Sebastian Trip, Claudio De Persis, Xiaodong Cheng, Antonella Ferrara, and Arjan van der Schaft. "A robust consensus algorithm for current sharing and voltage regulation in DC microgrids." IEEE Transactions on Control Systems Technology 27, no. 4 (2018): 1583-1595.
Trip, Sebastian, Michele Cucuzzella, Xiaodong Cheng, and Jacquelien Scherpen. "Distributed averaging control for voltage regulation and current sharing in DC microgrids." IEEE Control Systems Letters 3, no. 1 (2018): 174-179.
Tucci, Michele, Lexuan Meng, Josep M. Guerrero, and Giancarlo Ferrari-Trecate. "Stable current sharing and voltage balancing in DC microgrids: A consensus-based secondary control layer." Automatica 95 (2018): 1-13.
A. Karimpour, A. M. Amani, M. Karimpour, M. Jalili. "Enhanced voltage regulation in DC microgrids using a price incentive load management approach" IUST No. 4, 2021.
Tucci, Michele, Lexuan Meng, Josep M. Guerrero, and Giancarlo Ferrari-Trecate.Chetam Srivastava, Manoj Tripathy. "DC microgrid protection issues and schemes: A critical review" Elsevier, Renewable and sustainable energy reviews. 151, 2021.
Rabindra Mohanty, Ashok Kumar Pradhan, "Protection of smartDC microgrid with ring configuration using parameter estimation approach" IEEE Transaction on smart .grid Vol. 9, No. 6 Nov 2018
Rabindra Mohanty, Ashok Kumar Pradhan, Frede Blaajerg, "Cosine similarity-based centralized protection scheme for DC microgrid" IEEE Journal of emerging and selected topic in power electronics, Vol. 9, No. 5, Oct 2021
Lectures:
PowerPoint files related to the content are available. If you would like to acquire these file please get in touch with me at [karimpor@um.ac.ir/a.karimpoure@gmail.com] for further details.
The course begins with an introduction to key concepts and foundational principles. Students explore the application of dynamic average consensus in network systems, highlighting how this technique optimizes and stabilizes interconnected networks. They then examine a simple micro-grid model, illustrating the practical implementation of control strategies in a decentralized power system. This introductory section concludes with an overview, setting the stage for a comprehensive understanding of network control principles and their application in DC micro-grid operations.
In this section, the course delves into graph theory, covering essential concepts and matrices relevant to network control systems. Students learn about degree, indegree, and outdegree matrices, along with their weighted versions, to understand the connectivity and influence of nodes within a network. The course also explores the binary adjacency matrix and the weighted adjacency matrix, which represent the presence and strength of connections between nodes. The stochastic matrix is introduced to model probabilistic transitions, while the Laplacian matrix is examined for its role in capturing network dynamics and facilitating consensus algorithms. Lastly, the incidence matrix is discussed to illustrate the relationship between edges and vertices in a graph, providing a comprehensive toolkit for analyzing and designing networked control systems.
In this section, the course delves into DC microgrids, drawing on insights from various key references. Students examine decentralized and scalable approaches to voltage control, as outlined by Tucci et al. (2016), and robust consensus algorithms for current sharing and voltage regulation, as detailed by Cucuzzella et al. (2018). The section also covers distributed averaging control strategies for voltage regulation and current sharing, based on the work of Trip et al. (2018). Further discussions include consensus-based secondary control layers for stable current sharing and voltage balancing (Tucci et al., 2018), enhanced voltage regulation using price incentive load management (Karimpour et al., 2021).
In this section, the course addresses Dynamic Average Consensus, beginning with the fundamentals of average consensus and the inherent challenges associated with dynamic problems. It then delves into the formulation of the dynamic average consensus problem, providing a structured approach to understanding this complex issue. Students explore the practical application of dynamic average consensus in network systems, examining how it can be utilized to optimize performance and ensure stability. By the end of this section, learners will have a solid grasp of dynamic average consensus principles and their significant impact on network systems.