A survey on distributed optimisation approaches and applications in smart grids

Abstract

Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative, eco-friendly, stable, reliable and cost-efficient system as a whole. In order to unlock the full benefits, utilities need now to develop new technologies like distributed optimisation methods to excavate the latent value from the magnanimous data. This paper surveys recent advances of distributed optimisation and game algorithms with applications in power systems. In particular, this paper reviews distributed algorithms for model-based offline optimisation solution of dynamic economic dispatch problems, charging control problems for plug-in electric vehicles and risk-averse energy trading as well as model-free online algorithms for demand response problems.

Keywords:

• Smart grids
• energy trading
• distributed optimisation
• game
• online learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported in part by National Priority Research Project through the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP 9-166-1-031, in part by the NSFC 61503308, 11501070 and 61473326, in part by the Natural Science Foundation Projection of Chongqing cstc2017jcyjA0788, in part by China Postdoctoral Science Foundation under Grant 2017M620374.

Notes on contributors

Huiwei Wang

Huiwei Wang received the B.S. degree in Information and Computing Science and the M.E. degree in Computer Application from Chongqing Jiao Tong University, China, in 2008 and 2011, respectively, and the Ph.D. degree in Computer Science from Chongqing University, China, in 2014. He was a Postdoctoral Research Associate (2014–2016) at Texas A&M University at Qatar, Doha, Qatar. Currently, he is an Associate Professor at the College of Electronic and Information Engineering, Southwest University, China. His research interests include neural networks, multi-agent networks, wireless sensor networks and smart grids.

Chaojie Li

Chaojie Li received the B.Eng. and M.Eng. degrees from Chongqing University, Chongqing, China, in 2007 and 2011, respectively, and the Ph.D. degree from the School of Engineering, RMIT University, Melbourne, VIC, Australia, in 2017. He is currently a Research Fellow at RMIT University. His current research interests include big data analysis, distributed optimisation and control in smart grid. Dr. Li was a co-recipient of the best conference paper from PES General Meeting 2016.

Jueyou Li

Jueyou Li received his B.E. and M.E. degrees in Mathematical and Software Science from Sichuan Normal University, Chengdu, China, in 2003 and 2006, and Ph.D. degree in operation research from Federation University Australia, Australia, in 2014. He was a Postdoctoral Research Fellow at School of Electrical and Information Engineering, The University of Sydney, Australia, in 2015. He is currently an Associate Professor at the School of Mathematical Sciences, Chongqing Normal University, China. His current research interests include distributed optimisation and control, and its application in power engineering.

Xing He

Xing He received the B.S. degree in Mathematics and Applied Mathematics from the Department of Mathematics, Guizhou University, Guiyang, China, in 2009, and the Ph.D. degree in Computer Science and Technology from Chongqing University, Chongqing, China, in 2013. He is currently an Associate Professor at the School of Electronics and Information Engineering, Southwest University, Chongqing. From 2012 to 2013, he was a Research Assistant at Texas A&M University at Qatar, Doha, Qatar. From 2015 to 2016, he was a Senior Research Associate at the City University of Hong Kong, Hong Kong. His current research interests include neural networks, bifurcation theory, optimisation method, smart grid, and nonlinear dynamical system.

Tingwen Huang

Tingwen Huang received the B.S. degree from Southwest University, Chongqing, China, in 1990, the M.S. degree from Sichuan University, Chengdu, China, in 1993, and the Ph.D. degree from Texas A&M University, College Station, TX, USA, in 2002.

He was a Visiting Assistant Professor at Texas A&M University. He joined Texas A&M University at Qatar (TAMUQ), Doha, Qatar, as an Assistant Professor in 2003, where he is currently a Professor. His current research interests include neural networks, chaotic dynamical systems, complex networks, optimisation and control. His research is partially supported by the Qatar National Priorities Research Program.

Dr. Huang was awarded Dean's Fellow in 2014. He received the Faculty Research Excellence Award from TAMUQ in 2015. In addition, one of his projects for which he was the Lead PI received the Best Project by the Qatar National Research Fund in 2015.