Distributed bandit online optimisation for energy management in smart grids

ABSTRACT

This paper presents a distributed optimisation algorithm based on one-point bandit feedback (OPBF) which enables the solving of energy management problems (EMPs) over directed networks. Unlike existing EMPs with known cost functions, the proposed online energy management approach considers a time-varying and unknown cost function, which creates sampling difficulty. To tackle this challenge, a random gradient-free oracle is constructed, allowing for the facilitation of output generation updates. This construction significantly mitigates the need for explicit expressions of the cost function. Furthermore, the proposed algorithm successfully enforces both the supply-demand balance constraint and the generation constraint in EMPs. In order to evaluate performance, this study introduces a performance index referred to as regret, which exhibits sublinear convergence. This finding provides additional evidence that the algorithm can achieve optimal output generation at a rapid convergence rate, subject to certain step-size conditions. Finally, the performance of the algorithm is verified on both a modified 6-bus system and an IEEE 162-bus system. The results demonstrate the effectiveness and efficiency of the proposed algorithm in solving EMPs over directed networks.

KEYWORDS:

• Distributed optimisation
• one-point bandit
• randomised gradient-free oracles
• energy management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data available statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

This work was supported by the Science and Technology Project of SGCC, named Research on the Construction Technology of Artificial Intelligence Application Verification Platform for Power Grid Dispatching in Typical Scenarios (SGSH0000DKJS2200274).

Notes on contributors

Zhongyuan Zhao

Zhongyuan Zhao received his Ph.D. degree from Chongqing University, Chongqing, China, in 2019. He is currently a lecturer with the College of Automation, Nanjing University of Information Science and Technology. His research interests include cooperative control, event triggered control, and distributed optimization.

Lunchao Xia

Lunchao Xia received the B.Eng. degree from Anhui University of Science and Technology, Anhui, China, in 2021. He is currently working toward the M.S. degree at Nanjing University of Information Science and Technology. His research interests include distributed optimization and smart gr id.

Luyao Jiang

Luyao Jiang received the M.S. degree from Southeast University, Nanjing, China. in 2022. She is currently pursuing the Ph.D. degree in School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. Her current research interests include federated learning, smart service system, human factors, and their applications in medical care.

Quanbo Ge

Quanbo Ge (Member, IEEE) received the B.S. in computer and applications and M.S. degrees in applied mathematics from the College of Computer and Information Engineering, Henan University, in 2002 and 2005, respectively, and the Ph.D. degree in power electronics and power transmission from Shanghai Maritime University, in 2008. He was a Professor with the Institute of Systems Science and Control Engineering, the School of Automation, Hangzhou Dianzi University. From 2008 to 2010, he was a Lecturer and became an Associate Professor with the School of Automation, Hangzhou Dianzi University, in 2010. From 2009 to 2013, he was a Postdoctoral Fellow with the State Key Laboratory of Industrial Control Technology, Zhejiang University. From 2012 to 2013, he was a Visiting Scholar with the Optimization for Signal Processing and Communication Group, the Department of Electrical and Computer Engineering, Twin Cities Campus, University of Minnesota, USA. He is currently a Professor with the School of Automation, Nanjing University of Information Science and Technology, Nanjing, China. His research interests include information fusion, autonomous unmanned system, man-mechanic hybrid system, and machine vision.

Fang Yu

Fang Yu received a master's degree from Southeast University, Nanjing, China, in 2010. She jointed China Electric Power Research Institute in 2012. Her research interests include smart grid, power dispatch automation, power artificial intelligence platform and application.