A Hybrid Real-Time Electricity Pricing Strategy with Carbon Capture, Storage and Trading

Abstract

During the power system low carbonization process, various policies and technologies for low-carbon have been rapidly developed. Real-time demand side management by energy suppliers based on real-time pricing (RTP) has become a reality via smart meter technology. Motivated by this, an RTP mechanism is used to guide users’ demand response, and the carbon trading regulation (CTR) and carbon capture, storage (CCS) technology are used on the supply side. Through these means, we can reduce carbon emissions from the power system and achieve the goal of carbon neutrality. Furthermore, a game model is used to construct the relationship between the energy supplier and users. Since the energy supplier acts first and then the user make their decision, the leader is the energy supplier and the followers are users. Further, to protect the information privacy of them and independently solve the goals of them, a distributed solution approach combining a differential evolutionary (DE) algorithm and Gurobi solver is developed for finding the equilibrium solution. The proposed model’s economic and environmental benefits are verified and the effectiveness of CCS technology and CTR is accessed via the numerical analysis, what means the proposed strategy can serve as a guide for the system’s low-carbon management.

Keywords:

• low carbonization
• carbon capture and storage
• carbon trading regulation
• stackelberg game
• real-time pricing

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (Nos. 72071130, 71871144, 12071112) and the Innovation Program for Undergraduate Students in University of Shanghai for Science and Technology (No. XJ2023156).

Notes on contributors

Junxiang Li

Junxiang Li received his MS and PhD from the Dalian University of Technology, Liaoning Province, China in 2005 and 2008, respectively. From 2010 to 2011, he was a postdoctoral researcher in the School of Economics and Management, Tongji University, Shanghai, China. He was also a Postdoctoral Researcher in the Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Hong Kong, China in 2011 and an Academic Visitor at the Business School, Newcastle University, Newcastle, UK in 2018. Now he is a Professor and Supervisor of Postgraduate in the Business School, University of Shanghai for Science and Technology, Shanghai, China. His major is management science and engineering and his research interests include power system optimization and control and engineering management.

Ming Chen

Ming Chen is currently pursuing her Master degree in University of Shanghai for Science and Technology, Shanghai, China since 2021. And her major is management science and engineering. She obtained her Bachelor degree in logisitics management from Zhejiang Gongshang University, Hangzhou, China. Her research interests include game theory, real-time pricing on smart grid and power systems optimization.

Deqiang Qu

Deqiang Qu is a PhD candidate at University of Shanghai for Science and Technology, Shanghai, China. He obtained his Master degree in mathematics from Henan University of Science and Technology, Luoyang, China. His research interests are power system pricing and global optimization.

Xiaojia Ma

Xiaojia Ma is an undergraduate at University of Shanghai for Science and Technology, Shanghai, China. Her research interests are power system pricing and power systems optimization.