Unit Commitment in Achieving Low Carbon Smart Grid Environment with Virtual Power Plant

Hua, Weiqi; Li, Dan; Sun, Hongjian; Matthews, Peter

Abstract

This paper proposes a novel unit commitment (UC) model under smart grid (SG) environment, which intends to strike a balance pursuing minimum carbon emissions for policy maker, minimum costs for generators and minimum payment bills for consumers. This leads to a multiobjective optimization problem (MOP) which can be solved through the multiobjective immune algorithm (MOIA). Therefore, the energy market scheduling problem considering low carbon smart grid environment can be analysed. The case studies are conducted to demonstrate the proposed model and present the allocation of power generations as well as the daily energy market scheduling results. It has been proved that the penetration of SG contributes to the mitigation of carbon emissions during the peak demand time by around 500 ton/h. It is also suggested that if the policy maker can provide appropriate monetary compensation for the deployment of SG technologies, generators will be encouraged to participate in the SG deployment.

Citation

Hua, W., Li, D., Sun, H., & Matthews, P. (2017, September). Unit Commitment in Achieving Low Carbon Smart Grid Environment with Virtual Power Plant. Presented at 2017 3rd IEEE International Smart Cities Conference (ISC2 2017)., Wuxi, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 3rd IEEE International Smart Cities Conference (ISC2 2017).
• Start Date: Sep 14, 2017
• End Date: Sep 17, 2017
• Acceptance Date: Aug 18, 2017
• Online Publication Date: Nov 2, 2017
• Publication Date: Nov 2, 2017
• Deposit Date: Aug 28, 2017
• Publicly Available Date: Aug 29, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-6
• Book Title: 2017 IEEE International Smart Cities Conference (ISC2) : 14-17 September 2017, Wuxi, China ; proceedings.
• ISBN: 9781538625255
• DOI: https://doi.org/10.1109/isc2.2017.8090810
• Public URL: https://durham-repository.worktribe.com/output/1146755