LAPSE

LAPSE:2019.0284
Published Article
LAPSE:2019.0284
Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach
Zhenya Ji, Xueliang Huang, Changfu Xu, Houtao Sun
February 5, 2019
A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.
Keywords
Benders decomposition, electric vehicle, energy management system, microgrid, robust optimization, scenario-based model predictive control, Stochastic Optimization
Suggested Citation
Ji Z, Huang X, Xu C, Sun H. Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach. (2019). LAPSE:2019.0284
Author Affiliations
Ji Z: School of Electrical Engineering, Southeast University, Nanjing 210096, China; Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
Huang X: School of Electrical Engineering, Southeast University, Nanjing 210096, China; Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
Xu C: Electric Power Research Institute, State Grid Jiangsu Power Supply Company, Nanjing 211103, China
Sun H: School of Electrical Engineering, Southeast University, Nanjing 210096, China; Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E973
Year
2016
Publication Date
2016-11-22
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9110973, Publication Type: Journal Article
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LAPSE:2019.0284
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doi:10.3390/en9110973
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Feb 5, 2019
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CC BY 4.0
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Original Submitter
Calvin Tsay
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