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Energies 2016, 9(11), 973; doi:10.3390/en9110973

Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach

1
School of Electrical Engineering, Southeast University, Nanjing 210096, China
2
Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China
3
Electric Power Research Institute, State Grid Jiangsu Power Supply Company, Nanjing 211103, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paras Mandal
Received: 25 October 2016 / Revised: 15 November 2016 / Accepted: 17 November 2016 / Published: 22 November 2016
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Abstract

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. View Full-Text
Keywords: scenario-based model predictive control; robust optimization; stochastic optimization; electric vehicle; Benders decomposition; microgrid; energy management system scenario-based model predictive control; robust optimization; stochastic optimization; electric vehicle; Benders decomposition; microgrid; energy management system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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. Energies 2016, 9, 973.

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