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

Zhenya Ji, Xueliang Huang, Changfu Xu and Houtao Sun
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Zhenya Ji: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Xueliang Huang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Changfu Xu: Electric Power Research Institute, State Grid Jiangsu Power Supply Company, Nanjing 211103, China
Houtao Sun: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2016, vol. 9, issue 11, 1-18

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.

Keywords: scenario-based model predictive control; robust optimization; stochastic optimization; electric vehicle; Benders decomposition; microgrid; energy management system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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