Electric Power Systems Research 106 (2014) 29–35 Contents lists available at ScienceDirect Electric Power Systems Research jou rn al hom e page: www.elsevier.com/locate/epsr Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty Wencong Su a,∗ , Jianhui Wang b , Kuilin Zhang b , Alex Q. Huang c a Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA b Argonne National Laboratory, Argonne, IL 60439, USA c Future Renewable Electric Energy Delivery and Management (FREEDM) Systems Center, North Carolina State University, Raleigh, NC 27606, USA a r t i c l e i n f o Article history: Received 28 June 2013 Received in revised form 1 August 2013 Accepted 2 August 2013 Keywords: Smart Grid Microgrid Plug-in electric vehicle Model predictive control a b s t r a c t As an important component of Smart Grid, advanced plug-in electric vehicles (PEVs) are drawing much more attention because of their high energy efficiency, low carbon and noise pollution, and low opera- tional cost. Unlike other controllable loads, PEVs can be connected with the distribution system anytime and anywhere according to the customers’ preference. The uncertain parameters (e.g., charging time, initial battery state-of-charge, start/end time) associated with PEV charging make it difficult to predict the charging load. Therefore, the inherent uncertainty and variability of the PEV charging load have com- plicated the operations of distribution systems. To address these challenges, this paper proposes a model predictive control (MPC)-based power dispatch approach. The proposed objective functions minimize the operational cost while accommodating the PEV charging uncertainty. Case studies are performed on a modified IEEE 37-bus test feeder. The numerical simulation results demonstrate the effectiveness and accuracy of the proposed MPC-based power dispatch scheme. © 2013 Elsevier B.V. All rights reserved. 1. Introduction Advanced PEVs are drawing much more attention because of their relatively higher energy efficiency, lower carbon and noise pollution, and higher fuel economy (Miles per Gallon of Gasoline Equivalent) [1]. The U.S. Department of Energy projects that about 1 million PEVs will be on the road by 2015 and 425,000 PEVs will be sold in 2015 alone. At this rate, plug-in vehicles would account for 2.5% of all new vehicle sales in 2015 [2]. Using a moderate market penetration scenario, the Electric Power Research Institute (EPRI) projects that 62% of the entire U.S. vehicle fleet will consist of PEVs by 2050 [3]. However, the increasing market penetration of those plug-in vehicles has significantly complicated the oper- ations on the distribution system. Unlike any other controllable loads, these vehicles can be connected with a distribution system anywhere and anytime, bringing more spatial and temporal diver- sity and uncertainty [4]. As a result, the PEV charging load profile is highly uncertain and unpredictable. In last decade, a large number of literature [5–9] examined the impact of PEV charging load on power grids. They are based on a complete set of predefined data (e.g., when to charge and where to charge), which requires perfect ∗ Corresponding author. Tel.: +1 315 528 3344. E-mail addresses: wencong@umich.edu (W. Su), jianhui.wang@anl.gov (J. Wang), kzhang@anl.gov (K. Zhang), aqhuang@ncsu.edu (A.Q. Huang). forecasting PEV profiles over the entire energy scheduling horizon (e.g., next 24 h). Unfortunately, the perfect forecasting data is gen- erally not available in real-world power system operations. Even a small PEV charging load forecasting error may result in great uncer- tainties for the real-time operation for a distribution system. To the best of the authors’ knowledge, to date, the uncertainty issues of PEVs have not been well-discussed in any published literature work. Hence, a sophisticated power dispatch is highly needed to take these unique factors of PEVs into consideration. MPC is an advanced method for process control, which has been widely used in a variety of industries demonstrating the promising results for the complex dynamic systems [10–12]. The focus on this paper is to apply MPC-based methods to achieve optimal power dispatch for distribution systems with high pen- etration of renewable energy resources and PEVs. The proposed MPC-based methods incorporate the uncertainty of PEV charging loads by combining the updated current PEV charging informa- tion (e.g., number of PEVs connected to charging stations, instant PEV battery state-of-charge (SOC), and battery capacity) with the short-term forecasting model. Although the uncertainty modeling of renewable energy output is not within the scope of this paper, the proposed MPC-based approach provides a framework to address a wide range of uncertainties and variability (e.g., renewable energy, and customer preference) for the power dispatch of distribution systems. The major contributions of this paper include the following four aspects: 0378-7796/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.epsr.2013.08.001