In this paper, we develop formulation of a multiobjective optimization problem (MOOP) to optimally size a battery unit (BU)-ultracapacitor (UC) hybrid energy storage system (HESS) for plug-in electric vehicle (EV). In this application, the objectives were to minimize cost, weight, volume of the HESS simultaneously maximizing the remaining cycle life of the BU at the end of the driving cycle. The MOOP is solved by the Non-dominated Sorting Genetic Algorithm type 2 (NSGA-II) algorithm. Detailed mathematical models for the BU and UC are given. The thermal effect on performance and sizing are also included in the formulation. The power demand by the EV powertrain is shared amongst the BU and HESS by two methods: first is by using wavelet transformation (WT), while the second is by using power split ratio. The ratio of the power (i.e. power split) handled by each storage unit was determined by the optimizer. A sensitivity analysis was conducted for the power splitting ratio verification. The problem was solved for using the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HFET) driving profiles. This has resulted in sizing of a HESS with lower cost, volume and weight than those existing in literature. Finally, the effect of changing the motor type on the MOOP result was investigated.

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the Hierarchical Distributed Model Predictive Control (HDMPC) to solve the energy management problem in multi-time frame and multi-layer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized Supervisory Management (SM) layer and another short time-horizon during high power variability for a distributed Coordination Management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically as well as experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.

This paper presents the design and implementation of a communication and control infrastructure for smart grid operation. The proposed infrastructure enhances the reliability of the measurements and control network. The advantages of utilizing the data-centric over message-centric communication approach are discussed in the context of smart grid applications. The data distribution service (DDS) is used to implement a data-centric common data bus for the smart grid. This common data bus improves the communication reliability, enabling distributed control and smart load management. These enhancements are achieved by avoiding a single point of failure while enabling peer-to-peer communication and an automatic discovery feature for dynamic participating nodes. The infrastructure and ideas presented in this paper were implemented and tested on the smart grid testbed. A toolbox and application programing interface for the testbed infrastructure are developed in order to facilitate interoperability and remote access to the testbed. This interface allows control, monitoring, and performing of experiments remotely. Furthermore, it could be used to integrate multidisciplinary testbeds to study complex cyber-physical systems (CPS).