The transportation industry contributes a significant amount of carbon emissions and pollutants to the environment globally. The adoption of electric vehicles (EVs) has a significant potential to not only reduce carbon emissions, but also to provide needed energy storage to contribute to the adoption of distributed renewable generation. This paper focuses on a design model and methodology for increasing EV adoption through automated swapping of battery packs at battery sharing stations (BShS) as a part of a battery sharing network (BShN), which would become integral to the smart grid. Current battery swapping methodologies are reviewed and a new practical approach is proposed considering both the technical and socio-economic impacts. The proposed BShS/BShN provides novel solutions to some of the most preeminent challenges that EV adoption faces today such as range anxiety, grid reliability, and cost. Challenges and advancements specific to this solution are also discussed.

Distributed model predictive control is an effective method to realize the power system load frequency control (LFC). However, the frequency stability of the multi-area power system becomes quite challenging with incorporating high wind power penetration, as it brings strong uncertainties. This article proposes a tube-based distributed model predictive control (DMPC) for the LFC of the interconnected power system. In the scheme, the high penetrated wind power is taken as an independent generation area, in which the virtual inertia control system is incorporated for enhancing the inertia response. The wind turbines are operated under the de-load mode for participating in the frequency regulation, just like the thermal power plants. The tube-based DMPC consists of a nominal MPC and an ancillary feedback law, which aims at restraining the wind speed uncertainty. The neighboring areas’ information is incorporated into the ancillary controller as a feedback term, realizing effective coordination between generation areas. The input-to-state stability of the power system under the proposed tube-based DMPC can be guaranteed based on the Schur property and the bounded disturbance condition. The simulation on a four-area interconnected power system demonstrates the effectiveness of proposed algorithm on alleviating the frequency fluctuation caused by varying load and uncertain wind speed.

Model predictive iterative learning control (MPILC) is a popular approach to control batch systems with repetitive nature, as it is capable of tracking the plant reference trajectory with high accuracy and guaranteed closed-loop stability. However, varying reference trajectory often happens in batch processes, so that the tracking performance of a general MPILC can be deteriorated. With the reference variation being treated as bounded disturbance, this paper incorporates H∞ control with MPILC to constitute a robust MPILC (RMPILC), so as to restrain the fluctuation of tracking error caused by the varying reference. This RMPILC adopts the linear parameter varying (LPV) model to represent the dynamic property of the nonlinear system. It then solves the input vector of controlled system by optimizing the tracking performance objective function with constraints in the form of linear matrix inequalities. The robust stability and convergence condition of RMPILC are analyzed. The effectiveness of proposed algorithm is verified thorough the simulations on a numerical example, and also a continuous stirred tank reactor (CSTR) system.