The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of using an energy storage system (ESS) for loss reduction is investigated, where a novel two-stage method for key-bus selection and ESS scheduling is proposed. At the first stage, the most sensitive key buses to the variation of load are selected by using the loss sensitive factors (LSF) method. At the second stage, ESS scheduling is conducted by solving an optimization problem with uncertainties caused by high penetration of renewable energies, where the uncertainties are characterized by confidence levels. The optimal scheduling of ESS including locations, capacities, and working modes are obtained at the second stage. The effectiveness of the proposed method is demonstrated via numerical simulations. The influences of capacities of ESS and confidence levels with respect to uncertainties are also analyzed. It is demonstrated that the loss-reduction performances can be improved if the ESSs are deployed on the buses selected by the LSF method and operated under the developed optimal scheduling method.

In this paper, the semi-active suspension design problem is concerned by proposing a novel design method incorporating inerters, where the overall semi-active suspension is divided into a passive part and a semi-active part. A mechanical network composed of springs, dampers and inerters is employed as the passive part, and a semi-active damper constitutes the semi-active part. For the conventional semi-active suspensions, the passive part is merely a parallel connection of a spring and a damper, and the main focus is on improving the semi-active part by proposing more effective control algorithms. In contrast, in this paper, the main idea is to improve the overall vehicle performance by improving the passive part instead of the semi-active part. Low-order admittance networks are employed in the passive parts, where some low-order positive real admittance functions are optimized, and then realized as specific mechanical networks by using network synthesis. A suboptimal control law, called steepest gradient control, is utilized in the semi-active part for the control of the semi-active damper. The proposed method is illustrated based on a quarter-car model, and numerical simulations based on a quarter-car and full-car model are performed to show the effectiveness of the proposed method. In contrast to the conventional semi-active suspensions without inerters, the proposed method can provide significant improvements (over 10% and 8% improvements for the quarter-car and full-car model cases, respectively) for the overall suspension performance involving ride comfort, suspension deflection, and road holding.