Demand response (DR) is a vital part in smart grid to restore the balance between electricity demand and supply. In this paper, a real-time charging scheme is proposed to coordinate the electric vehicle (EV) charging and accommodate DR programs in the parking station. The charging scheduling is formulated as a binary optimization problem because the on-off strategy is leveraged to achieve faster charging speed of EV. Given the computationally expensive nature of exhaustive search in solving the optimal solution of binary optimization problem, a convex relaxation method is developed as an alternative to compute the near-optimal charging schedules. Extensive simulation results show that the proposed work is able to satisfy EV charging demand while accommodating both types of DR programs in the parking station. The proposed work is also able to simultaneously maximize the number of EVs for charging and minimize the monetary expenses.

The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles’ charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV’s charging and discharging.

Demand response (DR) is envisioned as a promising solution to address the demand-supply mismatch issue in electric power systems. A new demand bidding model is considered in this paper to facilitate demand response trading in load curtailment form between the aggregator (buyer) and the large contract capacity customers (seller). Different types of customers with different load curtailment characteristics are considered in the demand bidding, leading to the formulation of a nonlinear optimization problem that is computationally expensive to solve. Delicate mechanisms are incorporated into the proposed purchase optimization scheme to reformulate the nonlinear programming problem into an equivalent linear model that can be solved efficiently. Simulation studies show that the proposed work is promising to minimize the total bidding purchase cost while satisfying the given load curtailment constraints that include the load curtailment and the load curtailment patterns of different customers.

Ultra-wideband (UWB) sensors have been widely applied to indoor positioning. The indoor positioning of UWB sensors usually refers to the positioning of the mobile node that interacts with the anchors through radio for calculating the distance between the mobile node and each of the surrounding anchors. The positioning accuracy of the mobile node is affected by the installation positions of surrounding anchors. A mathematical model was proposed in this paper to respectively analyze the mobile node’s 2-dimensional (2D) and 3-dimensional (3D) positioning errors. The factors influencing the mobile node’s positioning errors were explored through the mathematical models. The best installation positions of surrounding anchors were obtained based on the mathematical models. The mobile node’s 2D and 3D positioning errors were reduced based on the anchor positions derived from the mathematical model. Both computer simulations and practical experiments were implemented to justify the results obtained in the mathematical models.

This study presents a resident-centric distributed community energy management system (CEMS). More specifically, the proposed resident-centric distributed CEMS allows residents to schedule their appliances autonomously, without the need to collaborate with the community and to consider whether their appliance scheduling is optimal from the perspective of the entire community. The central controller in the proposed CEMS will then determine a solution that is optimal for the entire community by dispatching the community's distributed energy sources according to the appliance scheduling of residents. In other words, the proposed distributed resident-centric CEMS allows residents to act autonomously while securing the collective goals of the community to a certain extent. In this paper, the collective goals of the community include participating in incentive-based demand response (IBDR) events at a specific time interval, and decreasing the total electricity cost of the community in response to time-varying electricity prices. The proposed distributed resident-centric CEMS is developed using the concept of distributed optimization and mixed-integer linear programming. Different types of public loads are incorporated into the proposed framework including stoppable and deferrable public loads. The simulation results show that the proposed framework dispatches power optimally.

The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO utilizes a set of ellipsoids to approximate the decision regions of the minority class. Furthermore, multi-objective particle swarm optimization (MOPSO) is integrated with the Gustafson–Kessel algorithm in EMDO to learn the size, center, and orientation of every ellipsoid. Synthetic minority samples are generated based on Mahalanobis distance within every ellipsoid. The number of synthetic minority samples generated by EMDO in every ellipsoid is determined based on the density of minority samples in every ellipsoid. The results of computer simulations conducted herein indicate that EMDO outperforms most of the widely used oversampling schemes.