Unmanned Aerial Vehicles (UAVs) technology has seen a significant boost in the past ten years and has been widely adopted in entertainment, rescue, intelligent transportation, no-touch delivery, environmental monitoring and other real world applications. However, the ranging limitation due to the shortage of battery energy capacity remains a major issue hindering further development of UAVs. Wireless power transfer (WPT) technology is a new technology of great potentials in improving the endurance of UAVs. The integration of WPT into UAV not only activates the recharging of UAVs batteries, but also enables UAVs to recharge other devices. Moreover, wireless charging technology allows the device to be fully enclosed and suitable for harsh weather conditions. This paper presents a comprehensive review in regard to the state-of-the-art of near-field WPT technologies for UAV charging, including technologies characteristics, design issues, as well as multiple case studies. A comparative analysis of existing technologies are also presented, associated with key future research discussions of WPT for UAVs.

The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this coordination, thereby amplifying their impact on the transmission level of the power grid. Further, a demand response program enhances the scheduling approach by managing the energy demands in parallel with the uncertain energy outputs of the DERs. This work presents a stochastic incentive-based demand response model for the scheduling operation of VPP comprising solar-powered generating stations, battery swapping stations, electric vehicle charging stations, and consumers with controllable loads. The work also proposes a priority mechanism to consider the individual preferences of electric vehicle users and consumers with controllable loads. The scheduling approach for the VPP is framed as a multi-objective optimization problem, normalized using the utopia-tracking method. Subsequently, the normalized optimization problem is transformed into a stochastic formulation to address uncertainties in energy demand from charging stations and controllable loads. The proposed VPP scheduling approach is addressed on a 33-node distribution system simulated using MATLAB software, which is further validated using a real-time digital simulator

Dynamically charging electric vehicles (EVs) have the potential to significantly reduce range anxiety and decrease the size of battery required for acceptable range. However, with the main driver for progressing EV technology being the reduction of carbon emissions, consideration of how a dynamic charging system would impact these emissions is required. This study presents a demand‐side management method for allocating resources to charge EVs dynamically considering the integration of local renewable generation. A multi‐objective optimisation problem is formulated to consider individual users, an energy retailer and a regulator as players with conflicting interests. A 19% reduction in the energy drawn from the power grid is observed over the course of a 24 h period when compared with a first‐come‐first‐served allocation method. This results in a greater reduction in CO ₂ emissions of 22% by considering the power grid's make‐up at each time interval. Furthermore, a 42% reduction in CO ₂ emissions is achieved compared to a system without local renewable energy integration. By varying the weights assigned to the players’ goals, the method can reduce overall demand at peak times and produce a smoother demand profile. System fairness is shown to improve with an average Gini coefficient reduction of 4.32%.