A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research

Planning and operation objectives of public electric vehicle charging infrastructures: a review

Planning public electric vehicle (EV) charging infrastructure has gradually become a key factor in the electrification of mobility and decarbonization of the transport sector. In order to achieve a high level of electrification in mobility, in recent years, different studies have been presented, proposing novel practices and methodologies for the planning and operation of electric vehicles charging infrastructure. In this paper, the authors present an up-to-date analysis of the existing literature in this research field, organized by considering the perspectives and objectives of the principal actors/operators of the EV public charging infrastructure value chain. Among these actors, the electric vehicle, the charging operators and service providers, and the power system infrastructure (transmission and distribution system) are analyzed in depth. By classifying the reviewed literature based on this manifold viewpoints approach, this paper aims to facilitate researchers and technology developers in exploring the state-of-the-art methodologies for each actor’s perspective, and identify conflicting interests and synergies in charging infrastructure operation and planning.The authors would like to thank the Research Council of Norway and industry partners for the support in writing this paper under project 295133/E20FuChar—Grid and Charging Infrastructure of the Future https://prosjektbanken.forskningsradet.no/en/project/FORISS/295133?Kilde=F ORISS&distribution=Ar&chart=bar&calcType=funding&Sprak=no&sortBy=score&sortOrder=desc& resultCount=30&offset=0&Fritekst=fuchar&source=FORISS&projectId=295133 (accessed on 23 June 2023). The authors gratefully acknowledge Michele Garau, Bendik Nybakk Torsæter, and Daniel Mota from SINTEF Energy Research for their contribution to the conceptualization and review of the article. The work of Andreas Sumper was supported by the Catalan Institution for Research and Advanced Studies (ICREA) Academia Program.Postprint (published version

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.info:eu-repo/semantics/publishedVersio

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue

Inductive Wireless Power Transfer Charging for Electric vehicles - A Review

Considering a future scenario in which a driverless Electric Vehicle (EV) needs an automatic charging system without human intervention. In this regard, there is a requirement for a fully automatable, fast, safe, cost-effective, and reliable charging infrastructure that provides a profitable business model and fast adoption in the electrified transportation systems. These qualities can be comprehended through wireless charging systems. Wireless Power Transfer (WPT) is a futuristic technology with the advantage of flexibility, convenience, safety, and the capability of becoming fully automated. In WPT methods resonant inductive wireless charging has to gain more attention compared to other wireless power transfer methods due to high efficiency and easy maintenance. This literature presents a review of the status of Resonant Inductive Wireless Power Transfer Charging technology also highlighting the present status and its future of the wireless EV market. First, the paper delivers a brief history throw lights on wireless charging methods, highlighting the pros and cons. Then, the paper aids a comparative review of different type’s inductive pads, rails, and compensations technologies done so far. The static and dynamic charging techniques and their characteristics are also illustrated. The role and importance of power electronics and converter types used in various applications are discussed. The batteries and their management systems as well as various problems involved in WPT are also addressed. Different trades like cyber security economic effects, health and safety, foreign object detection, and the effect and impact on the distribution grid are explored. Prospects and challenges involved in wireless charging systems are also highlighting in this work. We believe that this work could help further the research and development of WPT systems.publishedVersio

Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs