Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

GIS-Based Site Suitability Analysis for Wind and Solar Photovoltaics Energy Plants in Central North Region, Namibia

Increasing urbanisation and population growth are making it difficult for governments to achieve sustainable development. Provision of clean energy is among the seventeen sustainable development goals, as it reduces reliance on fossil fuels. In recent years, Namibia has rapidly increased her reliance on sustainable energy. The renewable energy sources (RESs), including wind and solar energy, can be described as clean sources which have lesser negative environmental impact compared to conventional energy sources. Amongst the pressing challenges today is finding solutions on efficient solar and wind energy production. It is imperative to work out the optimum location of RESs before installing them. This can significantly improve performance and establishes the foundation for studying both solar and wind power in a site selection problem. This study aims to determine potential locations for wind and solar photovoltaic (PV) energy plants installation using one of the multi-criteria decision-making (MCDM) methods, the analytical hierarchy process (AHP), and a geographic information system (GIS) within the Central North Regional Electricity Distributor (CENORED) supply area. Combining GIS with MCDM results in a powerful technique for selecting potential sites, since GIS provides effective analysis, manipulation, and visualization of geospatial data, whereas MCDM provides consistent weighing of criteria. In the evaluations of the location: topographical, environmental, climatic and regulations constraints were considered as factors that may facilitate or hinder the deployment of solarwind energy power plants. For solar PV energy plant, the highest potential areas are in the north-west, south-west and study area's southern regions, whereas for the wind power plant, only the northwest part is a highly suitable location for wind energy plants installation. These findings can be used to determine most favourable location of interest for solar PV and wind power plant development or to support the integration of electrical grid expansion and off-grid electrification strategies

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 184

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1978