A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

Local Market Mechanisms: how Local Markets can shape the Energy Transition

Europe has embarked on a journey towards a zero-emission system, with the power system at its core. From electricity generation to electric vehicles, the European power system must transform into an interconnected, intelligent network. To achieve this vision, active user participation is crucial, ensuring transparency, efficiency, and inclusivity. Thus, Europe has increasingly focused on the concept of markets in all their facets. This thesis seeks to answer the following questions: How can markets, often considered abstract and accessible only to high-level users, be integrated for end-users? How can market mechanisms be leveraged across various phases of the electrical system? Why is a market- driven approach essential for solving network congestions and even influencing planning? These questions shape the core of this research. The analysis unfolds in three layers, each aligned with milestones leading to 2050. The first explores how market mechanisms can be integrated into system operator development plans, enhancing system resilience in the face of changes. In this regard, this step addresses the question of how a market can be integrated into the development plans of a network and how network planning can account for uncertainties. Finally, the analysis highlights the importance of sector coupling in network planning, proposing a study in which various energy vectors lead to a multi-energy system. According to the roadmap to 2030, this layer demonstrates how markets can manage several components of the gas and electrical network. Finally, even though the robust optimisation increases the final cost in the market, it allows to cover the system operator from uncertainties. The second step delves into the concept of network congestion. While congestion management is primarily the domain of operators, it explores how technical and economic collaboration between operators and system users, via flexibility markets, can enhance resilience amid demand uncertainties and aggressive market behaviours. In addition to flexibility markets, other congestion markets are proposed, some radically different, like locational marginal pricing, and others more innovative, such as redispatching markets for distribution. Building upon the first analysis, this section addresses questions of how various energy vectors can be used not only to meet demand but also to manage the uncertainties associated with each resource. Consequently, this second part revisits the concept of sector coupling, demonstrating how various energy vectors can be managed through flexibility markets to resolve network congestion while simultaneously handling uncertainties related to different vectors. The results demonstrate the usefulness of the flexibility market in managing the sector coupling and the uncertainties related to several energy vectors. The third and most innovative step proposes energy and service markets for low-voltage users, employing distributed ledger technology. Since this step highlights topics that are currently too innovative to be realized, this third section offers a comparative study between centralised and decentralised markets using blockchain technology, highlighting which aspects of distributed ledger technology deserve attention and which aspects of low-voltage markets need revision. The results show that the blockchain technology is still in the early stage of its evolution, and several improvements are needed to fully apply this technology into real-world applications. To sum up, this thesis explores the evolving role of markets in the energy transition. Its insights are aimed at assisting system operators and network planners in effectively integrating market mechanisms at all levels of

Plug-in hybrid electric vehicle emissions impacts on control strategy and fuel economy

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, at the expense of increased tailpipe emissions due to multiple cold start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.The dissertation research focuses on the design of a vehicle supervisory control system for a pre-transmission parallel PHEV powertrain architecture. Energy management strategies are evaluated and implemented in a virtual environment for preliminary assessment of petroleum displacement benefits and rudimentary drivability issues. This baseline vehicle supervisory control strategy, developed as a result of this assessment, is implemented and tested on actual hardware in a controlled laboratory environment over a baseline test cycle. Engine cold start events are aggressively addressed in the development of this control system, which lead to enhanced pre-warming and energy-based engine warming algorithms that provide substantial reductions in tailpipe emissions over the baseline supervisory control strategy.The flexibility of the PHEV powertrain allows for decreased emissions during any engine starting event through powertrain torque shaping algorithms that eliminate high engine torque transients during these periods. The results of the dissertation research show that PHEVs do have the potential for substantial reductions in fuel consumption, while remaining environmentally friendly. Tailpipe emissions from a representative PHEV test platform have been reduced to acceptable levels through the development and refinement of vehicle supervisory control methods only. Impacts on fuel consumption are minimal for the emissions reduction techniques that are implemented, while in some cases, substantial fuel consumption reductions are observed

Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

As regulations on the emission of greenhouse gasses continue to tighten on the automotive industry, the production of hybrid electric vehicles has gained significant popularity in recent years. With the increase in production, there has been a parallel demand in the advancement of both mechanical hardware and control system implementation used in these vehicles. A critical factor in the efficient operation of a hybrid electric vehicle is the energy management strategy where the goal is to maximize the efficient use of fuel energy to propel the vehicle. Designing a fuel-efficient control system is a complex challenge due to the degrees of freedom that exist in the control of a hybrid electric vehicle. Several methods exist for the real-time implementation of control strategies that employ heuristic or optimization-based algorithms; however, these control strategies typically rely on the results of offline optimization as a benchmark against which the control strategies are evaluated. Offline energy management optimization strategies require a pre-defined driving schedule for which the operation of the powertrain can be evaluated to determine the globally optimal control policy. The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determine control trends that can be used to improve existing algorithms. The optimal combined CS fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6L 2019 Chevrolet Blazer

Internet of things for disaster management: state-of-the-art and prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE