Emissions Trading, CDM, JI, and More – The Climate Strategy of the EU

The objective of this paper is to assess the likely allocation effects of the current cli-mate protection strategy as it is laid out in the National Allocation Plans (NAPs) for the European Emissions Trading Scheme (ETS). The multi-regional, multi-sectoral CGE-model DART is used to simulate the effects of the current policies in the year 2012 when the Kyoto targets need to be met. Different scenarios are simulated in order to highlight the effects of the grandfathering of permits to energy-intensive installations, the use of the project-based mechanisms (CDM and JI), and the restriction imposed by the supplementarity criterion.Kyoto targets, EU, EU emissions trading scheme, National allocation plans, CDM and JI, Computable general equilibrium model, DART

Buildings-to-Grid Integration Framework

This paper puts forth a mathematical framework for Buildings-to-Grid (BtG) integration in smart cities. The framework explicitly couples power grid and building's control actions and operational decisions, and can be utilized by buildings and power grids operators to simultaneously optimize their performance. Simplified dynamics of building clusters and building-integrated power networks with algebraic equations are presented---both operating at different time-scales. A model predictive control (MPC)-based algorithm that formulates the BtG integration and accounts for the time-scale discrepancy is developed. The formulation captures dynamic and algebraic power flow constraints of power networks and is shown to be numerically advantageous. The paper analytically establishes that the BtG integration yields a reduced total system cost in comparison with decoupled designs where grid and building operators determine their controls separately. The developed framework is tested on standard power networks that include thousands of buildings modeled using industrial data. Case studies demonstrate building energy savings and significant frequency regulation, while these findings carry over in network simulations with nonlinear power flows and mismatch in building model parameters. Finally, simulations indicate that the performance does not significantly worsen when there is uncertainty in the forecasted weather and base load conditions.Comment: In Press, IEEE Transactions on Smart Gri

Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry.

The location-allocation (LA) problem concerns the location of facilities and the allocation of demand, to minimise or maximise a particular function such as cost, profit or a measure of distance. Many formulations of LA problems have been presented in the literature to capture and study the unique aspects of real-world problems. However, some real-world aspects, such as resilience, are still lacking in the literature. Resilience ensures uninterrupted supply of demand and enhances the quality of service. Due to changes in population shift, market size, and the economic and labour markets - which often cause demand to be stochastic - a reasonable LA problem formulation should consider some aspect of future uncertainties. Almost all LA problem formulations in the literature that capture some aspect of future uncertainties fall in the domain of dynamic optimisation problems, where new facilities are located every time the environment changes. However, considering the substantial cost associated with locating a new facility, it becomes infeasible to locate facilities each time the environment changes. In this study, we propose and investigate variations of LA problem formulations. Firstly, we develop and study new LA formulations, which extend the location of facilities and the allocation of demand to add a layer of resilience. We apply the population-based incremental learning algorithm for the first time in the literature to solve the new novel LA formulations. Secondly, we propose and study a new dynamic formulation of the LA problem where facilities are opened once at the start of a defined period and are expected to be satisfactory in servicing customers' demands irrespective of changes in customer distribution. The problem is based on the idea that customers will change locations over a defined period and that these changes have to be taken into account when establishing facilities to service changing customers' distributions. Thirdly, we employ a simulation-based optimisation approach to tackle the new dynamic formulation. Owing to the high computational costs associated with simulation-based optimisation, we investigate the concept of Racing, an approach used in model selection, to reduce the high computational cost by employing the minimum number of simulations for solution selection

Solid rocket booster internal flow analysis by highly accurate adaptive computational methods

The primary objective of this project was to develop an adaptive finite element flow solver for simulating internal flows in the solid rocket booster. Described here is a unique flow simulator code for analyzing highly complex flow phenomena in the solid rocket booster. New methodologies and features incorporated into this analysis tool are described

Modeling Storm Surge and Inundation in Washington, DC, during Hurricane Isabel and the 1936 Potomac River Great Flood

Abstract: Washington, DC, the capital of the U.S., is located along the Upper Tidal Potomac River, where a reliable operational model is needed for making predictions of storm surge and river-induced flooding. We set up a finite volume model using a semi-implicit, Eulerian-Lagrangian scheme on a base grid (200 m) and a special feature of sub-grids (10 m), sourced with high-resolution LiDAR data and bathymetry surveys. The model domain starts at the fall line and extends 120 km downstream to Colonial Beach, VA. The model was used to simulate storm tides during the 2003 Hurricane Isabel. The water level measuring 3.1 m reached the upper tidal river in the vicinity of Washington during the peak of the storm, followed by second and third flood peaks two and four days later, resulting from river flooding coming downstream after heavy precipitation in the watershed. The modeled water level and timing were accurate in matching with the verified peak observations within 9 cm and 3 cm, and with R2 equal to 0.93 and 0.98 at the Wisconsin Avenue and Washington gauges, respectively. A simulation was also conducted for reconstructing the historical 1936 Potomac River Great Flood that inundated downtown. It was identified that the flood water, with a velocity exceeding 2.7 m/s in the downstream of Roosevelt Island, pinched through the bank northwest of East Potomac Park near DC. The modeled maximum inundation extents revealed a crescent-shaped flooding area, which was consistent with the historical surveyed flood map of the event

Advancements and Challenges in IoT Simulators: A Comprehensive Review

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term “Internet of Things”, which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators’ capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain

Assessing the Sustainability Implications of Autonomous Vehicles: Recommendations for Research Community Practice

Autonomous vehicles (AV) are poised to induce disruptive changes, with significant implications for the economy, the environment, and society. This article reviews prior research on AVs and society, and articulates future needs. Research to assess future societal change induced by AVs has grown dramatically in recent years. The critical challenge in assessing the societal implications of AVs is forecasting how consumers and businesses will use them. Researchers are predicting the future use of AVs by consumers through stated preference surveys, finding analogs in current behaviors, utility optimization models, and/or staging empirical “AV-equivalent” experiments. While progress is being made, it is important to recognize that potential behavioral change induced by AVs is massive in scope and that forecasts are difficult to validate. For example, AVs could result in many consumers abandoning private vehicles for ride-share services, vastly increased travel by minors, the elderly and other groups unable to drive, and/or increased recreation and commute miles driven due to increased utility of in-vehicle time. We argue that significantly increased efforts are needed from the AVs and society research community to ensure 1) the important behavioral changes are analyzed and 2) models are explicitly evaluated to characterize and reduce uncertainty