Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor (DFF) and Agility Factor (AF), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience

A Novel Reinforcement Learning-Optimization Approach for Integrating Wind Energy to Power System with Vehicle-to-Grid Technology

High integration of intermittent renewable energy sources (RES), specifically wind power, has created complexities in power system operations due to their limited controllability and predictability. In addition, large fleets of Electric Vehicles (EVs) are expected to have a large impact on electricity consumption, contributing to the volatility. In this dissertation, a well-coordinated smart charging approach is developed that utilizes the flexibility of EV owners in a way where EVs are used as distributed energy storage units and flexible loads to absorb the fluctuations in the wind power output in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected trips, are also addressed by using an interactive mechanism in smart grid. First, a static deterministic model is formulated using multi-objective mixed-integer quadratic programming (MIQP) assuming known parameters day ahead of time. Subsequently, a formulation for real-time dynamic schedule is provided using a rolling-horizon with expected value approximation. Simulation experiments demonstrate a significant increase in wind utilization and reduction in charging cost and battery degradation compared to an uncontrolled charging scenario. Formulating the scheduling problem of the EV-wind integrated power system using conventional stochastic programming (SP) approaches is challenging due to the presence of many uncertain parameters with unknown underlying distributions, such as wind, price, and different commuting patterns of EV owners. To alleviate the problem, a model-free Reinforcement Learning (RL) algorithm integrated with deterministic optimization is proposed that can be applied on many multi-stage stochastic problems while mitigating some of the challenges of conventional SP methods (e.g., large scenario tree, computational complexity) as well as the challenges in model-free RL (e.g., slow convergence, unstable learning in dynamic environment). The simulation results of applying the combined approach on the EV scheduling problem demonstrate the effectiveness of the RL-Optimization method in solving the multi-stage EV charge/discharge scheduling problem. The proposed methods perform better than standard RL approaches (e.g., DDQN) in terms of convergence speed and finding the global optima. Moreover, to address the curse of dimensionality issue in RL with large action-state space, a heuristic EV fleet charging/discharging scheme is used combined with RL-optimization approach to solve the EV scheduling problem for a large number of EVs

Non-beta-lactamase-mediated beta-lactam resistance in Haemophilus influenzae. Mechanisms, epidemiology and susceptibility testing

Paper I and IV of this thesis are not available in Munin. Paper I: Skaare, D., Allum, A.-G., Anthonisen, I. L., Jenkins, A., Lia, A. Strand, L., Tveten, Y., Kristiansen, B.-E.: «Mutant ftsI genes in the emergence of penicillin-binding proteinmediated β-lactam resistance in Haemophilus influenzae in Norway”. Available in Clinical Microbiology and Infection 2010, 16(8):1117–1124. Paper IV: Skaare. D., Lia. A., Hannisdal, A., Tveten, Y., Matuschek, E., Kahlmeter, G., Kristiansen, B.-E.: “Haemophilus influenzae with non-beta-lactamase-mediated beta-lactam resistance: easy to find but hard to categorize”. Available in Journal of Clinical Microbiology 2015, 53(11):3589-3595. Haemophilus influenzae is a major pathogen, with the ability to cause a wide spectrum of invasive and non-invasive infections. Beta-lactams are first-line drugs but beta- lactam resistant strains are common. Beta-lactamase (bla) producing isolates emerged in the 1970s, and non-bla-mediated resistance due to mutations in the ftsI gene encoding penicillin-binding protein 3, denoted ‘rPBP3’ in this project, has increased in recent years. Low-rPBP3 H. influenzae are defined by the absence of the S385T substitution and the presence of R517H (group I) or N526K (group II); these genotypes predominate in Europe, North America and Australia, whereas high-rPBP3 isolates (defined by the additional S385T substitution) are common in Japan and Korea. Data from the Norwegian Surveillance System for Antimicrobial Drug Resistance (NORM) suggest that rPBP3 H. influenzae emerged in Norway in the early 2000s. In this project, two cross-sectional (I and II) and one longitudinal study (III) were performed to explore the resistance mechanisms, epidemiology and clinical characteristics of H. influenzae with non-bla-mediated beta-lactam resistance. The project was the first to characterize the resistance mechanism in Nordic H. influenzae with this phenotype. Study I encompassed 46 respiratory H. influenzae from NORM 2004, including 23 isolates with phenotypes suggesting the presence of non-bla-mediated beta-lactam resistance mechanisms and 23 susceptible control isolates. Study II encompassed 196 respiratory isolates from NORM 2007, including 177 with non-wild type susceptibility to beta-lactams not explained by bla, and 19 susceptible controls. Characterization included pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), ftsI sequencing with deduction of PBP3 substitution patterns (PBP3 typing), and susceptibility testing by determination of beta-lactam MICs. The prevalence of rPBP3 isolates in 2007 was 14.6%. The exact prevalence in 2004 could not be calculated (≥4.8%), but was estimated to 5.7% based on NORM data and the rPBP3 prevalence / amoxicillin-clavulanic acid resistance rate ratio in 2007. These results indicate that the rPBP3 prevalence increased significantly in Norway from 2004 to 2007. Estimated rPBP3 prevalences in more recent NORM populations suggest a further increase to 16.6% in 2014. Enhanced selection pressure due to a 76% increase in amoxicillin usage between 2000 and 2012 may have contributed to the increased frequency of rPBP3 H. influenzae in Norway. Isolates with group II low-rPBP3 genotypes accounted for most (96%) of rPBP3 H. influenzae in NORM 2007, and four clones with unique combinations of MLST allelic profiles and ftsI alleles accounted for 61% of all rPBP3 isolates. Analyses of clonality and comparison with other investigations showed that rPBP3 clones might persist over several years. The ST14/PBP3 type A clone appears to be particularly persistent, widespread and virulent. A few (n=13) bla-negative isolates with non-wild type beta- lactam susceptibility lacked rPBP3-defining substitutions in Study II, suggesting the existence of additional resistance mechanisms. Study III encompassed 30 high-rPBP3 H. influenzae from Norway (2006-2013). Characterization included MLST, PFGE, ftsI sequencing, PBP3 typing and determination of broth microdilution (BMD) MIC for a wide range of agents. The strain collection is unique outside Japan. Of particular notice is the large number (n=23) of group III isolates (N526K + S385T), including 12 isolates with the additional L389F substitution associated with increased resistance. We suggest adding the suffix ‘(+)’ for L389F positive isolates. The resistance rates for extended-spectrum cephalosporins were high in Study III, varying from 47% (ceftriaxone) to 97% (cefixime). Among the isolates were the first reported invasive group III(+) H. influenzae from Europe, and an extensively multi-drug-resistant (MDR) group III(+) high-rPBP3 ST159 strain, resistant to all extended-spectrum cephalosporins tested, and four classes of non-beta-lactams. This remarkable resistotype is previously unreported. The MDR strain was isolated from three patients at the same hospital within a period of four days, illustrating the potential for nosocomial spread. Study III documented the emergence and spread of high-rPBP3 H. influenzae in Norway during the 2000s. A contribution of selective antimicrobial pressure is suggested by a 158% increase in extended-spectrum cephalosporin usage from 2000 to 2012, further underlining the importance of rational use of antibiotics. This project was the first to report identical ftsI alleles in rPBP3 strains unrelated by MLST, suggesting that horizontal transfer of rPBP3-encoding ftsI gene sequences contributes to the evolution of new rPBP3 strains in vivo. The situation calls for improved surveillance. The MLST-ftsI typing approach, developed and validated in Study II, is a powerful tool for global molecular surveillance of rPBP3 H. influenzae. MLST-PBP3 typing offers lower resolution but may be used as a surrogate approach. In Study IV, 154 bla-negative H. influenzae from Study II were used to evaluate nine disks as screening for isolates with rPBP3 genotypes, and Etest and EUCAST disk diffusion were evaluated for categorization of susceptibility to beta-lactams with BMD MICs as the gold standard. The benzylpenicillin 1 unit disk, recommended for screening by EUCAST and first evaluated in this project, detected rPBP3 H. influenzae with high sensitivity (96.2%) and specificity (94.0%) but is unsuitable for screening of bla-positive isolates. The cefuroxime 5 μg disk demonstrated high sensitivity (94.2%) and acceptable specificity (88.0%) and was superior to previously evaluated disks with bla-stable agents, including cefaclor 30 μg and cefuroxime 30 μg. Cefuroxime 5 μg appears to be the best current option for screening of bla-positive H. influenzae but the disk is not available from all manufacturers. False susceptible rates were high with ampicillin Etest (88%) and disk diffusion with ampicillin 2 μg (EUCAST zone breakpoints, 77%; adjusted breakpoints, 28%). The poor performance may in part be explained by poor calibration of Etest and methodology-dependent test variation, but also reflects that current clinical breakpoints for aminopenicillins divide the low-rPBP3 population, making susceptibility categorization vulnerable to day-to-day variation. Breakpoint changes may improve agreement with reference methodology, but clinical data to support breakpoints for H. influenzae and beta-lactams are insufficient. To minimize the clinical consequences of very major errors, a warning comment should be added for rPBP3 screening positive isolates susceptible to aminopenicillins by disk diffusion and gradient tests. H. influenzae positive by rPBP3 screening should be reported ampicillin resistant in cases of meningitis, irrespective of results by agent- directed testing

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Service management for multi-domain Active Networks

The Internet is an example of a multi-agent system. In our context, an agent is synonymous with network operators, Internet service providers (ISPs) and content providers. ISPs mutually interact for connectivity's sake, but the fact remains that two peering agents are inevitably self-interested. Egoistic behaviour manifests itself in two ways. Firstly, the ISPs are able to act in an environment where different ISPs would have different spheres of influence, in the sense that they will have control and management responsibilities over different parts of the environment. On the other hand, contention occurs when an ISP intends to sell resources to another, which gives rise to at least two of its customers sharing (hence contending for) a common transport medium. The multi-agent interaction was analysed by simulating a game theoretic approach and the alignment of dominant strategies adopted by agents with evolving traits were abstracted. In particular, the contention for network resources is arbitrated such that a self-policing environment may emerge from a congested bottleneck. Over the past 5 years, larger ISPs have simply peddled as fast as they could to meet the growing demand for bandwidth by throwing bandwidth at congestion problems. Today, the dire financial positions of Worldcom and Global Crossing illustrate, to a certain degree, the fallacies of over-provisioning network resources. The proposed framework in this thesis enables subscribers of an ISP to monitor and police each other's traffic in order to establish a well-behaved norm in utilising limited resources. This framework can be expanded to other inter-domain bottlenecks within the Internet. One of the main objectives of this thesis is also to investigate the impact on multi-domain service management in the future Internet, where active nodes could potentially be located amongst traditional passive routers. The advent of Active Networking technology necessitates node-level computational resource allocations, in addition to prevailing resource reservation approaches for communication bandwidth. Our motivation is to ensure that a service negotiation protocol takes account of these resources so that the response to a specific service deployment request from the end-user is consistent and predictable. To promote the acceleration of service deployment by means of Active Networking technology, a pricing model is also evaluated for computational resources (e.g., CPU time and memory). Previous work in these areas of research only concentrate on bandwidth (i.e., communication) - related resources. Our pricing approach takes account of both guaranteed and best-effort service by adapting the arbitrage theorem from financial theory. The central tenet for our approach is to synthesise insights from different disciplines to address problems in data networks. The greater parts of research experience have been obtained during direct and indirect participation in the 1ST-10561 project known as FAIN (Future Active IP Networks) and ACTS-AC338 project called MIAMI (Mobile Intelligent Agent for Managing the Information Infrastructure). The Inter-domain Manager (IDM) component was integrated as an integral part of the FAIN policy-based network management systems (PBNM). Its monitoring component (developed during the MIAMI project) learns about routing changes that occur within a domain so that the management system and the managed nodes have the same topological view of the network. This enabled our reservation mechanism to reserve resources along the existing route set up by whichever underlying routing protocol is in place

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Transitioning to Affordable and Clean Energy

Transitioning to Affordable and Clean Energy is a collective volume which combines original contributions and review papers that address the question how the transition to clean and affordable energy can be governed. It will cover both general analyses of the governance of transition, including policy instruments, comparative studies of countries or policies, and papers setting out scientifically sound visions of a clean and just energy system. In particular, the following aspects are foregrounded: • Governing the supply and demand side transformation • Geographical and cultural differences and their consequences for the governance of energy transitions • Sustainability and justice related to energy transitions (e.g., approaches for addressing energy poverty) Transitioning to Affordable and Clean Energy is part of MDPI's new Open Access book series Transitioning to Sustainability. With this series, MDPI pursues environmentally and socially relevant research which contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries

Epidemiology Insights

This book represents an overview on the diverse threads of epidemiological research, brings together the expertise and enthusiasm of an international panel of leading researchers to provide a state-of-the art overview of the field. Topics include the epidemiology of dermatomycoses and Candida spp. infections, the epidemiology molecular of methicillin-resistant Staphylococcus aureus (MRSA) isolated from humans and animals, the epidemiology of varied manifestations neuro-psychiatric, virology and epidemiology, epidemiology of wildlife tuberculosis, epidemiologic approaches to the study of microbial quality of milk and milk products, Cox proportional hazards model, epidemiology of lymphoid malignancy, epidemiology of primary immunodeficiency diseases and genetic epidemiology family-based. Written by experts from around the globe, this book is reading for clinicians, researchers and students, who intend to address these issues