Strategic decision-making on low-carbon technology and network capacity investments using game theory

In recent years, renewable energy technologies have been increasingly adopted and seen as key to humanity’s efforts to reduce greenhouse gases emissions and combat climate change. Yet, a side effect is that renewables have reached high penetration rates in many areas, leading to undesired curtailment, especially if existing grid infrastructure is insufficient and renewable energy generated cannot be exported at areas of high energy demand. The issue of curtailment is compelling at remote areas, where renewable resources are abundant, such as in windy islands. Not only renewable production is wasted, but often curtailment comes with high costs for renewable energy developers and energy end-users. In fact, procedures on how generators access the grid and how curtailment is applied, are key factors that affect the decisions of investors about generation and grid capacity installed. Part of this thesis studies the properties of widely used curtailment rules, applied in several countries including the UK, and their effect on strategic interactions between self-interested and profit-maximising low-carbon technology investors. The work develops a game-theoretic framework to study the effects of curtailment on the profitability of existing renewable projects and future developments. More specifically, work presented in this thesis determines the upper bounds of tolerable curtailment at a given location that allows for profitable investments. Moreover, the work studies the effect of various curtailment strategies on the capacity factor of renewable generators and the effects of renewable resource spatial correlation on the resulting curtailment. In fact, power network operators face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this context, this thesis shows that fairness and equal sharing of imposed curtailment among generators is important to achieve maximisation of the renewable generation capacity installed at a certain area. A new rule is proposed that minimises disruption and the number of curtailment events a generator needs to respond to, while achieving fair allocation of curtailment between generators of unequal ratings. While curtailment can be reduced by smart grid techniques, a long term solution is increasing the network capacity. Grid reinforcements, however, are expensive and costs weight to all energy consumers. For this reason, debate in the energy community has focused on ways to attract private investment in grid reinforcement. A key knowledge gap faced by regulators is how to incentivise such projects, that could prove beneficial, especially in cases where several distributed generators can use the same power line to access the main grid, against the payment of a transmission fee. This thesis develops methods from empirical and algorithmic game theory to provide solutions to this problem. Specifically, a two-location model is considered, where excess renewable generation and demand are not co-located, and where a private renewable investor constructs a power line, providing also access to other generators, against a charge for transmission. In other words, the privately developed line is shared among all generators, a principle known as ‘common access’ line rules. This formulation may be studied as a Stackelberg game between transmission and local generation capacity investors. Decisions on optimal (and interdependent) renewable capacities built by investors, affect the resulting curtailment and profitability of projects and can be determined in the equilibrium of the game. A first approach to study the behaviour of investors at the game equilibrium, assumed a simple model, based on average values of renewable production and demand over a larger time horizon. This assumption allowed for an initial examination of the Stackelberg game equilibrium, by achieving an analytical, closed-form solution of the equilibrium and the investigation of its properties for a wide range of cost parameters. Next, a refined model is developed, able to capture the stochastic nature of renewable production and variability of energy demand. A theoretical analysis of the game is presented along with an estimation of the equilibrium by utilisation of empirical game-theoretic techniques and production/demand data from a real network upgrade project in the UK. The proposed method is general, and can be applied to similar case studies, where there is excess of renewable generation capacity, and where sufficient data is available. In practice, however, available data may be erroneous or experience significant gaps. To deal with data issues, a method for generating time series data is developed, based on Gibbs sampling. This attains an iterative simulation analysis with different time series data as an input (Markov Chain Monte Carlo), thus achieving the exploration of the solution space for multiple future scenarios and leading to a reduction of the uncertainty with regards to the investment decisions taken. Energy storage can reduce curtailment or defer network upgrades. Hence, the last part of this thesis proposes a model consisted of a line investor, local generators and a third independent storage player, who can absorb renewable production, that would otherwise have been curtailed. The model estimates optimal transmission, generation and storage capacities for various financial parameters. The value of storage is determined by comparing the energy system operation with and without energy storage. All models proposed in this thesis, are validated and applied to a practical setting of a grid reinforcement project, in the UK, and a large dataset of real wind speed measurements and demand. In summary, the research work studies the interplay among self-interested and indepen dent low-carbon investors, at areas of excess renewable capacity with network constraints and high curtailment. The work proposes a mechanism for setting transmission charges that ensures that the transmission line gets built, but investors from the local community, can also benefit from investing in renewable energy and energy storage. Overall, the results of this work show how game-theoretic techniques can help energy system stakeholders to bridge the knowledge gap about setting optimal curtailment rules and determining appropriate transmission charges for privately developed network infrastructure.Engineering and Physical Sciences Research Council (EPSRC

Optimisation de la gestion des interférences inter-cellulaires et de l'attachement des mobiles dans les réseaux cellulaires LTE

Driven by an exponential growth in mobile broadband-enabled devices and a continue dincrease in individual data consumption, mobile data traffic has grown 4000-fold over the past 10 years and almost 400-million-fold over the past 15 years. Homogeneouscellular networks have been facing limitations to handle soaring mobile data traffic and to meet the growing end-user demand for more bandwidth and betterquality of experience. These limitations are mainly related to the available spectrumand the capacity of the network. Telecommunication industry has to address these challenges and meet exploding demand. At the same time, it has to guarantee a healthy economic model to reduce the carbon footprint which is caused by mobile communications.Heterogeneous Networks (HetNets), composed of macro base stations and low powerbase stations of different types, are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. In such networks, intelligent user association and interference management schemes are needed to achieve gains in performance. Due to the large imbalance in transmission power between macroand small cells, user association based on strongest signal received is not adapted inHetNets as only few users would attach to low power nodes. A technique based onCell Individual Offset (CIO) is therefore required to perform load balancing and to favor some Small Cell (SC) attraction against Macro Cell (MC). This offset is addedto users’ Reference Signal Received Power (RSRP) measurements and hence inducing handover towards different eNodeBs. As Long Term Evolution (LTE) cellular networks use the same frequency sub-bands, mobile users may experience strong inter-cellxv interference, especially at cell edge. Therefore, there is a need to coordinate resource allocation among the cells and minimize inter-cell interference. To mitigate stronginter-cell interference, the resource, in time, frequency and power domain, should be allocated efficiently. A pattern for each dimension is computed to permit especially for cell edge users to benefit of higher throughput and quality of experience. The optimization of all these parameters can also offer gain in energy use. In this thesis,we propose a concrete versatile dynamic solution performing an optimization of user association and resource allocation in LTE cellular networks maximizing a certainnet work utility function that can be adequately chosen. Our solution, based on gametheory, permits to compute Cell Individual Offset and a pattern of power transmission over frequency and time domain for each cell. We present numerical simulations toillustrate the important performance gain brought by this optimization. We obtain significant benefits in the average throughput and also cell edge user through put of40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency. This work addresses industrial research challenges and assuch, a prototype acting on emulated HetNets traffic has been implemented.Conduit par une croissance exponentielle dans les appareils mobiles et une augmentation continue de la consommation individuelle des données, le trafic de données mobiles a augmenté de 4000 fois au cours des 10 dernières années et près de 400millions fois au cours des 15 dernières années. Les réseaux cellulaires homogènes rencontrent de plus en plus de difficultés à gérer l’énorme trafic de données mobiles et à assurer un débit plus élevé et une meilleure qualité d’expérience pour les utilisateurs.Ces difficultés sont essentiellement liées au spectre disponible et à la capacité du réseau.L’industrie de télécommunication doit relever ces défis et en même temps doit garantir un modèle économique pour les opérateurs qui leur permettra de continuer à investir pour répondre à la demande croissante et réduire l’empreinte carbone due aux communications mobiles. Les réseaux cellulaires hétérogènes (HetNets), composés de stations de base macro et de différentes stations de base de faible puissance,sont considérés comme la solution clé pour améliorer l’efficacité spectrale par unité de surface et pour éliminer les trous de couverture. Dans de tels réseaux, il est primordial d’attacher intelligemment les utilisateurs aux stations de base et de bien gérer les interférences afin de gagner en performance. Comme la différence de puissance d’émission est importante entre les grandes et petites cellules, l’association habituelle des mobiles aux stations de bases en se basant sur le signal le plus fort, n’est plus adaptée dans les HetNets. Une technique basée sur des offsets individuelles par cellule Offset(CIO) est donc nécessaire afin d’équilibrer la charge entre les cellules et d’augmenter l’attraction des petites cellules (SC) par rapport aux cellules macro (MC). Cette offset est ajoutée à la valeur moyenne de la puissance reçue du signal de référence(RSRP) mesurée par le mobile et peut donc induire à un changement d’attachement vers différents eNodeB. Comme les stations de bases dans les réseaux cellulaires LTE utilisent les mêmes sous-bandes de fréquences, les mobiles peuvent connaître une forte interférence intercellulaire, en particulier en bordure de cellules. Par conséquent, il est primordial de coordonner l’allocation des ressources entre les cellules et de minimiser l’interférence entre les cellules. Pour atténuer la forte interférence intercellulaire, les ressources, en termes de temps, fréquence et puissance d’émission, devraient être alloués efficacement. Un modèle pour chaque dimension est calculé pour permettre en particulier aux utilisateurs en bordure de cellule de bénéficier d’un débit plus élevé et d’une meilleure qualité de l’expérience. L’optimisation de tous ces paramètres peut également offrir un gain en consommation d’énergie. Dans cette thèse, nous proposons une solution dynamique polyvalente effectuant une optimisation de l’attachement des mobiles aux stations de base et de l’allocation des ressources dans les réseaux cellulaires LTE maximisant une fonction d’utilité du réseau qui peut être choisie de manière adéquate.Notre solution, basée sur la théorie des jeux, permet de calculer les meilleures valeurs pour l’offset individuelle par cellule (CIO) et pour les niveaux de puissance à appliquer au niveau temporel et fréquentiel pour chaque cellule. Nous présentons des résultats des simulations effectuées pour illustrer le gain de performance important apporté par cette optimisation. Nous obtenons une significative hausse dans le débit moyen et le débit des utilisateurs en bordure de cellule avec 40 % et 55 % de gains respectivement. En outre, on obtient un gain important en énergie. Ce travail aborde des défis pour l’industrie des télécoms et en tant que tel, un prototype de l’optimiseur a été implémenté en se basant sur un trafic HetNets émulé

AI meets CRNs : a prospective review on the application of deep architectures in spectrum management

The spectrum low utilization and high demand conundrum created a bottleneck towards ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

publishedVersio

Sustainable Industrial Engineering along Product-Service Life Cycle/Supply Chain

Sustainable industrial engineering addresses the sustainability issue from economic, environmental, and social points of view. Its application fields are the whole value chain and lifecycle of products/services, from the development to the end-of-life stages. This book aims to address many of the challenges faced by industrial organizations and supply chains to become more sustainable through reinventing their processes and practices, by continuously incorporating sustainability guidelines and practices in their decisions, such as circular economy, collaboration with suppliers and customers, using information technologies and systems, tracking their products’ life-cycle, using optimization methods to reduce resource use, and to apply new management paradigms to help mitigate many of the wastes that exist across organizations and supply chains. This book will be of interest to the fast-growing body of academics studying and researching sustainability, as well as to industry managers involved in sustainability management

Testing and troubleshooting with passive network measurements

Tämä diplomityö jakautuu kahteen osaan: kirjallisuuskatsaukseen ja empiiriseen osioon. Kirjallisuuskatsauksessa käsitellään passiivisissa mittauksissa testaamiseen ja ongelmanratkaisuun nykyään käytettäviä tekniikoita ja metodeita. Empiirisessä osiossa mitataan kahta reaalimaailman verkkoa ja verkkokomponenttia. Ensimmäisessä esimerkissä selvitetään palveluhotellin tietoliikenneverkon ongelmia ja toisessa testataan yksittäisen tietoliikennelaitteen verkko-ominaisuuksia. Passiivisten mittausten mittaustekniikka on pohjimmiltaan pysynyt samana, ainoastaan uudet linkkiteknologiat ja -nopeudet ovat uudistaneet sitä. Nykyaikaisilla kotitietokoneilla voidaan hoitaa joitakin päästä päähän -tyyppisiä mittauksia vaivattomasti. Mittauksiin ja tulosten analysointiin liittyvien metodien kehittymisen, uudistamisen ja lisääntymisen myötä asioita voidaan nykyään mitata helpommin, luotettavammin ja vähemmän mittaustiedon varassa. Tietoverkko-ongelmien syiden etsimiseen tarkoitettujen mallien sopivuus riippuu tapauksesta, esimerkiksi poissulkevaa mallia käytettiin pääasiassa palveluhotelliesimerkissä. Suurimpana ongelmana teollisuus-PC:itä käytettäessä passiivisiin mittauksiin on yhä edelleen tarpeeksi tarkan ajan saaminen. Lisäksi 1 Gbit/s ja sitä suurempien linjanopeuksien täysi talteenottaminen on yhä ongelmallista. Passiivinen pakettimittaus on tehokas, mutta välillä melko hidas tapa verkko-ongelmien selvittämiseksi ja verkkolaitteiden testaamiseksi.This thesis is divided into two parts. In the theory part, the state-of-the-art of passive measurement methods and mechanisms are presented with particular regard to testing and troubleshooting. Secondly, a real world network and network device are measured. The first measurements concentrate on the troubleshooting in a network of the service hotel. The second case's tests cover the network properties of a single network device. It is found that the fundamental measuring techniques of passive monitoring have remained unchanged, only the link speeds and technologies have renewed it. With modern home computing comes the possibility of doing end-to-end measurements easily. There are new amended methods to measure things easier, more reliably, and requiring smaller amounts of captured data. Troubleshooting the networks does not need to be done just by searching reasons for problems randomly. In the network troubleshooting the suitable model depends on a case. In Case I "A Service Hotel Network" the exclusion model was used partly for searching reasons for problems. The largest problem still remains in time-related multipoint measurements: the availability of cost effective accurate clock synchronization methods for PCs. In addition, capturing data fully from the link speed of 1 Gbit/s and more, is still a problem. Passive packet monitoring is a powerful – yet sometimes quite slow – way for troubleshooting and testing network devices

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications