Power system adequacy: on two-area models and the capacity procurement decision process

In this work, we explore methodological extensions to modelling practices in power system adequacy for single-area and two-area systems. Specifically, we build on top of some of the practices currently in use in Great Britain (GB) by National Grid, framing this in the context of the current technological transition in which renewable capacity is gradually replacing a considerable share of fossil-fuel-based capacity. We explore two-area extensions of the methodology currently used in GB to quantify risk in single-area models. By doing this, we also explore the impact of shortfall-sharing policies and wind capacity on risk indices and on the value of interconnection. Furthermore, we propose a model based on the statistical theory of extreme values to characterise statistical dependence across systems in both net demand (defined as power demand minus renewable generation) and capacity surpluses/deficits (defined as power supply minus demand), looking at how statistical dependence strength influences post-interconnection risk and the capacity value of interconnection. Lastly, we analyse the risk profile of a single-area system as reliance on wind capacity grows, looking at risk beyond the standard set of risk indices, which are based on long-term averages. In doing this, we look at trends which are overlooked by the latter, yet are of considerable importance for decision-makers. Moreover, we incorporate a measure of the decision-maker's degree of risk aversion into the current capacity procurement methodology in GB, and look at the impact of this and other parameters on the amount of procured capacity. We find that shortfall-sharing policies can have a sizeable impact on the interconnector's valuation in terms of security of supply, specially for systems that are significantly smaller than their neighbours. Moreover, this valuation also depends strongly on the risk indices chosen to measure it. We also find that the smoothing effect of parametric extreme value models on tail regions can have a material effect on practical adequacy calculations for post-interconnection risks, and that assumed independence between conventional generation fleets makes capacity shortfall co-occurrences only weakly dependent (in a precisely defined sense) across areas despite much stronger statistical dependence between system net demands. Lastly, as more wind capacity is installed, we find multiple relevant changes in the (single-area) system's risk profile that are not expressed by the standard risk indices: in particular, we find a substantial increase in the frequency of severe events, extreme year-to-year variability of outturn, and a progression to a system with fewer days of potentially much larger shortfalls. Moreover, we show that a high reliance on wind introduces a substantial amount of uncertainty into the calculations due to the limited number of available historic years, which cannot account for the wide range of possible weather conditions the system could experience in the future. Lastly, we also find that the a higher reliance on wind generation also impact the capacity procurement decision process, potentially making the amount of procured capacity considerably more sensitive to parameters such as the value of lost load

Design and implementation of architectures for the deployment of secure community wireless networks

Recientes avances en las tecnologías de la comunicación, así como la proliferación de nuevos dispositivos de computación, están plasmando nuestro entorno hacia un Internet ubicuo. Internet ofrece una plataforma global para acceder con bajo coste a una vasta gama de servicios de telecomunicaciones, como el correo electrónico, comercio electrónico, tele-educación, tele-salud y tele-medicina a bajo coste. Sin embargo, incluso en los países más desarrollados, un gran número de áreas rurales todavía están pobremente equipadas con una infraestructura básica de telecomunicaciones. Hoy en día, existen algunos esfuerzos para resolver esta falta de infraestructura, pero resultan todavía insuficientes. Con este objetivo presentamos en esta tesis RuralNet, una red comunitaria inalámbrica para proveer acceso a Internet de forma personalizada a los subscriptores de un área rural. Los objetivos de este estudio han sido el desarrollo de una nueva arquitectura para ofrecer un acceso a Internet flexible y seguro para zonas rurales aisladas. RuralNet combina el paradigma de las redes mesh y el uso de los dispositivos inalámbricos embebidos más económicos para ofrecer un gran número de servicios y aplicaciones basados en Internet. La solución desarrollada por RuralNet es capaz de cubrir grandes áreas a bajo coste, y puede también ser fácilmente desplegado y extendido tanto en términos de cobertura como de servicios ofrecidos. Dado que la implementación y la evaluación de RuralNet requiere un alto coste y una gran cantidad de mano de obra, hemos considerado que la simulación y la emulación eran una alternativa válida para ahorrar costes. Con este objetivo hemos desarrollado Castadiva, un emulador flexible proyectado para la evaluación de redes MANET y mesh. Castadiva es un emulador basado en dispositivos de bajo coste, utilizado para evaluar los protocolos y las aplicaciones desarrolladas.Hortelano Otero, J. (2011). Design and implementation of architectures for the deployment of secure community wireless networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10079Palanci

Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems

This dissertation investigates the strategic integration of Proof-of-Work(PoW)-based blockchains and ML models to improve transaction inclusion, and consequently molding transaction fees, for clients using cryptocurrencies such as Bitcoin. The research begins with an in-depth exploration of the Bitcoin fee market, focusing on the interdependence between users and miners, and the emergence of a fee market in PoW-based blockchains. Our observations are used to formalize a transaction inclusion pattern. To support our research, we developed the Blockchain Analytics System (BAS) to acquire, store, and pre-process a local dataset of the Bitcoin blockchain. BAS employs various methods for data acquisition, including web scraping, web browser APIs, and direct access to the blockchain using Bitcoin Core software. We utilize time-series data analysis as a tool for predicting future trends, and transactions are sampled on a monthly basis with a fixed interval, incorporating a notion of relative time represented by block-creation epochs. We create a comprehensive model for transaction inclusion in a PoW-based blockchain system, with a focus on factors of revenue and fairness. Revenue serves as an incentive for miners to participate in the network and validate transactions, while fairness ensures equal opportunity for all users to have their transactions included upon paying an adequate fee value. The ML architecture used for prediction consists of three critical stages: the ingestion engine, the pre-processing stage, and the ML model. The ingestion engine processes and transforms raw data obtained from the blockchain, while the pre-processing phase transforms the data further into a suitable form for analysis, including feature extraction and additional data processing to generate a complete dataset. Our ML model showcases its effectiveness in predicting transaction inclusion, with an accuracy of more than 90%. Such a model enables users to save at least 10% on transaction fees while maintaining a likelihood of inclusion above 80%. Furthermore, adopting such model based on fairness and revenue, demonstrates that miners' average loss is never higher than 1.3%. Our research proves the efficacy of a formal transaction inclusion model and ML prototype in predicting transaction inclusion. The insights gained from our study shed light on the underlying mechanisms governing miners' decisions, improving the overall user experience, and enhancing the trust and reliability of cryptocurrencies. Consequently, this enables Bitcoin users to better select suitable fees and predict transaction inclusion with notable precision, contributing to the continued growth and adoption of cryptocurrencies

Resilience to DDoS attacks

Tese de mestrado, Segurança Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasDistributed Denial-of-Service (DDoS) is one of the most common cyberattack used by malicious actors. It has been evolving over the years, using more complex techniques to increase its attack power and surpass the current defense mechanisms. Due to the existent number of different DDoS attacks and their constant evolution, companies need to be constantly aware of developments in DDoS solutions Additionally, the existence of multiple solutions, also makes it hard for companies to decide which solution best suits the company needs and must be implemented. In order to help these companies, our work focuses in analyzing the existing DDoS solutions, for companies to implement solutions that can lead to the prevention, detection, mitigation, and tolerance of DDoS attacks, with the objective of improving the robustness and resilience of the companies against DDoS attacks. In our work, it is presented and described different DDoS solutions, some need to be purchased and other are open-source or freeware, however these last solutions require more technical expertise by cybersecurity agents. To understand how cybersecurity agents protect their companies against DDoS attacks, nowadays, it was built a questionnaire and sent to multiple cybersecurity agents from different countries and industries. As a result of the study performed about the different DDoS solutions and the information gathered from the questionnaire, it was possible to create a DDoS framework to guide companies in the decisionmaking process of which DDoS solutions best suits their resources and needs, in order to ensure that companies can develop their robustness and resilience to fight DDoS attacks. The proposed framework it is divided in three phases, in which the first and second phase is to understand the company context and the asset that need to be protected. The last phase is where we choose the DDoS solution based on the information gathered in the previous phases. We analyzed and presented for each DDoS solutions, which DDoS attack types they can prevent, detect and/or mitigate

iURBAN

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city

Location Awareness in Multi-Agent Control of Distributed Energy Resources

The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

iURBAN

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city

Distributed Ledger Technology (DLT) Applications in Payment, Clearing, and Settlement Systems:A Study of Blockchain-Based Payment Barriers and Potential Solutions, and DLT Application in Central Bank Payment System Functions

Payment, clearing, and settlement systems are essential components of the financial markets and exert considerable influence on the overall economy. While there have been considerable technological advancements in payment systems, the conventional systems still depend on centralized architecture, with inherent limitations and risks. The emergence of Distributed ledger technology (DLT) is being regarded as a potential solution to transform payment and settlement processes and address certain challenges posed by the centralized architecture of traditional payment systems (Bank for International Settlements, 2017). While proof-of-concept projects have demonstrated the technical feasibility of DLT, significant barriers still hinder its adoption and implementation. The overarching objective of this thesis is to contribute to the developing area of DLT application in payment, clearing and settlement systems, which is still in its initial stages of applications development and lacks a substantial body of scholarly literature and empirical research. This is achieved by identifying the socio-technical barriers to adoption and diffusion of blockchain-based payment systems and the solutions proposed to address them. Furthermore, the thesis examines and classifies various applications of DLT in central bank payment system functions, offering valuable insights into the motivations, DLT platforms used, and consensus algorithms for applicable use cases. To achieve these objectives, the methodology employed involved a systematic literature review (SLR) of academic literature on blockchain-based payment systems. Furthermore, we utilized a thematic analysis approach to examine data collected from various sources regarding the use of DLT applications in central bank payment system functions, such as central bank white papers, industry reports, and policy documents. The study's findings on blockchain-based payment systems barriers and proposed solutions; challenge the prevailing emphasis on technological and regulatory barriers in the literature and industry discourse regarding the adoption and implementation of blockchain-based payment systems. It highlights the importance of considering the broader socio-technical context and identifying barriers across all five dimensions of the social technical framework, including technological, infrastructural, user practices/market, regulatory, and cultural dimensions. Furthermore, the research identified seven DLT applications in central bank payment system functions. These are grouped into three overarching themes: central banks' operational responsibilities in payment and settlement systems, issuance of central bank digital money, and regulatory oversight/supervisory functions, along with other ancillary functions. Each of these applications has unique motivations or value proposition, which is the underlying reason for utilizing in that particular use case