Scenarios for the development of smart grids in the UK: synthesis report

‘Smart grid’ is a catch-all term for the smart options that could transform the ways society produces, delivers and consumes energy, and potentially the way we conceive of these services. Delivering energy more intelligently will be fundamental to decarbonising the UK electricity system at least possible cost, while maintaining security and reliability of supply. Smarter energy delivery is expected to allow the integration of more low carbon technologies and to be much more cost effective than traditional methods, as well as contributing to economic growth by opening up new business and innovation opportunities. Innovating new options for energy system management could lead to cost savings of up to £10bn, even if low carbon technologies do not emerge. This saving will be much higher if UK renewable energy targets are achieved. Building on extensive expert feedback and input, this report describes four smart grid scenarios which consider how the UK’s electricity system might develop to 2050. The scenarios outline how political decisions, as well as those made in regulation, finance, technology, consumer and social behaviour, market design or response, might affect the decisions of other actors and limit or allow the availability of future options. The project aims to explore the degree of uncertainty around the current direction of the electricity system and the complex interactions of a whole host of factors that may lead to any one of a wide range of outcomes. Our addition to this discussion will help decision makers to understand the implications of possible actions and better plan for the future, whilst recognising that it may take any one of a number of forms

Optimizing Energy Efficiency in UAV-Based Wireless Communication Networks: A Comparative Analysis of TAODV and DSR Protocols using the Trust Score Algorithm for Signal Processing

This study presents a comprehensive analysis of energy efficiency optimization in signal processing algorithms for UAV-based wireless communication networks. Employing a multifaceted approach that integrates mathematical modeling, game theory analysis, and an array of testing methodologies, the research aims to address the critical challenge of enhancing communication protocol performance while minimizing energy consumption. Central to our investigation is the development and application of the Trust Score Algorithm (TSA), a novel quantitative tool designed to evaluate and compare the efficacy of various signal processing algorithms across multiple dimensions, including energy efficiency, reliability, adaptability, security, and latency. Through detailed comparative analysis and data visualization techniques, the study reveals that the Proposed_TAODV protocol significantly outperforms traditional TAODV and DSR protocols in several key metrics. These include throughput efficiency, end-to-end delay, and packet delivery ratio, particularly as the number of UAV nodes scales up. Such findings underscore the Proposed_TAODV protocol's superior stability and performance, advocating for its potential in improving the sustainability and effectiveness of UAV-based communication networks. The research methodology encompasses both theoretical and empirical testing phases, ranging from simulation-based analysis, to validate the performance of the signal processing algorithms under varied operational conditions. The results not only affirm the superior performance of the Proposed_TAODV protocol but also highlight the utility of the TSA in guiding the selection and optimization of signal processing algorithms for UAV networks

Knowing me, knowing you: the role of trust, locus of control and privacy concern in acceptance of domestic electricity demand-side response

Choosing to take part in a demand-side response (DSR) programme entails accepting external influence over one’s energy consumption patterns, such as through price or direct load control (DLC) signals. If participation is low, the programme will be ineffective. How might people’s perceptions of their relationship with the influencing entity affect the likelihood of participation? This study used a representative survey of Great Britain (N=2002) to explore the importance of trust, privacy concern and locus of control for acceptance of different approaches to influencing electricity consumption. Survey respondents were randomly shown a description of one of five DSR products (static time of use [TOU] tariff, static TOU with automated response to price changes, dynamic TOU, dynamic TOU with automated response, and DLC), framed as being offered by their electricity supplier. They then responded to a number of scales including those intended to measure trust in their supplier, privacy concern and locus of control. Controlling for demographic variables, trust in electricity supplier was significantly positively associated with acceptance of all tariffs, although the effect size was smaller for the automated TOU tariffs. The specific measure of trust in the supplier to ensure a reliable electricity supply was significantly negatively associated with acceptance of the dynamic TOU tariff. Privacy concern was significantly negatively associated with acceptance of all tariffs, with the strongest effect for the automated dynamic TOU tariff. Locus of control was a significant factor only in the case of DLC, where external locus was related to higher acceptance. These results suggest the existing low levels of trust in energy companies in the UK may present a challenge in securing uptake of DSR, and an opportunity to trusted entrants from other sectors. Automation within the home may mitigate trust concerns, but people must have confidence in the privacy of this arrangement. DLC may be viewed especially positively by people who currently perceive themselves to have little control over their energy use, but protections should be in place to ensure they are not exploited

Improving social engineering resilience in enterprises

A Engenharia Social é um problema significativo para as empresas. Os cyber-criminosos continuam a desenvolver novos e sofisticados métodos para ludibriar indivíduos, levando-os a divulgar informações confidenciais ou a conceder acesso não autorizado a sistemas de infraestruturas. Estes ataques continuam a constituir uma ameaça significativa para os sistemas empresariais, apesar dos investimentos significativos em arquitetura técnica e medidas de segurança. A formação e sensibilização dos funcionários, entre outras intervenções comportamentais, são fundamentais para melhorar a resiliência à Engenharia Social. Os programas de formação e educação dos funcionários são cruciais para a redução da probabilidade destes ataques. O cumprimento das políticas e procedimentos de segurança é significativamente melhorado através de formação baseada na educação. Uma cultura de segurança envolvendo todas as partes é também essencial, uma vez que uma comunicação aberta e honesta por parte da direção pode aumentar a consciência dos funcionários sobre potenciais ameaças. Os preconceitos e características emocionais como o medo, confiança e curiosidade têm também impacto na suscetibilidade a este tipo de ataques, mas, no entanto, as características pessoais que tornam os indivíduos vulneráveis exigem uma investigação profunda. Esta dissertação tem como objetivo fornecer uma avaliação abrangente do estado do conhecimento neste campo e propor uma Framework, identificando as melhores práticas para melhorar a resiliência à Engenharia Social nas empresas, enquanto apoia o desenvolvimento de novos estudos de investigação para abordar esta questão. O seu objetivo é ajudar as empresas de qualquer dimensão a utilizar esta Framework para reduzir o risco de ataques bem-sucedidos de Engenharia Social e melhorar a sua cultura de sensibilização para a segurança.Social Engineering is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving Social Engineering resilience. Training and education programs for users are crucial in reducing the probability of these attacks. Compliance with security policies and procedures is significantly improved through education-based training. A security culture involving all stakeholders is also essential, as open, and honest communication from management can increase user awareness of potential threats. Emotional biases such as fear, trust, and curiosity also impact susceptibility to attacks, but personal traits that make individuals vulnerable require further investigation. This dissertation aims to provide a comprehensive assessment of the state of knowledge in this field and propose a framework by identifying best practices for improving Social Engineering resilience in organizations, while supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size leverage this framework to reduce the risk of successful Social Engineering attacks and improve their culture of security awareness

Who needs XAI in the Energy Sector? A Framework to Upgrade Black Box Explainability

Artificial Intelligence (AI)-based methods in the energy sector challenge companies, organizations, and societies. Organizational issues include traceability, certifiability, explainability, responsibility, and efficiency. Societal challenges include ethical norms, bias, discrimination, privacy, and information security. Explainable Artificial Intelligence (XAI) can address these issues in various application areas of the energy sector, e.g., power generation forecasting, load management, and network security operations. We derive Key Topics (KTs) and Design Requirements (DRs) and develop Design Principles (DPs) for efficient XAI applications through Design Science Research (DSR). We analyze 179 scientific articles to identify our 8 KTs for XAI implementation through text mining and topic modeling. Based on the KTs, we derive 15 DRs and develop 18 DPs. After that, we discuss and evaluate our results and findings through expert surveys. We develop a Three-Forces Model as a framework for implementing efficient XAI solutions. We provide recommendations and a further research agenda