The Australian offshore wind sector is expanding, yet lacks a comprehensive, high-resolution national assessment of past and projected ocean wind climate. Understanding climate patterns over the next 30-50 years is vital for resource planning, safety, and the resilience of offshore wind infrastructure. This study applies the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA) Coordinated Regional Downscaling Experiment (CORDEX) seven-model ensemble to assess surface wind speed projections and their effect on offshore wind energy production. Across declared offshore wind farm regions, the projected signal of change is small and generally lower than seasonal and inter-annual variability. Areas with slight reductions in wind power density and production also show minor increases in variability, which may reduce turbine efficiency. These changes, however, fall within the uncertainty range of climate model projections, leaving future wind resource trends uncertain. Western Australia is an exception, showing a notable wintertime decrease in wind power density under both low-and high-emission scenarios. Overall, the reliability of wind energy production remains largely unaffected, with only slight reductions in the next 30 to 50 years, ranging from 0.1% to 2.6%, depending on the region.
This paper deals with subjects that are of essential concern to European and Spanish industries, and in doing so, it does not overlook social solidarity. In these stormy, crisis ridden times, recipes that applied to our more peaceful times are no longer of any practical use. This all calls for a change in the relationship between the individual and the organization. A new moral contract emerges, where the employee will not necessarily find that his loyal dedication is always rewarded by his being given a full time job for life. It will rather lead to conditions that allow continuous employability on an individual basis. The new concept of the social Europe takes solidarity as a down-to-earth condition for overcoming the crisis, instead of regarding it as support for conditions which may not be sympathethic towards today?s new realities. There is a need for competitiveness to be restored in European industry, but that competitiveness must be made to be compatible with the social and economic cohesion that characterises the European growth model.
Power Line Communication (PLC) systems are facing increasing security threats as adversaries leverage low-cost Software-Defined Radios (SDRs) to launch physical-layer attacks, e.g., jamming and Radio Frequency Fingerprinting (RFF), for communication disruption and unauthorized device tracking, respectively. This paper investigates the dual role of Radio Frequency (RF) wireless jamming for PLC environments, through two distinct scenarios: (i) friendly RF jamming for privacy preservation of (cabled) PLC devices against unauthorized RFF, and (ii) adversarial RF jamming to degrade the performance of legitimate RFF-based authentication systems. We conducted various systematic experiments using nine USRP X310 SDRs connected to actual PLC couplers exchanging signals modulated according to the Binary-Phase Shift Keying modulation scheme to analyze the behavior of RFF in PLC scenarios under different RF jamming levels. Our results demonstrate, for the first time, that strategic RF jamming effectively obscures device fingerprints in cabled PLC communications while maintaining communication quality, with bit error rates remaining acceptable across most configurations. We also demonstrate that device identification accuracy degrades significantly as the jamming intensity increases. Our findings establish fundamental trade-offs between privacy protection and authentication reliability, providing insights for the design of robust PLC systems.
Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.
Most building and energy management system (BEMS) solutions follow a set of rules (supervised or unsupervised learning) to make energy-saving recommendations to inhabitants. However, these systems are normally solely trained on energy data meaning that they do not consider other key factors, such as the inhabitants’ comfort or preferences. The lack of adaption to inhabitants renders these energy-saving solutions largely ineffective. Moreover, BEMS solutions are cloud-based entailing greater cyberattack risks and a high data transmission load. To address these problems, this research proposes an edge computing architecture based on virtual organizations and distributed explainable artificial intelligence (XAI) algorithms for optimized energy use in buildings/homes and demand response. Thanks to virtual organizations’ energy efficiency (EE) measures, which consider the inhabitants’ comfort and dynamically learn from real-time inhabitant data, the consumption patterns of the inhabitants are effectively optimized.
