ABSTRACT. Modern energy management systems, even within advanced energy internet (EI) infrastructures, remain fundamentally reactive, optimization-bound, and incapable of reasoning about context, intent, or uncertainty. While the EI paradigm has established a powerful cyber-physical architecture for interconnecting distributed energy resources via software-defined packetized networks, the question of how such systems should think, adapt, and govern energy decisions intelligently remains an open challenge. This paper introduces cognitive energy management (CEM); a new conceptual framework that addresses this gap by redefining how energy systems perceive, reason, learn, and act within complex operational environments. Grounded in the EI cyber-physical foundation, CEM extends beyond conventional optimization by embedding goal-directed reasoning and continuous adaptation into the energy management loop, positioning itself as the cognitive governance layer of EI-based infrastructures. We formally define CEM, distinguish it from rule-based and optimization-based paradigms through structured comparison, and articulate its core architectural layers. To demonstrate the framework's practical value, we develop a toy problem grounded in smart port energy management; one of the most operationally demanding EI node environments in modern infrastructure. Specifically, we model a predictive vessel turnaround scenario in which a CEM-enabled system plans energy procurement, storage pre-charging, and load scheduling across a six-hour operational horizon. The demonstration illustrates how CEM moves the EI beyond feasibility-seeking toward intelligent, anticipatory energy governance.

ABSTRACT. Countries all over the world face persistent grid instability driven by underinvestment, ageing infrastructure, and rapid demand growth. Kenya, for example, records an average of 9.42 hours of power outages and 3.57 interruptions per customer monthly—figures that exceed national and regional reliability benchmarks. Conventional backup solutions such as diesel gen- erators and lithium-ion battery systems remain inadequate due to high operating costs, emissions, and performance degradation under tropical temperatures. The proposed concept in the paper presents an energy storage and supply system that integrates Flywheel Energy Storage (FESS) with Molten Carbonate (MCFC) hydrogen fuel cell technology to enhance renewable integration and grid resilience. The system combines a high-speed carbon-fibre flywheel (50,000 RPM, 0.0005 N·m friction loss) for sub-second frequency response with a hydrogen subsystem for multi-day energy storage. A hier- archical IoT-based monitoring and predictive control framework employs an LSTM neural network to forecast grid anomalies 15 seconds in advance with 93.7% accuracy, enabling optimal power allocation between subsystems. Experimental hardware- in-loop testing over 1,000 hours demonstrates frequency stability within ±0.05 Hz during 90% of renewable fluctuation events and 72+ hours of hydrogen-powered backup. The results confirm the feasibility of AI-optimised hybrid storage as a scalable pathway toward resilient, low-carbon smart grids across Sub-Saharan Africa.

ABSTRACT. Reliable, low-carbon energy supply in Nordic climates requires coordinated use of multi-carrier energy systems (MESs) and advanced storage technologies. In the Finnish context, seasonal hydrogen storage (HSS) and sand battery–based high temperature thermal storage (SBS) are particularly important, as they enable shifting surplus renewable generation from windy and sunny periods to long, cold winters while reducing dependence on fossil heating fuels. This paper presents an energy hub (EH) optimization framework for such MESs with high renewable penetrations. The two-level model co-optimizes long-term planning and short-term operation of generation, hybrid daily/seasonal storage, energy conversion units, demand response, and vehicle-to-grid–enabled plug-in electric vehicles. Additionally, it explicitly includes battery degradation costs and assesses the coordinated use of seasonal HSS and SBS under uncertainty in renewable generations, demand, and prices. The case studies demonstrate operating cost and emission reductions of 4.8% and 6.3%, respectively, while achieving a renewable energy penetration of 91%.

ABSTRACT. This paper presents Pelagia as a simulation-based Decision-Intelligence framework linking an initial 3D concept design, a reproducible GitHub-based simulation workflow, and comparative evaluation across deterministic and stochastic studies. The 3D design defines a dock-adjacent near-shore system with a three-petal folding structure and coordinated body motion, from which motion-derived states and signal-informed features are derived for analysis. The Decision-Intelligence framework organises these features into an enterprise-oriented pipeline in which local behaviour is encoded, classified, reviewed locally, and prepared for privacy-preserving global sharing in reduced visual form. The simulated framework evaluation includes a 12-test deterministic baseline, a 120-test deterministic campaign, a 120-test stochastic campaign, and a repeated 120-stochastic x 100 study. Comparative confusion matrices, repeated-run accuracy, and macro-F1 analysis indicate strong state separability under deterministic conditions and its persistence under stochastic variation. The framework, therefore, provides a reproducible and interpretable basis for simulation-driven decision support in near-shore energy systems.

ABSTRACT. The increasing frequency of extreme meteorological events poses a critical challenge for the planning of renewable energy systems that depend on reliable forecasts. Green hydrogen produced through solar photovoltaic (PV) electrolysis emerges as a clean and decentralized energy vector; however, numerical weather prediction (NWP) models present systematic biases that are amplified under atypical conditions, compromising production estimation. This work proposes a two-pass bias-correction scheme applied to Global Forecast System (GFS) model forecasts using climate-regime-differentiated XGBoost, with explicit detection of heat and cold waves, evaluated using data from the solarimetric station of the Federal University of ABC (UFABC) in Santo André, Brazil. The results show reductions in temperature root mean square error (RMSE) of up to 1.662 °C and reductions in irradiance errors of up to 111.55 W/m², leading to improvements exceeding 18% in green hydrogen production estimation compared with raw GFS model forecasts.

ABSTRACT. An interactive workshop focused on career development, leadership, and professional growth in engineering. Through practical advice and shared experiences from women leaders in academia and industry, participants will explore how to build confidence, identify opportunities, overcome challenges, and shape a meaningful career path.

ABSTRACT. Shipboard power systems have traditionally relied on centralized power management logic to balance generator loading and restore nominal frequency after changes in loading or generation capacity. In inverter-dominated DC architectures, similar expectations often persist, with DC-link voltage deviation assumed to require explicit supervisory correction. This paper presents a system-level interpretation of exception handling in variable-voltage DC shipboard power systems, where coordination emerges from voltage-dependent local device behavior. Rather than treating DC-link voltage as a control error, voltage is used as the execution variable of coordination. During source loss, overload beyond droop capability, excessive current injection, and large load rejection events, DC-link voltage evolves according to instantaneous power balance. Voltage-responsive loads autonomously substitute their controlled variable when activation thresholds are reached, supporting voltage-forming sources and enabling progressive system response through local interaction. Voltage regulation is defined as active control of DC-link voltage from the prevailing operating point, independent of power flow direction; thus, load power reduction constitutes voltage support when driven by voltage-controlled operation. This behavior differs fundamentally from conventional AC frequency restoration, where nominal operating conditions are actively enforced. In the variable-voltage paradigm, controlled voltage deviation is an integral part of system operation and enables decentralized coordination without supervisory control.

ABSTRACT. The increasing adoption of converter-dominated DC microgrids has introduced new challenges related to source overloading and voltage stability during abnormal operating conditions. If overload progression is not resolved by power management or load-side response, voltage-supporting sources may eventually reach equipment protection boundaries such as current limitation or protective shutdown, which may reduce system survivability under severe loading conditions. This paper investigates the system-level behavior emerging when converter interfaced loads participate in DC-link voltage support during source overload situations. A modeling-based study is performed using a droop-controlled DC source supplying two converter interfaced loads. Two operating scenarios are compared: a case without load participation and a case in which one load exhibits voltage-responsive behavior during undervoltage conditions while the second load remains non-participating. The analysis focuses on the evolution of DC-link voltage, source loading, load-current behavior, and the resulting operating-point progression during overload conditions. The results demonstrate that participating loads can reduce source stress by decreasing their current demand as the DC-link voltage falls below a predefined participation threshold. Compared to the non-participating case, the voltage-responsive behavior reduces source stress and contributes to improved DC-link voltage stabilization. The study highlights how locally acting load participation can influence the overall operating behavior of DC microgrids during source overload events without requiring centralized coordination or communication between devices. The presented work focuses on the observed system-level behavior associated with load participation during overload conditions and provides a modeling-based demonstration of its influence on DC microgrid operating-point evolution.

ABSTRACT. This paper presents a compact, solid-state switch-based repetitive high-voltage nanosecond pulse generator (NSPG) to deliver repetitive high-voltage pulses with fast rise/fall time. The NSPG consists of an AC-DC conversion stage, a pulse generation stage, and a high-voltage pulse boosting circuit. The developed NSPG is experimentally tested up to 1 kHz pulse repetition frequency (PRF) across various capacitive-resistive (RC) loads chosen to match the dynamic electrical characteristics of an electron beam source. The results demonstrate that the NSPG efficiently generates pulses up to 8 kV with approximately 100 ns pulse duration, maintaining consistent performance across different RC loads. Additionally, it has been observed that the effect of circuit parasitics on the output pulse is minimal, even when increasing from lower to higher pulse voltage levels.

ABSTRACT. The transition toward low-carbon energy systems requires advanced solutions capable of integrating multiple energy sources while ensuring efficiency, reliability, and sustainability. Artificial intelligence (AI) has emerged as a transformative technology for optimizing multi-energy systems by enabling intelligent forecasting, control, and decision-making. This paper explores the role of AI in enabling low-carbon and multi-energy systems, focusing on intelligent optimization, predictive analytics, and integrated energy management. A conceptual framework is proposed to demonstrate how AI techniques such as machine learning, deep learning, and reinforcement learning can enhance energy efficiency, reduce carbon emissions, and improve system resilience. The study highlights key challenges, opportunities, and future research directions for AI-driven sustainable energy systems.

ABSTRACT. Kenya’s annual electrical energy consumption of 14,472 GWh continues to increase due to industrial expansion, technological advancement, and population growth. This rising demand necessitates the integration of renewable energy sources such as solar, wind, geothermal, hydropower, and bioenergy into the national grid. Safe integration requires strict synchronization of voltage, frequency (50 Hz), phase sequence, and phase angle to maintain grid stability and prevent system faults.

This paper presents an intelligent dual-layer PCB architecture-based grid protection and monitoring system incorporating multi-parameter sensing, smart grid communication, and predictive analytics. The system continuously monitors voltage, current, frequency, and phase variations across three-phase networks using true-RMS sensors and pulse-width modulation (PWM) frequency simulation for testing. Upon detecting deviations beyond synchronization thresholds, the controller executes automatic islanding protection, logs the event, and performs safe reconnection after stability restoration.

Real-time data is transmitted via Modbus RTU, LoRa, MQTT and Wi-Fi hybrid protocols through the dual-layer PCB architecture used to a cloud-based IoT dashboard for visualization, alerts, and trend analysis. A machine learning module processes historical data for fault prediction and adaptive threshold adjustment. Experimental results demonstrate accurate monitoring, predictive fault detection, and automated protection, offering a scalable pathway toward Kenya’s smart grid modernization and reliable renewable energy integration.

ABSTRACT. This paper presents a data-driven framework for optimizing energy resource usage in smart homes by combining advanced electricity price forecasting with adaptive battery scheduling. The proposed system integrates a hybrid prediction model—merging Facebook’s Prophet and Transformer-based deep learning—to capture both seasonal patterns and complex temporal dependencies in dynamic electricity pricing. Forecasts are then utilized in a tolerance-aware decision-making module that strategically controls battery charging and discharging operations. This module employs quantile-based thresholds and real-time price deviation checks to ensure cost-effective energy management under uncertainty. Simulation results using realworld data from April 2025 demonstrate the system’s ability to reduce electricity costs while respecting operational constraints, such as battery capacity and efficiency. The modular design supports customization for different battery configurations and pricing conditions, offering a scalable solution for intelligent, autonomous energy control in future smart home environments.

ABSTRACT. With energy crises always round the corner, the ongoing energy sustainability efforts have significant reliance on citizens. In that respect, citizen awareness around optimal electricity usage and the adoption of energy saving measures is often considered for granted at personal, household, and workplace level. However, this assumption is not always supported in practice and in this work we assess the extent to which this is the case. To achieve this, we carried out a quantitative survey based on a structured questionnaire that explored the habits, knowledge, perceptions, and attitudes of adult inhabitants in Cyprus primarily regarding electricity usage at home while also touching upon other energy-related topics. The results revealed widespread misconceptions and knowledge gaps, including underestimation or overestimation of appliance consumption, behaviors associated with inefficient energy use, and limited familiarity with concepts such as renewable energy sources and electromobility. Based on our findings we designed and developed an IoT-based residential energy monitoring system and deployed two prototypes in real households; one passive consumer household and one prosumer household equipped with a photovoltaic system. Through this work we demonstrate how identified energy-awareness gaps can be translated into a practical monitoring approach that supports more informed electricity use and provides a basis for future work aligned with the Sustainable Development Goals of the United Nations.

ABSTRACT. Battery energy storage systems (BESS) are increasingly commercialized through contracts that allocate operational control and merchant-revenue risk between asset owners and offtakers, including full tolling agreements, revenue floors with upside sharing, and profit-sharing structures.

In its simplest form, a BESS tolling agreement can be characterized by two parameters: a fixed payment ($P$) and a revenue-sharing coefficient ($\alpha$). The fixed payment is made by the offtaker to the battery owner to guarantee a minimum income and support project financing, whereas $\alpha$ determines the share of merchant revenues allocated to each party. These parameters depend on market characteristics and participants' risk preferences.

In this context, we propose a Stackelberg formulation to analyze representative BESS tolling agreements. For illustration, the offtaker acts as the leader, offering contract terms while anticipating the battery owner's decision under merchant-revenue uncertainty. The results show how revenue uncertainty and the risk aversion of both parties jointly shape contract design, with contracts shifting from pure tolling agreements to hybrid revenue-sharing structures as market volatility and risk preferences change.

ABSTRACT. Climate change and the growing demand for clean energy solutions have driven interest in green hydrogen as a key energy vector in the transition toward low-carbon systems. In this context, residential energy communities with distributed photovoltaic generation represent a favorable environment for local hydrogen production, as they generate solar surpluses that can be valorized through electrolysis rather than exported to the grid at reduced compensation rates. This work proposes a linear programming model for the optimal sizing of hydrogen production systems fed by photovoltaic surpluses in energy communities, minimizing the Annual Cost of the System as the objective function. The model integrates distributed photovoltaic generation, electrolysis, compression, and storage into a single linear optimization problem, validated over eleven real residential communities in Santo André, Brazil. The results provide valuable insights for the techno-economic planning of such systems, demonstrating that the community scale represents an effective intervention level for integrating green hydrogen production into the urban energy transition.

ABSTRACT. In Europe, sulphur hexafluoride (SF₆) remains essential for medium- and high-voltage switchgear due to its ex-ceptional dielectric and arc‑quenching properties. At the same time, its extremely high global warming potential places SF₆ under one of the most stringent regulatory regimes worldwide. With the adoption of Regulation (EU) 2024/573, the EU has clearly defined the transition to-wards closed-loop SF₆ lifecycle management, prioritising emission reduction, reclamation, and re‑use over contin-ued reliance on virgin gas. European and international standards, including IEC 60480, IEC 60376, and CIGRE TB 914, establish a com-prehensive technical framework for the reuse, purifica-tion, and quality control of SF₆. Within this framework, modern online gas drying and reclaiming technologies, applicable even during normal operation of gas‑insulated switchgear (GIS), enable compliance with regulatory requirements while significantly reducing outages, opera-tional costs, and environmental impact. A key focus is placed on humidity control, which is recognised as a critical factor influencing gas quality, insulation performance, and long‑term asset reliability. Periodic monitoring of SF₆ purity, humidity, and decom-position products supports predictive maintenance strat-egies aligned with European asset management and sus-tainability objectives. In addition, off‑site reclamation technologies based on membrane, pressure - cryogenic separation allow used SF₆ to be purified to a quality equal to or exceeding vir-gin gas, with substantially lower energy consumption and negligible emissions compared to new gas production. Together, these approaches demonstrate that compliance with European climate regulation, high grid reliability, and reduced lifecycle costs can be achieved simultaneous-ly, establishing closed‑loop SF₆ management as the new operational standard for the European power sector

ABSTRACT. To meet the European Green Deal’s goal of a 90% reduction in transport emissions by 2050, a significant modal shift from road and air toward rail and sustainable public transport is required. However, the current fragmentation of booking systems and the absence of a unified "travel contract" framework remain critical barriers to the cross-border adoption of Mobility as a Service (MaaS). This article explores the development of an integrated software platform designed to serve as the digital backbone for multimodal journeys, bridging the gap between long-distance rail and local "last mile" transit. By analyzing the current policy landscape and technical requirements, the study identifies that the primary bottleneck is increasingly policy-driven rather than technological. Furthermore, the article examines the shift in financial sustainability models, contrasting the early commercial pioneers with the public-sector-led initiatives. We conclude that the viability of such digital infrastructure must be evaluated through a holistic cost-benefit analysis that prioritizes decarbonization and public health over traditional ticket margins. By resolving local policy and financial hurdles, this integrated approach can finally empower European citizens to adopt sustainable mobility as a reliable travel option.