The growing imperative for energy efficiency and sustainable resource management necessitates advanced thermal energy storage (TES) solutions to bridge the temporal mismatch between energy supply and demand. Latent Heat Thermal Energy Storage (LHTES) systems, leveraging the high energy density associated with the phase change of materials, offer a particularly promising avenue. This article presents a comprehensive study on a packed-bed LHTES unit, combining experimental measurements and numerical simulations to evaluate its performance when utilizing different paraffin-based Phase Change Materials (PCMs). The investigation details the design and construction of the experimental setup, the meticulous measurement techniques employed (with uncertainty analysis), and the development and validation of a Computational Fluid Dynamics (CFD) model. The study systematically examines the charging and discharging characteristics, heat transfer rates, and thermal efficiency for various paraffins under different operating conditions. Findings reveal significant differences in performance attributed to the thermophysical properties of the chosen PCMs. The validated numerical model further enables detailed parametric studies, providing critical insights into the underlying heat transfer mechanisms and offering practical guidance for optimizing packed-bed LHTES designs for enhanced energy storage and release, thereby contributing to the broader application of sustainable energy technologies.

This research presents a stochastic multi-objective optimization framework for the dispatch and decision-making processes in integrated electric and thermal energy systems (IETES). The approach considers uncertainties in renewable energy generation, load demands, and market prices to ensure reliable and efficient energy management. A multi-objective evolutionary algorithm is employed to simultaneously minimize operational cost, emission levels, and unmet energy demand. The model integrates both electric and thermal subsystems, including combined heat and power (CHP) units, boilers, and energy storage devices. Pareto-optimal solutions are evaluated using a decision-making tool to identify the best trade-offs among conflicting objectives. The results demonstrate the proposed framework’s robustness under stochastic conditions and its potential to support sustainable and economically viable energy planning in modern smart grids.

This study explores the techno-economic feasibility and optimal configuration of off-grid hybrid renewable energy systems (HRES) for rural electrification in Ethiopia. The proposed systems integrate solar photovoltaic (PV), wind turbines, and battery storage, with or without diesel backup, to ensure reliable and sustainable energy access in remote areas. Using HOMER Pro software, various system combinations were simulated based on local resource availability, load profiles, and economic parameters. The analysis reveals that optimized HRES configurations can significantly reduce the levelized cost of electricity (LCOE), greenhouse gas emissions, and reliance on fossil fuels. Solar-wind-battery systems, in particular, showed promising results in terms of cost-effectiveness and environmental sustainability. This work supports Ethiopia’s energy access goals and provides a viable framework for clean energy implementation in underserved rural regions.