Vol. 4 No. 3 (2026): In Progress

  • Open Access

    Article

    Article ID: 4347

    Monte Carlo simulation of a direct beta-radiation harvester: Impact of electric fields

    by Itzhak Orion, Adir Cohen, Elroei Damri

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    The viability of high-efficiency direct energy harvesting from a beta-emitting radioactive source was previously experimentally investigated using an apparatus based on an ionization chamber equipped with an internal positioning stage for the source. This novel concept uniquely leverages the direct collection of beta particles on a converter electrode, converting their kinetic energy into electrical power and enabling compact power sources. This approach offers ultra-long-life, maintenance-free solutions in applications where conventional batteries are impractical. To evaluate system performance, we employed Monte Carlo simulations to model electron transport, governed by continuous slowing down due to Coulomb interactions. Electron interactions in matter, dominated by elastic and inelastic scattering, can lead to ionization events accompanied by X-ray fluorescence or Auger electron emission. EGS5 Monte Carlo simulations were performed to study the response of an ionization chamber to a Ni-63 beta source under various electric field strengths. The simulated dosimetric response was compared with previous experimental measurements. Gas-filled ionization chambers, consisting of two electrodes in a controlled electric field, are widely used for radiation dosimetry. Simulations using four different chamber gases across a wide range of field strengths showed consistent agreement with measured dose responses. These results demonstrate that the developed simulation framework is an accurate and versatile tool for investigating electron behavior in gas-filled detectors. It can be confidently applied in future studies to explore alternative gases, field configurations, or radiation sources. Additionally, the findings support the feasibility of direct beta radiation harvesting, highlighting its potential for developing compact and efficient power sources.

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  • Open Access

    Article

    Article ID: 4509

    Degradation-aware AI energy management for hybrid supercapacitor–battery energy storage systems in microgrids

    by Lalit Sachdeva, Utkarsh Anand

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    The presence of intermittent sources of renewable energy in power systems requires ESSs to manage temporal imbalance in energy supply and demand. In this study, we introduce a hybrid energy storage system (HESS) coupled with an AI energy management system (EMS) that uses deep reinforcement learning (DRL) for optimal scheduling of renewable energy utilization within grid-connected and islanded microgrids. AI-enabled EMS utilizes a DRL agent with proximal policy optimization (PPO) to make optimal decisions regarding energy generation based on state space and economic considerations, while accounting for SoC constraints of batteries. An important aspect of the proposed system is the design of HESS architecture and reward function based on DRL. Further improvements are made via analysing the PPO clipping sensitivity, Pearson correlation analysis on the relationship between the intermittency of renewables and response latency, and Monte Carlo uncertainty analysis with a 95% confidence interval. For a 24-hour simulation period, the developed system is able to cut down on grid power imports by 43.2%, have an 87.3% renewable energy utilization rate, extend the lifespan of the battery from 8.1 to 12.5 years, and have 91.4% peak shaving efficiency through 100 Monte Carlo runs and without any SoC violations (25%–90%). Net benefit analysis is estimated to be $56,000–$66,000 for 15 years at a 6% discount rate, while a 120 ms response time and one-way ANOVA with Tukey's HSD confirm statistical significance.

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  • Open Access

    Article

    Article ID: 4586

    Coal fly ash—Municipal solid waste gasification char hybrid electrodes: Pore structure and charge-transport characteristics

    by Satria Pinandita, Rustam Asnawi, Mochamad Syamsiro

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    The development of sustainable and cost-effective electrode materials is crucial for advancing metal–air battery technology and reducing dependence on conventional carbon resources. This study explores coal fly ash and Municipal Solid Waste Gasification (MSWG) char as waste-derived electrode precursors, utilizing their mineral-rich and carbonaceous characteristics to improve electrochemical performance. The objectives were to characterize the raw materials’ elemental composition and surface functional groups and to comparatively evaluate three hybrid-electrode formulations based on their pore characteristics, electrochemical response, and charge-transfer behaviour. SEM–EDX showed coal fly ash with spherical, microsphere-like particles rich in O, Si, and Al, indicating its role as a mineral-rich silicate and aluminosilicate precursor. MSWG char, however, exhibited irregular, rough, agglomerated morphology with higher carbon content, serving as the main carbon source. FTIR confirmed silicate, oxide, hydroxyl, carbonate, and carbon–mineral bonding. Among the three formulations tested, the formulation containing 20 wt.% coal fly ash (F20) showed the best overall performance, with the highest micropore surface area (1.601 m2 g⁻1) and volume (0.0006110 cm3 g⁻1). It delivered the largest CV response, energy density of ~208 Wh kg⁻1, power density of 17.8 W kg⁻1, and the lowest ΔZ′ of 0.137 Ω. The descriptive results indicate that the measured performance may depend on active-pore accessibility and ion–electron transport characteristics rather than on total BET surface area alone. Further replicated experiments are required to establish the statistical reliability of the observed differences. This study advances waste-ash-derived electrodes for sustainable metal–air battery applications. The novelty of this work is the integrated composition–pore–transport evaluation of directly blended coal fly ash and MSWG char hybrid electrodes.

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  • Open Access

    Article

    Article ID: 4664

    An integrated LSTM-MPA framework for intelligent hybrid renewable energy forecasting and charging optimization of campus electric bicycle and motorcycle systems

    by Mochamad Subchan Mauludin, Yuki Trisnoaji, Arif Rifan Rudiyanto, Singgih Dwi Prasetyo

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    The increasing adoption of electric bicycles and motorcycles has intensified the demand for sustainable and reliable charging infrastructures, particularly in campus environments characterized by fluctuating mobility patterns and renewable energy variability. This study proposes an intelligent hybrid solar–wind renewable charging framework integrated with Long Short-Term Memory–Marine Predators Algorithm (LSTM-MPA) optimization to improve adaptive forecasting, charging stability, and renewable energy utilization. The methodology combined systematic meta-analysis and deep learning simulation approaches. A total of 30 empirical studies were analyzed using PRISMA-based selection procedures, risk-of-bias assessment, effect-size evaluation, and publication bias analysis. Experimental renewable energy datasets consisting of photovoltaic (PV), wind turbine, and charging parameters were modeled using the proposed hybrid LSTM-MPA architecture. The results demonstrated that Solar-Wind-Deep Learning systems achieved the highest average effect size of 20.653. The forecasting model achieved Root Mean Square Error (RMSE) values of 115.70–125.65 and Mean Absolute Error (MAE) values of 90.34–93.64, while training and validation losses decreased by more than 39%, indicating stable convergence. Operational analysis showed that hybrid renewable generation consistently exceeded campus charging demand, maintaining battery State of Charge (SoC) near 100% and resulting in a cumulative renewable energy generation of 43,235.30 kWh. Annual renewable energy exceeded 2,070 kWh with a maximum energy balance of 882.64 kWh. The system was economically feasible, achieving an IRR of 14.9% and positive cumulative cash flow after the seventh operational year, while reducing cumulative CO₂ emissions by more than 22,450 kg over 20 years.

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  • Open Access

    Article

    Article ID: 4726

    Comparative analysis of steady-state and dynamic models for grid-connected photovoltaic farm performance monitoring: A case study of Huawei SUN2000-60KTL-M0 inverter

    by Dias Prihatmoko, Rustam Asnawi, Moh. Khairudin, Nor Azlan Othman

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    This study evaluates the solar-to-electrical energy conversion performance of a 63.24 kWp grid-connected photovoltaic (PV) farm equipped with a Huawei SUN2000-60KTL-M0 (60 kW) inverter by comparing a steady-state diagnostic model against a dynamic observability assessment. A single-instant snapshot of 12 PV strings on 26 June 2026 identified three non-producing strings (PV6, PV7, PV8), reducing string availability to 75.0% and causing an instantaneous DC power shortfall of approximately 6.0 kW (23.6% of currently achievable capacity). Inverter DC-to-AC conversion efficiency was 98.72% and the observed daily-equivalent capacity factor was 15.48%, both computed directly from measured values. Performance Ratio is reported as not evaluable due to the absence of temporally overlapping plane-of-array irradiance data. The dynamic assessment evaluates day-to-day energy temporal variability and historical hourly weather variability from measured June 2026 and 2020–2025 records (mean absolute daily ramp 12.66%, daily energy CV 19.45%), while sub-minute electrical transient characteristics remain not evaluable owing to the lack of sub-minute electrical logging. A Model Observability Score (MOS) and Data Resolution Adequacy Index (DRAI) are computed from an a priori parameter registry. The steady-state model achieves an MOS of 87.5% (7 of 8 applicable parameters), while the dynamic model achieves 25.0% (4 of 16), with the gap concentrated in the electrical-transient domain and attributable to instrumentation limitations. A phased instrumentation roadmap is proposed. The findings provide a transparent, auditable template for determining appropriate monitoring depth in tropical grid-connected PV operations and maintenance practice.

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  • Open Access

    Article

    Article ID: 4414

    Renewable Energy Systems for Multi-Form Coupling: Design Strategies and Energy Storage Solutions

    by Shanshan Sun

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    To transition to carbon-neutral energy systems, it is necessary to transform traditional single-energy infrastructure into integrated renewable systems capable of combining electricity, heating, cooling, hydrogen, gas fuels, and transport. This survey explores the renewable energy systems to be coupled with multi-form systems with special emphasis on design and energy storage strategies. First, the architectural properties of coupled systems are discussed in terms of integration of renewable resources, inter-energy conversion pathways, and building, microgrid, industrial, and regional structural configurations. Second, the major design strategies are discussed, including system planning, capacity configuration, operational scheduling, control architecture, and resilience design. Third, the review assesses the contributions of electrical, thermal, hydrogen-based, chemical, and hybrid storage technologies to allow short-term regulation, long-duration balancing, and cross-sector flexibility. The comparison, modeling, and optimization techniques are also discussed to elucidate the role of techno-economic performance, environmental impact, and constraints on operational capabilities in system design choices. The review also specifies the key challenges associated with multi-timescale coordination, uncertainty propagation, interoperability, investment incentives, safety, and standardization. Based on this, trends in future development are emphasized, such as sector-coupling architecture hybrids, long-term storage, AI-based energy management, and modular deployment channels. This review provides a unified view that connects architecture and design, storage, and system analysis, and argues that coordinated multi-form coupling will be fundamental to creating flexible, resilient, and profoundly decarbonized renewable energy systems. To transition to carbon-neutral energy systems, it is necessary to transform traditional single-energy infrastructure into integrated renewable systems capable of combining electricity, heating, cooling, hydrogen, gas fuels, and transport. This survey explores the renewable energy systems to be coupled with multi-form systems with special emphasis on design and energy storage strategies. First, the architectural properties of coupled systems are discussed in terms of integration of renewable resources, inter-energy conversion pathways, and building, microgrid, industrial, and regional structural configurations. Second, the major design strategies are discussed, including system planning, capacity configuration, operational scheduling, control architecture, and resilience design. Third, the review assesses the contributions of electrical, thermal, hydrogen-based, chemical, and hybrid storage technologies to allow short-term regulation, long-duration balancing, and cross-sector flexibility. The comparison, modeling, and optimization techniques are also discussed to elucidate the role of techno-economic performance, environmental impact, and constraints on operational capabilities in system design choices. The review also specifies the key challenges associated with multi-timescale coordination, uncertainty propagation, interoperability, investment incentives, safety, and standardization. Based on this, trends in future development are emphasized, such as sector-coupling architecture hybrids, long-term storage, AI-based energy management, and modular deployment channels. This review provides a unified view that connects architecture and design, storage, and system analysis, and argues that coordinated multi-form coupling will be fundamental to creating flexible, resilient, and profoundly decarbonized renewable energy systems.

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  • Open Access

    Article

    Article ID: 4742

    A comprehensive optimization and energy conservation assessment of R1234yf-based hybrid nanorefrigerants for sustainable HVAC applications: Thermodynamic, economic, environmental and AI

    by Jay Patel, Shailesh K. Patel, Choon Kit Chan

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    In this study, authors present a complete energy system optimization framework of R1234yf based hybrid nanorefrigerant and hybrid nanolubricant system for improving the thermodynamic, economic, environmental and energy performance of vapor compression refrigeration system for Indian operating conditions. Six nano-particles (Al₂O₃, TiO₂, CuO, SiO₂, graphene nanoplatelets (GNP) and multi-walled carbon nanotubes (MWCNT)) were assessed for the thermophysical parameters, dispersion stability, coefficient of performance (COP) of the system, exergy efficiency, energy consumption of the compressor, life-cycle economics, environmental impact, and artificial intelligence-based prediction. The results show that the thermal conductivities of nanorefrigerants are significantly better than those of the conventional refrigerants, and the COP of the R1234yf system is improved closer to the COP of the conventional refrigerants, with the Al₂O₃–SiO₂/PAG hybrid nanolubricant being more stable, while also reducing the power consumption of the compressor and improving the exergy efficiency. Both economic analysis and environmental assessment show good payback periods and significant reductions in indirect carbon emissions. The energy optimization of the system can be performed quickly and accurately with high reliability using the artificial neural network models. The proposed hybrid nanorefrigerant approach not only enables energy conservation and sustainable cooling but also paved a way for the HVAC systems to be compatible with renewable energy resources and to become low-carbon refrigeration technologies, directly contributing to Sustainable Development Goal 7 (Affordable and Clean Energy) via its high energy efficiency and its contribution to cooling in an environmentally friendly way.

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  • Open Access

    Article

    Article ID: 4609

    Comparative analysis of half-cut monofacial monocrystalline solar panel and traditional full-cell monofacial monocrystalline solar panel

    by Akpovi Oyubu Oyubu, Ufuoma Kazeem Okpeki, Anthony Onoharigho Okpare, Ikechukwu Emmanuel Onuigbo, Favour Emmanuel Egwuenu, Akpoghene Destiny Dibie

    Energy Storage and Conversion, Vol.4, No.2, 2026;

    Despite the theoretical benefits of the half-cut cells over the traditional full-cell monocrystalline panel, there is limited comparative analysis showing its power output by comparing voltage, current, efficiency and other parameters between both panels under varying sunlight conditions; thus this research aimed to evaluate and compare the performance of both panel technologies in terms of voltage, current, and power output at different times of the day, while also considering environmental factors such as temperature, humidity, and solar irradiance. An experimental research design was adopted using two 400 W solar panels with measurements taken simultaneously at 2.5 h intervals between 10:00 am and 3:00 pm for a period of three months under two scenarios—without shading and with shading. The findings revealed that while the traditional panels consistently produced higher voltage values, the half-cut panels generated significantly higher current outputs resulting in greater overall power generation. Statistical analysis including percentage variance, and standard deviation reveal that the energy yield of the half cut is slightly higher than that of the full cut; but the Analysis of Variance (ANOVA) carried out to deepen the result of the statistical analysis depict that there is no significant difference in the power generation of both technologies under the ‘without shading’ scenario. However, from the ANOVA, a significant difference under the ‘with shading’ scenario with the half-cut panels having a higher power generation thus confirming the superior energy yield and performance stability of the half-cut panels under all environmental conditions including shading when compared to their traditional full-cell counterpart.

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