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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