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.

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.

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.

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.

Open Access

Article

Article ID: 4210

Analysis of electricity production and consumption in Morocco: Assessing the electricity deficit and key contributing factors

by Ilham Ait-Oujalla, Jamal Mabrouki, Driss Azdem, Salima Boudraham, Najat Qisse, Ibrahim Alsayer, Younes Abrouki

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

This paper examines the electricity sector of Morocco from 2011 to 2022. Based on the data from the U.S. Energy Information Administration, the World Bank, and ONEE, four major indicators were computed: electricity deficit (ED), electricity import dependency (EID), self-sufficiency ratio (SSR), and per capita electricity consumption (PCEC). The findings indicate that electricity production increased from 20.3 TWh in 2011 to 41.2 TWh in 2022, while consumption climbed from 26 TWh to 35 TWh for the same period. The self-sufficiency ratio remained above 100% after 2015 and reached a maximum of 121.2% in 2019, clearly indicating Morocco's transition from an importer to a net exporter of electricity. Although the renewable capacity has grown to make up 38% of the mix, coal stayed the main source (59.4% in 2022). The per capita consumption went up from 790 kWh to 934 kWh. These results unveil that Morocco has made remarkable strides in electricity self-sufficiency while still facing the challenge of fossil fuel lock-in. Accelerating the integration of renewables, making the grid more flexible, and formulating specific coal transition policies are the study's main recommendations for ensuring sustainability in the long run.

Open Access

Article

Article ID: 4291

Performance comparison of solar fed MPC and AI controller for fast battery charging in electric vehicles

by Apoorva Srivastava, Mohammad Saif Raza, Faiz Haider, Prasant Shukla

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

The rapid growth of electric vehicles (EVs) has increased the demand for charging infrastructure that is not only fast and efficient but also environmentally sustainable. Solar-powered EV charging stations, which integrate photovoltaic (PV) systems, offer a promising solution by reducing dependence on the electrical grid and lowering carbon emissions. However, the intermittent nature of solar energy creates significant challenges for maintaining stable and efficient fast-charging operations. This study evaluates the performance of different control strategies for a solar-powered EV fast-charging system. A comparative analysis was conducted between Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), and Artificial Neural Network (ANN)-based controllers. The system consisted of a 100 kWp PV array, a 400 V DC bus, a bidirectional DC–DC converter operating at 20 kHz, and a 60 kWh EV battery charged at 1C–2C rates. The MPC controller was designed with a prediction horizon of 10, a control horizon of 3, and a sampling time of 100 μs using quadratic cost optimization, while the DRL controller employed a Deep Q-Network framework. Simulation results demonstrated that the DRL-based controller achieved superior performance under varying irradiance conditions. Compared with MPC, it increased solar energy utilization by 8%, improved charging efficiency by 12.8%, and reduced battery degradation by approximately 15% over 1000 charge–discharge cycles. In addition, DRL exhibited faster transient response, achieving system stabilization within 0.21 s during sudden irradiance changes, compared with 0.35 s for MPC. The findings indicate that advanced adaptive control strategies can enhance energy utilization, charging performance, and battery longevity in solar-powered EV charging applications.