https://espo.kut.ac.ir/ International Journal of Energy Systems Planning and Optimization (ESPO) en daily 1 Thu, 01 Jan 2026 00:00:00 +0330 Thu, 01 Jan 2026 00:00:00 +0330 https://espo.kut.ac.ir/article_732695.html Accurately modeling electric vehicle (EV) charging behavior is crucial for the design and operation of charging stations that offer multiple charging options to drivers. A realistic representation of driver decision-making enables better planning, load management, and energy efficiency. This paper proposes a weighted index-based model that captures the heterogeneous preferences of EV drivers, incorporating four key factors: battery state of charge (SoC), time sensitivity, price sensitivity, and environmental impact. Each factor is assigned a weight and combined into separate indices for slow and fast charging options. The final charging decision for each EV is determined by selecting the option with the lower weighted index, reflecting the drivers’ priorities and real-world behavior. Randomized input within predefined ranges allows the model to replicate variability among drivers. The proposed methodology captures both deterministic tendencies, such as preference for fast charging when the battery is low, and stochastic variations reflecting human behavior. Simulation results demonstrate that the model produces consistent, interpretable, and realistic charging patterns, with the distribution of slow and fast charging choices closely aligning with expected driver behavior across multiple scenarios. The simulation results indicate that the model converges well under stochastic input variables. Specifically, running 20 simulations for five EVs showed that, in most cases, either three EVs chose fast charging and two chose slow charging, or vice versa. A sensitivity analysis of the parameter weights further revealed that changing the weights of the environmental, price, and time indices led to a respective increase of 12%, 30%, and 54% in the probability of choosing slow charging. https://espo.kut.ac.ir/article_731350.html Biodiesel, a renewable and biodegradable fuel, offers a promising alternative to fossil fuels. This study investigates the production of biodiesel using a novel, cost-effective, and environmentally sustainable catalyst derived from waste materials. Specifically, biochar from pine fruit was used as the catalyst support, while eggshells, rich in calcium oxide, served as the active catalytic component. The catalyst and its support were characterized using FESEM, XRD, EDX, and FT-IR. The transesterification reaction was optimized using response surface methodology with Design-Expert 7.0.0 software, varying the oil to methanol volume ratio (1, 2, and 3 v/v), reaction time (60, 90, and 120 min), and eggshell percentage in the catalyst (20, 30, and 40 wt.%). The optimal conditions were determined to be an oil to methanol ratio of 2 v/v, a reaction time of 90 min, and an eggshell percentage of 30 wt.%, resulting in a biodiesel purity of 96.83%. These findings demonstrate the potential of utilizing waste-derived catalysts for efficient and sustainable biodiesel production, offering a promising avenue for future research focusing on process optimization and catalyst longevity. https://espo.kut.ac.ir/article_735025.html As distributed generation units and renewable energy sources become increasingly integrated into modern power systems, ensuring the stability of the DC-link voltage—especially in the presence of potential cyber threats—poses a significant challenge. Traditional control methods, such as proportional–integral (PI) controllers, and even many contemporary intelligent algorithms, often struggle to maintain performance under unexpected cyberattacks or falsified data due to their reliance on accurate system models or extensive retraining. This study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control strategy that merges the adaptive learning ability of neural networks with the robustness of fuzzy logic, enabling real-time adjustment of control parameters. The primary contribution of this approach is its capacity to autonomously detect and mitigate sophisticated cyber threats—including False Data Injection Attacks (FDIA), Denial-of-Service (DoS) attacks, and cyber-induced load fluctuations—without the need for predefined system models or extensive retraining. Simulation results on the IEEE 13-bus network with integrated solar and wind generation, implemented in MATLAB/Simulink, show that the proposed controller significantly improves DC-link voltage stability, shortens recovery time, and enhances overall network resilience compared to conventional PI and other intelligent controllers. These findings highlight that the ANFIS-based controller effectively addresses the limitations of traditional methods, offering a practical and robust solution for modern smart and resilient power grids.