Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Efficient electricity distribution in rural areas is strongly affected by seasonal agricultural energy demand, particularly in irrigation-intensive regions where electricity consumption increases substantially during summer periods. Conventional transformer operation strategies in such rural grids often fail to adapt to seasonal load variability, leading to unnecessary idle operation, increased technical losses, and reduced infrastructure efficiency. Existing approaches generally rely on static assumptions or simulated data, limiting their ability to represent real irrigation-driven seasonal load asymmetry. To address this issue, this study proposes a data-driven multi-objective seasonal transformer scheduling framework using a bio-inspired Artificial Immune System (AIS) algorithm. The model was developed using two years of empirical hourly SCADA load data and transformer operation records obtained from a real 380/154 kV TEİAŞ transmission substation in Central Anatolia, Türkiye. Hourly SCADA measurements were used for seasonal load characterization and objective-function evaluation, while transformer scheduling decisions were defined at the seasonal operational level. The proposed AIS-based scheduling strategy reduced annual technical energy losses by approximately 5.4 GWh, decreased operational costs by 10.81 million TL (≈360,000 USD), and lowered carbon emissions by about 2270 metric tons of CO2 compared with conventional static transformer operation. The study presents a proof-of-concept framework integrating empirical SCADA measurements with AIS-assisted seasonal transformer scheduling for practical utility-scale operational planning in irrigation-dominated rural electricity networks....
In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demandside management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction....
The increasing penetration of inverter-based renewable energy sources has significantly reduced system inertia, leading to faster frequency deviations in low-inertia power systems. This paper proposes an asynchronous distributed model predictive control (AD-MPC) strategy to coordinate flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) for enhanced frequency stability in low-inertia power grids. A modified IEEE 39-bus system integrating a 3 MW wind energy conversion system (WECS), a 2 MW PV solar unit, and an electric vehicle (EV) load emulator unit was simulated to evaluate the system performance of the controller under a 30% increase in load disturbance. The results show that the coordinated FESS–BESS operation using the proposed AD-MPC controller achieves faster frequency recovery and reduces frequency deviation by 4% compared to single storage configurations. The proposed approach demonstrates that the high-speed FESS can provide a rapid inertial response, while the BESS delivers primary frequency support, offering a promising solution for maintaining dynamic stability in future renewable-dominated power systems....
Real-time monitoring of marine ecosystems is crucial for global climate change research. In extreme marine environments such as the westerly regions in the Arctic and Antarctic, monitoring buoys and platforms often suffer from severe challenges, including insufficient energy supply, limited battery life, and difficult maintenance. Triboelectric nanogenerators (TENGs) offer a promising strategy for self-powered marine sensing. However, conventional tubular liquid–solid triboelectric nanogenerators (LS-TENGs) suffer from low efficiency of interfacial charge transfer due to limited contact area and excessive internal resistance, which restricts their output. In this study, a multi-tube nested liquid–solid triboelectric nanogenerator (MLS-TENG) is proposed, and the suitable filling ratio is determined through comparative experiments on structural parameters. This design significantly increases the effective contact area, reduces internal resistance, and improves synergistic charge transfer at multiple interfaces. Experimental results demonstrate that the MLSTENG exhibits substantially improved electrical output compared with the corresponding single-tube structures. When integrated with a power management module, the capacitor charging efficiency is improved by approximately 120 times. In real sea trials, an array composed of MLS-TENG units successfully drives a self-powered sensing system, achieving stable 4G transmission of environmental parameters. This work provides a scalable structural optimization strategy for constructing high-performance blue energy-harvesting self-powered nodes for the marine Internet of Things....
The intermittent nature of renewable energy sources such as solar and wind power poses significant challenges for grid stability and energy management. Accurate forecasting is crucial for mitigating these challenges, as traditional models such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) often fail to capture the non-linear relationships between weather patterns and energy generation. To address this limitation, this research proposes a machine learning framework leveraging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Recurrent Neural Networks (RNNs) for time-series forecasting. By integrating system design parameters with meteorological data, the framework aims to enhance prediction accuracy. The potential outcomes of this framework are not just improved grid stability, optimized energy storage utilization, and reduced operational costs, but also a significant step towards the efficient integration of renewable energy into the power system, fostering a sense of optimism for the future of renewable energy forecasting....
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