ABSTRACT Microgrids are expected to play a central role in future smart grids due to their ability to enhance flexibility, controllability, and service provision at the distribution level. In such systems, the energy management system (EMS) and its supervisory control functions are critical for coordinating operation across grid‐connected and islanded conditions. IEEE 2030.7 provides a comprehensive functional framework for microgrid controller interoperability and operational coordination. This paper presents an offline PSCAD–Python co‐simulation framework for evaluating the impact of different microgrid operational states on control variables and pricing‐related outcomes. The proposed framework enables supervisory EMS assessment while capturing the dynamic behaviour of converter‐based distributed energy resources and microgrid transitions. It is applied to steady‐state and transition operational states, including grid‐connected operation, islanded operation, planned islanding, unplanned islanding, and reconnection. The results indicate that operational‐state changes substantially influence system response, supervisory control actions, and pricing‐related outcomes. These findings demonstrate the value of the proposed framework as a simulation‐based platform for assessing microgrid EMS behaviour under diverse operational conditions.
ABSTRACT Cryptocurrencies have emerged as a transformative force within the global financial ecosystem, propelled by the innovation of blockchain technology. This decentralized ledger system has garnered widespread attention for its potential to reshape traditional financial paradigms. However, the substantial electric energy consumption by cryptocurrency miners poses a significant environmental challenge, necessitating urgent intervention to mitigate its ecological impact. Renewable energies are great choices to deal with this challenge, presenting opportunities for promoting eco‐friendly practices within the blockchain industry. This article reviews the literature on different aspects including the electric energy demand of cryptocurrency, the application of renewable energies such as hydro, solar, wind, geothermal, and bio energies in cryptocurrency mining, and cryptocurrency policies, seeking to present the green solution for cryptocurrency mining. It aims to empower cryptocurrency miners, energy providers, and researchers in advocating for environmentally friendly practices within the blockchain industry. Unlike previous reviews that often focus on a single or two renewable sources or only on Bitcoin, this study provides a comprehensive comparison of five renewable technologies across multiple cryptocurrencies and ranks them based on LCOE, maturity, and operational suitability for continuous mining. With the increasing accessibility and affordability of renewable energy sources globally, there is optimism for transitioning towards cleaner power options in cryptocurrency mining. Through collaborative efforts and informed decision‐making, a future is envisioned where energy‐efficient solutions take precedence in sustainable cryptocurrency mining practices. The findings show that wind and solar energies are the best selections for cryptocurrency mining, respectively.
ABSTRACT This study examines the ambient‐adjusted rating (AAR) of flexible busbar conductors in power substations, a simple form of dynamic thermal rating that is directly applicable with existing meteorological infrastructure. One year of weather data measured at a Slovak transmission substation was used to rate the ACSR 758‐AL1/43‐ST1A conductor with two AAR models: M1, with variable ambient temperature and constant solar irradiation, and M2, with both quantities variable. The resulting dynamic ratings are compared with the static ratings of four Central European countries. The median rating of model M2 is 39% above the Slovak static value at the 80∘C conductor temperature limit, with an overload potential index of 1.91. The revision extends the original analysis in three directions requested by practice: a wind‐speed sensitivity study (0.2–2.0 m/s), a full dynamic rating model M3 that uses the measured wind speed, and a statistical assessment covering seasonal variability, rating distributions, thermal risk at static‐rating loading, and measurement uncertainty. Model M3 reaches a median of 1851 A, 24% above M2, which quantifies the capacity given up by the conservative wind assumption of the AAR. A thermal‐risk analysis shows that continuous loading at the Slovak static rating never brought the conductor above 77∘C during the analysed year. The results support AAR as a low‐cost first step for both transmission and distribution system operators, with straightforward validation through conductor temperature monitoring in the confined substation environment.
ABSTRACT Cables are widely used in renewable‐energy stations, and the reliable assessment of their insulation condition is essential for safe and stable system operation. In this study, the harmonic characteristics of the loss current in 10 kV XLPE cables containing conductor spike defects were investigated through simulation and experimental analysis. Based on the bipolar charge transport model, the nonlinear conductivity behavior of XLPE under high electric fields was analyzed, and the effects of tip curvature radius, defect location, and electrode spacing on the loss current and conductivity characteristics were evaluated. The results show that conductor spike defects cause severe local electric‐field distortion, which induces periodic fluctuations in insulation conductivity and distorts the loss‐current waveform, leading to the appearance of higher‐order harmonics. The harmonic components are dominated by the 3rd and 5th harmonics, and their contribution ratios vary with defect severity and location. As the defect becomes more severe, the amplitude and total harmonic distortion of the loss current, as well as the conductivity distortion rate, all increase significantly. Moreover, defects located at the conductor core produce higher distortion levels than those located at the shielding layer. A representative experimental condition supports the principal 3rd‐ and 5th‐harmonic signatures predicted by the simulation, providing a physical basis for further investigation of loss‐current harmonics as comparative indicators of conductor spike defects in 10 kV XLPE cables.
ABSTRACT Indoor localization using Wi‐Fi fingerprinting has gained significant attention due to its cost‐effectiveness and the widespread availability of Wi‐Fi infrastructure. However, the high dimensionality of data, along with signal fluctuations and redundant access points (APs), often degrades localization accuracy and increases computational complexity. This paper proposes an improved binary particle swarm optimization‐based feature selection strategy to identify the most informative APs for fingerprint‐based indoor localization. The approach integrates a novel hybrid transfer function combining V‐shaped and U‐shaped characteristics with an enhanced learning mechanism to balance exploration and exploitation while preserving population diversity. An improved weighted k‐nearest neighbours algorithm is employed to evaluate the selected AP subsets. Experiments conducted on the UJIIndoorLoc benchmark dataset demonstrate that the proposed method achieves an overall weighted mean localization error of 6.27 m while selecting approximately 85% fewer APs than the original feature set. The proposed AP selection strategy reduces computational overhead during localization, making the method suitable for deployment on resource‐constrained devices without compromising localization performance.
