Journal of Green Energy Research and Innovation

Abstract In this paper, a multi-carrier energy hub that can generate and deliver electricity, heating, and cooling ‎energy from different sources, such as wind, solar, fuel cells, batteries, and compressed air is ‎proposed. The intelligent energy hub can also participate in electrical and thermal demand response, ‎which aims to reduce peak demand and enhance overall system efficiency. The scheduling problem is ‎a mixed-integer linear programming problem that seeks to minimize the system cost and carbon ‎dioxide emissions. To obtain optimal solutions that strike a balance between cost, emissions, and ‎decision maker's preferences, an augmented epsilon-constraint min-max fuzzy method is employed. ‎The proposed strategy's advantages are demonstrated through a case study, where it is compared with ‎other methods. The results show that the proposed approach effectively reduces the cost and ‎emissions of the smart energy hub while improving the load shape and energy hub efficiency. ‎Moreover, the results showed that the integration of compressed air systems and demand response ‎programs enhances the performance of the smart energy hub, making it more flexible and reliable. ‎The GAMS software is employed for the modeling and resolution of the scheduling issue.‎

Abstract This paper investigates the wind energy potential in Markazi Province, Iran, focusing on three ‎cities: Tafresh, Khomein, and Saveh. The primary objective of this study is to provide a ‎comprehensive analysis of wind patterns using a combination of statistical approaches and ‎artificial intelligence techniques. Wind data was collected from advanced meteorological ‎stations in these cities over two years (2018–2020), including detailed measurements of ‎wind speed and direction at 10-minute intervals. This high-resolution dataset facilitated an in-depth examination of wind behavior and its suitability for energy production. Statistical ‎analysis was conducted using the Weibull distribution and wind rose diagrams, which provided ‎insights into the wind characteristics and their spatial variations. Additionally, Long Short-Term Memory (LSTM) networks were employed to predict wind speeds and temporal trends. ‎These models effectively captured the complex relationships within the wind data and produced ‎highly accurate forecasts. The comparison of actual and predicted wind rose diagrams ‎demonstrated a strong alignment, validating the reliability of the LSTM-based predictions in ‎reflecting real-world wind patterns. The results of this study demonstrate that combining ‎traditional statistical methods with modern machine learning techniques provides a robust ‎framework for analyzing wind energy potential. By leveraging these tools, the study offers ‎valuable insights for sustainable energy planning and supports informed decision-making for ‎renewable energy investments in Markazi Province‎.‎

Abstract The research introduces a hybrid light trapping structure designed to enhance the absorption ‎bandwidth in carbon-based perovskite solar cells. Silver nanoparticles coated with silica are ‎included within the active layer of this structure. An anti-reflective coating is applied to the ‎upper surface to enhance the absorption of additional wavelengths. The influence of geometric ‎parameters, such as the radius and period of silver nanoparticles, the thickness of the silica ‎protective shell, and the thickness of the anti-reflection coating, on light absorption is ‎examined. The finite difference time domain technique in Lumerical software is employed to ‎examine the specified parameters. A carbon-based perovskite solar cell was first introduced as ‎a reference, followed by an examination of the proposed structure utilizing various geometric ‎light absorption factors. The simulation findings indicate that nearly total light absorption may ‎be attained using the ideal structural parameters for a 600 nm thick perovskite layer utilizing ‎this configuration. A short-circuit current density of 25.264 mA/cm² can be attained utilizing ‎silver-silica nanoparticles with a radius of 100 nm, a period of 280 nm, and a 60 nm thick ‎PMMA anti-reflection coating over a 600 nm thick perovskite layer. This metric indicates a ‎‎22% enhancement relative to carbon-based perovskite solar cells lacking light control. The ‎suggested hybrid light-trapping architecture enhances light usage and reduces material ‎consumption in carbon-based perovskite solar cells.‎‎

Abstract The significance of transmission network expansion planning (TNEP) in a restructured power ‎system is underscored by the urgent need to integrate renewable energy sources. As the world ‎shifts towards sustainability, effective transmission planning becomes critical for accommodating ‎diverse energy sources, particularly wind and solar, which are frequently situated far from ‎consumption centers. This integration is not only essential for achieving sustainability goals and ‎reducing greenhouse gas emissions but also for ensuring a reliable and efficient power supply. ‎Moreover, strategic transmission planning plays a vital role in minimizing congestion within the ‎network, which can escalate costs and compromise reliability. By anticipating future demand and ‎generation patterns especially the intermittent nature of renewables planners can optimize the ‎placement of transmission lines and substations to mitigate potential bottlenecks. In a competitive ‎market, a resilient transmission infrastructure is crucial for providing equitable access to all ‎market participants, thereby fostering innovation and competition. Additionally, effective ‎planning must address regulatory requirements and stakeholder interests, promoting transparency ‎and collaboration among various entities in the power sector. This comprehensive approach not ‎only ensures compliance but also builds public trust in the energy system. In summary, developing ‎an efficient transmission network is imperative for supporting a reliable, competitive, and ‎sustainable power system that prioritizes renewable energy sources. This paper aims to provide an ‎overview of the challenges ahead in TNEP while proposing necessary solutions to effectively ‎address these challenges‎.‎

Abstract In this paper, a single-stage three-level z-source inverter is utilized for connecting PV panels to the grid. The use of a three-level z-source inverter not only allows adjustment of output voltage but also facilitates the elimination of harmonic components. To extract maximum power from the PV panels, a model predictive control (MPC) strategy based on maximum power point tracking (MPPT) is employed. This method allows the MPC to predict the optimal operating point one step ahead, resulting in a faster response compared to conventional perturb and observe (P&O) method under rapid changes. Finally, the three-level z-source inverter is simulated using MATLAB/Simulink software, and its performance using the MPC method is analyzed. The simulation results have verified that the converter operates effectively.‎

Abstract Since the most important issue in the production of digital currencies is energy consumption, the ‎use of illegal electricity in mining farms has become very popular. Illegal mining is particularly ‎important in countries such as Iran where the price of electrical energy is extremely low. This issue ‎has caused numerous problems such as frequent blackouts, large losses for industries and even ‎daily power cuts in several large cities. Previous machine learning approaches for miner detection ‎are mostly supervised methods which rely on labeled data. Due to the fact that the number of ‎labeled data is very limited in reality, we propose unsupervised methods in this paper. A real data ‎set from Markazi Province Distribution Company in Iran has been employed to produce the results. ‎The classification process consists of two stages: in the first stage, Dynamic Mode Decomposition ‎‎(DMD) has been used to extract new features which compose the set of features along with certain ‎factors from the Advanced Metering Infrastructure (AMI). These features are selected for 58 ‎subscribers with positive and negative labels. In the second stage, a number of unsupervised models ‎are built from the results of the first stage. The highest accuracy of classification obtained is 74% ‎from unsupervised algorithms and 85% for supervised algorithms, which is very significant ‎considering the fact that unsupervised algorithms do not need labeled data‎.‎