Journal of Green Energy Research and Innovation

Abstract This study aims to enhance regional planning for green energy synergy in Yazd Province by ‎addressing energy imbalances and promoting sustainable development. Significant disparities ‎in production and consumption persist, with Yazd County exhibiting an 8.4-million-kWh ‎surplus in 2022, while counties such as Ashkezar and Bafgh show deficits of approximately 1 ‎million kWh. The analysis indicates that, without intervention, surpluses will decrease and ‎deficits will increase by 2035. Using quantitative trend analysis in MATLAB and participatory ‎Delphi-based scenario development, the study constructs four scenarios: synergistic ‎development, which maximizes renewable energy use; foreign dependency, which sustains ‎disparities; local self-sufficiency, which supports localized solutions; and energy isolation, ‎which exacerbates imbalances. Proposed strategies include decentralized solar power plants, ‎smart grid systems, and a green investment fund. The concept of “green energy synergy” offers ‎a novel framework for transforming regional disparities into opportunities for renewable ‎energy development. This research provides a scalable model for sustainable planning in Yazd ‎and comparable regions, contributing to more effective energy policy formulation‎‎‎.‎

Abstract This paper introduces a new approach for short-term forecasting of solar and wind energy potential in Ahvaz City. The method is based on the StackedBoost-XG model, a hybrid ensemble that combines Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with XGBoost as the final estimator. The study focuses on accurately predicting energy generation using real-time meteorological data. Key inputs include temperature, humidity, wind speed, and solar irradiance factors that are crucial for reliable energy forecasting. These variables are integrated into energy production formulas to estimate outputs for both solar and wind sources. This improves prediction accuracy. The model’s performance is assessed using standard evaluation metrics: RMSE, MAE, and R². Results indicate that StackedBoost-XG significantly outperforms the individual SVM and KNN models. It shows higher accuracy in forecasting both solar and wind energy. The research also explores the effect of wind turbine height. It finds that optimal energy output occurs at heights between 15 and 25 meters. In addition, the study highlights the importance of managing thermal losses in solar panels, especially during warmer months, to maintain system efficiency. Finally, it emphasizes the complementary nature of solar and wind energy. Solar power offers relatively stable output throughout the year, while wind energy provides higher peaks in specific seasons. By integrating both energy sources, the study proposes a promising solution to address energy demand imbalances in Ahvaz. This study introduces a hybrid forecasting method that uses advanced machine learning and weather data. Its goal is to optimize renewable energy systems and enhance the management of the energy grid. 

Abstract Integrating photovoltaic (PV) generation with active filtering (AF) in three-phase four-wire distribution networks enhances power quality while maximizing renewable energy utilization. This study presents two advanced control strategies—the corrected p-q theory and the enhanced vectorial control method—to optimize system performance under unbalanced and distorted grid conditions. The corrected p-q strategy ensures harmonic compensation, reactive power balance, and neutral current elimination, stabilizing DC-link voltage with THD levels below 3%. Meanwhile, the enhanced vectorial control approach provides superior harmonic suppression, reducing THD to 0.88% and neutral current RMS to 0.035, while maintaining DC-link voltage at a stable level, optimizing energy storage and power conversion. Both strategies are validated through PSCAD/EMTDC simulations, demonstrating their adaptability in dynamically adjusting PV power injection in response to irradiance and temperature variations. The MPPT algorithm effectively tracks optimal power points, ensuring efficient grid interaction and power stabilization. A comparative analysis confirms the enhanced vectorial control method’s advantages in harmonic reduction and reactive power compensation, making it preferable for stringent grid applications while reinforcing the role of PV systems as intelligent multi-functional energy units.‎
 

Abstract Environmental and economic concerns of utilizing fossil fuels reveal the need to use alternative ‎sources. Due to various kinds of energy demands, choosing the proper generation units is the ‎main aim of energy administrators.   Simultaneous generation units are the proper choice to ‎meet several kinds of demands. Combined hydrogen, heat, and power (CHHP) is a cogeneration ‎system to generates three kinds of energy demands.  Presenting the comprehensive reliability ‎model of CHHP is the main part of this study. State-space and continuous Markov models with ‎hydrogen, heat, and power generation systems are considered in the reliability model of CHHP. ‎Loss of load expectation (LOLE) and expected energy not supplied (EENS) are considered as ‎reliability indices to verify the efficiency of the proposed reliability model of the CHHP. Due ‎to the important role of communication and data gathering, sending and receiving, the necessity ‎to have a system to minimize the errors in data gathering and processing, and sending is ‎unavoidable. Natural language program (NLP) is the best tool for managing data received, ‎processing, and sending within the system with high accuracy.‎
 

Abstract This paper addresses the issue of flexibility pricing for grid-connected energy hubs (EHs) under the influence of uncertain energy generation resources. An optimization-based scheme is proposed to tackle this challenge. The objective of the problem is to maximize the profitability of these resources within the flexibility market. Key constraints include the flexibility model of energy storage systems, load response mechanisms, and controllable distributed generation units. Additional restrictions involve the optimal load distribution equations across energy networks, the utilization model of EHs incorporating various active resources and loads, as well as the flexibility constraints specific to EHs. The flexibility model hinges on the active power generated by these resources. The flexibility price is determined by the Lagrange coefficients corresponding to the flexibility constraint. To compute this price, a penalty function associated with the flexibility constraint is incorporated into the objective function. The proposed framework is implemented in the GAMS software environment and tested on 9-bus electric grid, a 4-bus gas system, and a 7-bus thermal network. Ultimately, the scheme’s effectiveness in enhancing operational performance, flexibility, and economic outcomes for both energy networks and EHs is thoroughly analyzed. So that based on optimal operation of resources, storage devices, and responsive loads, total energy losses, maximum voltage drop, and temperature improvements of approximately 14.9%, 41.2%, and 39.7%, respectively, when compared to load distribution studies.

Abstract Fault detection and classification in transmission lines equipped with Flexible Alternating ‎Current Transmission System (FACTS) devices is one of the basic challenges in line protection. ‎In the event of a fault on one side of this equipment, relays installed at the other terminal often ‎struggle to detect the fault and determine the faulty phases due to control system interruptions ‎in the line. The Series Static Synchronous Compensator (SSSC) is a series-connected device in ‎transmission lines that addresses reactive power control challenges in the network. This study ‎proposes a highly accurate and fast algorithm to detect and classify various short-circuit faults ‎in transmission lines compensated with an SSSC. Crucially, the SSSC in this study is connected ‎via its DC link to a solar photovoltaic (PV) farm, specifically utilizing Trina Solar Vertex N ‎‎210R-N-66 panels, allowing it to act as both a reactive power compensator and a means to ‎seamlessly integrate this significant source of green energy into the grid. This integration ‎highlights the method’s relevance to modern renewable energy systems, particularly in ‎enhancing the protection and monitoring of solar-powered infrastructures. The algorithm ‎analyzes voltage signals from one side of the line, employing a discrete wavelet transform and ‎a decision-making algorithm. The proposed method was simulated and implemented for at least ‎‎4000 fault scenarios under normal and critical conditions. Based on the extensive fault ‎scenarios and reported results, the algorithm's performance accuracy is estimated to be ‎approximately 98%, demonstrating its potential in improving the reliability and performance of ‎smart and green power systems‎.‎