Journal of Green Energy Research and Innovation

Abstract The choice between residing in tall buildings and in few-story or detached dwellings has ‎been the subject of considerable debate, with each approach attracting its own proponents ‎and critics. This study investigates and compares the heating and cooling energy ‎consumption of multi-story and low-elevation buildings, incorporating the Fanger comfort ‎index as a measure of thermal comfort. Energy performance is evaluated on eight building ‎configurations with varying numbers of floors, under three distinct climatic conditions, ‎using the Predicted Mean Vote (PMV) index as the primary comfort criterion. The building ‎scenarios range from single-story detached houses to 50-story high-rise structures. The ‎climatic cases—representing hot (Yazd), moderate (Arak), and cold (Shahr-e Kord) ‎environments—are simulated and analyzed using DesignBuilder software. The results ‎section presents detailed analyses of heating and cooling loads, comfort index values, and ‎electricity and gas demand for each building–climate combination, with monthly and annual ‎performance trends. The findings reveal that the number of shared walls exerts a greater ‎influence on energy consumption than the number of floors. Specifically, detached single-‎story buildings, which lack shared walls, exhibit up to 58.9% higher heating demand and ‎‎67.1% higher cooling demand compared to their counterparts with shared walls‎‎.‎

Abstract In recent years, distributed generation as a source of local loads and continuous economic operation has gathered ‎attention. In this thread, this study focuses on distributed generation using a photovoltaic package with batteries, so that ‎the power drawn from the distributed generation system for injection into the main grid or receiving it is adjusted based ‎on the battery charge status. The goal is to absorb the maximum power received from the photovoltaic system at any ‎temperature and hypothetical radiation. If the battery charge is not optimal, part of this power is applied to the battery ‎for charging. By presenting a suitable structure, a photovoltaic system with a battery package is presented as a ‎distributed generation source with the design of appropriate controllers. The results show that at any temperature and ‎radiation, the maximum power received from the photovoltaic system can be estimated. By controlling a converter ‎switching the required amount of energy can be obtained from the photovoltaic system. It can be concluded that such a ‎structure, as a desirable distributed generation source, is realized. With the proper design of the necessary controllers, ‎optimal management can be done for power management‎‎‎.‎

Abstract In this article, the modified fuzzy controller tracks the maximum power point (MPP) ‎in a photovoltaic (PV) system connected to the grid under variable and standard ‎solar radiation and variable temperature conditions. The perturb and observe ‎‎(P&O) method has also been employed for MPP tracking (MPPT), and it has been ‎compared with the modified fuzzy method. Ultimately, the superiority of the ‎modified fuzzy method has been proven. In addition, the particle swarm algorithm ‎‎(PSO) is employed to make the fuzzy groups optimal, thereby enhancing the ‎performance of the fuzzy controller. In conclusion, implementing the designed phase ‎control for the PV system connected to the single-phase grid is paramount. ‎Furthermore, utilizing the hysteresis current control method facilitates inverter ‎switching, thereby ensuring the injection of maximum power into the grid‎‎‎‎.‎

Abstract This study proposes an integrated framework that combines photovoltaic (PV) generation with hydrogen-based storage to enhance energy management in smart distribution systems. The framework is designed to address the multi-objective concerns of distribution system operators (DSOs), focusing on minimizing operating costs, reducing energy losses, and mitigating greenhouse gas emissions. To ensure technical rigor and practical applicability, the model incorporates alternating current (AC) power flow equations along with operational constraints and system-specific performance characteristics. Recognizing the inherent uncertainties associated with load demand, renewable PV output, and fluctuating market prices, the research employs a scenario-based stochastic optimization method. This approach integrates the Kantorovich method, which efficiently manages complex multi-dimensional problems, with the Roulette Wheel Mechanism (RWM), a probabilistic selection tool that enhances solution robustness under uncertainty. Numerical simulations validate the effectiveness of the proposed method, demonstrating significant improvements compared with conventional load flow analyses. The results show reductions of approximately 14.5% in operational costs, 28.9% in energy losses, and 21% in emissions, indicating the capacity of the approach to promote sustainable and cost-efficient system operation. Beyond its quantitative achievements, the study provides meaningful insights for DSOs, offering a structured roadmap to navigate the technical, economic, and environmental challenges posed by evolving energy systems. Ultimately, the research underscores the transformative potential of PV-hydrogen integration for building resilient, efficient, and environmentally responsible distribution networks, contributing both theoretical advancements and practical guidance to the broader discourse on sustainable energy management.‎

Abstract This study presents a comprehensive framework for enhancing power quality in radial distribution networks by simultaneously optimizing the placement and size of capacitor banks and distributed generation (DG) units. Employing the biogeography-based optimization (BBO) algorithm, this research addresses key objectives, including minimizing power losses and improving voltage profiles. The methodology incorporates critical operational constraints, such as voltage limits and permissible installation locations for DG units and capacitors. The proposed approach is validated using the IEEE 33-bus radial distribution system, where numerical results demonstrate a reduction in power losses by 88.28% with the simultaneous placement of DGs and capacitors (Mode 4), compared to the base case. Voltage profiles improved significantly, with the lowest voltage rising from 0.9117 pu in the base mode to 0.9835 pu. Additionally, Mode 5, involving variable power factors, achieved a 94.4% reduction in losses, further enhancing system efficiency. These results highlight the BBO algorithm's superior performance and computational efficiency in addressing complex distribution system challenges. This study is particularly relevant for optimizing renewable energy integration and future power system resilience.‎

Abstract Solar energy has become an important global energy research topic, recognized for its potential to ‎address sustainability challenges.‎‏ ‏While photovoltaic (PV) technology offers clean energy generation, ‎its broader adoption is constrained by limitations such as suboptimal conversion efficiency and high ‎perceived initial costs. This study explores the influence of various maximum power point tracking ‎‎(MPPT) methods on the reliable performance of PV systems that operate in network-connected mode. It ‎investigates how these power optimization strategies impact overall operational reliability, emphasizing ‎the role of MPPT in achieving stable and efficient grid integration. By categorizing MPPT techniques ‎into offline, online, and hybrid groups, the research assesses their impact on reliability metrics within a ‎standardized distribution network. Using the recursive least squares (RLS) method, localized solar cell ‎parameters are dynamically estimated under different MPPT configurations. To isolate the effects of ‎methodologies, irradiation fluctuations are treated as controlled set-point adjustments. Simulation ‎results conducted with MATLAB/Simulink reveal statistically significant correlations between MPPT ‎selection and system reliability, providing actionable insights for enhancing PV grid integration‎‎‎.‎