Journal of Green Energy Research and Innovation

Abstract Grounding systems in wind turbines ‎are critical for lightning protection ‎and managing GPR. This study ‎investigates the influence of different ‎soil models (uniform, two-layer, and ‎three-layer) on GPR across six ‎distinct geographical regions: desert, ‎forest, agricultural, mountainous, ‎coastal, and frozen. Simulations were ‎performed using the CDEGS software ‎on a standard grounding system ‎comprising a ring electrode, ‎horizontal electrodes, and vertical ‎electrodes. The results reveal a strong ‎dependence of GPR on soil ‎characteristics and regional ‎conditions. In desert regions, the high ‎resistivity of dry soil significantly ‎increases GPR, whereas in coastal ‎areas, water-saturated layers ‎markedly reduce GPR. In frozen ‎regions, surface layer freezing ‎substantially elevates GPR despite ‎lower resistivity in deeper layers. The ‎study demonstrates that increasing ‎the complexity of the soil model (i.e., ‎the number of layers) does not ‎necessarily mitigate GPR, ‎underscoring the need for region-‎specific data in grounding system ‎design. Numerical results show the ‎largest peak GPR for the uniform ‎model in the frozen region during ‎winter (≈2,197,587 V), reduced to ‎‎802,833.2 V with the three-layer ‎model (≈63.5% reduction). Overall, in ‎high-resistivity regions (desert, ‎mountainous, frozen), multilayer ‎models yield substantial GPR ‎reductions, whereas in coastal areas, ‎changes in the soil model cause only ‎minor decreases (≈13.5%). These ‎findings highlight the importance of ‎tailoring grounding system designs to ‎geographical conditions, potentially ‎enhancing the safety and efficiency ‎of wind turbines against lightning ‎strikes‎‎‎‎.‎

Abstract The main objective of this study is to evaluate the impact of building energy efficiency ‎measures on the cost of solar electricity in a climate-compatible villa located in the suburbs ‎of Saman, Chaharmahal and Bakhtiari Province, Iran. Enhancing building energy efficiency ‎while lowering the cost of renewable electricity generation is particularly vital in Iran’s off-‎grid residential sector, where growing energy demand and dependence on fossil fuels ‎necessitate sustainable, climate-compatible solutions. A baseline case and five optimization ‎scenarios were modeled using DesignBuilder (v6.1.0.6) to estimate annual energy ‎consumption, followed by techno–economic–environmental assessment of an off-grid ‎solar–battery–diesel generator system using HOMER (v2.81). Results show that the net ‎present cost (NPC) of the baseline system is $947,243, with cost reductions of 17.6%, 5.4%, ‎‎22.7%, 63.1%, and 79.5% achieved through polystyrene insulation, green roof, UPVC ‎windows, VRF HVAC, and all measures combined, respectively. The optimal integrated ‎scenario also reduces annual emissions by ~130 tons and increases the return on investment ‎‎(ROI) by 146%. This work uniquely couples building-level energy efficiency modeling with ‎techno–economic–environmental optimization of a hybrid off-grid PV– Battery–Diesel ‎generator system, quantifying for the first time how demand-side measures propagate into ‎key renewable electricity cost metrics in off-grid residential contexts. These findings ‎highlight the substantial economic and environmental benefits of combining building ‎optimization strategies with renewable energy deployment in off-grid residential ‎applications‎‎‎‎‎.‎

Abstract This study explores the effective energy management strategies employed by ‎electricity and heat grids hubs, emphasizing multi-criteria objectives that balance ‎economic performance and operational efficiency for network operators. The ‎primary objective of this study is to optimize the integration of multiple renewable ‎energy sources, namely solar energy, bio-waste units, and wind turbines, within a ‎unified management framework. The system employs advanced energy storage ‎technologies, including compressed air, thermal, and hydrogen storage units. ‎Thermal energy production is achieved through electrically powered heat pumps, ‎while combined heat and power (CHP) systems are utilized to enhance the ‎performance of both bio-waste and hydrogen storage subsystems. The proposed ‎approach seeks to optimize energy procurement costs across these networks, ‎aligning with their operational models. A key challenge tackled involves ‎efficiently managing the interdependencies of energy sources and storage systems ‎within the conceptual framework of an energy hub. By addressing these ‎complexities, the strategy demonstrates measurable improvements in both ‎technical and financial outcomes for electricity and heat grids. The numerical ‎analysis highlights the efficacy of the proposed approach, demonstrating ‎significant improvements in both economic viability and operational efficiency. ‎Specifically, the integration of renewable energy hubs, storage, and heat pump ‎systems, has achieved an approximate 44.1% enhancement in economic conditions ‎and operational improvements ranging from 28% to 90%. These gains signify a ‎clear advantage over traditional load flow methodologies, reaffirming the potential ‎of advanced hub energy management in modern networks.‎

Abstract This study introduces a comprehensive multi-objective harmony search algorithm designed to ‎simultaneously minimize total monetary costs and pollutant emissions while explicitly accounting ‎for uncertainties associated with electrical load demand and electricity market prices. To ‎effectively capture and model these inherent uncertainties, a Monte Carlo simulation (MCS) ‎framework is employed, enabling a probabilistic assessment of system behavior under varying ‎operating conditions. The formulated optimization problem integrates six distinct types of ‎distributed energy resources (DER), namely wind turbines, photovoltaic systems, fuel cells, ‎micro-turbines, gas turbines, and diesel generators. This diverse portfolio of DER technologies ‎allows the model to accurately reflect the operational flexibility and heterogeneity of modern ‎distributed energy systems. Within the proposed multi-objective harmony search framework, a ‎non-dominated sorting mechanism is applied to systematically classify candidate solutions and ‎extract the Pareto-optimal front, thereby revealing the trade-offs between economic and ‎environmental objectives. To further support practical decision-making, a fuzzy decision-making ‎methodology is incorporated to identify the most suitable compromise solution from the set of ‎Pareto-optimal alternatives, taking into account decision-makers’ preferences and system ‎priorities. The simulation results demonstrate that higher penetration of renewable energy sources ‎plays a crucial role in reducing energy losses, mitigating environmental impacts, and improving ‎overall system efficiency. These findings highlight the effectiveness of the proposed optimization ‎framework in enhancing the economic and environmental performance of distributed energy ‎systems under uncertainty.‎‎

Abstract In this study, a hybrid microbial fuel cell–electrokinetic remediation system (MSRC) was developed to ‎remediate soil contaminated with hexavalent chromium while simultaneously generating bioelectricity. ‎Two configurations were compared: MSRC-1 with plain graphite electrodes and MSRC-2 with graphite ‎electrodes modified using activated carbon granules. The results demonstrated that electrode modification ‎significantly enhanced biofilm development and electron transfer, leading to higher system efficiency. ‎MSRC-2 achieved an open-circuit voltage of 641 mV, a maximum power density of 4.21 W/m³, and 83.5% ‎COD removal, compared to 406 mV, 1.23 W/m³, and 62.3% in MSRC-1. Chromium migration toward the ‎cathode was also more effective in MSRC-2, reducing soil concentrations to 68–99 µg/g. These findings ‎highlight the novelty of integrating activated-carbon-modified electrodes into a microbial fuel cell–‎electrokinetic system, offering an efficient and environmentally friendly approach for simultaneous energy ‎recovery and in-situ remediation of Cr‏ ‏‎(VI)-polluted soils‎‎‎‎.‎

Abstract Photovoltaic (PV) systems have become indispensable in the renewable energy landscape, harnessing the sun’s abundant and clean potential. However, their efficiency is often compromised by low conversion rates, particularly under partial shading conditions (PSC). This study introduces a novel quadratic spline-based maximum power point tracking (QS-MPPT) technique to optimize PV array performance under both uniform irradiance and PSC. Unlike conventional methods such as Perturb and Observe (P&O) or Incremental Conductance (INC), which struggle to pinpoint the global maximum power point (GMPP) amid the multi-peak power-voltage (P-V) curves typical of PSC, QS-MPPT employs a straightforward quadratic interpolation approach. By leveraging a minimal set of sampled points, this method rapidly and accurately locates the GMPP, ensuring stability without oscillations around the operating point. Simplicity of the proposed method also makes it ideal for implementation on cost-effective microcontrollers, broadening its practical appeal for real-world PV applications. The efficiency of the proposed method is shown by the time domain simulation in the MATLAB/SIMULINK environment and implementation in the form of a processor-in-the-loop (PIL). Through MATLAB simulations, QS-MPPT performance is evaluated and compared with MPPT techniques like P&O, Particle Swarm Optimization (PSO), and Flower Pollination Algorithm (FPA) in three- and four-peak PSC scenarios, where the proposed method shows higher accuracy and faster convergence‎.‎