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 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 scheme delves into how electrical and thermal network hubs efficiently manage their energy consumption, focusing on the multi-criteria objectives that balance economic and operational factors for network operators. These hubs integrate various sustainable energy sources such as solar power, bio-waste units, and wind turbines. They also feature storage units for compressed air, heat, and hydrogen. Thermal energy is generated via heat pumps using electrical energy, while the bio-waste unit and hydrogen storage utilize combined heat and power technology. The strategy proposed in this context aims to minimize overall energy procurement costs within these networks, adhering to their operational models. Additional challenges include managing the operational model of the energy sources and storages, conceptualized as an energy hub. In conclusion, the numerical results demonstrate the benefits of the proposed approach in enhancing both the technical and financial performance of thermal and electrical networks through effective hub energy management. The integration of renewable hubs with storage units and heat pumps has led to improvements in economic conditions by approximately 44.1%, and operational conditions by 28% to 90%, compared to traditional load flow methods.

Abstract This study presents a multi-objective harmony search algorithm aimed at minimizing monetary costs and pollutant emissions while considering uncertainties in electrical load and electricity market prices. To address these uncertainties, a Monte Carlo simulation (MCS) is employed. The optimization problem incorporates six diverse types of DER, including wind turbines, photovoltaics, fuel cells, micro-turbines, gas turbines, and diesel generators. Non-dominated sorting is utilized within the multi-objective harmony search algorithm to identify Pareto-optimal solutions. Furthermore, a fuzzy decision-making approach is implemented to select the most viable solution among the optimal alternatives. The results indicate that the integration of renewable energy sources significantly contributes to reducing losses and enhancing overall system efficiency.

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 way of 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, which the proposed method shows higher accuracy and faster convergence