Renewable Energy Focus最新文献

The use of solar radiation for lighting purposes has gained significant attention in recent years because of its potential to provide a sustainable and renewable source of energy. One approach to harnessing solar radiation for lighting is through the use of optic fiber technology, which allows for the efficient transmission of light from a source to a desired location. This review provides a comprehensive analysis of the different technologies and methods used for the transmission of solar radiation for lighting purposes using optic fibers. The first topic of our discussion was the basic principles of optic fiber technology and its applications in solar lighting to examine the different methods used for coupling solar radiation into optic fibers, such as the use of solar concentrators, mirrors, and lenses. Finally, this review introduces the challenges and prospects of using optic fiber technology for solar lighting applications and the current development status of this technology. This review concludes that optic fiber technology is a promising approach for the transmission of solar radiation for lighting purposes and has the potential to provide significant energy savings and environmental benefits. However, further research is needed to optimize the efficiency of optic fiber systems and to develop cost-effective solutions for their implementation in real-world applications.

In the face of the global climate crisis, geothermal energy emerges as a crucial, sustainable, and low-carbon solution to reduce greenhouse gas emissions and foster economic growth. Despite significant research into geothermal energy in Indonesia, gaps remain in understanding how dynamic interactions among variables can enhance its potential for emission reduction. Therefore, this research aims to identify and analyze the variables influencing geothermal development and their impact on emissions. Employing the system dynamics approach, the study examines the interactions among key factors such as economic growth, energy demand, and policy measures over a projected period from 2022 to 2122. Key findings reveal that strategic interventions like increasing the carbon credit price, implementing carbon taxes, and enhancing renewable energy mix can dramatically reduce national and internal company emissions while advancing geothermal capacity. The study recommends robust government policies and incentives to foster investment in renewable energy, highlighting the crucial role of financial strategies and external funding in achieving Indonesia’s geothermal targets efficiently.

The present study optimizes a novel developed cycle including solid oxide fuel cell (SOFC) fed by synthesis gas produced from biomass as well as gas turbine (GT), supercritical carbon dioxide cycle (SCO₂), transcritical carbon dioxide cycle (TCO₂), Organic Rankine Cycle (ORC), thermoelectric generator (TEG), and reverse osmosis (RO)- based desalination. Energy, exergy, exergoeconomic and exergoenvironmental analyses on the developed cycle were investigated. Multi-objective optimization was carried out using of Genetic algorithm using generated power and exergy destruction as objective functions. Sankey diagram data indicate that afterburner holds the highest portion of the total exergy destruction 46.5% (692.24 kW), followed by SOFC which is 20.48% (304.51 kW). Moreover, optimization results showed that the total net power, first and second laws of thermodynamic efficiencies increased by 2.6%, 0.96% and 0.83%, respectively, while exergy destruction decreased by 1%. Furthermore, such a power increase (18.53 kW) using the freshwater produced by RO leads to daily production of 17040 liters of drinking water. According to the exergoeconomic analysis, the minimum flow value pertains to GT at a value of 0.0119 $/GJ, while the TCO₂ turbine has the highest value which is 0.2867 $/GJ. The system product cost rate and exergy destruction cost rate reached 27.0353 $/h, and 10.7012 $/h, respectively. In the case of the exergoenvironmental one, the maximum environmental impact is related to the SCO₂ turbine 0.0212 Pts/GJ, while SOFC has the lowest (0.0002 Pts/GJ). The system product environmental impact and exergy destruction were achieved at optimum values of 2.7503 $/h, and 4.1576 $\times {10}^{-7}$ $/h, respectively.

Offshore wind farms (OWF) and floating solar photovoltaic farms (FPV) are becoming crucial parts of global renewable energy plans. Combining OWF and FPV offers a promising approach to improving energy generation efficiency and cutting costs through shared infrastructure and operational synergies. This systematic review assesses key criteria for identifying suitable co-location sites; focusing on environmental regulations, resource availability, economic viability, social acceptance, and technological readiness. The study highlights that environmental protection laws and legal limitations are the primary factors affecting site feasibility, in addition to factors such as distance from existing infrastructure and economic considerations. Despite potential benefits, the existing challenges are the early stage of FPV technology and its low resilience in offshore environments. The findings underline the potential of co-located OWF-FPV projects to reduce the costs associated with offshore renewables, particularly in densely populated coastal areas with limited land availability. Strategic resource allocation and policy support are essential for overcoming these obstacles and promoting the development of sustainable offshore energy solutions. These findings serve researchers and practitioners alike, by offering insights for a better allocation of resources and efforts to foster the co-location development of OWF and FPV in the future.

This paper focuses on optimal sizing of building-integrated photovoltaic (BIPV) without energy storage system (ESS) in a zero power/energy export (ZE) power system, considering several types of buildings/consumers. BIPV systems have gained significant popularity in the development of low-carbon smart cities because they offer several key advantages, such as utilizing locally available renewable energy sources (RES) and reducing dependence on fossil fuels and greenhouse gases emissions. However, the implementation of BIPV system faces challenges due to legal, regulatory, and technical restrictions imposed by the power distribution system operator, sometimes resulting in ZE requirements. In this case, one of the major challenges is the optimal sizing of BIPV system, considering both technical and economic parameters, especially if there is no ESS. The objective function presented in this paper integrates the internal rate of return on investment and the self-sufficiency rate of BIPV system. The primary goal is to optimize both the cost-effectiveness and self-sufficiency of BIPV system, along with minimizing the cost of energy consumption from the power grid over a ten-year period. Additionally, the presented approach accounts for varying tariff rates, different load profiles, price fluctuations during the exploitation period, and the variation of the efficiency of BIPV system over time. As case studies, the presented approach is validated and assessed on real data sets of several different examples of BIPV systems without ESS, considering ZE as the constraint.

In recent years, the widespread adoption of renewable energy sources for electricity generation has been driven by their minimal environmental impact and easy accessibility. However, without adequate load frequency control to balance production and demand, the variability in wind energy production can cause significant frequency fluctuations. Additionally, the anticipated increase in the use of plug-in hybrid electric vehicles (PHEVs) on the demand side, with their substantial battery storage and bidirectional charge/discharge capabilities, presents an opportunity to mitigate these fluctuations. Therefore, it is essential to design controllers that account for the uncertainties in renewable energy parameters, such as variable wind power and load. This study employs the Ant Lion Optimization (ALO) algorithm to optimally set the parameters for Model Predictive Control (MPC) and Proportional-Integral (PI) controllers in the load frequency control section. The goal is to efficiently regulate the charging rate of PHEV batteries while utilizing renewable energy sources. The proposed method was tested by optimizing the battery charge of four different PHEV models—V1G, V2G, smart charge, and smart discharge—based on load frequency control using MPC design in a smart, interconnected, two-area power system. The results indicate that the MPC controller outperforms the PI controller in reducing network frequency fluctuations and enhancing power control in a smart, interconnected, two-area power system.

In variable speed and variable pitch large-scale wind turbines, the pitch controller plays a crucial role in optimizing power output near the rated value during wind speeds that exceed the rated threshold. Nevertheless, the erratic nature of wind speeds poses challenges to the pitch controller’s efficacy, leading to a decline in generator power. So, there has been a growing interest among researchers in the development of machine learning-based pitch controllers. This paper introduces an improved recurrent radial basis function neural network and its parameters were tuned using the modified particle swarm optimization algorithm to enhance neural network performance. The proposed controller is validated in a benchmark wind turbine and comparative analysis are conducted against existing controllers in the literature. Through a series of comprehensive studies, the proposed controller consistently outperforms its counterparts, particularly in achieving power output close to the rated value.

It is not easy to make precise predictions about solar energy generation in the coming decades. However, it is generally expected to play an increasingly important role in the global energy mix in the near future. There are several trends that suggest solar energy will continue to grow in the coming years meet the global energy needs. Therefore, a mathematical model based on the diffusion of installation sales of solar photovoltaic (S-PV) power in a certain market is presented. The present study begins with some mathematical modifications to the Bass diffusion model (BDM), and applies these modifications to the S-PV worldwide-market and different countries. Calculations using the BDM leads to the saturation of the “market,” which means a saturation of S-PV production, corresponding to the saturation of the S-PV market’s production (national/country production). This leads to precise predictions of S-PV production values at the national levels.

The integration of conventional synchronous generators (SGs) and virtual synchronous generators (VSGs) in microgrids (MGs) is increasingly common due to the growth of renewable energies. However, demand fluctuations and grid faults can pose significant challenges to the stability of the MG, causing system frequency and active power oscillations. This study proposes control approaches for an isolated/islanded AC MG that consists of an VSG and an SG to mitigate power and frequency oscillations. The proposed methods utilize the VSG’s adjustable damping coefficient, which is determined by intelligent controls. The proposed strategies significantly reduce power fluctuations by 53% and frequency deviations by 75%, thereby improving system stability and reliability. The suggested controllers use short-term large damping to effectively amplify the system’s AC frequency dynamic response and enhance power delivery across different power-sharing modes. The controllers do not require additional communication infrastructure and rely solely on the local AC frequency for feedback. Furthermore, a novel synchronization mechanism with positive effects on system stability is presented.