Search Results (436)

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar systems can exacerbate voltage unbalance in these networks. This paper investigates the application of a Static Synchronous Compensator (STATCOM) for the improvement of voltage regulation in four-wire LV distribution feeders through reactive power management as a means of mitigating voltage regulation and unbalance challenges. To demonstrate the performance of the STATCOM with varying loads and PV output, a Q-V droop curve is applied to specify the level of reactive power injection/absorption required to maintain appropriate voltage regulation. A practical four-wire feeder from New South Wales, Australia, has been used as a case study network to analyse improvements in system performance through the use of the STATCOM. The outcomes indicate that the STATCOM has a high degree of efficacy in mitigating voltage regulation and unbalance excursions. In addition, compared to other solutions identified in the existing literature, the STATCOM-based solution requires no sophisticated communication infrastructure. Full article

Excessive reliance on traditional energy sources such as coal, petroleum, and gas leads to a decrease in natural resources and contributes to global warming. Consequently, the adoption of renewable energy sources in power systems is experiencing swift expansion worldwide, especially in offshore areas. Floating solar photovoltaic (FPV) technology is gaining recognition as an innovative renewable energy option, presenting benefits like minimized land requirements, improved cooling effects, and possible collaborations with hydropower. This study aims to assess the levelized cost of electricity (LCOE) associated with floating solar initiatives in offshore and onshore environments. Furthermore, the LCOE is assessed for initiatives that utilize floating solar PV modules within aquaculture farms, as well as for the integration of various renewable energy sources, including wind, wave, and hydropower. The LCOE for FPV technology exhibits considerable variation, ranging from 28.47 EUR/MWh to 1737 EUR/MWh, depending on the technologies utilized within the farm as well as its geographical setting. The implementation of FPV technology in aquaculture farms revealed a notable increase in the LCOE, ranging from 138.74 EUR/MWh to 2306 EUR/MWh. Implementation involving additional renewable energy sources results in a reduction in the LCOE, ranging from 3.6 EUR/MWh to 315.33 EUR/MWh. The integration of floating photovoltaic (FPV) systems into green hydrogen production represents an emerging direction that is relatively little explored but has high potential in reducing costs. The conversion of this energy into hydrogen involves high final costs, with the LCOH ranging from 1.06 EUR/kg to over 26.79 EUR/kg depending on the complexity of the system. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors. Full article

At present, transportation energy comes primarily from fossil fuels. In order to mitigate the effects of greenhouse gas emissions, it is necessary to transition to low-carbon transportation technologies. These technologies can include battery electric vehicles, fuel cell vehicles and biofuel vehicles. This transition includes not only the development and production of suitable vehicles, but also the development of appropriate infrastructure. For example, in the case of battery electric vehicles, this infrastructure would include additional grid capacity for battery charging. For fuel cell vehicles, infrastructure could include facilities for the production of suitable electrofuels, which, again, would require additional grid capacity. In the present paper, we look at some specific examples of infrastructure requirements for battery electric vehicles and vehicles using hydrogen and other electrofuels in either internal combustion engines or fuel cells. Analysis includes the necessary additional grid capacity, energy storage requirements and land area associated with renewable energy generation by solar photovoltaics and wind. The present analysis shows that the best-case scenario corresponds to the use of battery electric vehicles powered by electricity from solar photovoltaics. This situation corresponds to a 47% increase in grid electricity generation and the utilization of 1.7% of current crop land. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWh at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure. Full article

The European Union’s 2030 target of decreasing net greenhouse gas emissions by at least 55% has resulted in a significant uptake of renewable energy sources (RESs) in the European power system, primarily wind and solar power, as well as the closure of conventional power plants that mostly used fossil fuels. The European Union’s members have accelerated the process of energy transition driven by climate change, and public authorities’ involvement in this process is impressive. The goal of this study is to present a broad overview of the existing challenges for the energy transition in Europe and how they can affect the reliability and stability of the interconnected power system in Europe and future investments, focusing especially on Greece. Unfortunately, this environmentally friendly transition is taking place without the required amount of investment in electrical energy storage technology, which raises the risk of a blackout due to the high predicted variability of RES. The gradual abandonment of conventional energy production units such as natural gas in the coming decades will intensify the problem of frequency regulation, which will become even more acute due to the particularly increased installed capacity in RESs across Europe and Greece. The European Power System, being partially unprepared for the energy transition, frequently faces a paradox: it rejects green power originating from high-RES production because of low demand, a lack of transmission line interconnections, or extremely low energy storage capacity. This paper examines all the prerequisites, including how the European electrical transmission system will be developed in the future and how new energy storage technologies will be used. Lastly, Greece’s energy future and potential risks associated with realizing the environmental goals of the European Green Deal is studied using a PESTEL analysis. Full article

Large-scale photovoltaic (PV) development has been widely promoted in northwest China and has yielded notable economic and industrial outcomes. However, the existing literature has not adequately examined the relationship between large-scale PV development and regional economic growth, particularly in high-altitude and ecologically fragile areas. This study selects eight prefecture-level cities in Qinghai Province from 2014 to 2023 and employs a static fixed-effects panel regression model to empirically investigate the association between solar PV generation and regional economic performance. The findings indicate a significant positive correlation between PV power generation and regional GDP, with clear regional heterogeneity. In developed regions, the association is stronger, while in less developed regions, the effect is positive but comparatively weaker. Furthermore, the analysis reveals a nonlinear (inverted U-shaped) relationship between PV generation and economic growth in less developed areas, with a critical threshold beyond which the marginal economic benefit declines. These results provide empirical insights into optimizing PV development strategies based on local economic conditions. Notably, the study focuses on identifying statistical associations rather than establishing causality. Full article

The interest in flexible and wearable electronics is increasing in both scientific research and in multiple industry sectors, such as medicine and healthcare, sports, and fashion. Thus, compatible power sources are needed to develop secondary batteries, fuel cells, supercapacitors, sensors, and dye-sensitized solar cells. Traditional liquid electrolytes pose challenges in the development of textile-based electronics due to their potential for leakage, flammability, and limited flexibility. On the other hand, gel electrolytes offer solutions to these issues, making them suitable choices for these applications. There are several advantages to using gel electrolytes in textile-based electronics, namely higher safety, leak resistance, mechanical flexibility, improved interface compatibility, higher energy density, customizable properties, scalability, and easy integration into manufacturing processes. However, it is also essential to consider some challenges associated with these gels, such as lower conductivity and long-term stability. This review highlights the application of gel electrolytes to textile materials in various forms (e.g., fibers, yarns, woven, knit, and non-woven), along with the strategies for their integration and their resulting properties. While challenges remain in optimizing key parameters, the integration of gel electrolytes into textiles holds immense potential to enhance conductivity, flexibility, and energy storage, paving the way for advanced electronic textiles. Full article

Energy-efficient and cost-effective water desalination systems can significantly replenish freshwater reserves without further stressing limited energy resources. Currently, the majority of the desalination systems are operated by non-renewable energy sources such as fossil fuel power plants. The viability of any desalination process depends primarily on the type and amount of energy it utilizes and on the product recovery. In recent years, membrane distillation (MD) and forward osmosis (FO) have drawn the attention of the scientific community because of FO’s low energy demand and the potential of MD operation with low-grade heat or a renewable source like geothermal, wind, or solar energy. Despite the numerous potential advantages of MD and FO, there are still some limitations that negatively affect their performance associated with the water–energy nexus. This critical review focuses on the hybrid forward osmosis–membrane distillation (FO-MD) processes, emphasizing energy demand and product quality. It starts with exploring the limitations of MD and FO as standalone processes and their performance. Based on this, the importance of combining these technologies into an FO-MD hybrid process and the resulting strengths of it will be demonstrated. The promising applications of this hybrid process and their advantages will be also explored. Furthermore, the performance of FO-MD processes will be compared with other hybrid processes like FO–nanofiltration (FO-NF) and FO–reverse osmosis (FO-RO). It will be outlined how the FO-MD hybrid process could outperform other hybrid processes when utilizing a low-grade heat source. In conclusion, it will be shown that the FO-MD hybrid process can offer a sustainable solution to address water scarcity and efficiently manage the water–energy nexus. Full article

As the global shift towards renewable energy sources accelerates, the challenge of effectively modeling the inherent uncertainty associated with these energy units becomes increasingly significant. Sustainable energy sources, like solar and wind power sources, are highly variable and difficult to predict, making their integration into power systems complex. Beyond renewable energy, other critical sources of uncertainty also influence power systems’ operations, including fluctuations in electricity prices and variations in load demand. To address these uncertainties, stochastic programming has become a widely adopted approach. Preparation of the required scenarios for a stochastic programming framework typically includes two main components: scenario generation and reduction. Scenario generation involves creating a diverse set of possible future outcomes based on various uncertainties considered, while scenario reduction focuses on refining these scenarios to a manageable number without losing any essential piece of information. In this paper, we explore the innovative methods used for scenario generation and scenario reduction, with a special emphasis on deep learning approaches. Additionally, we provide future research recommendation, identify areas for further development, and discuss the challenges associated with these deep learning methods. Full article

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints. Full article

Evaluating the competitiveness of electricity is the most important issue. The main aim of this study was to determine the cost-effectiveness of renewable energy production in the European Union (EU) using the levelized cost competitiveness of renewable energy sources. The weighted average cost of capital (WACC) for onshore wind was calculated for European (EU) countries. The levelized cost of electricity (LCOE) approach was used to evaluate the energy costs of renewable energy sources. Energy production costs were compared across different technologies. The capital expenditures associated with solar PV are expected to decrease from USD 810/kW in 2021 to USD 360/kW in 2050. The power factor will remain stable at 14% during the analyzed period. Fuel, CO₂, and operation and maintenance (O&M) costs will be maintained at USD 10/MWh at all three time points of the analysis (2021, 2030, and 2050), whereas the LCOE will decrease from USD 50/MWh in 2021 to USD 25/MWh in 2050. The capital expenditures associated with onshore wind energy will decrease from USD 1590/kW in 2021 to USD 1410/kW in 2050. The power factor will increase from 29% to 30%, and fuel, CO₂, and O&M costs will reach USD 15/MWh in all three years. The LCOE will decrease from USD 55/MWh in 2021 to USD 45/MWh in 2050. In offshore wind projects, capital expenditures are expected to decrease considerably from USD 3040/kW in 2021 to USD 1320/kW in 2050. Full article

The increasing complexity and uncertainty associated with high renewable energy penetration require forecasting methods that provide more comprehensive information for risk analysis and energy management. This paper proposes a novel probabilistic forecasting model for solar power generation based on a non-homogeneous multi-observation Hidden Markov Model (HMM). The model is purely data-driven, free from restrictive assumptions, and features a lightweight structure that enables fast updates and transparent reasoning—offering a practical alternative to computationally intensive neural network approaches. The proposed framework is first formalized through an extension of the classical HMM and the derivation of its core inference procedures. A method for estimating the probability density distribution of solar power output is introduced, from which point forecasts are extracted. Thirteen model variants with different observation-dependency structures are constructed and evaluated using real PV operational data. Experimental results validate the model’s effectiveness in generating both prediction intervals and point forecasts, while also highlighting the influence of observation correlation on forecasting performance. The proposed approach demonstrates strong potential for real-time solar power forecasting in modern power systems, particularly where speed, adaptability, and interpretability are critical. Full article