Search Results (34)

The integration of Reverse Osmosis (RO) desalination with Renewable Energy (RE) sources offers a sustainable approach to freshwater production, particularly in remote and off-grid regions. However, the variable and intermittent output of RE power can cause operational instability that affects membrane performance and system reliability. This study experimentally evaluated a flat sheet seawater RO membrane under variable conditions emulating a Photovoltaic (PV)-powered system over three days. Three scenarios were examined: (i) steady full-load operation representing PV with battery storage, (ii) variable operation representing sunny-day PV output, and (iii) highly variable operation representing cloudy-day PV output. A Variable Frequency Drive (VFD) regulated by an Arduino microcontroller adjusted high-pressure pump operation in real time to replicate power fluctuations without energy storage. Each scenario operated for eight hours per day and was tested with and without end-of-day rinsing. Under the highly variable cloudy-day scenario without rinsing, water permeability decreased by 37%, salt rejection decreased by 18%, and membrane resistance increased by 37%, indicating compaction and fouling effects. Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR) confirmed structural changes in membranes exposed to fluctuating conditions. These results highlight the need for improved operational strategies to protect membrane longevity in RE-powered desalination systems. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

The growing demand for electric vehicles (EV) has increased the need for reliable and sustainable charging infrastructure. To address this challenge, autonomous charging stations powered by renewable energy sources (RES) are a promising solution. This paper presents a simulation-based study that determines the optimal contribution of wind farms, photovoltaic systems, and energy storage to power an autonomous EV charging station. The simulation takes into account historical weather data, EV charging patterns, and renewable energy storage capacity. The results show that by combining RES and batteries, the charging station can operate autonomously minimizing the dependence on the power grid. Battery energy storage plays a key role in balancing intermittent RES generation and variable demand from the charging station. The simulation highlights the importance of adjusting parameters to optimize the energy utilization of the charging station and minimize the dependence on the grid. Further research is warranted to optimize the design, operation, and integration with advanced energy management systems to increase the efficiency and effectiveness of these charging stations. The development of a widespread autonomous charging infrastructure powered by renewable energy sources can accelerate the transition to clean transportation and support the energy system. Full article

The intermittent nature of renewable energy sources (RES) restricts their widespread applications and reliability. Nevertheless, with advancements in the field of artificial intelligence, we can predict the variations in parameters such as wind speed and solar irradiance for the short, medium and long terms. As such, this research attempts to develop a machine learning (ML)-based framework for predicting solar irradiance at Muscat, Oman. The developed framework offers a methodological way to choose an appropriate machine learning model for long-term solar irradiance forecasting using Python’s built-in libraries. The five different methods, named linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), support vector regression (SVR), Prophet, k-nearest neighbors (k-NN), and long short-term memory (LSTM) network are tested for a fair comparative analysis based on some of the most widely used performance evaluation metrics, such as the mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²) score. The dataset utilized for training and testing in this research work includes 24 years of data samples (from 2000 to 2023) for solar irradiance, wind speed, humidity, and ambient temperature. Before splitting the data into training and testing, it was pre-processed to impute the missing data entries. Afterward, data scaling was conducted to standardize the data to a common scale, which ensures uniformity across the dataset. The pre-processed dataset was then split into two parts, i.e., training (from 2000 to 2019) and testing (from 2020 to 2023). The outcomes of this study revealed that the SARIMAX model, with an MSE of 0.0746, MAE of 0.2096, and an R² score of 0.9197, performs better than other competitive models under identical datasets, training/testing ratios, and selected features. Full article

The necessity for transitioning to renewable energy sources and the intermittent nature of the natural variables lead to the integration of storage units into these projects. In this research paper, wind turbines and solar modules are combined with pumped hydro storage, batteries, and green hydrogen. Energy management strategies are described for five different scenarios of hybrid renewable energy systems, based on single or hybrid storage technologies. The motivation is driven by grid stability issues and the limited access to fresh water in the Greek islands. A RES-based desalination unit is introduced into the hybrid system for access to low-cost fresh water. The comparison of single and hybrid storage methods, the exploitation of seawater for the simultaneous fulfillment of water for domestic and agricultural purposes, and the evaluation of different energy, economic, and environmental indices are the innovative aspects of this research work. The results show that pumped hydro storage systems can cover the energy and water demand at the minimum possible price, 0.215 EUR/kWh and 1.257 EUR/m³, while hybrid storage technologies provide better results in the loss of load probability, payback period and CO₂ emissions. For the pumped hydro–hydrogen hybrid storage system, these values are 21.40%, 10.87 years, and 2297 tn/year, respectively. Full article

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity. Full article

A matrix-integrated single-stage isolated MF/HF AC-AC/DC-AC/AC-DC converter topology stands out as an innovative concept, offering a multitude of advantages including minimal output current THDs, near UPF, 4Q operation, smooth BPF capability, and increased power density leading to the converter’s enhanced efficiency, cost-effectiveness, and reliability. These characteristics render it an exemplary choice for RE-based power conversion applications. In fact, the matrix-integrated single-stage isolated MF/HF converters have witnessed an increased adoption of RE-based grid interconnection in recent years, specifically within solar PV, WECS, grid-tied offshore WF, and FC-based applications. RE sources produce variable and intermittent AC power by nature, further necessitating conversion to a stable and grid-compatible AC voltage and frequency. This is where MCs offer distinct advantages when contrasted with the conventional indirect dual-stage VSC-based rectifier–inverter topology. In this paper, a total of 22 matrix-integrated HF isolated converter topologies are broadly explored. Our study provides a comprehensive analysis and classification of matrix-integrated isolated single-stage MF/HF AC-AC converters, DC-AC inverters, and AC-DC rectifier topologies including modified topology architectures, control method, modulation techniques along with significant applications. Within this scope, the matrix-integrated converter topologies are categorized based on their architectures and other relevant subvariants. Our primary objective of this study is to impart a clear understanding of the overarching framework and principles of the matrix-integrated single-stage isolated MF/HF converter topologies and stimulate the creation of new topologies that cater to specific requirements for grid-interconnected systems. Full article

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of RESs have a substantial adverse effect on the accuracy of MG energy management. In order to obtain accurate outcomes, the analysis of the MG operation must consider the uncertainty parameters of RESs, market pricing, and electrical loads. As a result, our study has focused on load demand variations, intermittent RESs, and market price volatility. In this regard, energy storage is the most crucial facility to strengthen the MG’s reliability, especially in light of the rising generation of RESs. This work provides a two-stage optimization method for creating grid-connected MG operations. The optimal size and location of the energy storage are first provided to support the hosting capacity (HC) and the self-consumption rate (SCR) of the RESs. Second, an optimal constrained operating strategy for the grid-connected MG is proposed to minimize the MG operating cost while taking into account the optimal size and location of the energy storage that was formerly determined. The charge–discharge balance is the primary criterion in determining the most effective operating plan, which also considers the RES and MG limitations on operation. The well-known Harris hawks optimizer (HHO) is used to solve the optimization problem. The results showed that the proper positioning of the battery energy storage enhances the MG’s performance, supports the RESs’ SCR (reached 100% throughout the day), and increases the HC of RESs (rising from 8.863 MW to 10.213 MW). Additionally, when a battery energy storage system is connected to the MG, the operating costs are significantly reduced, with a savings percentage rate of 23.8%. Full article

The transition to renewable energy sources for a sustainable, low-carbon future is driven by the need for the mitigation of climate change. The integration of RES-based systems and storage units can deal with the intermittent nature of natural variables. The selection of storage technology is determined by various parameters related to space, topography and water resource availability. In the present study, two different storage methods, wind-powered pumped hydro storage and hydrogen fuel cells, are compared in terms of fulfillment energy and water demand of a small Aegean Sea island for the project’s 25-year lifespan. Full article

Many issues pioneered by Jackson Herring deal with how nonlinear interactions shape atmospheric dynamics. In this context, we analyze new direct numerical simulations of rotating stratified flows with a large-scale forcing, which is either random or quasi-geostrophic (QG). Runs were performed at a moderate Reynolds number $Re$ and up to 1646 turn-over times in one case. We found intermittent fluctuations of the vertical velocity w and temperature $\theta$ in a narrow domain of parameters as for decaying flows. Preliminary results indicate that parabolic relations between normalized third- and fourth-order moments of the buoyancy flux $\propto ⟨w\theta ⟩$ and of the energy dissipation emerge in this domain, including for passive and active scalars, with or without rotation. These are reminiscent of (but not identical to) previous findings for other variables and systems such as oceanic and atmospheric flows, climate re-analysis data, fusion plasmas, the Solar Wind, or galaxies. For QG forcing, sharp scaling transitions take place once the Ozmidov length scale ${\ell }_{Oz}$ is resolved—${\ell }_{Oz}$ being the scale after which a turbulent Kolmogorov energy spectrum likely recovers at high $Re$. Full article

The Association of the Southeast Asian Nations (ASEAN) region is a critical contributor to global development from an environmental perspective. This study has reviewed carbon emissions from energy generation, influence factors from the population, economic growth and renewable energy, emission and energy intensity projection, spatial distribution characteristics, and decarbonization strategy. This work utilizes a comparison methodology between ASEAN countries in the emission intensity and energy intensity in the future projection of electricity conditions in 2030 or 2040, as well as opportunities for reducing greenhouse gas (GHG) emissions as determined by the national policies of each government. The results show that Indonesia, Vietnam, Thailand, and Malaysia produce 79.7% of the electricity in the ASEAN region. As a developing country, Indonesia has the largest population and gross domestic product (GDP) but has the highest predicted emission intensity, of 0.97 CO₂e/MWh, in 2030. Vietnam is predicted to have an emission intensity of about 3.56t-CO₂e/cap and 0.747t-CO₂e/GDP in 2030. Vietnam is expected to increase in energy intensity to 1241 MWh/GDP, while Brunei Darussalam has a high energy intensity of 11.35 MWh/cap. However, the capacity of solar power plants (more than 11 GW) and wind-power plants (2384 MW) have generally increased in ASEAN from 2015 to 2019, indicating the positive development of renewable energy source (RES) use. The national policies strongly influence the estimated GHG emission in ASEAN by aggressively replacing fossil fuels with RESs. Support, via government policies, can reduce the cost of electricity generation from RESs is needed to increase and enhance the installment of clean power generation systems. In future work, the research needs to consider the intermittent characteristics of variable RES in power system operation. Full article

Real-time energy management of battery storage in grid-connected microgrids can be very challenging due to the intermittent nature of renewable energy sources (RES), load variations, and variable grid tariffs. Two reinforcement learning (RL)–based energy management systems have been previously used, namely, offline and online methods. In offline RL, the agent learns the optimum policy using forecasted generation and load data. Once the convergence is achieved, battery commands are dispatched in real time. The performance of this strategy highly depends on the accuracy of the forecasted data. An agent in online RL learns the best policy by interacting with the system in real time using real data. Online RL deals better with the forecasted error but can take a longer time to converge. This paper proposes a novel dual layer Q-learning strategy to address this challenge. The first (upper) layer is conducted offline to produce directive commands for the battery system for a 24 h horizon. It uses forecasted data for generation and load. The second (lower) Q-learning-based layer refines these battery commands every 15 min by considering the changes happening in the RES and load demand in real time. This decreases the overall operating cost of the microgrid as compared with online RL by reducing the convergence time. The superiority of the proposed strategy (dual-layer RL) has been verified by simulation results after comparing it with individual offline and online RL algorithms. Full article

Background/Aims: The management of hepatic hydrothorax (HH) remains a challenging clinical scenario with suboptimal options. We investigated the effect and safety of pigtail catheter drainage compared to intermittent thoracentesis. Methods: This multicenter, retrospective study included 164 cirrhotic patients with recurrent pleural effusion from March 2012 to June 2017. Patients with neoplasms, cardiopulmonary disease, and infectious conditions were excluded. We compared the clinical outcomes of pigtail catheter drainage versus thoracentesis for variables including complications related to procedures, overall survival, and re-admission rates. Results: A total of 164 patients were divided into pigtail catheter (n = 115) and thoracentesis (n = 49) groups. During the follow-up period of 6.93 months after discharge, 98 patients died (pigtail; n = 47 vs. thoracentesis; n = 51). The overall survival (p = 0.61) and 30-day mortality (p = 0.77) rates were similar between the pigtail catheter and thoracentesis groups. Only MELD scores were associated with overall survival (adjusted HR, 1.08; p < 0.01) in patients with HH. Spontaneous pleurodesis occurred in 59 patients (51.3%) in the pigtail catheter group. Re-admission rates did not differ between the pigtail catheter and thoracentesis groups (13.2% vs 19.6% p = 0.7). A total of five complications occurred, including four total cases of bleeding (one patient in the pigtail catheter group and three in the thoracentesis group) and one case of empyema in the pigtail catheter group. Conclusions: Pigtail catheter drainage is not inferior to that of intermittent thoracentesis for the management of HH, proving it may be an effective and safe clinical option. Full article

The high share of PV energy requires greater system flexibility to address the increased demand/supply imbalance induced by the inherent intermittency and variability of the solar resource. In this work, we have developed a methodology to evaluate the margins for imbalance reduction and flexibility that can be achieved by advanced solar/wind forecasting and by strengthening the national transmission grid connecting the Italian market areas. To this end, for the forecasting of the day-ahead supply that should be provided by dispatchable generators, we developed three advanced load/PV/wind forecasting methodologies based on a chain or on the optimal mix of different forecasting techniques. We showed that, compared to the baseline forecast, there is a large margin for the imbalance/flexibility reduction: 60.3% for the imbalance and 47.5% for the flexibility requirement. In contrast, the TSO forecast leaves only a small margin to reduce the imbalance of the system through more accurate forecasts, while a larger reduction can be achieved by removing the grid constrains between market zones. Furthermore, we have applied the new forecasting methodologies to estimate the amount of imbalance volumes/costs/flexibility/overgenerations that could be achieved in the future according to the Italian RES generation targets, highlighting some critical issues related to high variable renewable energy share. Full article