Search Results (85)

In many countries located in Central–Eastern Europe, there is a need for heating in the autumn and winter seasons. In Poland, this has been met over the years, mainly through the development of centralized heating systems. The heat sources in such systems are based on fossil fuels like coal or gas. New regulations and climate concerns are forcing a transformation of existing systems towards green energy. The research presents two scenarios of such a change. The first focuses on maintaining centralized heat sources but increases the share of renewables in the heat supply. This can be realized by weather-independent, high-power sources such as biomass boilers and/or high-temperature heat pumps (HP) such as sewage heat pumps or ground source HP. The second scenario changes the location of the heat sources to more dispersed locations so that the unit power can be lower. In this case, renewable heat sources can be used at favorable locations in the system. Among the sources included in this scenario are solar panels, photovoltaic panels, micro wind turbines, and ground source heat pumps with local heat storage. These are characterized by low energy density. Their dispersion in the urban space can contribute to the desired energy generation, which would be impossible to achieve in the centralized scenario. Furthermore, the transmission losses are lower in this case, so lower heating medium temperatures are required. The existing district heating network can be used as a buffer or heat storage, contributing to stable system operation. The article presents a comparative analysis of these solutions. Full article

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure. Full article

The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism (ERCS_Lossy_Flag), which incorporates a flag bit for each dimension, indicating if the prediction error exceeds a threshold, followed by further compression using Golomb-Rice coding. We conducted experiments on 24-dimensional weather and wave element data from a single buoy, and the results show that ERCS_Lossless achieves an average compression rate of 47.40%. In real communication scenarios, splicing and byte alignment operations are performed on multidimensional data, and the results show that the variance of the payload increases but the mean decreases after compression, realizing a 38.60% transmission energy saving, which is better than existing real-time lossless compression methods. In addition, ERCS_Lossy_Flag further reduces the amount of data and improves energy efficiency when lower data accuracy is acceptable. Full article

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems. Full article

Autonomous Underwater Vehicles (AUVs) operate independently using onboard batteries and data storage, necessitating periodic recovery for battery recharging and data transfer. Traditional surface-based launch and recovery (L&R) operations pose significant risks to personnel and equipment, particularly in adverse weather conditions. Subsurface docking stations provide a safer alternative but often involve complex fixed installations and costly acoustic positioning systems. This work introduces a comprehensive docking solution featuring the following two key innovations: (1) a novel deployable docking station (DDS) designed for rapid deployment from vessels of opportunity, operating without active acoustic transmitters; and (2) an innovative sensor fusion approach that combines the AUV’s onboard forward-looking sonar and camera data. The DDS comprises a semi-submersible protective frame and a subsurface, heave-compensated docking component equipped with backlit visual markers, an electromagnetic (EM) beacon, and an EM lifting device. This adaptable design is suitable for temporary installations and in acoustically sensitive or covert operations. The positioning and guidance system employs a multi-sensor approach, integrating range and azimuth data from the sonar with elevation data from the vision camera to achieve precise 3D positioning and robust navigation in varying underwater conditions. This paper details the design considerations and integration of the AUV system and the docking station, highlighting their innovative features. The proposed method was validated through software-in-the-loop simulations, controlled seawater pool experiments, and preliminary open-sea trials, including several docking attempts. While further sea trials are planned, current results demonstrate the potential of this solution to enhance AUV operational capabilities in challenging underwater environments while reducing deployment complexity and operational costs. Full article

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. Full article

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability. Full article

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product. Full article

Precise surveys are indispensable in coastal engineering projects. The extensive presence of sand in the coastal area leads to significant attenuation of seismic waves within unsaturated loose sediments. As a result, it becomes challenging for seismic waves to penetrate the weathered zone and reach the desired depth with significant amount of energy. In this study, the application of three-dimensional (3D) cross-well elastic reverse time migration (RTM) imaging based on multi-wave and multi-component techniques in coastal engineering exploration is explored. Accurate decomposition of vector compressional (P) and shear (S) waves is achieved through two wavefield decoupling algorithms without any amplitude and phase distortion. Additionally, compressional wave pressure components are obtained, which facilitates subsequent independent imaging. This study discusses and analyzes the imaging results of four imaging strategies under cross-correlation imaging conditions in RTM imaging. The analysis leads to the conclusion that scalarizing vector wavefields imaging yields superior imaging of P- and S-waves. Furthermore, the imaging results obtained through this approach are of great physical significance. In order to validate the efficacy of this method in 3D geological structure imaging in coastal areas, RTM imaging experiments were performed on two representative models. The results indicate that the proposed 3D elastic wave imaging method effectively generates accurate 3D cross-well imaging of P- and S-waves. This method utilizes the multi-wave and multi-component elastic wave RTM imaging technique to effectively leverage the Earth’s elastic medium without increasing costs. It provides valuable information about the distribution of subsurface rock layers, interfaces, and other structures in coastal engineering projects. Importantly, this can be achieved without resorting to extensive excavation or drilling operations. This method addresses the limitations of current cross-well imaging techniques, thereby providing abundant and accurate geological and geophysical information for the analysis and interpretation of 3D geological structures in coastal engineering projects. It has important theoretical and practical significance in real-world production, as well as for the study of geological structures in coastal engineering. Full article

Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 10² to 5.68 × 10³) than estimates that assume coincident failures are the result of independent hydrologic events. Full article

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters. Full article

The unmanned aerial vehicle (UAV) communication network has emerged as a promising paradigm capable of independent operation and as a relay to enhance communication coverage and efficiency. However, densely distributed terrestrial base stations with shared communication frequencies inevitably generate co-channel interference (CCI). The interference effect can be effectively eliminated by implementing free-space optical (FSO) communication in the UAV communication network. This paper proposes a solution for the UAV communication network to address interference effectively, specifically by employing a hybrid millimeter-wave radio frequency (RF)/FSO communication system. The RF links serve as the primary means of communication, while the FSO links act as a backup means of communication in the case of CCI. The exact outage probability (OP) and average symbol error rate (SER) expressions are derived for the hybrid RF/FSO communication network. The decision to switch between them depends on the signal-to-interference-plus-noise ratio (SINR). Furthermore, the SINR switching threshold value, which satisfies the target SER, has been calculated numerically for the proposed model. Simulation results indicate that the proposed network notably enhances the OP and attains a signal-to-noise ratio gain of approximately 4.6 dB in the average SER, particularly in scenarios where the RF links are subjected to severe interference or adverse weather conditions, as opposed to a pure RF communication network. Full article

To obtain new information about surface waves, it is proposed to use an underwater acoustic wave gauge, and an assessment of its effectiveness can be performed using a numerical simulation and field experiment. A new device, an underwater acoustic wave gauge named “Kalmar”, was developed by the Institute of Applied Physics of the Russian Academy of Sciences for long-term, all-weather monitoring of wind waves. The instrument uses ultrasound to probe the water surface from underwater and can be used to verify remote sensing data. In this work, the capabilities of the device are tested and compared with ADCP data. Two independent methods for processing underwater acoustic wave gauge data are discussed and compared. One of them is completely new for acoustic measurements and is based on the analysis of the shape of the reflected acoustic pulse averaged over space and time. The other allows processing individual reflected pulses and calculating the time implementation of the distance to the water surface. It is shown that two independent methods of significant wave height retrieval from the acoustic wave gauge measurements are highly correlated. The “Kalmar” acoustic wave gauge and the RDI WH-600 acoustic Doppler current profiler operated simultaneously at the test site in Gelendzhik from 1 February to 10 February 2020. The significant wave heights measured by the two instruments are in good agreement. Full article

Distributed resources at a grid’s end cannot upload operational power data to local centers due to data transmission and privacy issues. This leaves the centers with incomplete information, thus impacting decision making. This paper presents a virtual aggregation-based model for such scenarios. We define four virtual aggregate types based on resource response characteristics. Using characteristic coefficients, we identify these aggregates’ categories and proportions from bus power. To address blind source separation in single-channel power signals, we apply the Ensemble Empirical Mode Decomposition-Fast Independent Component Analysis (EEMD-FastICA) method. This helps extract and analyze bus power, thereby deriving power curves for different aggregates. Moreover, we use a graph convolutional network to explore how factors like date, time, weather, and pricing intertwine with aggregate power. We develop a predictive model with an advanced SpatioTemporal Graph Convolutional Network (STGCN), thus facilitating proactive power forecasting for virtual aggregates. Case studies show our method’s efficacy in extracting power curves under limited information, with the STGCN ensuring accurate, forward-looking predictions. Full article

Precise knowledge of the antenna pointing direction is a key facet to ensure the accuracy of observations from scanning weather radars. The sun is an often-used reference point to aid accurate alignment of weather radar systems and is particularly useful when observed as interference during normal scanning operations. In this study, we combine two online solar interference approaches to determine the pointing accuracy of an X-band mobile weather radar system deployed for 26 months in northern England (54.517°N, 3.615°W). During the deployment, several shifts in the tilt of the radar system are diagnosed between site visits. One extended period of time (>11 months) is shown to have a changing tilt that is independent of human intervention. To verify the corrections derived from this combined approach, quantitative precipitation estimates (QPEs) from the radar system are compared to surface observations: an approach that takes advantage of the variations in the magnitude of partial beam blockage corrections required due to tilting of the radar system close to mountainous terrain. The observed improvements in QPE performance after correction support the use of the derived tilt corrections for further applications using the corrected dataset. Finally, recommendations for future deployments are made, with particular focus on higher latitudes where solar interference spikes show more seasonality than those at mid-latitudes. Full article