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This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

In this report, a 5 kW PEFC system running on dry hydrogen with an appropriately sized Balance of Plant (BoP) was used to conduct experimental studies and analyses of gas supply subsystems. The improper rating and use of BoP components has been found to increase parasitic loads, which consequently has a direct effect on the polymer electrolyte fuel cell (PEFC) system efficiency. Therefore, the minimisation of parasitic loads while maintaining desired performance is crucial. Nevertheless, little has been found in the literature regarding experimental work on large stacks and BoP, with the majority of papers concentrating on modelling. A particular interest of our study was the anode side of the fuel cell. Additionally the rationale behind the use of hydrogen anode recirculation was scrutinised, and a novel anode purging strategy was developed and implemented. Through experimental modelling, the use of cathode air blower was minimised since it was found to be the biggest contributor to the parasitic loads.

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

To achieve sustainable development of hydroelectric resources, it is necessary to understand their availability, variability, and the expected impacts of climate change. Current research has mainly focused on estimating hydropower potential or determining the optimal locations for hydropower projects without considering the variability and historical trends of the resources. Herein, the hydropower potential variability from reconstructed streamflow series estimated with a non-parametric gap-filling method and geographic information systems (GIS) techniques are analyzed. The relationships between hydropower and large-scale climate variability, expressed by sea surface temperature, are explored. Finally, we project hydropower potential through 2050 using 15 global circulation models with representative concentration pathway (RCP) 4.5. We used four watersheds in central Chile as a case study. The results show significant interannual and inter-basin hydropower potential variability, with decreasing trends over time modulated by alternating positive and negative decadal trends; these modulations exhibit greater intensities than the general trends and are attributable to climatic oscillations such as El Niño. Future scenarios indicate high hydropower availability and a possible over-investment in hydroelectric plants in two of the four studied watersheds. Results show the need to improve the current policies that promote hydropower development including hydropower resource variability in order to achieve optimal, sustainable hydropower development worldwide.

This work focuses on preparing TiO2, CdS, and composite TiO2:CdS thin films for photovoltaic applications by thermal evaporation. The suggested materials exhibit very good optical and electrical properties and can play a significant role in enhancing the efficiency of the device. Various microscopy and spectroscopy techniques were considered to investigate the optical, morphological, photoluminescence, and electrical properties. FTIR confirms the material identification by displaying some peaks in the fingerprint region. UV Vis spectroscopy yields high transmission (80–90%) and low absorbance (5–10%) within the spectral region from 500 nm to 800 nm for the composite thin films. The optical band gap values for CdS, TiO2, and TiO2:CdS thin films are 2.42 eV, 3.72 eV, and 3.6 eV. XRD was utilized to analyze the amorphous nature of the thin films, while optical and SEM microscopy were employed to examine the morphological changes caused by the addition of CdS to TiO2. The decrease in the bandgap of the composite thin films was determined by the Tauc plot, which is endorsed due to the band tailing effects. Photoluminescence spectroscopy depicts several emission peaks in the visible region when they are excited at different wavelengths, and the electrical measurement enhances the material conductivity. Furthermore, the proposed electron transport materials (TiO2, CdS, TiO2:CdS) were simulated with different perovskite materials to validate their design by employing the SCAPS-1D program and assess their performance in commercial implementation. The observed results suggest that TiO2:CdS is a promising candidate to be used as an ETM in PSC with enhanced productivity.

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements.

At autonomous electricity grids Renewable Energy (RE) contributes significantly to energy production. Offshore resources benefit from higher energy density, smaller visual impacts, and higher availability levels. Offshore locations at the West of Crete obtain wind availability ≈80%, combining this with the installation potential for large scale modern wind turbines (rated power) then expected annual benefits are immense. Temporal variability of production is a limiting factor for wider adaptation of large offshore farms. To this end multi-generation with wave energy can alleviate issues of non-generation for wind. Spatio-temporal correlation of wind and wave energy production exhibit that wind and wave hybrid stations can contribute significant amounts of clean energy, while at the same time reducing spatial constrains and public acceptance issues. Offshore technologies can be combined as co-located or not, altering contribution profiles of wave energy to non-operating wind turbine production. In this study a co-located option contributes up to 626 h per annum, while a non co-located solution is found to complement over 4000 h of a non-operative wind turbine. Findings indicate the opportunities associated not only in terms of capital expenditure reduction, but also in the ever important issue of renewable variability and grid stability.

The road to decarbonization has led to the exploration of sustainable energy sources for domestic and industrial use [...]