The use of smart meters enables the emergence of innovations in the electricity sector, such as smart grids, prosumers and Peer-to-Peer trading, which can play an important role in realizing the energy transition. These developments rely on the processing of personal data, triggering the application of data protection legislation in addition to the legislation organizing the electricity markets. This article examines the interaction between the General Data Protection Regulation (GDPR) and the Directive (EU) 2019/944 on common rules for the internal market for electricity in the European Union, following the method of doctrinal legal research. Concretely, this article investigates what kinds of tensions may arise from the coexistence of these two legal regimes and whether there are mechanisms in place to prevent or mitigate such tensions. Three main tensions are identified. The first tension lies in the fact that some of the innovations facilitated by smart metering in the energy secto... [more]

This study investigates using tailor-developed combustion and air-handling system concepts to achieve high-efficiency, clean gasoline compression ignition (GCI) combustion, aimed at addressing a future heavy-duty ultralow NOx standard of 0.027 g/kWh at the vehicle tailpipe and the tightening CO2 limits around the world by combining GCI with a cost-effective engine aftertreatment system. The development activities were conducted based on a 15 L heavy-duty diesel engine. By taking an analysis-led design approach, a first-generation (Gen1) GCI engine concept was developed and tested, encompassing tailor-designed piston bowl geometry, fuel spray pattern, and swirl motion paired with a customized, fixed-geometry, two-stage turbocharging system and a high-pressure EGR loop with two-stage cooling. Across four key steady-state operating points, the Gen1 GCI concept demonstrated 85−95% lower smoke and 2−3% better diesel-equivalent gross indicated fuel consumption compared to the diesel baseline... [more]

This study explores Bangladesh’s present energy condition, renewable energy (RE) possibilities and designs an optimal 100% RE-based off-grid power system for St. Martin’s Island, Bangladesh. The optimal size of a hybrid renewable microgrid based on photovoltaic (PV) cells, a battery energy storage system (BESS), fuel cells (FC), and an electrolysis plant (EP) is proposed. Advanced direct load control (ADLC) and rooftop PV meet the energy demand at the lowest cost, and profits are maximized by selling chemical products produced by seawater electrolysis. Four cases are explored with the mixed-integer linear programming (MILP) optimization technique using MATLAB® software to demonstrate the efficacy of the suggested power system. The system cost in case 1 is lower than in the other cases, but there is no chance of profiting. Cases 2, 3, and 4 have greater installation costs, which may be repaid in 8.17, 7.72, and 8.01 years, respectively, by the profits. Though the revenue in case 3 is 6.... [more]

T oilfield is the fractured-vuggy carbonate reservoir at a temperature of around 130 °C, with salinity of up to 22 × 104 mg/L. In order to solve the problem of the high water cut in the late development stage of T oilfield, we selected XN-T from 27 kinds of swelling retarding particles by testing their swelling capacity, and coated a thin film to improve its retarding swelling capacity. The mechanisms of strong water absorption and water-holding abilities of particles were analyzed by infrared spectrometry and SEM. In the core flow experiment, the plugging rate was found to be 98.42%. Finally, the injection parameters of the coated particles were optimized to maximize the water plugging and profile control ability, resulting in an optimal particle size of 0.4−0.6 mm and a mass fraction of 10%.

The objective of this article is to review the methodologies used in the last 15 years to estimate the power loss in wind turbines due to their exposure to adverse meteorological conditions. Among the methods, the use of computational fluid dynamics (CFD) for the three-dimensional numerical simulation of wind turbines is highlighted, as well as the use of two-dimensional CFD simulation in conjunction with the blade element momentum theory (BEM). In addition, a brief review of other methodologies such as image analysis, deep learning, and forecasting models is also presented. This review constitutes a baseline for new investigations of the icing effects on wind turbines’ power outputs. Furthermore, it contributes to a continuous improvement in power-loss prediction and the better response of icing protection systems.

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

Several countries around the world are undergoing a radical transformation of their electricity networks, with the widespread integration of distributed generation based on renewable energy sources at the center of the change. Under these scenarios, DC links are becoming an attractive option for the infrastructure of modern networks, especially at medium voltage levels. This work is devoted to the integration of DC systems in existing AC networks by converting AC power lines to DC. The work presents a novel method for converting a three-conductor medium voltage AC transmission line to DC. The efficiency of the proposed conversion is assessed in terms of increased power capacity. It is shown that the proposed approach allows for increasing the power capacity of the line by at least 10%. The feasibility and effectiveness of the proposed method are then confirmed by laboratory experiments.

The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users’ EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the cost... [more]

To achieve sustainable development goals (SDGs), it is necessary to solve the problem of assessing and measuring energy sustainability performance. A popular indicator used for this purpose is the World Energy Council (WEC) energy sustainability index, or the Energy Trilemma Index, which is based on such key metrics as energy security, energy equity, environmental sustainability, and country context. Each of the four metrics, or dimensions, includes many indicators that depend on both internal and external factors. By combining a variety of indicators into integral ones, WEC ranks countries in terms of energy sustainability. However, what is not taken into account is how countries differ in terms of economic development, income, energy mix, renewables use, ownership in the energy sector, and other factors, and neither is the methodology itself disclosed by the developers. As a provider for all other sectors of the economy, the energy sector plays an important role in developing countri... [more]

The concept of the modular multilevel converter (MLC) has been raising interest in research in order to improve their performance and applicability. The potential of an MLC is enormous, with a great focus on medium- and high-voltage applications, such as solar photovoltaic and wind farms, electrified railway systems, or power distribution systems. This concept makes it possible to overcome the limitation of the semiconductors blocking voltages, presenting advantageous characteristics. However, the complexity of implementation and control presents added challenges. Thus, this paper aims to contribute with a critical and comparative analysis of the state-of-the-art aspects of this concept in order to maximize its potential. In this paper, different power electronics converter topologies that can be integrated into the MLC concept are presented, highlighting the advantages and disadvantages of each topology. Nevertheless, different modulation techniques used in an MLC are also presented a... [more]

Metal additive manufacturing (AM) has been growing remarkably in the past few years. Thanks to the advantages of unmatched flexibility and zero material waste, this clean technology opens the door for new design solutions with greater material efficiency, which are not possible through conventional machining techniques. In this paper, we provide a technology overview of metal AM techniques that can be utilized in a wide range of applications, including constructing electrical machines. Different techniques of metal AM are discussed and compared. Additionally, the impact of the material forms (powder/wire) on printing speed and quality are studied. Based on the industrial and technical literature, this paper provides a comprehensive review of metal AM in the fabrication of electrical machines and their applications. This includes the current state of the art and associated benefits of AM in these applications.

Alternative sources of fuel have been a concern in the last few decades. The growth of urbanization and industrialization will lead to the exhaustion of fossil fuels, attracting studies on alternative routes. The main aim of this study was to produce biodiesel from waste cooking oil (WCO) by methyl transesterification using sodium hydroxide as a catalyst. For this, the physicochemical parameters of biodiesel were studied in triplicate (density, acidity, saponification, viscosity, corrosiveness to copper, visual appearance, and cloud point). An analysis by thin layer chromatography and infrared spectrometry was also performed. The increase in yield (83.3%) was directly proportional to the increase in the catalyst (0.22 g of NaOH). The infrared absorption spectra of WCO and biodiesel showed the presence of common and singular bands of each material. Furthermore, a simple and low-cost mechanism was proposed for purifying glycerol. The spectra of glycerol versus purified glycerin showed th... [more]

This article discusses a sustainable low-carbon development strategy that uses nuclear heat sources to replace coal boilers at existing coal power plants in China, to help support a resource and cost-effective low-carbon development. Based on the local situation in China, a three-stage strategy to explore the potential of repowering coal power by nuclear energy is proposed. The main focus of this study is to conduct a more detailed exploration of the 1st stage of this strategy, which includes coal plants located on the coast in regions that already have nuclear power installations. The study makes use of HTR-PM modular reactor for retrofit analysis for the types of coal units present in 1st stage of the strategy. The results show that: 1. There is a technical and economic basis for exploring nuclear power retrofit decarbonization. This conclusion is backed up by on-site transformation analysis and demonstration of the conversion of representative plant units to ensure the validity and... [more]

Lowering the regeneration temperature for solid CO2-capture materials is one of the critical tasks for economizing CO2-capturing processes. Based on reported pKa values and nucleophilicity, we compared two different polyethylenimines (PEIs): branched PEI (BPEI) and linear PEI (LPEI). LPEI outperformed BPEI in terms of adsorption and desorption properties. Because LPEI is a solid below 73−75 °C, even a high loading amount of LPEI can effectively adsorb CO2 without diffusive barriers. Temperature-programmed desorption (TPD) demonstrated that the desorption peak top dropped to 50.8 °C for LPEI, compared to 78.0 °C for BPEI. We also revisited the classical adsorption model of CO2 on secondary amines by using in situ modulation excitation IR spectroscopy, and proposed a new adsorption configuration, R1(R2)-NCOOH. Even though LPEI is more expensive than BPEI, considering the long-term operation of a CO2-capturing system, the low regeneration temperature makes LPEI attractive for industrial a... [more]

When a lot of surplus power occurs in wind power system, an output limit is implemented to directly or indirectly curtail the output to maintain a balance between the supply and demand of the power system. The curtailment process of a large-scale wind farm causes loss of power and mechanical loads. Resultantly, imbalanced curtailments often occur, resulting in unilateral burdens for the owners of wind farms. Considering the curtailment issue, the study for minimizing system loss of power plants is required in terms of operational efficiency. This paper proposes an algorithm to achieve flexible control during the actual power curtailment process in a wind farm, considering the wake effect. Here, the Monte Carlo method was adopted to calculate the curtailment weight in wind farms by using power loss terms. In addition, an equivalent model of a real wind farm was implemented and simulated through real simulation computer-aided design (RSCAD) software. This paper verified the effectiveness... [more]

Large databases of legacy hydrocarbon reservoir and well data provide an opportunity to use modern data mining techniques to improve our understanding of the subsurface in the presence of uncertainty and improve predictability of reservoir properties. A data mining approach provides a way to screen dependencies in reservoir and fluid data and enable subsurface specialists to estimate absent properties in partial or incomplete datasets. This allows for uncertainty to be managed and reduced. An improvement in reservoir characterisation using machine learning results from the capacity of machine learning methods to detect and model hidden dependencies in large multivariate datasets with noisy and missing data. This study presents a workflow applied to a large basin-scale reservoir characterization database. The study aims to understand the dependencies between reservoir attributes in order to allow for predictions to be made to improve the data coverage. The machine learning workflow comp... [more]

Feeding IC engines with hydrogen-rich syngas as an admixture to hydrocarbon fuels can decrease pollutant emissions, particularly NOx. It offers a potential technique for low-environmental impact hydrocarbon fuel use in automotive applications. However, hydrogen-rich reformate gas (syngas) production via fuel reforming still needs more research and optimization. In this paper, we describe the effect of a plasma torch assembly design on syngas yield and composition during plasma-assisted reforming of gasoline. Additionally, erosion resistance of the cathode-emitting material under the conditions of gasoline reforming was studied, using hafnium metal and lanthanated tungsten alloy. The gasoline reforming was performed with a noncatalytic, nonthermal, low-current plasma system in the conditions of partial oxidation in an air and steam mixture. To find the most efficient plasma torch assembly configuration in terms of hydrogen production yield, four types of anode design were tested, i.e.,... [more]

3D concrete printing has gained tremendous popularity as a promising technique with the potential to remarkably push the boundaries of conventional concrete technology. Enormous research efforts have been directed towards improving the material properties and structural safety of 3D printed concrete (3DPC) over the last decade. In contrast, little attention has been accorded to its sustainability performance in the built environment. This study compares the energy efficiency, operational carbon emission, and thermal comfort of air cavity 3DPC building envelopes against insulated models. Four insulations, namely expanded polystyrene (EPS), extruded polystyrene (XPS), polyurethane foam (PUF), and fiberglass (FG), are iteratively paired with three different 3DPC mix designs, and their resulting performances are reported. A numerical optimization analysis is performed to obtain combinations of 3DPC building models and insulation with the least energy expenditure, carbon production, and the... [more]

Microwave soil heating deactivates weed seeds; however, in many modern agricultural settings, weed seeds are mostly found in the top 1−2 cm of the soil profile. Until recently, microwave soil heating has been achieved using various antennas, which project the microwave energy deeply into the soil. The aim of this research was to develop new microwave applicators that provide shallow heating (less than 50 mm). This paper presents two applicator designs, one based on a comb slow-wave structure and the other on the frustrated total internal reflection (FTIR) principle, which utilise evanescent microwave fields to restrict the depth of microwave heating. The background theory to their performance is presented, followed by experimental evidence of their constrained heating performance under different soil moisture scenarios. Experimental measurements of the heating performance of these applicators, in soils of varying moisture content, demonstrate that the evanescent microwave fields restri... [more]

In smart grids, a hybrid renewable energy system that combines multiple renewable energy sources (RESs) with storage and backup systems can provide the most cost-effective and stable energy supply. However, one of the most pressing issues addressed by recent research is how best to design the components of hybrid renewable energy systems to meet all load requirements at the lowest possible cost and with the best level of reliability. Due to the difficulty of optimizing hybrid renewable energy systems, it is critical to find an efficient optimization method that provides a reliable solution. Therefore, in this study, power transmission between microgrids is optimized to minimize the cost for the overall system and for each microgrid. For this purpose, artificial bee colony (ABC) is used as an optimization algorithm that aims to minimize the cost and power transmission from outside the microgrid. The ABC algorithm outperforms other population-based algorithms, with the added advantage of... [more]

This paper presents the final development of an expert system utilizing a measurement of cable screen earthing transient current. The developed system allows for identification and location of earth fault in underground cable and mixed lines (underground cable and overhead line) and monitoring of an earthing system and cable screen connections. The unique feature of the developed earth fault locating system is the possibility of identification of line type and branch of the MV underground cable or mixed feeder under earth fault conditions. As a result, the time to remove failure can be greatly reduced and the number of earth fault indicators installed in the distribution network can also be reduced. Unfortunately, in order to operate properly, the previously developed system requires a fundamental—50 Hz component of the measured zero-sequence cable core current and cable screen earthing current; therefore, short transient earth faults without steady-state earth fault currents cannot be... [more]

The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification... [more]

The aim of the research, the results of which are used in this publication, was to identify the motives for mergers and acquisitions in the energy sector after the introduction of green economy elements in Western Europe. The mentioned region is the group of countries where changes related to the move to green energy are most visible (in addition to some countries from other regions, such as Singapore, New Zealand, or countries of the Arabian Peninsula). The research assumed the hypothesis that over the years since the Paris Conference the themes of mergers and acquisitions have changed from motives close to the views related to energy generation in traditional systems (black energy in large, monopolistic systems) to motives close to green energy (the research hypothesis). This was confirmed in the research, as business risk diversification (defined as diversification of the power sources from black to green) was the most popular M&A motive. In addition to emphasizing the direction of... [more]

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are u... [more]

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well... [more]