Building electricity load forecasting plays an important role in building energy management, peak demand and power grid security. In the past two decades, a large number of data-driven models have been applied to building and larger-scale energy consumption predictions. Although these models have been successful in specific cases, their performances would be greatly affected by the quantity and quality of the building data. Moreover, for older buildings with sparse data, or new buildings with no historical data, accurate predictions are difficult to achieve. Aiming at such a data silos problem caused by the insufficient data collection in the building energy consumption prediction, this study proposes a building electricity load forecasting method based on a similarity judgement and an improved TrAdaBoost algorithm (iTrAdaBoost). The Maximum Mean Discrepancy (MMD) is used to search similar building samples related to the target building from public datasets. Different from general Boos... [more]

This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions of this article are to present a system where Q-learning is successfully integrated with MPEI to reduce the impact of power contingencies on critical loads and to explore the effectiveness of the subsequent system. The feasibility of the proposed method has been proven through simulation and experiments. It has been demonstrated that the proposed method can effectively improve the reliability of the local power system—for a case study where 20% of the total loads are classified as critical loads, the system average interruption duration index (SAIDI) has been improved by 75% compared to traditional microgrids with no load schedule.

Bifacial technology is attracting the attention of the photovoltaic community. Although considered premature, research and development activities still need to be carried out to improve bPV performance. In addition, the need for a standard test reference will aid bankability and increase confidence in this technology. This article describes the state of the art of bifacial technology, going through the bPV cell and its difference compared to conventional monofacial cells and listing the different sources of limitations, with an identification of different parameters that characterize the performance of the bifacial. Then, the paper reviews the different modeling methods that allow predicting the performance of bPV systems, and ends with the most important applications, whether for dual use of land to produce energy and food (agrivoltaic) or for placing bPV modules on water bodies instead of on the ground (aquavoltaics), or for vertical use as solar fences, acoustic barriers, or buildin... [more]

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation for... [more]

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This dat... [more]

With the rapid expansion of new energy in China, the large-scale grid connection of new energy is increasing, and the operating safety of the new energy power system is being put to the test. The static security and stability region (SSSR) with hyper-plane expression is an effective instrument for situational awareness and the stability-constrained operation of power systems. This paper proposes a hybrid improved particle swarm optimization (IPSO) and recursive least square (RLS) approach for rapidly approximating the SSSR boundary. Initially, the operating point data in the high-dimensional nodal injection space is examined using the IPSO algorithm to find the key generators, equivalent search space, and crucial points, which have a relatively large impact on static stability. The RLS method is ultimately utilized to fit the SSSR border that best suits the crucial spots. Consequently, the adopted algorithm technique was used to rapidly approximate the SSSR border in power injection sp... [more]

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the a... [more]

Biomass is one of the most important sources of renewable energy in the energy industry. It is assumed that by 2050 the global energy deposit could be covered in 33−50% of biomass combustion. As with conventional fuels, the combustion of biomass produces combustion by-products, such as fly ash. Therefore, along with the growing interest in the use of biomass as a source of energy, the production of ash as a combustion by-product increases every year. It is estimated that approximately 476 million tons of ashes per year can be produced from biomass combustion. For example, the calorific value of dry wood mass tends to be between 18.5 MJ × kg−1 and 19.5 MJ × kg−1, while the ash content resulting from thermal treatment of wood is from 0.4 to 3.9% of dry fuel mass. However, biomass ash is a waste that is particularly difficult to characterize due to the large variability of the chemical composition depending on the biomass and combustion technology. In addition, this waste is, on the one h... [more]

Methane hydrate applications in gas storage and desalination have attracted increasing attention in recent years. In the present work, the effect of isopentane (IP), sodium dodecyl sulfate (SDS), and IP/SDS blends as promoters on methane hydrate formation kinetics, in terms of the pressure−temperature (P-T) profile, gas uptake, hydrate induction time (HIT), and water-to-hydrate conversion ratio (WHCR), were studied for distilled water and seawater samples with an IP/water sample ratio of 3:10 (by volume) and an SDS/water sample ratio of 1:1000 (by mass). Each solution was tested in a stirred tank at 600 rpm at a temperature and pressure of 2 °C and 5.2−5.3 MPa. In the case of methane hydrate formation in distilled water, the highest WHCR attained was 9.97% without additives, and 45.71% and 72.28% for SDS and isopentane additives, respectively. However, when using seawater at a salinity of 3.9%, the highest WHCR attained was 2.26% without additives and 9.89% and 18.03% for SDS and IP pr... [more]

Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection perfor... [more]

The Paris Agreement requires a drastic reduction of global carbon emissions towards the net zero transition by mid-century, based on the large-scale transformation of the global energy system and major emitting sectors. Aviation and shipping emissions are not on a trajectory consistent with Paris goals, driven by rapid activity growth and the lack of commercial mitigation options, given the challenges for electrification of these sectors. Large-scale models used for mitigation analysis commonly do not capture the specificities and emission reduction options of international shipping and aviation, while bottom-up sectoral models do not represent their interlinkages with the entire system. Here, I use the global energy system model PROMETHEUS, enhanced with a detailed representation of the shipping and aviation sector, to explore transformation pathways for these sectors and their emission, activity, and energy mix impacts. The most promising alternative towards decarbonizing these secto... [more]

The bidirectional dc-dc converter is a critical component for extending the use of renewable energy and improving the efficiency of high-power electronic systems. This paper presents the analysis of the stray inductance of a commutation loop and the commutation behavior of IGBT devices in a flying capacitor three-level bidirectional DC-DC converter (FC-3L-BDC) phase-leg power electronic building block (PEBB). An FC-3L-BDC phase-leg PEBB was designed as an example, which can be used to build 400 kW to MW-grade light rail train chargers, battery energy storage interface converters, or metro regenerative braking energy recovery converters with a single PEBB or several PEBBs interleaved parallel. In order to optimize the stray inductance of commutation paths and realize snubberless operation, a five-layer laminated bus bar was carefully designed, and the stray inductance of the bus bar was extracted by three-dimensional finite element analysis simulation. To obtain higher accuracy, the str... [more]

The dynamic performance of the DC bus significantly influences the impedance characteristics of MMC and the system stability in a high-voltage direct current system. However, most of the existing MMC-HVDC system stability research simplifies the DC side as an ideal voltage source and ignores the impacts of voltage feedforward control, which affects the accuracy and practicability of stability analysis. In this paper, a sequence impedance model considering both DC voltage control and voltage feedforward control is developed, and the necessity of considering DC control and voltage feedforward control for MMC-HVDC stability analysis is illustrated. Then, the impact of control parameters on MMC-HVDC impedance is discussed, and the boundary conditions of control parameters are also derived. Finally, a method of control parameters design and impedance optimization for MMC-HVDC based on the stability boundary is proposed. Compared to the traditional optimization method, the system stability i... [more]

Nuclear power plants have a complex structure and changeable operation mode, which induces low setting calculation efficiency. After analyzing the technology, architecture, and functional logic of a variety of relay protection setting calculation systems and combining the characteristics of the setting calculation of nuclear power plants, the relay protection setting calculation system in nuclear power plants based on B/S architecture and cloud computing is studied in this paper. The system adopts three-tier B/S architecture, applies two key technologies, the cloud computing task distribution synchronization mechanism and the cloud component automatic assembly mechanism, and introduces a particle swarm optimization algorithm to provide technical support for nuclear power plant setting calculation; the running example of the nuclear power plant system fully proves the efficiency and reliability of the relay protection setting calculation system of the nuclear power plant, which has high... [more]

The key goal of this research was to figure out the short and long run relationship between environmental degradation caused by carbon dioxide (CO2) emissions and energy consumption, the level of GDP economic growth, and urbanization in the Visegrad Region countries (V4). The study used data from the years 1996−2020. In the methodological area, ARDL bound test, and ARDL and ECM models were used to determine the directions and strength of interdependence. The results show that in the case of some V4 countries (Poland, Slovakia, and Hungary), changes in the urbanization rate affect CO2 emissions. Moreover, it was confirmed that the phenomenon of urbanization influences the enhanced energy consumption in the studied countries. In the case of individual countries, these relationships were varied, both unidirectional and bidirectional. Their nature was also varied—there were both long and short-term relationships. These findings suggest that the V4 countries should increase renewable and ec... [more]

Transient electromagnetic (TEM) surveys constitute an important element in exploration projects and can be successfully used in the search for oil and gas. Different modifications of the method include shallow (sTEM), 2D, 3D, and 4D (time-lapse) soundings. TEM data allow for solving a large scope of problems for estimating resources and reserves of hydrocarbons, discriminating reservoir rocks, detecting tectonic features, and characterizing drilling conditions. TEM surveys are applicable at all stages, from initial prospecting to production, and are especially efficient when combined with seismic surveys. Each stage has its specific objectives: estimation of net pay thickness, porosity, and fluid type during prospecting, optimization of well placement and prediction of drilling conditions in exploration, and monitoring of flooding during production. Electromagnetic soundings resolve permafrost features well and thus have a high potentiality for exploration in the Arctic petroleum provi... [more]

Energy deposition for flow and flight control has received significant interest in the past several decades due to its potential application to high-speed flow and flight control. This paper reviews recent progress and recommends future research.

In this paper, the transient state analysis of a Line-Start Permanent Magnet Synchronous Motor (LSPMSM) and the optimum design for high efficiency were studied. In the case of an LSPMSM, aluminum bars and permanent magnets are inserted in the rotor. Since it has aluminum bars, it can be directly started on-line without closed-loop control at the time of starting, like an induction motor. Furthermore, once driven, it rotates at a synchronous speed due to the permanent magnets in the steady state. Theoretically, since the rotor bars have no induced current, copper loss does not occur in the rotor bars. Further, because of the inserted permanent magnets, an LSPMSM has a higher power density than an induction motor with the same output power. However, since it is driven directly on-line, the transient state is longer than that of a synchronous motor driven by an inverter. Therefore, it is important to analyze the characteristics of the transient state depending on the rotor shape in the LS... [more]

An important feature of this research is the investigation of the de-alloyed catalysts based on the nanoparticles with a simple structure (alloy) and a complex structure (gradient). The resulting samples exhibit the 2−4 times higher mass activity in the ORR compared with the commercial Pt/C. The novelty of this study is due to the application of the express-electrochemical experiment to register the trend of changes in the ORR activity caused by rearranging the structure of bimetallic nanoparticles. The state-of-the-art protocol makes it possible to establish the dependence of properties of the de-alloyed catalysts on the nanoparticles’ structure obtained at the stage of the material’s synthesis. The study shows the possibility of determining the rate of the ongoing reorganization of bimetallic nanoparticles with different architectures. The PtCu/C electrocatalysts for proton-exchange membrane fuel cells presented in this work are commercially promising in terms of both the high functi... [more]

In this paper, an integrated system based on low-concentrated photovoltaic/thermal (LCPV/T) technology and efficient vacuum membrane distillation (VMD) seawater desalination utilizing the energy of solar is established. Through a theoretical analysis and a series of experiments, this paper explores the temperature change of a single VMD process, and the variation trend of single-day membrane flux with solar irradiation and temperature parameters. In addition, the changes in solar irradiation, temperatures of the integrated system, membrane flux, and thermoelectric properties in different seasons are also analyzed. A mathematical model was established to calculate the relationship between membrane flux and temperature difference. The experimental results show that the membrane flux of VMD is 2.73 L/(m2·h); the simulated seawater can achieve a desalination rate of 99.9%. After economic analysis, the operating incomes of the system under sunny weather conditions in different seasons were... [more]

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve th... [more]

Determining the geomechanical properties of hydrate-bearing sands (HBS), such as strength and stiffness, are critical for evaluating the potential for the economic and safe recovery of methane gas from HBS reservoirs. To date, results from numerous independent laboratory studies on synthesized HBS have shown that strength and stiffness are largely influenced by hydrate saturation, the method adopted for hydrate formation, and to a lesser extent, the confining stresses applied during testing. However, a significant scatter is observed in the data even when these conditions are similar. These include recent studies on natural HBS where sands with larger particle size distribution (PSD) exhibited higher strengths despite lower hydrate saturation. To investigate the impact of PSD, and the role that specific hydrate formation conditions might impose, on the strength and stiffness of HBS, a series of laboratory tests were carried out on sand specimens formed with different particle size dist... [more]

A cooling tower is an important guarantee for the proper operation of a solar system. To ensure proper operation of the system and to maintain high-efficiency points, the cooling tower must operate year-round. However, freezing is a common problem that degrades the performance of cooling towers in winter. For example, the air inlet forms hanging ice, which clogs the air path, and the coil in closed cooling towers freezes and cracks, leading to water leakage in the internal circulation. This has become an intractable problem that affects the safety and performance of cooling systems in winter. To address this problem, three methods of freeze protection for cooling towers are studied: (a) the dry and wet mixing operation method—the method of selecting heat exchangers under dry operation at different environments and inlet water temperatures is presented. The numerical experiment shows that the dry and wet mixing operation method can effectively avoid ice hanging on the air inlet. (b) The... [more]

The integration of renewable resources with distribution networks (DNs) is an effective way to reduce carbon emissions in energy systems. In this paper, an economic and low-carbon-oriented optimal planning solution for the integration of photovoltaic generation (PV) and an energy storage system (ESS) in DNs is proposed. A convolutional neural network (CNN)-based prediction model is adopted to characterize the uncertainties of PV and load demand in advance. Then, taking the lowest total economic cost, the largest carbon emission reduction, and the highest system power supply reliability as the optimization objectives, the optimal distribution network planning model is constructed. The improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the optimization model, and the effectiveness of the proposed solution is confirmed through a comparative case study on the IEEE-33 bus system. Simulation results show that the proposed solution can better maintain the... [more]

Nuclear technologies have strong potential and a unique role to play in delivering reliable low carbon energy to enable a net-zero society for future generations. However, to assure the sustainability required for its long-term success, nuclear will need to deliver innovative solutions as proposed in iMAGINE. One of the most attractive features, but also a key challenge for the envisaged highly integrated nuclear energy system iMAGINE, is the need for a demand driven salt clean-up system based on the principles of reverse reprocessing. The work described provides an insight into the dynamic interplay between a potential salt clean-up system and reactor operation in a plutonium-started core in a dynamic approach. The results presented will help to optimise the parameters for the salt clean-up process as well as to understand the differences which appear between a core started with enriched uranium and plutonium as the fissile material. The integrated model is used to investigate the eff... [more]