A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, th... [more]

In this paper, a blockchain-enabled energy trading method is proposed to deal with the inefficiency and security issues in energy trading for electric vehicles in smart grids. It includes the design of a smart contract and the excitation mechanism of energy sharing. The credit points of each vehicle as a node are considered in the design of the smart contract, which is used to supervise the process of energy trading. A strategy to estimate the credit points of each node and describe the excitation mechanism is illustrated. The connection between the credit points and the probability that a node would be accepted for energy trading has been established. To control the energy trading access, a double auction method is used to choose the part of the nodes participating in energy trading. Only selected nodes with winning bids can supply or request energy from the blockchain-based platform. Then it reaches the conclusion that the higher the credit points they have, the more trading initiati... [more]

The presented work contains the comparison of a different photovoltaic system approach, in terms of monthly energy production profiles, with detailed user needs profiles. Two types of investors were taken into account—a company where the yearly energy consumption exceeded 50 MWh and a standard household where the yearly energy consumption was around 6 MWh. Moreover, two different scenarios were considered; the first one was focused on the maximization of the energy production during a year by choosing optimal azimuth and tilt angles, whereas the second PV system was based on the East−West azimuth angles, which usually enables one to have better energy self-consumption, which leads to sustainable energy usage. Energy production profiles, along with users’ energy needs profiles, were analyzed in terms of sustainability, investment payback time, and energy loss in the face of the new Polish Renewable Energy Law. Recent changes in the Renewable Energy Law in Poland, which came into force o... [more]

We investigated the dynamics of nanofluid and heat transfer in a three-dimensional circular annular using the κ−ε turbulence model and energy equations. The pipe contained two concentric and rotating cylinders with a constant speed in the tangential direction. A heat flux boundary condition was executed at the inner cylinder of the annular. The pipe was settled vertically, and water alumina nanofluid was allowed to enter, with the initial velocity depending on the Reynolds number, ranging from 30,000 to 60,000. The volume fraction of the solid particles was tested from 0.001 to 0.1. The speed of the rotation of the cylinders was tested in the range from 0.5 to 3.5. The simulations were developed using COMSOL Multiphysics 5.6, adopting the finite element procedure for governing equations. The results were validated using the mesh independent study and the average Nusselt number correlations. We found that the average Nusselt number in the middle of the channel decreases linearly with th... [more]

Photovoltaic systems (PVs) are promising low-carbon technologies playing a major role in the electricity business. In terms of voltage variation and feeder usage capacity, high PV penetration levels have significant technical implications for grid stability, particularly in Low Voltage (LV) networks. This paper presents a comprehensive PV integration analysis on real-life residential LV networks in Malta using recorded smart metering data. The methodology framework and tools developed are highlighted through step-by-step results on their usefulness. First, at the substation level, an LV network with seven LV feeders is analyzed using Monte Carlo simulations and OpenDSS. Then, Cumulative Distribution Functions (CDFs) are extracted to establish the likelihood of LV network challenges. Afterwards, 95 multi-feeder analyses assess the impact assessment on the first occurrence of LV network challenges and predominant issues. Finally, a Regression Analysis Tool, considering the regression’s s... [more]

The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness... [more]

In this study, we assessed a lifestyle in which occupants adjust the fittings based on climate, weather, and time, in terms of energy efficiency and thermal conditions. The proposed solution is a Zero Energy House (ZEH) with high thermal performance. The thermal performance of the building envelope can be adjusted by changing the operation of fittings based on the indoor and outdoor environments, as well as air conditioning usage. Many studies have achieved zero energy by increasing the thermal performance of an envelope and using highly efficient energy-saving facilities; however, uniquely, here we focus on occupant behavior to change the building envelope condition. In this paper, numerical analysis was used to investigate the effect of adjusting the fittings on buildings with different thermal performances of the envelope. The analysis demonstrates that, while more research into measures is needed in the summer, the adjustment of fittings and thermal storage properties in the winter... [more]

The rural electrification of Sub-Saharan Africa and South-East Asia is crucial to end the energy poverty in which around 1 billion people are trapped. Swarm electrification, i.e., the progressive building of decentralized and decarbonized electric infrastructure in a bottom-up manner, tackles rural electrification challenges by quickly providing modern and reliable electricity services to unelectrified communities while fostering local socio-economic development. This paper follows the technological approach of this electrification model and presents the development of a DC microgrid with decentralized production and storage suitable for rural electrification. This DC microgrid aims at interconnecting nanogrids, small collective autonomous power units composed of a solar panel and a lead−acid battery for 4 to 6 households, to increase the electrical services brought to the community and enhance the economic sustainability of this rural electrification model. The design of the proposed... [more]

As energy consumption for building engineering systems is a major part of the total energy spent, it is necessary to reduce it. This leads to the need for the development of new solutions for the control of heating, ventilation, and conditioning (HVAC) systems that are responsive to humans and their demands. In this review article, the existing research and technology advancements of the modern technologies of computer vision and neural networks for application in HVAC control systems are studied. Objectives such as human detection and location, human activity monitoring, skin temperature detection, and clothing level detection systems are important for the operation of precise, high-tech HVAC systems. This article tries to compile the latest achievements and principal solutions on how this information is acquired. Moreover, it how parameters such as indoor air quality (IAQ), variable air volume ventilation, computer vision, metabolic rate, and human clothing isolation can affect final... [more]

Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This research focused on a European city, Lisbon, and the e-bike sharing system GIRA, in its early deployment, in 2018, where it had about 409 bikes of which 30% were non-electric conventional bikes and 70% were e-bikes. The research aims at answering the main research questions: (1) What is the number of trips per day and travel time in conventional bikes and e-bikes?; (2) Do the daily usage peaks follow the trends of other modes of transport in terms of rush hours?; (3) Are there seasonality patterns in its use (weekdays and weekends, workdays and holiday periods)?; (4) How do climate conditions affect its use?; and finally, (5) What would be t... [more]

A substation is the portion of a power grid that forms a link between the cyber system and the physical system. Reliability evaluation of smart substations based on a time-varying probabilistic hybrid attack graph (TVPHAG) is studied in this paper. First, the topology network of the smart substation is established, whose attributes are represented by probability. Then, in order to solve the problem of asynchrony in the cyber-physical system and the hybrid caused by heterogeneity, time-varying state equation in topology and cuts in algebra are introduced to TVPHAG. Based on TVPHAG, the evaluation of the reliability of cyber-physical systems with multiple equipment and multiple timescales is established. On this basis, the influences of physical conditions, cyberattacks, physical attacks, and cyber-physical attacks on substations are analyzed, respectively. Finally, the simulation shows that the method is effective in evaluating the reliability of smart substations, providing a new metho... [more]

A novel energetic co-crystal composed of hydrazinium 3-nitro-1,2,4-triazol-5-one (HNTO) and ammonium nitrate (AN), as a composite solid propellant oxidizer, was recently developed to substitute either pure ammonium perchlorate (AP) or nitrate. Unfortunately, the thermodynamic properties of this co-crystal or even the pure HNTO are not available in the open literature. Therefore, in this work, the low-temperature heat capacities of HNTO and HNTO·AN co-crystal were measured in the temperature range from 213.15 K to 378.15 K using differential scanning calorimetry. By fitting the heat capacity data, the thermodynamic functions ΔH298.15K, ΔG298.15K, and ΔS298.15K were derived. In addition, the standard molar energies of combustion ΔcU° of HNTO and HNTO·AN co-crystal were determined, and from the combustion results, the standard molar enthalpies of combustion ΔcHmo and formation ΔfHmo of these energetic compounds were derived at T = 298.15 K. The set of thermochemical data has been proposed... [more]

Ferry operators in Sweden provide transportation for tens of millions of people annually. As electric vehicles (EVs) are becoming more commonplace, ferries and ferry terminals seem like suitable places for providing charging services. However, high costs and low occupancy rates means that it is challenging to design profitable business models for charging services in general. This paper reports on a market review of the charging services that ferry operators in Sweden provide and a case study of suitable business model design elements for operators that intend to offer charging on board or in terminals. While only two of fifteen ferry operators offer EV charging on board, four more operators indicated that they were planning to provide such services in the near future. Nine operators offered charging in or close to ferry terminals. The results also indicate that business model design focuses primarily on safety, leading to higher costs for onboard charging due to hardware and staffing... [more]

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square... [more]

Diesel engines are widely used in agricultural tractors. During field operations, the tractors operate at low speed and high load for a long time, the fuel efficiency is only about 15% to 35%, and the exhaust waste heat accounts for 38% to 45% of the energy released from the fuel. The use of tractor exhaust waste heat can effectively reduce fuel consumption and pollutant emissions, of which the organic Rankine cycle (ORC)-based waste heat recovery conversion efficiency is the highest. First, the diesel engine map is achieved through the test rig, a plate-fin evaporator is trial-produced based on the tractor size, and the thermodynamic and economic performance model of the ORC are established. Then, taking the thermal efficiency of ORC and the specific investment cost (SIC) as the objective function, the particle swarm optimization (PSO) algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) decision method were used to obtain the optimal operating... [more]

This paper presents the integrated optimal design of the powertrain of a hybrid regional aircraft using multidisciplinary design optimization (MDO). The sizing of the main components of the propulsion chain is performed over the flight mission under various scenarios regarding energy management strategies and technological assessments. For that purpose, a complete set of multidisciplinary surrogate models are integrated into the MDO process, taking account not only of the main electrical, thermal and mechanical aspects but also of environmental constraints such as partial discharges in electric motors regarding flight conditions. Several MDO formulations are investigated comparing local (i.e., motor mass minimization) and global optimizations (i.e., powertrain mass then fuel burn minimization at aircraft level). Results emphasize main systemic couplings showing that despite future technological progress, the series hybrid architecture is heavier than a conventional thermal aircraft. Ne... [more]

The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function−support vector regression and genetic algorithms is presented in this paper. Firstly, key features of coal gangue gathered from major coal mines are measured and used to build a sample set. Then, the forecasting performance of single kernel function-based models is established, and linear kernel and Gaussian kernel functions are chosen according to forecasting results. Next, a hybrid kernel combined with the two kernel functions mentioned above is used to establish a calorific value forecasting model. In addition, a genetic algorithm is introduced to optimize critical parameters of SVR and the adjustable weight. Finally, the forecasting model based on hybrid kernel function−support vector regression and genetic algorithms is built to predict the calorific value of new c... [more]

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number... [more]

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy a... [more]

The transition from the use of fossil fuels to renewable and green energy is a worldwide challenge that must be seriously considered in order to ensure sustainable development and the preservation of the environment. The conversion of wet biomasses (i.e., sewage sludge) into energy through thermochemical processes in general and hydrothermal carbonization (HTC) in particular has been pointed out as an interesting and attractive approach for the energetic and agricultural valorization of the produced solid residues, named hydrochars. The success of such valorization options is highly dependent on these hydrochars’ physico-chemical and energetic properties that are influenced not only by the type of the sludge (urban or industrial) and its nature (primary, secondary, or digested) but also by the HTC parameters, especially temperature, pressure, and residence time. This editorial provides a summary of the latest studies regarding the impact of the cited above parameters on the properties... [more]

In order to explore the development characteristics and influencing factors of microscale pores in lacustrine organic-rich muddy shale, this study selected five shale samples with different mineral compositions from the Qingshankou Formation in the Songliao Basin. The oil content and mineralogy of the shale samples were obtained by pyrolysis and X-ray diffraction analysis, respectively, while the porosity of the samples was computed by micro-CT imaging. Next, based on the CT images, the permeability of each sample was calculated by the Avizo software. Results showed that the continuous porosity of Qingshankou shale in the Songliao Basin was found between 0.84 and 7.79% (average 4.76%), the total porosity between 1.87 and 12.03% (average 8.28%), and the absolute permeability was calculated between 0.061 and 2.284 × 10−3 μm2. The total porosity of the samples has a good positive correlation with the continuous porosity and permeability. This means higher values of total porosity suggeste... [more]

Using thermal insulation materials to reduce energy loss in buildings is a key action. For reducing the building’s energy use, firstly, the internal unheated spaces (attics, cellars) should be insulated, followed by the insulation of the external walls, and changing the doors and windows. Finally, the building can be completed with the renovation/maintenance of its service systems. Newly designed and constructed buildings are subject to increasingly strict regulations, which highlight the minimization and elimination of wasteful energy use and the resulting emissions of harmful substances. Therefore, the use of thermal insulation is the first step in making buildings more energy efficient. In this editorial, seven articles covering thermal insulation possibilities and topics are highlighted. This paper reflected on the use of thermal insulations both for internal and external applications. This editorial also promotes the use of super insulation materials such as aerogels and vacuum in... [more]

One of the by-products of a mechanical-biological waste treatment plant is the under-sieve fraction, which requires separation prior to further processing of municipal mixed waste. This stream usually follows the fate of landfilling. Instead, it could be introduced to the hydrothermal carbonization (HTC) process to improve its fuel properties and become solid recovered fuel. The organic fraction and high moisture content (approximately 26%) of under-sieve fraction are favorable properties for the HTC process. In this study, hydrochars, the solid product of HTC, were produced at 200 and 220 °C with residence times of 1, 4, and 8 h. The main aim of this investigation was to establish the influence of different process parameters on hydrochars’ fuel properties. Thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) were employed in the analyses. The results confirmed the positive effects of hydrothermal carbonization on the... [more]

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30−59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this paper... [more]

Quantitative accounting of greenhouse gas (GHG) emissions has become an important global focus. GHG emissions from the waste sector have high potential in GHG emissions reduction. We analyzed the GHG emissions inventory in the waste sector of the European Union, Germany, the United Kingdom, the United States of America, and Canada from 1990 to 2019. Landfill disposal was the main category of GHGs from the waste sector, with a contribution rate between 69% and 95%. Landfill disposal also played a prominent role in emission reduction, with a contribution rate higher than 86%. GHG emissions from landfill sites in China were calculated using the inventory analysis method recommended by the IPCC and combined with actual situations. The results showed that the highest GHG emissions from landfill disposal in China occurred in 2020, with an estimated 165 million tons of carbon dioxide (CO2) equivalent. In 2019, the per capita GHG emissions from landfill sites in China was 117 kg CO2 equivalent... [more]