Hydrate formation from cyclopentane (CP) and carbon dioxide was measured at 281 K by powder X-ray diffraction (PXRD) and macroscopic methods. The effect of initial pressure and CP mass fraction in liquid phase was analyzed. The results showed that hydrate formation was assumed to start with the nucleation of the mixed CP-CO2 hydrate with small fraction of CO2 followed by a large continuous CO2 adsorption. Initial pressure was found to have a positive correlation with the total CO2 consumptions when the initial pressure was below 2.5 MPa. However, the total CO2 consumptions dropped by over a half as the initial pressure was 3.0 MPa. PXRD revealed that all the hydrate samples formed at different initial pressures were structure II. The CO2 consumptions were assumed to be inhibited by the competitive occupation of 51264 cages between CP and CO2 molecules when the initial pressure was above 2.5 MPa. The CO2 consumptions were also found to be reduced as the CP mass fraction was above 0.25.... [more]

Electric vehicles (EVs) and charging piles have been growing rapidly in China in the last five years. Private charging piles are widely adopted in major cities and have partly changed the charging behaviors of EV users. Based on the charging data of EVs in Hefei, China, this study aims to assess the impacts of increasing private charging piles and smart charging application on EVs’ charging load profiles. The charging load profiles of three types of charging piles which are public, employee-shared, and private ones, are simulated in three different scenarios. The results of scenario simulation indicate that the increase in EVs will reinforce the peak value of the total power load, while increasing private charging piles and the participation rate of smart charging piles will have peak-load shifting effects on the power load on weekdays. Specifically, 12% of the charging load will be shifted from public piles to private ones if the ratio of EVs and private piles increases from 5:3 to 5:... [more]

This article assesses the costs and benefits of incorporating battery energy storage systems (BESS) in transmission network expansion planning (TEP) over multiple time periods. We propose a mixed-integer programming model (MIP) for joint planning of the installation of battery energy storage systems (BESS) and construction of new transmission lines in multiple periods of time. The mathematical formulation of the presented model is based on the strategies of the agents of a transmission network to maximize their benefit, and on the operational restrictions of the power flows in transmission networks. This analysis is performed for the Garver 6 node test system takes into account the power losses in the lines and the restrictions for the energy stored in BESS. The power flows obtained with the MIP model are compared with AC power flows generated with specialized software for flows in power systems. This allows us to demonstrate the potential of models based on DC power flows to achieve a... [more]

The paper proposes a general description system of the five-phase two-level inverter. The two base methods are presented and discussed. The first one is based on the standard space vector transformation, while the other uses state vectors which enable the definition of the basic physical quantities of the inverter: current and voltage. The proposed notation system offers a general simplification of vector identification. It comprises a standardized proposal of notation and vector marking, which may be extremely useful for the specification of inverter states. The described notation system makes it possible to reach correlation between state and space vectors. It presents space and state vectors using the same digits. These properties suggest that the proposed notation system is a useful mathematical tool and may be really suitable in designing control algorithms. This mathematical tool was verified during simulation tests performed with the use of the Simulation Platform for Power Elec... [more]

Building energy performance benchmarking increases awareness and enables stakeholders to make better informed decisions for designing, operating, and renovating sustainable buildings. In the era of nearly zero energy buildings, the embodied energy along with operational energy use are essential for evaluating the environmental impacts and building performance throughout their lifecycle. Key metrics and baselines for the embodied energy intensity in representative Hellenic houses are presented in this paper. The method is set up to progressively cover all types of buildings. The lifecycle analysis was performed using the well-established SimaPro software package and the EcoInvent lifecycle inventory database, complemented with national data from short energy audits carried out in Greece. The operational energy intensity was estimated using the national calculation engine for assessing the building’s energy performance and the predictions were adapted to obtain more realistic estimates.... [more]

This study aims to find the relationship between energy resource dependence and economic growth in consideration of interprovincial heterogeneity. This paper first uses panel data from 14 provinces with rich energy resources in China between 2001 and 2016 as a whole to test the energy resource curse hypothesis. It finds that there is no obvious resource curse from a general perspective. It further makes time prediction and transmission channel analysis based on regressions of each province and classifies them into four groups according to the different degrees of the resource curse. It shows the different roles of resource dependencies in different groups. Twelve provinces are subject to different degrees of the resource curse, among which, six provinces would eventually experience negative economic growth if they increase the degree of resource dependence. Next, this study discusses the mechanism of one particular group, “invisible energy resource curse”, which is when energy resource... [more]

In recent years, there has been growing interest in the market interactions between carbon (or clean/renewable energy) and traditional fossil energy such as coal and oil, but few studies have discussed their dynamic volatility spillover and time-varying correlation. To investigate these issues, we used the weekly data of the European Union carbon emission allowance (EUA) futures, biofuel and Brent oil prices from 25 October 2009 to 5 July 2020. We employed the vector autoregressive-generalized autoregressive conditional heteroscedasticity (VAR-GARCH) model with the Baba, Engle, Kraft and Krone (BEKK) specification. Our main findings are summarized as follows: First, we identified the sudden changes and the volatility persistence in the EUA, biofuel, and Brent oil markets, and also confirmed that the volatility of the markets has changed significantly over time. Second, we found a weak volatility spillover effect among the three markets, and a strong spillover effect between the EUA and... [more]

The interface algorithm is critical for accuracy of the real-time integration simulation system of renewable energy and the power grid. To improve the overall performance of the existing interface algorithms, this paper proposes an optimized interface algorithm based on the auxiliary damping impedance method interface current feedback. We explain in detail the implementation principle of the new interface algorithm and the calculation method of impedance matching and also provide a parallel timing control logic. Using the new interface algorithm, we derive equations for voltage and current of the digital simulation system side and the device under test side and also compare it with the naturally coupled system without interface delay. Finally, we verify the accuracy of the new interface algorithm via establishing a complete model of the real-time integration simulation system with a wind turbine and the power grid. The results show that the accuracy can be improved 95% in the digital s... [more]

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossov... [more]

In AC railway electrification systems, the impact of reactive power flow in the feeding voltage magnitude is one aspect contributing to the quality of supply degradation. Specifically, this issue results in limitations in the infrastructure capacity, either in the maximum number of trains and in maximum train power. In this paper, two reactive power compensation strategies are presented and compared, in terms of the theoretical railway infrastructure capacity. The first strategy considers a static VAR compensator, located in the neutral zone and compensating the substation reactive power, achieving a maximum capacity increase up to 50% without depending on each train active power. The second strategy adapts each train reactive power, achieving also a capacity increase around 50%, only with an increase of the train apparent power below 10%. With a smart metering infrastructure, the implementation of such compensation strategy is viable, satisfying the requirements of real-time knowledge... [more]

The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further fee... [more]

In view of the serious threat of gas accumulation in the coal mine goaf and the limitations of the existing gas sealing materials, the orthogonal experiment was developed to study a new type of foamed concrete for mine gas sealing. Dry density, gas permeability, and compressive strength were studied as the material indicators according to the demands of the gas isolation material in the coal mine goaf, and the experimental results showed that foam content was the most important factor. Meanwhile, the optimum mix was selected according to the influence of foam content as well as the engineering requirement. Then two application modes of this foamed concrete for goaf gas isolation were put forward, after which the convection-diffusion model of gas was built by COMSOL Multiphysics (COMSOL Inc., Stockholm, Sweden) to reveal the mechanism of different application modes using the parameters of the new foamed concrete. Simulation results showed that this foamed concrete used as isolating mate... [more]

Investigations were performed in relation to the thermal gasification of wood granulate using steam in an allothermal reactor with electric heaters. They studied the impact of the temperature inside the reactor and the steam flow rate on the percentage shares of H2, CH4, CO, and CO2 in synthesis gas and on the calorific value of syngas. The tests were conducted at temperatures inside the reactor equal to 750, 800, and 850 °C and with a steam flow rate equal to 10.0, 15.0, and 20.0 kg∙h−1. The intensity of gasified biomass was 20 kg∙h−1. A significant impact of the temperature on the percentages of all the components of synthesis gas and a significant impact of the steam flow rate on the content of hydrogen and carbon dioxide in syngas were found. The highest percentage of hydrogen obtained was 43.3%. The calorific value of the gas depended significantly on the temperature inside the reactor and the correlation between the temperature and the steam flow rate. Its maximum value was 13.3... [more]

The coal was gasified in a fluidized bed reactor with CO2 as a gasifying agent at 889−980 °C. The coal and gasification residue produced during gasification was burned at temperatures up to 900 °C. The petrographic analysis, gasification residues, and fly and bottom ash resulting from the combustion of coal and chars showed the efficiency of the gasification and combustion processes. The gasification residue primarily comprised inertoids and crassinetwork, which accounted for 60% of the sample. The analysis of the petrographic composition of fly ash revealed that the fly ash formed during the combustion of gasification residue had a higher mineral content. The fly ash from the combustion of gasification products contained significantly less unburned coal compared to that from coal. The samples of the bottom ash from coal combustion were composed of approximately 25% organic matter, most of which was chars. The bottom ash formed from the combustion of coal gasification products was comp... [more]

Rechargeable magnesium (Mg)-based energy storage has attracted extensive attention in electrochemical storage systems with high theoretical energy densities. The Mg metal is earth-abundant and dendrite-free for the anode. However, there is a strong Coulombic interaction between Mg2+ and host materials that often inhibits solid-state diffusion, resulting in a large polarization and poor electrochemical performances. Herein, we develop a Mg−Li hybrid battery using a Mg-metal anode, an FeSe2 powder with uniform size and a morphology utilizing a simple solution-phase method as the counter electrode and all-phenyl-complex/tetrahydrofuran (APC)-LiCl dual-ion electrolyte. In the Li+-containing electrolyte, at a current density of 15 mA g−1, the Mg−Li hybrid battery (MLIB) delivered a satisfying initial discharge capacity of 525 mAh g−1. Moreover, the capacity was absent in the FeSe2|APC|Mg cell. The working mechanism proposed is the “Li+-only intercalation” at the FeSe2 and the “Mg2+ dissolve... [more]

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economi... [more]

In this paper, the development of the wind energy sector in 28 European Union countries in 2017 is described. By means of taxonomic methods—i.e., Ward’s method and the Wroclaw taxonomic methods—clusters of countries similar in terms of their potential and level of development of the wind energy sector in the EU are distinguished. The main purpose of the paper is to separate and cluster EU countries due to the current development potential of the wind energy sector and determinants stimulating the development of this sector. By means of the ranking methods of linear ordering (Technique for Order of Preference by Similarity to Ideal Solution—TOPSIS method), a ranking of EU countries that defines their position in the development of this very important wind energy sector was determined. The results show that the research hypothesis of a great diversity of EU countries considering the development potential of the wind energy sector is justified. The countries of the former European Union,... [more]

Fossil energy inputs and greenhouse gas (GHG) emissions associated with the cultivation and transport of sugarcane (Saccharum officinarum) for bioethanol production in Tanegashima, Japan, were estimated by life cycle assessment (LCA). The aim was to understand the effects of combined systems of polyethylene mulching treatment (mulching at planting and every ratooning, MM; mulching only at planting, MU; and untreated, i.e., no mulching at all, UU) and cultivar (a cold-tolerant genotype, NiTn18, and a conventional variety, NiF8). The mulch treatments and cultivars were combined to create six cultivation systems that were used to conduct a comparative assessment of cradle-to-gate energy inputs and emissions for bioethanol production. The LCA results showed that the energy inputs and GHG emissions resulting from the MM/NiF8 system were 6.29 MJ L−1 and 0.500 kg CO2e L−1, which were 14% and 23% lower, respectively, than the corresponding values in the UU/NiF8 system. In contrast, the MU/NiF8... [more]

The present paper reports the impact of the Covid-19 pandemic on the electricity peak load and power generation in the State of Kuwait during the partial and full curfews imposed in March, April and May 2020 using historic data measured data and the predictions provided by a statistical genetic algorithm model. A quantitative assessment is made of the economic and environmental impacts caused by partial and full lockdowns. Comparison of measured peak demand for 2019 and 2020 with predicted peak demand for 2020 has: (i) enabled an accurate evaluation of residential energy consumption in the state of Kuwait at nearly 18 MWh yearly the highest energy consumption per capita in the world, (ii) shown that the imposition of the curfews to reduce the spread of COVID-19 caused a fall in the demand for electrical power of 17.6% compared with the expected demand and (iii) quantified the reduction in CO2, NOx and CO pollutant emissions produced by power plants due to less fuel being consumed. A ma... [more]

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

Recently, the demand for electricity has been increasing worldwide. Thus, more attention has been paid to renewable energy. There are acceptable limits during the integration of renewable energy into distribution systems because there are many effects of integrating renewable energy. Unlike previous studies that have estimated the distributed energy resource (DER) hosting capacity using the standard high voltage and probability approach, in this study, we propose an algorithm to estimate the DER hosting capacity by considering DER outages due to abrupt disturbances or uncertainties based on the generator ramp rate and voltage stability, which involves analysis of the low-voltage aspects. Furthermore, this method does not involve a complicated process or need large amounts of data to estimate the DER hosting capacity because it requires only minimum data for power flow. The proposed algorithm was applied to the IEEE-33 radial distribution system. According to the DER capacity, a voltage... [more]

Densely populated areas with large incoming populations have difficulty achieving high separate collection rates of municipal solid waste. The manuscript analyzes the link between biowaste collection and circular economy requirements as a fulfilment of the recycling rates and using biogas as a sustainable energy source. Three biowaste collection scenarios and three technical scenarios for its treatment are considered. The first scenario assumes only composting for biowaste treatment, the next includes also anaerobic digestion. In the years 2020−2050, the separate biowaste collection level will increase, depending on the scenario, from 26.9 kg/inh. up to 148.1kg/inh. By 2030, the quantity of biogas generated from biowaste can grow to almost 9 million m3/year, enabling the production of renewable energy at annual levels of almost 17 GWh and 69 TJ. Using the third scenario, the quantity of biogas generated grows more than twice (in 2035). If the capture rate of biowaste increases from 15%... [more]

An increase in variable renewable energy sources and soaring electricity demand at peak hours undermines the efficiency and reliability of the power supply. Conventional supply-side solutions, such as additional gas turbine plants and energy storage systems, can help mitigate these problems; however, they are not cost-effective. This study highlights the potential value of electric vehicle demand response programs by analyzing three separate scenarios: electric vehicle charging based on a time-of-use tariff, smart charging controlled by an aggregator through virtual power plant networks, and smart control with vehicle-to-grid capability. The three programs are analyzed based on the stochastic form of a power system optimization model under two hypothetical power system environments in Jeju Island, Korea: one with a low share of variable renewable energy in 2019 and the other with a high share in 2030. The results show that the cost saving realized by the electric vehicle demand respons... [more]

The aim of this paper is to estimate the probability of bankruptcy of the companies from the Slovak electrical engineering industry based on data obtained from financial statements. Parameters of the predictive model were estimated using binary logistic regression. This model is able to predict the probability of a company’s bankruptcy based on values of significant explanatory variables (accounts payable turnover ratio (APTR), return on sales (ROS), quick ratio (QR), financial leverage (FL), net working capital/assets (NWC/A)). The model is constructed using the financial data of a large sample of electrical engineering companies from 2017. Resulting estimated odds ratios show that, in the electrical engineering industry, ROS, QR, and NWC/A significantly reduce the likelihood of bankruptcy. In other words, if these financial indicators increase, the probability of bankruptcy decreases. Our results are also applicable to other industries connected with industrial production, especially... [more]

Air conditioning in buildings accounts for 60% of the total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs), which simply and accurately allow us to forecast energy consumption with limited variables. Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month’s worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period... [more]