Agricultural land and forestland are considered as two largest potential biomass sources for meeting the Renewable Fuel Standard (RFS) mandate for cellulosic biofuels. However, the land use change and greenhouse gas (GHG) savings with both agricultural and forest biomass production are yet to be examined systematically. This paper examines the effects of implementing a 16-billion gallon (60 billion liters) cellulosic biofuel mandate by 2035 on the mix of agricultural and forest biomass, land use change and GHG emissions by using a dynamic partial equilibrium model of the agricultural, forestry and transportation sectors in the US. Our results show that crop residues play a significant role in supplying cellulosic ethanol before 2030, while energy crops are the major feedstocks used for meeting the RFS cellulosic mandate after 2030. Milling and logging residues are economically viable supplements to agricultural biomass for cellulosic ethanol production, though their role in total bioma... [more]

Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The... [more]

The study was carried out on a group of 85 public buildings, which differed in type of use, construction technology and heating systems. From the collected data, a set of qualitative and quantitative variables characterizing them in terms of heat demand was extracted. In this paper, the authors undertook to test the suitability of a model based on rough set theory (RST), which allows the analysis of imprecise, general and uncertain data. To obtain input data for the RST model in quantitative form, the authors used an alternative approach, which is a method based on the thermal properties of buildings. The quality of the predictive model was evaluated based on the following indicators, such as the coefficient of determination (R2), the mean bias error (MBE), the coefficient of variance of the root mean square error (CV RMSE) and the mean absolute percentage error (MAPE), which are accepted as statistical calibration standards by ASHRAE (American Society of Heating, Refrigerating and Air... [more]

Due to the ever-growing importance of rechargeable lithium-ion batteries, the development of electrode materials and their processing techniques remains a hot topic in academia and industry. Even the well-developed and widely utilized active materials present issues, such as surface reactivity, irreversible capacity in the first cycle, and ageing. Thus, there have been many efforts to modify the surface of active materials to enhance the electrochemical performance of the resulting electrodes and cells. Herein, we review the attempts to use polymer coatings on the anode active materials. This type of coating stands out because of the possibility of acting as an artificial solid electrolyte interphase (SEI), serving as an anode protective layer. We discuss the prominent examples of anodes with different mechanisms: intercalation (graphite and titanium oxides), alloy (silicon, tin, and germanium), and conversion (transition metal oxides) anodes. Finally, we give our perspective on the fu... [more]

Energy policy is becoming a key aspect of the everyday worldwide agenda, and the decisions in this field are directly affecting many aspects, such as energy security, energy supply, and consumer final prices, as well as environmental aspects, among others, and will also affect conditions in the coming years with regard to aspects such as energy resource availability decay, climate change effects, or air contamination. During the last decades, many specific efforts in energy planning research have been carried out by different scientists around the world, but very few of their scientifically based conclusions and recommendations have been transferred into energy planning and energy policy. As a consequence, the energy availability and the environmental situation of the world are worsening; the objectives which aim to achieve a maximum of a 1.5 °C increase are far from being achieved, and many different regions are suffering energy supply disruptions and lack of accessible and secure ene... [more]

In order to clarify the influence in the volcanic mode and structure on the oil reservoirs, the volcanic reservoir characteristics, volcanic eruption pattern, and volcanic eruption period of the third member of the Shahejie formation in the Dagang Oilfield Zao35 fault block are studied by combining logging, 3D seismic, and production data, and to provide geological basis for the subsequent development of volcanic reservoirs. The results show that the volcanic body of the Zao35 fault block is jointly controlled by the fissure-centered eruption mode, and there are three strings of bead-shaped eruption centers as well as a fault overflow channel. Based on the seismic response characteristics, the volcanic rocks can be divided into three main eruption cycles. Moreover, combined with the relatively stable mudstone interlayer encountered by the single well, it can be further divided into eight volcanic eruption periods. There are three different lava units in the overflow facies of each stag... [more]

Renewable energy sources have power quality and stability issues despite having vast benefits when integrated with the utility grid. High currents and voltages are introduced during the disconnection or injection from or into the power system. Due to excessive inverter switching frequencies, distorted voltage waveforms and high distortions in the output current may be observed. Hence, advancing intelligent and robust optimization techniques along with advanced controllers is the need of the hour. Therefore, this article presents an improved arithmetic optimization algorithm and an offset hysteresis band current controller. Conventional hysteresis band current controllers (CHCCs) offer substantial advantages such as fast dynamic response, over-current, and robustness in response to impedance variations, but they suffer from variable switching frequency. The offset hysteresis band current controller utilizes the zero-crossing time of the current error for calculating the lower/upper hyst... [more]

In this paper, an ocean wave energy conversion system (OWECS) is modeled and experimented in the Yellow Sea near Lianyungang port, and an optimized control method based on the sliding mode control is proposed to improve the efficiency of OWECS. Firstly, a motion model of a double-buoy OWECS is presented using a complex representation method, and the analysis results indicate that the efficiency of converting ocean wave energy into the outer buoy’s mechanical power is highest in a suitable ocean wave period. Secondly, a double-buoy OWECS is constructed and experimented in the Yellow Sea near Lianyungang port, which verified the correctness of the above analysis results. Lastly, in order to further improve the efficiency of the double-buoy OWECS, a sliding mode control method based on a linear generator is proposed to realize the phase synchronization between the outer buoy and ocean waves, and the simulation results may be beneficial for the next ocean test of the double-buoy OWECS.

Urban wastewater could be converted into energy if microbial electrochemical technologies (METs) like microbial dual-chamber electrolysis cells (MDEC) or microbial fuel cells (MFC) are applied as a treatment method. Mathematical modelling of MFC and MDEC for wastewater treatment and energy recovery has been developed in this study. The Radaue method has been used to solve ordinary differential equations (ODEs), and the model outputs were successfully validated with previous experimental and modelling data. A case study in Montreal, Canada, has also been considered for testing the application of METs on an urban scale with a total daily wastewater flow of 75,000 L/day. The results show that from 1 m3 of wastewater, MDEC and MFC can generate 0.077 kg H2 and 0.033 kWh, respectively.

Pollution of water by plastic contaminants has received increasing attention, owing to its negative effects on ecosystems. Small plastic particles propagate in water and can travel long distances from the source of pollution. In order to research the settling motion of particles in water flow, a small-scale experiment was conducted, whereby spherical plastic particles of varying diameters were released in an open-channel flow. Three approaches were investigated to numerically simulate the motion of particles. The numerical simulation results were compared and validated with experimental data. The presented methods allow for deeper insight into particle motion in fluid flow and could be extended to a larger scale to predict the propagation of mesoplastics in natural environments.

Supercritical carbon dioxide (SCO2) is widely used in many fields of energy and power engineering, such as nuclear reactors, solar thermal power generation systems, and refrigeration systems. In practical applications, SCO2 undergoes a cooling process significantly when it is cooled near the pseudo−critical point. Because of the drastic variations in thermo−physical properties, the heat transfer characteristics fluctuate, affecting the heat exchange and overall cycle performance. This paper summarizes extensive experiments and numerical simulations on the cooling process of SCO2 in various application scenarios. The effects of various working conditions, such as mass flow, working pressure, pipe diameter, flow direction, and channel shapes, are reviewed. The applicability and computational results using different numerical methods under different working conditions are also summarized. Furthermore, empirical correlations obtained in experiments at different conditions are included. The... [more]

In the context of carbon neutrality, the development of new digital infrastructure (NDI) and the improvement of digital capabilities are essential, in order to speed up the transformation of the energy structure. Based on the balanced panel data of 30 provinces in China from 2008 to 2019, we empirically analyzed the impact of NDI on the structural transformation of energy in China and its mechanisms of action. The results demonstrated that (1) NDI had a positive impact on China’s energy transition, and the empirical results were robust. (2) The mediating effect showed that NDI had a positive impact on the transformation of energy structure, through improving green total factor productivity and green finance. (3) The heterogeneity analysis indicated that NDI made a more significant contribution to the transformation of the energy structure in regions with lower pollution levels and in those with energy cooperation policies. This study provides a policy reference for Chinese energy trans... [more]

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference.... [more]

This paper reports on a review on combined heat and power (CHP). A historical examination points out that combined heat and power was primarily used for hot heat valorizing (CHHP). The technological aspects evolved with this configuration first in industrial size. More recently, configuration with cold heat and power production (CCHP) appeared. Then, the immediate extension of this configuration led to trigeneration configuration, providing three useful effects: power and hot and cold heat. We suggest in the paper that progress regarding this last approach remains to be achieved towards the extension of trigeneration to polygeneration, whatever the form of energy and substance (water uses, for example). More generally, we consider that the goal, regarding the energy uses, is the integration of all needs in the design stage of the whole system (design optimization). Then, the evolution of the system in time should be considered, this being the purpose of control command of the optimized... [more]

Due to the uncertainty of the nodal power caused by the varying renewable energies and the variety of loads, the line power of the distribution network (DN) is uncertainty also. In extreme scenarios, the line power may exceed the loading limits and incur overload violations. In this paper, a risk analysis specifically for overload violations based on the security region of the DN is established. This method takes the N-0 security of the DN as the reference to determine the bidirectional security region and violation distances. The calculation of the probability distribution of the overload violation in the distribution lines is established according to the distribution of node injections of the DN by using the semi-invariant algorithm. By referring to the security boundaries, the optimization model of the anti-violation strategy to minimize the cost of anti-violation is derived, by which the severity of violation risk events is obtained accordingly. Assessment of the risk cost is built... [more]

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]