The main purpose of the paper was to identify the most frequently discussed directions of research on green transformation. In the article, both the significant similarities in the existing studies in this field, as well as the newly emerging topics of research, are presented. For this purpose, the authors used a systematic literature review with elements of statistical analyses. This kind of approach is not popularly used in literature review papers, as it differs from the research practices employed previously, which mostly concentrated on applying qualitative methods, alternatively supported by the analysis of the co-occurrence of keywords. In this paper, the authors decided to include selected methods of dimensional analysis in the systematic literature review, namely the log-linear and correspondence analyses. The main results of the presented analyses are a more detailed division of studies related to green transformations into groups focused on the areas more difficult to distin... [more]

Carbon capture and storage (CCS) is an essential technology for achieving carbon neutrality. Depositional environments with sandstone and interbedded shale layers are promising for CO2 storage because they can retain CO2 beneath continuous and discontinuous shale layers. However, conventional numerical simulation of shale−sandstone systems is computationally challenging due to the large contrast in properties between the shale and sandstone layers and significant impact of thin shale layers on CO2 migration. Extending recent advancements in Fourier neural operators (FNOs), we propose a new deep learning architecture, the RU-FNO, to predict CO2 migration in complex shale−sandstone reservoirs under various reservoir conditions, injection designs, and rock properties. The gas saturation plume and pressure buildup predictions of the RU-FNO model are 8000-times faster than traditional numerical models and exhibit remarkable accuracy. We utilize the model’s fast prediction to investigate the... [more]

Because reliance on gas for electricity generation rises over time, the natural gas and electricity markets are highly connected. However, both of them are susceptible to various risk factors that endanger energy security. The intricate interactions among multiple risks and between the two markets render risk assessment more challenging than for individual markets. Taking a systematic perspective, this study first undertook a thorough analysis of the evolution mechanism that indicated the key risk factors and dual interactions, with real-world illustrative examples. Subsequently, a system dynamics model was constructed for understanding the causal feedback structures embedded in the operation of a coupled natural gas−electricity market in the face of risks. Quantitative experiments were conducted by using data from China’s Energy Statistical Yearbook, China’s Statistical Yearbook and other reliable sources to assess the effects of individual risks, depict the evolutionary behavior of c... [more]

Ion flow field is an important aspect of high voltage direct current (HVDC) transmission lines. In this paper, we apply the upwind finite volume method for solving the ion flow field of three HVDC configurations to clarify the effect of the wind and the role of shielding conductors. For the monopolar configuration installation, the ground current distribution with underbuilt shield wires was studied numerically and experimentally. For the ±250 kV bipolar configuration, the calculated peak electric field and current density are verified with the values in a reference. The ±500 kV bipolar configuration is used to investigate the change in electric field and ion current within the same corridors of the existing HVAC lines. We analyze the ion flow field with and without the dedicated metallic return conductor (DMRC). In the absence of wind, the maximum of the electric field is lower than that of the HVAC lines and the current density is very low on the ground. In the presence of wind, the... [more]

Fluctuations in the critical current along the length of high-temperature superconducting (HTS) tapes manufactured in the form of coated conductors is a common manufacturing phenomenon. These fluctuations originate in the generation of weak points through the length of HTS tapes that may cause quenching later. By means of the propagation of quenches in HTS tapes, the reliability, stability, and the performance of the device and the system that contain HTS tapes could be seriously degraded. In this study, an artificial intelligence technique based on artificial neural networks (ANN) was proposed to estimate the probability of quenches in HTS tapes caused by weak points. For this purpose, six different HTS tapes were considered with different widths, total thicknesses, and thicknesses of sub-layers. Then, for each one of these tapes, different operating conditions were considered, where the operating temperature changed from 40 K to 80 K, in 1 K steps. Under each operating temperature, d... [more]

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be... [more]

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for futur... [more]

This paper mainly discusses the demand bidding and risk management of user aggregators by considering profit and risk. The covariance matrix of demand price was used to analyze the risk model under an uncertain demand price. By considering revenue and cost, the demand bidding strategy of user aggregators was derived to obtain the maximum profit. By using a risk-tolerance parameter β, a new demand bidding model for the user aggregators that takes both risk and profit into consideration was formulated. We simulated the risk posed by fluctuating demand prices for user aggregators using this model. Finally, this paper proposes Feasible Particle Swarm Optimization (FPSO) to solve the demand bidding model of user aggregators. Through the dynamic adjustment of control factor parameters in the FPSO, we changed the behavioral characteristics of various types of particles, improved the search efficiency and stability of particles in high-dimensional space, and sought the optimal solution for the... [more]

Presently, among the global ocean energy technologies, the most conventional one is the wave energy power generation device based on the oscillating water column (OWC) wave energy converter. Given the fluctuation and randomness of waves and the complexity of the current power grid, the dynamic response of grid connections must be considered. Furthermore, considering the characteristics of the wave energy converter, this paper proposed an adaptive intelligent controller (AIC) for the permanent magnet synchronous generator (PMSG) in an OWC. The proposed controller includes a grey predictor, a recurrent wavelet-based Elman neural network (RWENN), and an adaptive critical network (ACN) to improve the stability of OWC power generation. This scheme can increase the maximum power output and improve dynamic performance when a transient occurs under the operating conditions of random wave changes. The proposed AIC for the PMSG based on OWC has a faster response speed, a smaller overshoot, and b... [more]

The traditional ampere-hour (Ah) integration method ignores the influence of battery health (SOH) and considers that the battery capacity will not change over time. To solve the above problem, we proposed a joint SOH-SOC estimation model based on the GWO-BP neural network to optimize the Ah integration method. The method completed SOH estimation through the GWO-BP neural network and introduced SOH into the Ah integration method to correct battery capacity and improve the accuracy of state of charge (SOC) estimation. In addition, the method also predicted the SOH of the battery, so the driver could have a clearer understanding of the battery aging level. In this paper, the stability of the joint SOH-SOC estimation model was verified by using different battery data from different sources. Comparative experimental results showed that the estimation error of the joint SOH-SOC estimation model could be stabilized within 5%, which was smaller compared with the traditional ampere integration... [more]

The European Union (EU) has committed to supporting the United Nations’ efforts in line with the Paris Agreement for addressing climate change and has set ambitious targets to reduce primary energy consumption and emissions. Similar commitments have also been set by EU-27 member states. For this purpose, it is necessary to use a primary energy factor (PEF) for converting electricity use to primary energy units and for assessing energy conservation measures. Lower PEFs reflect efficiency improvements in power generation, an increased share of renewable energy sources in the fuel mix for electricity generation, and lower transmission and distribution losses. Over the past decades, there have been intensive efforts and notable progress in the EU-27 for increasing the use of renewables in the energy mix for electricity generation. However, the EU default PEF value for electricity was not regularly updated and remained at 2.5 for several years till it was finally recalculated at 2.1 in the... [more]

This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min−max scale method and converted into a multivariate time series. The overall model’s architecture is mainly based on building blocks consisting of separable convolutional neural networks and efficient channel attention (ECA) modules. The experimental results show that the model can learn good representations from sufficient data covering different operation conditions. These results also suggest that ECA modules can improve the model’s performance. The comparative study shows our model’s strong performance compared to other NOx prediction models. Then, we demonstrate the effectiveness of the model proposed in this paper in terms of predicting NOx emissions.

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of... [more]

The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techn... [more]

Soiling significantly impacts PV systems’ performance, but this can be mitigated through optimized frequency and timing of cleaning. This experimental study focused on the conditions leading to soiling. It utilized a novel method to evaluate the effectiveness of different cleaning frequencies. The transmittance of horizontally mounted glass coupons exposed outdoors in a warm and humid location was measured weekly and these measurements were used (i) to evaluate the variability of soiling and its seasonal correlations with environmental factors using linear regression models and (ii) to assess the effectiveness of the different cleaning cycles using statistical (F- and t-test) analysis. The minimum transmittance loss occurred during the season with the most frequent rainfall, which acted as the dominant natural cleaning agent. The experimental campaign showed that rainfalls do not completely clean soiling; a minimum intensity threshold has to be achieved to have a cleaning effect. The t... [more]

The green economy is a form of economic progress. It promotes environmentally sustainable, low-carbon, and inclusive development. It ensures environmental sustainability and preserves the conditions for social progress. The current model of resource management is not sustainable and puts pressure on the environment. The proposed steps toward a green economy are intended to benefit both the environment, the economy, and society. The aim of the study was to assess the spatial differentiation of the relationship between the green economy and the zero waste concept of Eastern Poland’s municipalities. The assessment was performed using a synthetic measure. The choice of variables was determined by the availability of data in the Bank of Local Data of the Central Statistical Office (BDL GUS) for the years 2010−2020 in spatial terms (709) municipalities of Eastern Poland. The synthetic measure of green economy ranged from 0.28 to 0.56 in 2010 and from 0.28 to 0.59 in 2020; and for the waste m... [more]

Every type of agricultural production is a burden for the natural environment. The paper’s objective is to assess the energy use efficiency, GHG emissions, and provide an economic analysis of buckwheat production for Central Europe (Poland). The analysis and comparison involved two production systems: low-input and high-input ones. The experiment involved three varieties of buckwheat, Panda, Volma, and Mancan. The yields for analysis were obtained from the field experiment which was set up in 3k-p fractional design was applied in two replications in which at the same time five factors were tested (A—variety, B—mineral fertilisation, C—sowing rate, D—weed control, E—growth regulator). A quartile was used as a statistical tool to select production systems. A high-input buckwheat production regime required, on average, 74.00% more energy than a low-input system. The total mean energy input for three varieties ranged from 7532.7 to 13,106.9 MJ ha−1 for low- and high-input systems, respecti... [more]

Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was deve... [more]

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are many limitations to traditional oil-bearing assessment due to low resolution and excessive reliance on geological expert experience, which may lead to inaccurate and uncertain predictions. Based on information gain, three data-driven models were established in this paper to re-evaluate the oil bearing of long-term production wells. The results indicated that the RF model performed best with an accuracy of 95.07%, while the prediction capability of the neural network model was the worst, with only 79.8% accuracy. Moreover, an integrated model was explored to improve model accuracy. Compared with the neural network, support vector machine, and random forest models, the accuracy of the fusion mo... [more]

Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, existing data-driven methods are usually based on a fixed voltage segment or state of charge, which would be failed if the charging process does not cover the predetermined segment due to the user’s free charging behavior. This paper proposes a capacity estimation method using multiple small voltage sections and back propagation neural networks. It is intended to reduce the requirement of the length of voltage segment for estimating the complete battery capacity in an incomplete charging cycle. Firstly, the voltage segment most possibly covered is selected and divided into a number of small sections. Then, sectional capacity and skewness of the voltage curve are extracted from these small... [more]

There is a growing interest of estimating the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to mitigate accompanying risks for system operators. This study aims to advance the field of probabilistic PV power forecast by introducing and extending deep Gaussian mixture density networks (MDNs). Using the sum of the weighted negative log likelihood of multiple Gaussian distributions as a minimizing objective, MDNs can estimate flexible uncertainty distributions with nearly all neural network structures. Thus, the advantages of advances in machine learning, in this case deep neural networks, can be exploited. To account for the epistemic (e.g., model) uncertainty as well, this study applies two ensemble approaches to MDNs. This is particularly relevant for industrial applications, as there is often no extensive (manual) adjustment of the forecast model structure for each site, and only a limited amount of training data are available during co... [more]

Considering the importance of lithium-ion (Li-ion) batteries and the attention that the study of their degradation deserves, this work provides a review of the most important battery state of health (SOH) estimation methods. The different approaches proposed in the literature were analyzed, highlighting theoretical aspects, strengths, weaknesses and performance indices. In particular, three main categories were identified: experimental methods that include electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), model-based methods that exploit equivalent electric circuit models (ECMs) and aging models (AMs) and, finally, data-driven approaches ranging from neural networks (NNs) to support vector regression (SVR). This work aims to depict a complete picture of the available techniques for SOH estimation, comparing the results obtained for different engineering applications.

Modern aircraft and missiles are gradually integrating thrust vector control systems to enhance their military capabilities. Bypass Dual-Throat Nozzle (BDTN) control is a new fluidic thrust vectoring technique capable of achieving superior performance with large vector angles and low thrust loss. In this study, we analyzed the flow characteristics and performance parameters of BDTN by varying the bypass angle, nozzle convergence angle, and bypass width. The flow governing equations are solved according to a finite volume discretization technique of the compressible RANS equations coupled with the Renormalization Group (RNG) k-ϵ turbulence model for Nozzle Pressure Ratio (NPR = 2~10) to capture the significance of under-expanded and over-expanded jets. Results show that by decreasing the bypass angle from 90° to 35°, there is a 6% increase in vectoring angle while the vectoring efficiency is enhanced by 18%. However, a decrease of 3% in the thrust and discharge coefficients is also obse... [more]

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The mod... [more]

Background: N-heptyl-D-galactonamide (GalC7) is a small synthetic carbohydrate derivative that forms a biocompatible supramolecular hydrogel. In this study, the objective was to analyze more in-depth how neural cells differentiate in contact with GalC7. Method: Direct (ex vivo) cells of the fresh hippocampus and culture (In vitro) of the primary cells were investigated. In vitro, investigation performed under three conditions: on culture in neurospheres for 19 days, on culture in GalC7 gel for 7 days, and on culture in both neurospheres and GalC7 gel. Total RNA was isolated with TRIzol from each group, Sox8, Sox9, Sox10, Dcx, and Neurod1 expression levels were measured by qPCR. Result: Sox8 and Sox10, oligodendrocyte markers, and Sox9, an astrocyte marker, were expressed at a much higher level after 7 days of culture in GalC7 hydrogel compared to all other conditions. Dcx, a marker of neurogenesis, and Neurod1, a marker of neuronal differentiation, were expressed at better levels in th... [more]