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It is patently obvious that renewable energy sources like solar and wind play a crucial part in preventing environmental degradation. This research analyses the interplay between energy transitions, technology, disaggregated energy consumption, and long-term economic growth in 21 states of the Middle East and North Africa countries (MENA). Using cutting-edge econometric techniques, the study conduct empirical research covering the years 1997–2021 and uncover persistent connections between the variables of interest. In a long-run analysis, energy transitions (ET), economic globalization (EG), technology (T), consumption of renewable energy (RE), and consumption of non-renewable energy (NRE) positively impact economic growth (EGR), while natural resources (N) exhibit no effects on economic growth. In the short-run analysis, energy transitions, economic globalization, and natural resources, on the other hand, revealed negative impacts on economic growth. Whereas other variables like technology, consumption of renewable energy, and consumption of non-renewable energy showed no effects on the economy’s growth in the short term analysis. Policymakers in MENA countries are urged to lower carbon costs and taxes, work together more on R&D, bring technologies with low CO2 emissions to market, cut subsidies for non-renewable sources, set up a framework for the spread of new technologies, and adopt a green trade strategy in order to achieve sustainable development.

Solar photovoltaic (PV) electricity offers a promising solution for the increasing demand for clean and sustainable energy, particularly in developing countries like Ghana. Power utility operators maintain power quality as their utmost priority per benchmarks established by regulators. Some researchers argue that increasing variable renewable sources in the grid compromises its stability, leading to poor power quality. This paper aims to evaluate the suitability of reactive power management (technical aspect) in a solar PV microgrid in meeting regulators' power quality benchmarks. It also presents the economics associated with reactive power management. The study uses Python optimisation algorithms to achieve the technical objective. The net present value (NPV), profitability index (PI) and discounted payback period (DPP) are the econometrics used to assess the economic viability of reactive power management in this study. The results show that optimising reactive power in microgrids reduces power losses, increasing the utility's energy sales. An annual energy savings of 1,985,600 MWh was made due to the reactive power optimisation, leading to significant economic returns for the microgrid. The NPV, PI and DPP calculated from the study were GHS 12,528,583,512, 10,404.3, and one year, respectively. Therefore, it suggests reactive power optimisation to microgrid and grid operators as a viable solution to meet regulators' benchmarks.

Previous studies suggested that, at the aggregate growth level, feedback effects often exist between energy consumption and overall growth. This study further tested for the long- and short-run relationships between primary energy use and key industrial sector variables in China. The annual data spanned the period from 1953 to 2018. The results show that energy use and the changes in three key sector output values were cointegrated. In the long-run, at the sectoral level, there was unidirectional causality running from sectors to energy use, which follows the conservation hypothesis. Hence, strict energy-saving measures in industrial, construction, and wholesale and retail sectors will not curb their respective long-run growth The growth in the construction and wholesale and retail sectors can lead to a slight decline in energy use. Thus, China can encourage long-run growth in construction and business at a low environmental cost. In the short-run, bidirectional causality and small elasticities existed between energy use and three key sectors. China can stimulate the short-run growth in the industry, construction and domestic trade while taking strict energy-saving measures in these sectors.

This article analyzed the causal link between the growth in agricultural output value and total primary energy consumption for both the 1953 to 2020 and 1980 to 2020 periods. The main method applied is the Toda–Yamamoto​ Wald-χ2test. Empirical results show that from 1953 to 2020, feedback existed between the growth in agricultural output and energy use. From 1980 to 2020, unidirectional Granger causality existed from energy consumption to the aggregate economy and agriculture. Thus, the study suggests the growth hypothesis in energy economics. In the short-run, agricultural growth will not increase energy consumption. However, strict energy-saving measures in the agricultural sector may curb its growth. During the period after 1980, agricultural energy use efficiency improved. Energy consumption was a short-run determinant of agricultural growth.

The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly.

The remaining useful life (RUL) is the key element of fault diagnosis, prediction and health management (PHM) during the equipment operation service period. The prediction result of RUL is the premise for equipment to adopt preventive maintenance, condition-based maintenance, fault maintenance and other maintenance strategies. Lithium battery is an important energy component of new energy vehicles, mobile phones, etc. Its RUL is related to the state of its equipment system. Many model-based methods have been used to predict the lithium batteries’ RUL, and some studies have begun to use lithium battery monitoring data to predict its remaining service life. With the continuous detection and monitoring capability of equipment throughout its life cycle gradually improved, a large number of monitoring and detection data promote the wide application of data-driven residual life prediction in the field of equipment. At present, the data-driven prediction method of the lithium batteries’ RUL mostly adopts a single time-series forecasting model. The robustness and generalization of the prediction method are insufficient. It needs to be further improved to improve the prediction accuracy and robustness. Preventive maintenance measures shall be taken immediately according to the prediction results to ensure the effective supply of energy at any time. In this paper, an integrated learning algorithm based on monitoring data is proposed to fit the degradation model of lithium batteries and predict their RUL. The ensemble learning method consists of 5 basic learners to achieve better prediction performance, including relevance vector machine (RVM), random forest (RF), elastic net (EN), autoregressive model (AR), and long short-term memory (LSTM) Network. The genetic algorithm (GA) is used in the ensemble learning method to find and determine the optimal weights of the basic learners, and obtain the final prediction result of lithium batteries. Then, the simulation is carried out on the CS2_35 lithium battery data set. The simulation results show that the method proposed in this paper has a smaller Root Mean Square Error (RMSE) than another 5 single methods. The RMSE is respectively 0.00744 for RVM, 0.01097 for RF, 0.01507 for EN, 0.03223 for AR, 0.01541 for LSTM, and 0.00483 for ensemble learning, and the RMSE of ensemble learning is reduced by 0.0274 at the highest and 0.00261 at the lowest, so the ensemble learning algorithm has better robustness and generalization effect.

The converter transformer is an electrical equipment with a symmetrical structure. Due to its symmetry, the ground current should be small when the core is grounded at one point. The research and analysis of grounding currents in the converter transformer can effectively help staff to judge the working state of the converter transformer. Therefore, it is very meaningful to study the calculation problem of the grounding current under the multi-point grounding state. First of all, a homogenization modeling method considering the characteristics material of the core silicon steel sheet is proposed; secondly, on this basis, the finite element simulation model of multi-point grounding of the core of the converter transformer is established and simulated; then a multi-point grounding test was conducted using the shrinkage model of the converter transformer to verify the finite element simulation results; the final consequence show that the homogenization modeling method can effectively calculate the grounding current at multi-grounding.

Anti-surge control of centrifugal compressors is an essential issue for the operation of long-distance natural gas pipeline systems. A suitable controller can make a centrifugal compressor runs smoothly and stably and improve the economy. This work presents a new intelligence control strategy with self-adapting ability. The strategy includes the proportional integral (PI) control self-tuned by radial basis function neural network (RBF-NN), recycle trip control, special derivative control, surge line correction, and asymmetric output of the controller. A hybrid numerical simulation platform is built to validate the anti-surge strategy, and a real centrifugal compressor is simulated. The results show that the strategy makes the anti-surge valve respond quickly, decreases the surge control line’s margin and backflow rate, and improves the economy. In the controller, the special derivative control can make the anti-surge valve open earlier and effectively reduce the fluctuating of inlet flow rate. Aiming at the problem that the gradient descent method is more sensitive to the initial value when solving RBF-NN, a hybrid algorithm of k-means, recursive least square, and gradient descent (KRG algorithm) is proposed. It is successfully applied in the anti-surge controller. Even if the given RBF-NN initial parameters are not good enough, the KRG algorithm illustrates good learning stability and increases the adaptive ability of RBF-NN.