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High torque ripple can be observed with a 6/4 variable flux reluctance machine (VFRM). In order to minimize the torque ripple in VFRMs, this paper presents a harmonic injection method for 6/4 VFRMs with an open-winding configuration. By analyzing the impact of harmonics on VFRMs, the method involves detecting the third harmonic using a first-order low-pass filter (FLPF). Subsequently, the extracted harmonics are controlled and shifted to counteract the voltage harmonics in both inverters without inducing phase imbalance or overvoltage. With the proposed method, the torque ripple can be significantly reduced by about 50% under load conditions. The effectiveness of the harmonic injection method is validated through a prototype VFRM.

Due to uncertain photovoltaic (PV) power generation, analyzing the voltage stability of transmission networks with a large PV plant is challenging. The stability of PV output power is a critical factor in establishing PV penetration levels in active transmission networks when assessing loading capabilities. Therefore, this article contributes to enhancing the understanding of how a static synchronous compensator (STATCOM) could be used as part of the power system network. STATCOMs are often used to improve the static and transient voltage, maintain transmission limits, and reduce low-frequency disturbances. With the help of a STATCOM unit, a proper investigation and analysis of dynamic voltage stability in a transmission system are presented. The validity of the study is evaluated by the Saudi Arabia grid code (SAGC). The system being tested is comprised of a 300 MW PV plant in Sakaka, Saudi Arabia, integrated into a 17-bus transmission power network and a STATCOM, which is employed at one of the buses. PSAT software is used to evaluate a number of case studies to determine the transient voltage stability with and without the STATCOM and the PV plant. The simulation findings demonstrate how STATCOM functions to improve the quality of the power system in accordance with the SAGC. In addition, the analysis of voltage stability demonstrates that the power network’s resilience to short circuits at the PV system’s grid connection point significantly impacts the voltage’s dynamic behavior.

The appropriate design of the power oscillation damping controllers guarantees that distributed energy resources and sustainable smart grids deliver excellent service subjected to big data for planned maintenance of renewable energy. Therefore, the main target of this study is to suppress the low-frequency oscillations due to disruptive faults and heavy load disturbance conditions. The considered power system comprises two interconnected hydroelectric areas with heavy solar energy penetrations, severely impacting the power system stabilizers. When associated with appropriate controllers, FACTs technology such as the static synchronous series compensator provides efficient dampening of the adverse power frequency oscillations. First, a two-area power system with heavy solar energy penetration is implemented. Second, two neuro-based controllers are developed. The first controller is constructed with an optimized particle swarm optimization (PSO) based neural network, while the second is created with the adaptive neuro-fuzzy. An energy management approach is developed to lessen the risky impact of the injected solar energy upon the rotor speed deviations of the synchronous generator. The obtained results are impartially compared with a lead-lag compensator. The obtained results demonstrate that the developed PSO-based neural network controller outperforms the other controllers in terms of execution time and the system performance indices. Solar energy penetrations temporarily influence the electrical power produced by the synchronous generators, which slow down for uncomfortably lengthy intervals for solar energy injection greater than 0.5 pu.

The feasibility and potential assessment (PA) of solar PV energy is one of the key factors in identifying the most promising areas for the installation of solar PV stations. It determines the useful energy generated in the given area. This paper assesses the solar energy distribution and PA in the North Shewa administration zone. Based on the data collected and analysis made, it is found that more than 80% of the North Shewa areas are suitable for the solar energy generation for off-grid and on-grid systems. Hence, the solar potential of the North Shewa zone completely fulfills the standards of sunshine, solar radiation, and temperature. That is, most of the areas have solar radiation of 5.2 kWh/m2, and daily sunshine is greater than 7 h. The maximum energy production is found in December in Shewa Robit, Mehal Meda, Eneware, Debre Berhan, Alem Ketema, and Sela Dengay with 175.35 kWh, 188.18 kWh, 180.78 kWh, 189.54 kWh, 175.78 kWh, and 189.63 kWh, respectively. This is a good opportunity for investors to invest in solar PV electricity generation. It will solve the issue of electricity supply to the community, which currently relies on wood and fossil fuels. It also highlights the positive opportunities for the future implementation of solar energy.

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.

Integration of a grid with an under-developed remote hilly area faces various technical and geographical challenges. Thus, generation of power from renewable resources in off-grid conditions has become one of the most cost-effective and reliable solutions for such areas. The present research deals with the possible application of an integrated solar/hydro/biomass/battery-based system to generate power in autonomous mode for a remote hilly town of a northeastern Indian state. Four different cases of the integrated energy system (IES) were designed using the hybrid optimization model for electric renewable (HOMER Pro), examining the performance of each case. The best combination of the integrated system was chosen out of several cases depending upon the optimized solution that can meet the load demand of the proposed hilly town sustainably, reliably and continuously. The simulation results show that the integrated battery/biomass/hydro/solar-based system is the best optimized, cheapest and most suitable solution to generate renewable-based power for the specified location, having the lowest net present cost (NPC) of USD 644,183.70 with a levelized cost of energy (COE) of 0.1282 USD/kWh. Further, the result also indicates that the optimized configuration reduces the emission of CO2 gas in the environment compared to the battery/biomass/hydro system having the worst emission rate. A sensitivity study was also carried out with variation in load, hydro stream flow and solar irradiation, respectively that may largely affect the technical as well as economical aspect of an integrated energy system.

Modern power systems are structurally complex and are vulnerable to undesirable events like faults. In the event of faults in transmission line, accurate fault location improves restoration process, thereby enhancing the reliability of the overall system. Fault location methods (FLMs) are tools which assist in identifying fault locations quickly. However, the accuracy of these FLMs gets affected in the presence of flexible alternating current transmission system (FACTS) devices. Therefore, in this work, the performance of four different signal decomposition techniques aided traveling wave aided FLMs are qualitatively compared in the context of fault localization in FACTS-compensated systems. FLMs based on intrinsic time decomposition (ITD), empirical mode decomposition (EMD), S-transform (ST), and estimation of signal parameters via rotational invariance technique (ESPRIT) are investigated. The accuracy of FLMs is tested for different cases of series, shunt, and series-shunt FACTS-compensated systems. A 500 kV system employed with 100 MVAr FACTS device is used for simulation. The instant of arrival wave at end of transmission line is from all aforementioned FLMs. The obtained ATWs are used in fault localization. Further, the associated percentage errors are calculated. The results suggest that EMD and ESPRIT-based FLMs are more accurate than others.

Different configurations of on/off-grid-connected hybrid renewable energy systems (HRESs) are analyzed and compared in the present research study for optimal decision making in Sub-Saharan Africa, facing the problems of electricity deficit. A multi-criteria analysis is performed for this purpose using MATLAB software for simulation. The obtained results show that the levelized cost of energy (LCOE) corresponding to 0% power supply deficit probability (PSDP) is 0.0819 USD/kWh, 0.0925 USD/kWh, 0.3979 USD/kWh, 0.3251 USD/kWh, 0.1754 USD/kWh, 0.1641 USD/kWh, 0.5385 USD/kWh, and 1.4515 USD/kWh, respectively, for the Grid-PV/Wind/Battery, Grid-PV/Battery, Grid-Wind/Battery, Grid-Wind, PV/Wind/Battery, PV/Battery, Wind/Battery, and stand-alone Wind systems. The CO2 emissions are 14,888.4 kgCO2/year, 16,916.6 kgCO2/year, 13,139.7 kgCO2/year, 6430.4 kgCO2/year, 11,439 kgCO2/year, 14,892.5 kgCO2/year, 10,252.6 kgCO2/year, and 1621.5 kgCO2/year, respectively, for the aforementioned systems. It is found that the Grid-connected PV/Wind/Battery is the most cost-effective system leading to a grid energy cost reduction of 30.89%. Hybridization of different renewable energy sources (RESs) could significantly improve the electricity cost and reduce the CO2 emissions. However, this improvement and this reduction depend on the used RES and the system configuration. On-grid-connected HRESs are more cost-effective than off-grid-connected HRES. The least polluting energy system is the stand-alone Wind system, which allows a reduction in the grid CO2 emissions by 93.66%. The sensitivity analysis has proven that the long-term investment, the decrease in the battery cost, and the decrease in the discount rate could lead to the reduction in the LCOE.

The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values.

Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.

The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper.

This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults.

The increase in power demand, nonlinearity, complexity, varying structure, and other important causes has necessitated the implementation of artificial intelligent control methodologies for safe and acceptable operation of the electric power systems. Therefore, in this article, an improved two-degrees-of-freedom (2DOF-PID) control scheme is proposed for power/frequency control of a two-area interconnected electric power system. The parameters of the 2-DOF-PID control scheme are optimized using the Dragonfly Algorithm (DA) via a new integral-based weighted goal fitness function (IB-WGFF) (i.e., DF-2DOF-PID-IB-WGFF). The superiority of the suggested scheme is proved by comparing the results obtained using the proposed IB-WGFF with those obtained using the conventional controllers, and the 2DOF-PID controllers optimized using the DA and Genetic Algorithm (GA) via the frequently published performance criterion. To verify the stability, efficacy, and robustness of the proposed control scheme, a load disturbances and parameters perturbations with various percentages are implemented in the controlled system under the same controllers. Finally, verification results proved that the proposed 2DOF-PID optimized using DA via the IB-WGFF is more stable, efficient, and robust than the other controllers recently used in the literature.

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

Improving the stability of power systems using FACT devices is an important and effective method. This paper uses a static synchronous series compensator (SSSC) installed in a power system to smooth out inter-area oscillations. A meta-heuristic optimization method is proposed to design the supplementary damping controller and its installation control channel within the SSSC. In this method, two control channels, phase and magnitude have been investigated for installing a damping controller to improve maximum stability and resistance in different operating conditions. An effective control channel has been selected. The objective function considered in this optimization method is multi-objective, using the sum of weighted coefficients method. The first function aims to minimize the control gain of the damping controller to the reduction of control cost, and the second objective function moves the critical modes to improve stability. It is defined as the minimum phase within the design constraints of the controller. A hybrid of two well-known meta-heuristic methods, the genetic algorithm (GA) and grey wolf optimizer (GWO) algorithm have been used to design this controller. The proposed method in this paper has been applied to develop a robust damping controller with an optimal control channel based on SSSC for two standard test systems of 4 and 50 IEEE machines. The results obtained from the analysis of eigenvalues and nonlinear simulation of the power system study show the improvement in the stability of the power system as well as the robust performance of the damping in the phase control channel.

In this study, an algae biomass gasification process using a dual fluidized bed with combined power and methanol cogeneration was developed. The gasification process was modeled using Aspen Plus and validated using experimental data of two microalgae species (Nannochloropsis oculata and Dunaliella salina) commonly found on the western coast of Saudi Arabia. The impacts of different operating conditions, including the gasifier temperature, steam-to-biomass ratio, and algae-char split ratio, on the compositions of four main gases (CO, CO2, CH4, and H2) were investigated. The results of the parametric studies indicated that the gasification temperature has a significant effect on the composition of the synthesis gas, where 700–850 °C was the ideal operating range for gasification. Altering the ratio of biomass to steam showed a slightly smaller effect on the synthesis gas composition. The char split ratio should be kept below 75% to ensure an adequate heat supply to the process. The proposed process successfully converted 45.7% of the biomass feed to methanol at a production capacity of 290 metric tons per day. On the other hand, 38 MW of electricity capacity was generated in the combined power cycle.

In this paper, optimal allocation of a distributed thyristor-controlled series compensator (DTCSC) in a power system is presented to minimize overload, voltage deviations, and power losses while improving system reliability. The decision variable was defined as the optimal reactance of the DTCSC in the power system, which was determined using a new meta-heuristic algorithm named the improved equilibrium optimization algorithm (IEOA). A nonlinear inertia weight reduction strategy was used to improve the performance of traditional EOA in preventing premature convergence and facilitate a quick global optimum solution. The effect of system critical line outage was evaluated for each of the considered goals. To evaluate the effectiveness of the proposed methodology, IEOA capability was compared with particle swarm optimization (PSO) and manta ray foraging optimizer (MRFO) methods. Simulations were carried out considering different scenarios on 14- and 118-bus test systems. The results showed that, for all scenarios, optimal allocation of DTCSC could result in a significant reduction in overloading, voltage deviation of network buses, as well as power losses under the condition of line outage, due to the optimal injection of reactive power. In all investigated scenarios, our results attested to the superiority of the IEOA over the traditional EOA, PSO, and MRFO in achieving a better value for the objective function. In addition, the results showed that improving reliability in the objective function could eliminate overloading, and hence, introduce further improvement in each of the objectives.

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 in this work for the first time and will be undoubtedly indispensable information for the development of energetic materials based on HNTO and HNTO·AN co-crystal.

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.

Distribution static compensators (D-STATCOMs) can enhance the technical performance of the power distribution network by providing rapid and continuous reactive power support to the connected bus. Accurate modeling and efficient utilization of D-STATCOMs can maximize their utility. In this regard, this article offers a novel current-injection-based D-STATCOM model under the power control mode of operation for the reactive power compensation of the power distribution network. The versatility of the proposed D-STATCOM model is demonstrated by combining it with two of the most established distribution load flow techniques, viz., the forward–backward sweep load flow and the BIBC–BCBV-matrix-based direct load flow. Further, the allocation of the proposed D-STATCOM model is carried out under a multiobjective mathematical formulation consisting of various technical and economic indices such as the active power loss reduction index, voltage variation minimization index, voltage stability improvement index and annual expenditure index. A novel parameter-free metaheuristic algorithm, namely a student-psychology-based optimization algorithm, is proposed to determine the optimal assignment of the different number of D-STATCOM units under the multiobjective framework. The proposed allocation scheme is implemented on a standard 33-bus test system and on a practical 51-bus rural distribution feeder. The obtained results demonstrate that the proposed D-STATCOM model can be efficiently integrated into the distribution load flow algorithms. The student-psychology-based optimization algorithm is found to be robust and efficient in solving the optimal allocation of D-STATCOMs as it yields minimum power loss compared to other established approaches for 33-bus PDNs. Further, the economic analysis carried out in this work can guide network operators in deciding on the number of D-STATCOMs to be augmented depending on the investment costs and the resulting savings.

This paper uses two competing machine learning models, namely the Support Vector Regression (SVR) and the eXtreme Gradient Boosting (XGBoost) against the Autoregressive Integrated Moving Average ARIMAX (p,d,q) model to identify their predictive performance of the crude oil volatility index before and during COVID-19. In terms of accuracy, forecasting results reveal that the SVR model dominates the XGBoost and ARIMAX models in predicting the crude oil volatility index before COVID-19. However, the XGBoost model provides more accurate predictions of the crude oil volatility index than the SVR and ARIMAX models during the pandemic. The inverse cumulative distribution of residuals suggests that both ML models produce good results in terms of convergence. Findings also indicate that there is a fast convergence to the optimal solution when using the XGBoost model. When analyzing the feature importance, the Shapley Additive Explanation Method reveals that the SVR performs significantly better than the XGBoost in terms of feature importance. During the pandemic, the predictive power of the CBOE Volatility Index and Economic Policy Uncertainty index for forecasting the crude oil volatility index is improved compared to the pre-COVID-19 period. These findings imply that investor fear-induced uncertainty in the financial market and economic policy uncertainty are the most significant features and hence represent substantial sources of uncertainty in the oil market.

A power system’s nonlinearity and complexity increase from time to time due to increases of power demand. Therefore, properly designed power system controlsare required. Without these, system instability will cause equipment failures, and possibly even cascading events and blackouts. To cope with this, intelligent controllers using soft computing are necessary for real time operation. In this paper, the reheat type three-area thermal power system is considered, and the output scaling factors, gain parameters of fuzzy membership functions, and parameters of fuzzy-proportional integral derivative (FPID) controllers are optimized using a differential evolution (DE) optimization techniqueand integral time multiplied absolute error (ITAE) as objective functions. To improve the limitations of the controller and to enhance stability of the system, high voltage direct current (HVDC) technology is advantageous due to its quickresponse capabilities. In this paper, a HVDC is connected in parallel to the system, revealing that a FPID controller with a HVDC provides better and more accurate resultscompared to a system without a controller. The test results presented in this paper show the proposed controller’s suitability for managing random load changes.

Universal power converters (UPCs) have aroused significant attention in performing multiple operations in a single power converter. Furthermore, they contribute to economic operation and improved system performance. In this work, a new configuration of the universal power converter (UPC) was proposed by using a simple switching arrangement. It can perform different modes of operations, such as AC–DC, DC–DC, DC–AC, AC–AC, and cyclo-converter operations. In DC–DC conversion, the proposed configuration can perform buck mode, boost mode, and buck–boost mode of operations. Moreover, in DC–AC conversion, it gives better total harmonic distortion (THD). The effectiveness of the proposed configuration was verified by an extensive simulation, using MATLAB/Simulink environment. A low-power prototype circuit was designed to test the viability of the proposed circuit configuration and validated with simulation results.

In this manuscript, we propose a single-phase UPC (universal power compensator) system to extensively tackle power quality issues (voltage and current) with an equal VAR (volt-ampere reactive) sharing approach between the series and shunt APF (active power filter) of a UPC system. The equal VAR sharing feature facilitates the series and shunt APF inverters to be of an equal rating. An SRF (synchronous reference frame)-based direct PA (power angle) calculation technique is implemented to realize equal VAR sharing between the APFs of the UPC. This PA estimation utilizes d and q axis current parameters derived for the reference signal generation of the shunt APF. An SRF-based method is highly useful for power estimations in distorted supply voltage conditions compared with other conventional methods, i.e., the PQ method. It comprises a reduced complexity and estimations with an easiness to retain two APF inverters of equal rating. A rigorous simulation analysis is performed with MATLAB/SIMULINK and a real-time digital simulator (OPAL-RT) for addressing different power quality-disturbing elements such as current harmonics, voltage harmonics, voltage sag/swell and load VAR demand with the proposed method.

Over time, the dependence on oil has increased to meet industrial and domestic needs. Enhanced oil recovery (EOR) techniques in this regard have captured immense growth as EOR is not only used to increase the oil recovery but also to augment the sweep efficiency. Several techniques over the past decades have been used to improve oil recovery with cost-effectiveness. Cost-effective alkaline flooding has been effective for those oil reservoirs with a high total acid number. In this review, the significance of alkaline flooding has been discussed in detail, as well as the features of alkaline flooding in comparison to other modes of flooding. This review entails (1) alkaline flooding, (2) hybrid modes of injection, (3) experimental work, (4) pilot projects, (5) screening criteria, and (6) field applications. The findings of this study can help increase the understanding of alkaline flooding and provide a holistic view of the hybrid modes of flooding.

In a quest to solve the multi-objective optimal planning problem using a simple parameter-free metaheuristic algorithm, this paper establishes the recently proposed student psychology-based optimization (SPBO) algorithm as the most promising one, comparing it with the other two popular nonparametric metaheuristic optimization algorithms, i.e., the symbiotic organisms search (SOS) and Harris hawk optimization (HHO). A novel multi-objective framework (with suitable weights) is proposed with a real power loss minimization index, bus voltage variation minimization index, system voltage stability maximization index, and system annual cost minimization index to cover various technical, economic, and environmental aspects. The performances of these three algorithms are compared extensively for simultaneous allocation of multitype distributed generations (DGs) and D-STACOM in 33-bus and 118-bus test systems considering eight different cases. The detailed analysis also includes the statistical analysis of the results obtained using the three algorithms applied to the two test distribution systems.

Optimal planning of renewable energy generator (REG) units helps to meet future power demand with improved flexibility. Hence, this paper proposes a grid-oriented genetic algorithm (GOGA) based on a hybrid combination of a genetic algorithm (GA) and a solution using analytical power flow equations for optimal sizing and placement of REG units in a power system network. The objective of the GOGA is system loss minimization and flexibility improvement. The objective function expresses the system losses as a function of the power generated by different generators, using the Kron equation. A flexibility index (FI) is proposed to evaluate the improvement in the flexibility, based on the voltage deviations and system losses. A power flow run is performed after placement of REGs at various buses of the test system, and system losses are computed, which are considered as chromosome fitness values. The GOGA searches for the lowest value of the fitness function by changing the location of REG units. Crossover, mutation, and replacement operators are used by the GOGA to generate new chromosomes until the optimal solution is obtained in terms of size and location of REGs. A study is performed on a part of the practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India for the base year 2021 and the projected year 2031. Load forecasting for the 10-year time horizon is computed using a linear fit mathematical model. A cost–benefit analysis is performed, and it is established that the proposed GOGA provides a financially viable solution with improved flexibility. It is established that GOGA ensures high convergence speed and good solution accuracy. Further, the performance of the GOGA is superior compared to a conventional GA.

A multi-fidelity micro combustor design approach is developed for a small-scale combined heat and power CHP system. The approach is characterised by the coupling of the developed preliminary design model using the combined method of 3D high-fidelity modelling and experimental testing. The integrated multi-physics schemes and their underlying interactions are initially provided. During the preliminary design phase, the rapid design exploration is achieved by the coupled reduced-order models, where the details of the combustion chamber layout, flow distributions, and burner geometry are defined as well as basic combustor performance. The high-fidelity modelling approach is then followed to provide insights into detailed flow and emission physics, which explores the effect of design parameters and optimises the design. The combustor is then fabricated and assembled in the MGT test bench. The experimental test is performed and indicates that the designed combustor is successfully implemented in the MGT system. The multi-physics models are then verified and validated against the test data. The details of refinement on lower-order models are given based on the insights acquired by high-fidelity methods. The shortage of conventional fossil fuels and the continued demand for energy supplies have led to the development of a micro-turbine system running renewable fuels. Numerical analysis is then carried out to assess the potential operation of biogas in terms of emission and performance. It produces less NOx emission but presents a flame stabilisation design challenge at lower methane content. The details of the strategy to address the flame stabilisation are also provided.

The research proposes a new oppositional sine cosine muted differential evolution algorithm (O-SCMDEA) for the optimal allocation of distributed generators (OADG) in active power distribution networks. The suggested approach employs a hybridization of the classic differential evolution algorithm and the sine cosine algorithm in order to incorporate the exploitation and exploration capabilities of the differential evolution algorithm and the sine cosine algorithm, respectively. Further, the convergence speed of the proposed algorithm is accelerated through the judicious application of opposition-based learning. The OADG is solved by considering three separate mono-objectives (real power loss minimization, voltage deviation improvement and maximization of the voltage stability index) and a multi-objective framework combining the above three. OADG is also addressed for DGs operating at the unity power factor and lagging power factor while meeting the pragmatic operational requirements of the system. The suggested algorithm for multiple DG allocation is evaluated using a small test distribution network (33 bus) and two bigger test distribution networks (118 bus and 136 bus). The results are also compared to recent state-of-the-art metaheuristic techniques, demonstrating the superiority of the proposed method for solving OADG, particularly for large-scale distribution networks. Statistical analysis is also performed to showcase the genuineness and robustness of the obtained results. A post hoc analysis using Friedman–ANOVA and Wilcoxon signed-rank tests reveals that the results are of statistical significance.

Meeting the generation schedule in a wind farm is a major issue. This work utilized battery energy storage systems (BESS) integrated wind farms (WF) to supply energy to the power grid at a pre-determined generation schedule, which was set previously based on the meteorological forecast and BESS characteristics. This study proposed the integration of two independently controlled BESS into the WF to balance stochastic power deviations between actual wind power and scheduled power. By utilizing linear optimization and solving in MATLAB, simulation models of the operations of BESS-integrated WF have been developed. The technical performance of the BESS-integrated wind farm on meeting the generation schedule, along with the cost benefits and profit attributed to the BESS, is therefore measured by a series of indices. The simulation on a practical wind farm, i.e., Adama-I WF, Ethiopia shows that even though it depends on the type of state exchanging strategy adopted, the developed methodology of integrating BESS into the WF is effective and BESS profits can totally cover the cost. Technical and economic indices that resulted from the integration of two separate BESSs with independent control were compared with indices that resulted from integrating a single BESS. Simulation results show that operating the wind farm with two independently controlled batteries has better performance as compared to operating with a single battery. It also shows that the discharging and charging state exchanging approaches of the BESS (in the case of two battery integration), as well as the number of batteries integrated into the wind farm, have significant impacts on the performance of the WF integrated with BESS.

The potential of bio-electro-jet fuel (BEJF) production with integration into an existing biomass-based combined heat and power (CHP) facility was investigated. The BEJF is produced via Fischer–Tropsch (F–T) synthesis from biogenic CO2 and H2 obtained by water electrolysis. Techno-economic (TEA)- and life. cycle (LCA)- assessments were performed to evaluate the production cost and environmental impact of the BEJF production route. The BEJF mass fraction reached 40% of the total F–T crude produced. A reduction of 78% in heating demands was achieved through energy integration, leading to an increase in the thermal efficiency by up to 39%, based on the F–T crude. The total production cost of BEJF was in the range of EUR 1.6–2.5/liter (EUR 169–250/MWh). The GWP of the BEJF was estimated to be 19 g CO2-eq per MJ BEJF. The reduction potential in GWP in contrast to the fossil jet baseline fuel varied from 44% to more than 86%. The findings of this study underline the potential of BEJF as a resource-efficient, cost-effective, and environmentally benign alternative for the aviation sector. The outcome is expected to be applicable to different geographical locations or industrial networks when the identified influencing factors are met.

Increased deployment of variable renewable energy (VRE) has posed significant challenges to ensure reliable power system operations. As VRE penetration increases beyond 80%, the power system will require long duration energy storage and flexibility. Detailed uncertainty analysis, identifying challenges, and opportunities to provide sufficient flexibility will help to achieve smooth operations of power system networks during the scenario of high share of VRE sources. Hence, this paper presents a comprehensive overview of the power system flexibility (PSF). The intention of this review is to provide a wide spectrum of power system flexibility, PSF drivers, PSF resources, PSF provisions, methods used for assessment of flexibility and flexibility planning to the researchers, academicians, power system planners, and engineers working on the integration of VRE into the utility grid to achieve high share of these sources. More than 100 research papers on the basic concepts of PSF, drivers of the PSF, resources of PSF, requirement of the PSF, metrics used for assessment of the flexibility, methods and approaches used for measurement of flexibility level in network of the power system, and methods used for the PSF planning and flexibility provisions have been thoroughly reviewed and classified for quick reference considering different dimensions.

The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.

Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.

The impacts of the fast growth of renewable energy sources (RESs) and distribution static synchronous compensators (D-STATCOMs) on unbalanced radial distribution networks (URDNs) are analyzed with three-phase power flow algorithms (PFAs). As the URDNs are unbalanced, they can experience voltage unbalance (VU). This paper proposes a novel three-phase PFA for URDNs with multiple RES and D-STATCOM device integrations. The bus number matrix (BNM) and branch number matrix (BRNM) developed in this paper make the implementation of the proposed PFA simple. These matrices are developed to store the bus numbers and branch numbers of newly created sections of the URDN. Both PQ and PV modeling of RES and PV modeling of D-STATCOM devices are effectively integrated into the proposed three-phase PFA. The accuracy of the proposed PFA has been tested on the IEEE-13 bus URDN and the results are found to be accurate with the IEEE results. Several study examples have been conducted on the IEEE-13 bus and the IEEE-34 bus URDNs with multiple integrations of three-phase RESs and three-phase D-STATCOMs. Test results indicate that these integrations improve the voltage profile, reduce the power loss and reduce the severity of the VU.

Commercializing direct methanol fuel cells (DMFC) demands cost-effective cation exchange membranes. Herein, a polymeric blend is prepared from low-cost and eco-friendly polymers (i.e., iota carrageenan (IC) and polyvinyl alcohol (PVA)). Zirconium phosphate (ZrPO4) was prepared from the impregnation–calcination method and characterized by energy dispersive X-ray analysis (EDX map), X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM), then incorporated as a bonding and doping agent into the polymer blend with different concentrations. The new fabricated membranes were characterized by SEM, FTIR, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and XRD. The results revealed that the membranes’ physicochemical properties (oxidative stability, tensile strength) are enhanced with increasing doping addition, and they realized higher results than Nafion 117 because of increasing numbers of hydrogen bonds fabricated between the polymers and zirconium phosphate. Additionally, the methanol permeability was decreased in the membranes with increasing zirconium phosphate content. The optimum membrane with IC/SPVA/ZrPO4-7.5 provided higher selectivity than Nafion 117. Therefore, it can be an effective cation exchange membrane for DMFCs applications.

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

A modelling approach based on the Substitutive Coefficients Network (SCN) is developed to predict the thermal behavior of a system in the dynamic state-space, without requiring knowledge of the thermal mass. The method can apply either to large- (building, combined solar systems, geothermal energy, and thermodynamic installations) or to small-scale systems (heat exchangers, electronic devices cooling systems, and Li-ion batteries). This current method is based on a dimensionless formulation of the simplified dynamic thermal balance model, using relaxation time as a key parameter to establish the model. The introduction of relaxation time reduces the parameters set as guidance coefficients. The parameters are finally expressed by a combination of global heat transfer coefficients related to each layer and/or sub-layer of the system. Advantages of the method are reliability, “non-destructibility”, i.e., it allows a reliable prediction of the thermal behavior which experimentally is inaccessible, and reducibility of the parameters size estimate. Additionally, the method is inexpensive in terms of computation memory. It is also easy to implement in practical numerical schemes. In this paper, the method leads to a simplified mathematical model that predicts the thermal behavior of a mono-zone eco-cottage building installed at Lorraine University (in Longwy, France) as a case study. Thermal performance of the building is estimated under the hourly weather conditions onsite, as obtained from the Meteonorm software. The thermal dynamics within hourly Typical Meteorological Year 2 (TMY2) Meteonorm data disturbances and the internal heating input state in the winter period were simulated with a simplified numerical discretization method. Results provide a general dynamic state of the different sub-components of the system, with limited design of the model parameters.

The effect of drying temperature and the application of ultrasound on drying kinetics of Lawsonia inermis (henna) leaves and the extraction of lawsone from the dried samples was addressed. Indeed, henna leaves were dried with and without the application of ultrasound (21.7 kHz, 30.8 kW/m3) at 40, 50 and 60 °C with a constant air velocity (1 m/s). As expected, both the increase of temperature and the application of ultrasound decreased the drying time and increased the rate of extraction of the lawsone. The values of the effective diffusion coefficients obtained were used to quantify this influence showing the value increases with higher drying temperature and the application of ultrasound. Moreover, the influence of temperature was quantified by the estimation of the activation energy from an Arrhenius-type equation (46.25 kJ/mol in the case of drying without ultrasound application and 44.06 kJ/mol in the case of ultrasonically-assisted drying). Regarding the influence of studied variables on lawsone extraction yield, the higher is the temperature, the lower is the yield, probably linked with lawsone degradation reaction due to thermal treatment. On the contrary, the application of ultrasound improved the extraction yield mainly at the lower drying temperature tested of 40 °C.