Volume 26, Issue 3

Distributed generators (DGs) have the potential to act as alternative power sources for balancing and restoring service in distribution system breakdowns. Nevertheless, DGs variety of characteristics and uncertain behavior pose difficulties in optimizing their operation while restoring the system. In this paper, a hierarchical structure is introduced to manage the unbalanced and islanded distribution networks with the optimal coordination strategy of DGs. The proposed approach encompasses multiple control tiers that manage DGs coordination process into three specific levels, each accountable for distinct functions involving voltage and frequency regulation, power distribution within individual islands, and overseeing power exchange between these units for minimizing imbalance and maximizing the load restoration. This research delivers a comprehensive solution for optimally coordinating DGs in unbalanced and isolated networks during restoration, thus elevating system reliability and resilience. The proposed model for distributed control restoration is validated using a modified IEEE 123-bus test setup.

Developing nations are facing market, regulatory, and monetary issues that often prevent them from being on the cutting edge of the energy industry. Climate commitments set forth by international cooperation like the Paris Agreement are calling for countries to decarbonize their energy infrastructure. The International Finance Corporation (IFC) predicts that much of the development in the renewable energy sector is poised to come from developing nations; this is where the greatest opportunities lie for systemic change. This study will highlight the shifting trend towards wind-plus-storage, the development cycle of disruptive technologies, key characteristics of wind-plus-storage projects, and a discussion on best practices to stimulate market demand, technological innovation, and associated regulations. A case study in Oceania, in Papua New Guinea was constructed using ArcGIS as a proof of concept to highlight data that can be leveraged to preliminarily identify high potential sites for wind-plus-storage project development. A detailed map was revealed and the different wind-plus-storage options for future project development were found. It has not been studied so far how a wind-plus-storage project can contribute to more holistic energy systems in emerging markets, such as in the case of Papua New Guinea.

In literature, to generate gate pulses for any inverter topology, most of the authors are using a single modulating signal with (level-1) or (level-1)/2 carrier-based pulse width modulation (PWM) techniques and single carrier (SC) signal with (level-1)/2 modulating signal based PWM techniques. Then, while going to higher-level inverter topologies, the complexity of the PWM control approach in both cases can increase. To reduce the control complexity, few authors have reported a single modulating signal with single carrier-based PWM techniques. However, these techniques need more mathematical equations and if-else loops. This article proposes a new single-modulating and single-carrier signal-based generalized PWM control approach using floor function (FF). This technique has been verified by considering one of the single-phase conventional symmetrical and asymmetrical multilevel inverter (MLI) topologies. Generally, FF rounds the value to the nearest integer towards the negative infinity. The proposed PWM control implementation process is very simple and suitable for both high switching frequency (HSF) and low switching frequency (LSF) operations. The proposed control technique (PCT) has been experimentally verified with different steady-state and transient-state studies of results under standalone operation.

Renewable energy utilization has gained much attention in the recent time owing to the sharp increase in the power demand. Out of different renewable sources, photovoltaic (PV) energy utilization in different forms is one of key focus. Power converter such as multilevel inverters (MLIs) are proven as key components for PV systems as well as for other applications such as motor drives, electric vehicles, and charging stations. With the advancement of the design of MLIs, the switched-capacitor (SC) based MLIs have been widely deployed for their boosting capability which is a major requirement for PV systems. In this paper, a reduced switch single input step-up 9-level SC MLI is first proposed. Followed by this, a new Rat Swarm Optimization (RSO) based Selective Harmonic Elimination (SHE) control method is incorporated for switching operation. RSO has the ability to provide the solution with higher competency within less time and a less computationally complex approach. The operation of proposed structure is analyzed and its advantages are justified by comparison with exiting topologies. The effectiveness of RSO based SHE scheme is also verified by comparing it with other nature-inspired algorithms. The effectiveness and workability of the proposed system are examined using simulation and experimental tests.

Accurate harmonic estimation is essential for effective power quality assessment, designing appropriate harmonic filters, and ensuring the reliable operation of electrical equipment. This article proposes a novel hybrid harmonic estimation technique combining recursive least square (RLS) and arithmetic optimization algorithm (AOA) for accurate estimation of harmonics, inter-harmonics and sub-harmonics. AOA is a new meta-heuristic method based on distribution behaviour of main arithmetic operators such as addition, subtraction, multiplication and division. RLS is used for estimation of amplitude of harmonics, whereas phase is estimated by AOA. The performance of AOA–RLS is investigated in detail for estimation of power system signals using two set of test signals buried with noise. The proposed AOA–RLS is proved to be efficient for estimating both phase and amplitude parameters under different signal to noise ratio (SNR) conditions with an estimation of error of E −3 . The efficacy of AOA–RLS technique is demonstrated by comparing with competitive existing techniques. The performance of AOA–RLS also verified in experimental studies.

The optimizing of renewable energy use and grid integration relies on accurate solar power predictions. In order to predict the amount of power that solar photovoltaic (PV) systems would produce inside an IoT framework, this study suggests a new method that integrates Singular Spectrum Analysis (SSA) with Extreme Learning Machine technology. The SSA algorithm makes sense of solar power data by separating it into its component parts, such as trend, seasonality, and noise. The ELM model, a quick and effective feedforward neural network with a single hidden layer, takes these broken-down parts as input characteristics. In order to enhance the accuracy of solar power forecasts, the suggested strategy combines the decomposition skills of SSA with the predictive capability of ELM. Data acquired by solar PV sensors is input into the IoT-based forecasting model, which then undergoes preprocessing with SSA, feature extraction, model training with ELM, and performance evaluation. The SSA-ELM methodology has been successfully tested on real solar power data and has shown promising results in terms of accuracy measures such as low mean absolute error and mean absolute percentage error. By implementing the suggested method, accurate projections of solar output can be made, leading to better energy management, lower costs, and the smooth incorporation of renewables into smart grids. A dependable and computationally efficient method for solar forecasting in Internet of Things applications is provided by the combination of SSA and ELM.

In the era of smart grids and microgrids, the transformation of the traditional grid system brings many operational, technical, and economic benefits. However, the complexity of the network due to the integration of various distributed generations (DGs), continuous change of topology, and non-linear load make fault detection a major issue that forces power engineers to focus on. In this paper, a novel fault detection scheme is suggested based on the multivariate variational mode decomposition mode (MVMD) and differential cumulative sum (DCUSUM). As a generalized extension of the original variational mode decomposition (VMD) algorithm for multivariate data residing in multidimensional spaces, the main goal of MVMD is to decompose the input signal into different band-limited intrinsic mode functions (IMFs). Due to the inherent characteristics of being insensitive to noise and very effective in decomposing the local features even with similar frequencies, it is very effective for fault detection in microgrid distribution systems. The proposed DCUSUM algorithm computes the differential cumulative energy for the remaining significant modes. A fault detection index is considered in this approach and applied for fault detection by adaptively through the threshold setting to accurately result in fault detection. To justify the proposed approach, a standard AC microgrid test system is considered and the approach is verified for fault detection under various fault conditions and resistances. The obtained results and the comparative analysis with other methods reflect the better accuracy, robustness, and reliability of the proposed approach.

This paper introduces a novel approach to address the optimal coordination of directional overcurrent relays (DOCRs) in modern power distribution networks. By utilizing the different optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), pattern search (PS), grey wolf optimization (GWO), the study aims to tackle the inherent complexity and nonlinearity of the relay coordination problem effectively. GWO stands out due to its ability to handle highly nonlinear optimization problems by leveraging the social behavior and hunting mechanisms of grey wolves and its ability to quickly converge to near-optimal solutions make it a popular choice. This unique feature enables the algorithm to explore the solution space more efficiently by repositioning solutions around each other, thereby facilitating better exploitation of the solution space. The effectiveness of the proposed GWO algorithm is evaluated using fault data generated from various test systems ranging from small-scale 8-bus networks to large 15-bus systems. The results demonstrate several key advantages, reduced operating time, robust coordination, and reduced coordination interval. Compared to other optimization algorithms, the GWO algorithm achieves a reduced coordination interval between primary and backup relay pairs. This optimization contributes to faster and more precise fault detection and isolation within the network in comparison to other techniques. Overall, the findings highlight the superior performance and robustness of the GWO algorithm in addressing the optimal coordination challenges of DOCRs in modern power distribution networks thereby enhancing the efficiency and reliability of protection systems in complex electrical grids.

This study introduces a cutting-edge Matrix Pencil-based Dual Grid Energy Management System (MP-DEMS) aimed at seamlessly integrating electric vehicles (EVs) into power grids while enhancing power quality (PQ) and reliability (PR). Operating within an AC-DC hybrid microgrid (HMG) framework, the MP-DEMS reduces the necessity for additional conversion devices, simplifying the integration of EVs and batteries. The core of the proposed DEMS comprises three key modules: the Wind Energy Management Module (WEMS), Smart Storage and EV Power Coordination (SS-EVPC), and Coordinated Power Conversion Control (CPCC). These modules work together to optimize energy usage, leverage renewable sources effectively, and manage EV charging/discharging schedules to minimize grid impact. An innovative aspect of the MP-DEMS is its use of Matrix Pencil-based techniques, offering several advantages over traditional Discrete Fourier Transform (DFT) methods. These advantages include swift dynamic response, resilience to voltage variations, and precise voltage phase estimation. Software simulations and Hardware-in-the-Loop (HIL-402) testing validate the efficacy of the MP-DEMS approach, showcasing enhanced power transfer capabilities, improved harmonic performance, and superior voltage/frequency regulation within EV-HMS applications. Overall, the MP-DEMS presents a promising solution for advancing the integration of EVs into microgrid systems, contributing to a more sustainable and reliable energy future.

To reduce greenhouse emission and achieve sustainability in the electric power industry, improved efficiency and energy conservation are key to finding a sound solution. Energy conservation through voltage reduction (CVR) is one such program. To implement effective CVR programs, load models are of utmost importance in determining proper feeders and methods to implement the CVR programs. Exponential and polynomial load models stand out as the most widely adopted models. The polynomial load model, often referred to as the ZIP (constant impedance, current and power) model, is especially notable for its ability to represent the relationship between the applied voltage and power consumption. This paper sheds light on the limitations of commonly used ZIP load model estimation methods and highlights the significance of using ap-propriate measurement samples and understanding the true nature of the load. Two distinct ZIP load models, i.e., the regular ZIP (RegZIP) and the Non-traditional ZIP (NTZIP) models, have been used for ZIP parameter estimation. The RegZIP load model consumes equal or more power when voltage increases, while the NTZIP model consumes equal or less power when voltage increases. It is emphasized that the RegZIP is an existing load model and the NTZIP is an artificial load model this research created for comparison purposes. The intention of this paper is not to propose any new load model, but to investigate the implications of choosing the wrong model and the impacts of measurement errors of voltage and power data. The research has shown that the two different models can fit the same measurement data equally well for some measured data. The results have demonstrated that simply using certain methods such as the optimization method for estimating load model parameters using unfiltered real time measurements may yield misleading and unreliable results. Wrong load models will inevitably lead to wrong CVR assessment. Identifying the correct measurement samples, obtained during natural or staged events causing substantial voltage variations, is critically important in calculating load parameters and ensure the soundness of the obtained results. The accurate load parameters will be important in the assessment of CVR program effectiveness and will ensure that power system analysis applications that utilize the load models will provide reliable results.

Integrating renewable energy sources like solar power into traditional power systems poses challenges. One such challenge is the effect of renewable power plants, which use power electronics, on the grid’s stability. Specifically, these plants can impact small-signal stability by either damping or exacerbating low-frequency oscillations. This paper introduces a novel Unified Power Flow Controller (UPFC) based damping controller specifically designed for Solar Photovoltaic (PV) integrated power systems. It employs an Arithmetic Optimization Algorithm (AOA) to optimize the UPFC damping controller parameters and mitigate low-frequency oscillations in the power system. The objective function minimizes the Integral Time Absolute Error (ITAE) of speed deviations under varying loading conditions. The proposed technique is utilized simultaneously to control the modulation index of series and phase angle of shunt converters of UPFC. The MATLAB/simulation results obtained effectively from the proposed technique which is actualized and identify both detrimental and beneficial impacts of increased PV penetration for small signal stability performance. The study reveals both the small-signal stability of the system and its response to large disturbances that alter the active power balance and frequency stability. The results of the analysis demonstrated with single and multimachine environment by comparing with the other optimizations like PSO, DE, DE-PSO and GWO, the proposed one is effective for damping out the oscillations. The effectiveness of the proposed damping controller is further confirmed through real-time validation using the OPAL-RT setup.

In this paper, the use of complex differentiation is presented as a robust and concise tool for modeling and sensitivity analysis of large-scale electrical networks. A complex power flow model was employed to determine the network’s operating point, from which the sensitivity analysis of the electrical system is conducted. The proposed methodology, utilizing complex differentiation, was applied to two systems with data extracted from the MATPOWER software. The obtained results are depicted in a network topology graph, highlighting the identified sensitivities.

The reliability of any electrical power system must be evaluated in order to increase durability. In accordance with the IEEE standard 1366, 2012, reliability is modeled by quality of service and profitability indices. This reliability highly depends on the applied grid code. An investigation was conducted on data recorded during these last five years since 2018 from OUED KEBRIT PV power plant located in Algeria. It was highlighted in this paper that the inverter power controller can decrease significantly the reliability. In many cases electrical protections of PV power plant react by disconnecting it, whereas the fault is outside. Data mining shows that many PV power plant breakdowns are not necessary. A modified active and reactive power control strategy was applied to the PV power plant and compared to the existing one. The proposal is based on software and hardware management. Thanks to the proposed strategy control, the PV power plant decoupled control is more reliable and was highly improved in this paper.