The objective of this research is to identify oscillation modes from real-world measurement data captured by phasor measurement units (PMUs) and make distinction of their characteristics. To this end, dynamic mode decomposition (DMD) is applied. This paper improves DMD performance by data stacking. This enables DMD to accurately identify system eigenvalues and reconstruct signals in the time-evolving format. While data stacking raises the computation cost, we further implement a randomization technique for DMD to radically reduce the size of the data matrix. The randomized DMD (rDMD) is shown to achieve both efficiency in computing and accuracy in mode identification. PMU data from three real-world oscillation events are used for demonstration.

Over the last decade, significant advancements have occurred in global electricity networks due to the widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation of solar photovoltaic and wind power stations and reduction of environmental hazards related to traditional power sources, they have also introduced various challenges to power network scheduling and operation. The traditional optimal power flow (OPF) problem, which is inherently complex, has become even more intricate with the integration of RES alongside traditional thermal power generators. This complexity arises from the unpredictable and intermittent nature of those resources. To tackle the intricacies of incorporating RES into conventional electric power systems, this study utilizes a pair of probability distribution functions to predict the power generation of wind and solar PV systems, respectively. The comprehensive OPF, which includes RES components, is expressed as a singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, and voltage deviations. To tackle this challenge, a novel hybrid metaheuristic optimization algorithm (ACGO) is introduced. The ACGO algorithm combines Chaos game optimization (CGO) with the artificial ecosystem-based optimization (AEO) method to obtain the optimum solution for the OPF problem considering stochastic RES. This technique aims to enhance solution precision by increasing solution diversity through an optimization process. The modified optimizer’s validation begins by examining its performance using well-known benchmark optimization functions, demonstrating its superiority over CGO, AEO, and other competitive algorithms. Subsequently, the modified optimizer is applied to a combined model of a wind and PV-incorporated IEEE 30-bus system. The ACGO technique proves to be highly effective, yielding the lowest fitness values of 781.1675 $/h and 808.4109 $/h in their respective scenarios for the modified IEEE 30-bus system. Additionally, the proposed ACGO method achieves the optimal total cost of 31623.5 $/h and 31601.55 $/h for the modified IEEE 57-bus system. These results emphasize the accuracy and robustness of ACGO in effectively addressing various instances of the OPF problem. The performance of ACGO in solving the OPF issue is verified through statistical boxplot comparisons, non-parametric tests, and robustness analyses. The evaluations indicate that the ACGO technique outperforms other well-known optimization algorithms in achieving the optimum values for the OPF problem involving stochastic PV and wind power systems. Additionally, the results show that ACGO offers faster convergence rates and higher precision in convergence compared to conventional artificial ecosystem-based optimization, Chaos game optimization, and other recent heuristic, metaheuristic, and hybrid optimization algorithms. The effectiveness of the ACGO technique has been proven to be robust and efficient, making it suitable for multidisciplinary problems and engineering optimization challenges.

Sustainable energy management hinges on precise forecasting of renewable energy sources, with a specific focus on solar power. To enhance resource allocation and grid integration, this study introduces an innovative hybrid approach that integrates meteorological data into prediction models for photovoltaic (PV) power generation. A thorough analysis is performed utilizing the Desert Knowledge Australia Solar Centre (DKASC) Hanwha Solar dataset encompassing PV output power and meteorological variables from sensors. The aim is to develop a distinctive hybrid predictive model framework by integrating feature selection techniques with various regression algorithms. This model, referred to as the PV power generation predictive model (PVPGPM), utilizes meteorological data specific to the DKASC. In this study, various feature selection techniques are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward selection (SFS), backward elimination (BE), recursive feature elimination (RFE), and embedded method (EM), to identify the most influential factors for PV power prediction. Furthermore, a hybrid predictive model integrating multiple regression algorithms is introduced, including linear regression, ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) regressor, eXtreme Gradient Boosting (XGBoost) Regressor, and a hybrid model thereof. Extensive experimentation and evaluation showcase the effectiveness of the proposed approach in achieving high prediction accuracy. Results demonstrate that the hybrid model comprising XGBoost Regressor, Extra Trees Regressor, and GB regressor surpasses other regression algorithms, yielding a minimal root mean square error (RMSE) of 0.108735 and the highest R‐squared (R²) value of 0.996228. The findings underscore the importance of integrating meteorological insights into renewable energy forecasting for sustainable energy planning and management.

Over the last twin decades, significant advancements have occurred in global electricity grids due to the widespread adoption of renewable energy resources (RES). While these sources play an essential role in total generation cost reduction, transmission power loss minimization, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a primary challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses’ in the nature is introduced. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. The obtained results demonstrate that the DMOA realized exceptional performance for both the test networks, with total generation cost minimized values of 780.982 ${\$}$ /h and 8283.942 US ${\$}$ /h, respectively. These results highlight the precision and robustness of DMOA in effectively addressing various instances of the OPF problem Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.

Fuel cells (FCs) have garnered significant attention due to their versatile applications, but their nonlinear characteristics pose challenges in the modeling process. This research presents a unique enhanced artificial hummingbird algorithm (EAHA) aimed at identifying the seven unknown parameters of the proton exchange membrane fuel cells (PEMFCs) stack by utilizing their experimental datasets. To accomplish this, the objective is to achieve accurate current/voltage (I/V) curves where a cost function is defined using the aggregation of quadratic deviations (AQD) between the measured dataset points and the appropriate model‐based estimations. The presented EAHA combines several territorial foraging techniques with a linear regulating mechanism. The performance of the conventional AHA is compared with the suggested EAHA using three commonly employed PEMFC modules. Furthermore, a comparative analysis is conducted against previously published methodologies and newly developed optimizers such as the equilibrium optimizer (EO), social networking search (SNS) technique, slim mold algorithm (SMA), heap‐based optimizer (HBO), and African vultures optimization (AVO) technique. The findings are compared to existing methodologies and other state‐of‐the‐art optimizers, providing valuable insights into the efficacy of the proposed approach. For the 250 W PEMFC stack, the proposed EAHA shows improvements of 2.966%, 6.493%, 1.491%, 7.080%, 1.131%, and 2.875% over AHA, AVO, EO, HBO, SMA, and SNS, respectively, depending on the mean AQD values. Similar findings are attained for the other two stacks. For example, for the test case of the BCS 500 W PEMFCs stack, the proposed EAHA demonstrates improvements of 64.228%, 82.859%, 66.140%, 81.156%, 46.302%, and 71.635% over AHA, AVO, EO, HBO, SMA, and SNS, respectively.