The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

In rechargeable battery control and operation, one of the primary obstacles is safety concerns where the battery degradation poses a significant factor. Therefore, in recent years, state-of-health assessment of lithium-ion batteries has become a noteworthy issue. On the other hand, it is challenging to ensure robustness and generalization because most state-of-health assessment techniques are implemented for a specific characteristic, operating situation, and battery material system. In most studies, health status of single cell batteries is assessed by using analytical or computer-aided deep learning methods. But, the state-of-health characteristics of series-connected battery systems should be also focused with advances of technology and usage, especially electric vehicles. This study presents a data-driven, deep learning-based hybrid decision approach for predicting the state-of-health of series-connected lithium-ion batteries with different characteristics. The paper consists of generating series-connected battery degradation dataset by using of some mostly used datasets. Also, by employing deep learning-based networks along with hybrid-classification aided by performance metrics, it is shown that estimating and predicting the state-of-health can be achieved not only by using sole deep-learning algorithms but also hybrid-classification techniques. The results demonstrate the high accuracy and simplicity of the proposed novel approach on datasets from Oxford University and Calce battery group. The best estimated mean squared error, root mean square error and mean-absolute percentage error values are not more than 0.0500, 0.2236 and 0.7065, respectively which shows the efficiency not only by accuracy but also error indicators. The results show that the proposed approach can be implemented in offline or online systems with best average accuracy of 98.33 % and classification time of 58 ms per sample.

This paper introduces three new variations of the HFO-1 (Honey Formation Optimization with Single Component) algorithm, namely HFO-1a, HFO-1b and HFO-1c, adapted to address the optimal power flow (OPF) problem. The original HFO-1 algorithm has shown success in solving various numerical problems in recent years; however, it assumes a single search range for all dimensions of the solution space, making it unsuitable for direct application to the OPF problem. Modifications to both the honey formation and mixing phases of the HFO-1 algorithm were made to improve solution quality and convergence speed, resulting in three new variants of HFO-1. The newly developed variants aim to minimize even the most challenging objective functions of the complex OPF problem, which has been further complicated by the integration of renewable energy sources into power systems. The paper provides a comprehensive and transparent comparison of the three types of IEEE 30-bus test systems and 118-bus test systems with existing methods, meticulously adhering to practical, technical, operational, and safety constraints. Following successful results on the CEC 2021 standard benchmark functions, the proposed HFO-1 variants have been thoroughly validated through extensive analysis. Experimental results demonstrate that the proposed approach can achieve lower costs ($800.5972/hour and $800.3871/hour) in two types of IEEE 30-bus systems without integrating renewable resources while maintaining system constraints. Furthermore, HFO-1a (achieving 3.0776261 MW) and HFO-1b achieve the lowest values in the literature with a multi-fuel cost of (646.375893 $/h) and a valve point effective fuel cost of (823.981360 $/h), respectively, while HFO-1c exhibits a voltage deviation of (0.083498 p.u.) and Prohibited Operating Zones (POZ) cost of generator (800.665078 $/h).