The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA.
Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.
- Keywords
- cat and mouse, optimization, optimization problem, population-based, stochastic,
- MeSH
- Algorithms * MeSH
- Movement MeSH
- Problem Solving MeSH
- Models, Theoretical * MeSH
- Learning MeSH
- Publication type
- Journal Article MeSH
This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.
This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA's performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed-approaching the maximum Power Point 10-15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm's overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.
The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV's impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV's role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.
Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional-integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm's balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25-50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller's effectiveness and reliability in modern power systems with hybrid and renewable energy sources.
- Keywords
- Load frequency control, PI controller, Sinh cosh optimizer (SCHO), Two-area system,
- Publication type
- Journal Article MeSH
Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95-1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.
- Keywords
- Distribution system, GHG emissions, Grid-connected, Power losses, Voltage deviation,
- Publication type
- Journal Article MeSH
