Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the ...entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.
The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power ...dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.
Summary
The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method “Chaotic MayFly optimization ...algorithm” (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I–V characteristics of the PEMFC's are non‐linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non‐linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.
Implement a novel improved CMOA to obtain an accurate PEMFC model.
PEMFC model affects dynamic analyses in several energy applications.
Seven unknown parameters are optimally extracted using the proposed model.
The model results are verified by experimental results for three PEMFCs.
The proposed model has outstanding results over models.
Abstract Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require ...robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources.
This paper presents a novel endeavor to use the Heap optimization algorithm (HOA) to solve the problem of optimal power flow (OPF) in the electricity networks. The key objective is to optimize the ...cost of fuel of the conventional generators under the system limitations. Various scenarios are studied in a later stage considering the addition of the PV panel and/or wind farm with changing load curves during a typical day. The active output power of the generators is selected to be the OPF problem search space. The HOA is employed to get the best solution of the fitness function and provides the corresponding best solutions. The modeling of the heap-based optimizer (HBO) depends on three levels: the relation between the subordinates and the boss, the relation between the same level employees, and the contribution of the employee oneself. The validity of the proposed algorithm is tested for a variety of electric grids, the IEEE 30-bus, IEEE 57-bus and 118 bus networks. These networks are simulated under various scenarios. Real load curves, in this study, are considered to achieve a practical outcome. The simulation outcomes are evaluated and tested. The results indicate that the implemented HOA-based OPF methodology is flexible and applicable compared with that achieved by using the genetic algorithm.
This paper provides an application of the hybrid Grey Wolf Optimization and Particle Swarm Optimization (GWO-PSO) method to reach a solution to the optimal reactive power dispatch (ORPD) problem in ...the scope of electric power networks. PSO is a swarm based meta-heuristic optimization algorithm whose target is to seek the best solution to a problem by moving particles in a specific exploration field. On the other hand, GWO is a meta-heuristic optimization technique which is inspired by grey wolves. In this article, GWO is hybridized with a PSO method to improve the progress of the GWO. There are two objectives minimized in this research study to improve the electric power network performance. They are: 1) power losses in the transmission systems, and 2) the deviation of voltages at the load buses. The problem of ORPD has many restrictions on the networks which must be considered during the solution. The hybrid GWO-PSO is proven as an effective optimization technique when seeking the global best solution to an optimization problem. The success of the introduced hybrid technique is verified utilizing more than one standard IEEE test system. A valuation to the introduced technique is performed by comparing it with other optimization techniques stated through the literature. The simulation results confirm that the usage of the hybrid GWO-PSO techniques causes an observable improvement in a wide scale of the electric power networks behavior.
This paper introduces a novel enhancement to the Transient Search Optimization (TSO) algorithm to estimate an accurate electrical model of the proton exchange membrane fuel cell (PEMFC). The PEMEFC ...model is a non-linear model that includes seven unknown variables which cannot be calculated analytically. The TSO is enhanced by inserting two new factors, the Levy function and the Weibull distribution function. The proposed enhanced Transient Search Optimization (ETSO) and TSO algorithms are applied to estimate the seven variables by minimizing the sum of the squared errors (SSEs) between the measured and calculated voltages. The error is defined as the difference between the measured and the calculated output voltage of the PEMFC. Three different commercial types of PEMFCs are modeled: i) Ballard, Mark V 5 kW, ii) Horizon H-12, and iii) 6 kW Nedstack PS6 stacks PEMFC. The estimated seven variables and the minimum SSE of electrical PEMFCs using ETSO and TSO algorithms are compared with the results obtained by using other optimization algorithms like whale optimization algorithm, genetic algorithm, neural network algorithm and others. The results obtained by the ETSO are better than that obtained by TSO by more than 10% and this percentage increases with other algorithms. The accuracy of the proposed PEMFC model is verified by comparing the estimated V–I and P–I characteristics with the measured data. The effectiveness of the proposed ETSO based model is verified by an investigation of sensitivity analysis for design variables and the robustness of the ETSO algorithm via the statistical analysis and the parametric t-test.
•This paper presents a novel improved TSO algorithm to extract PEMFC model parameters.•Three commercial PEMFC models are implemented in this paper.•The parameters of proposed model are compared with other recent models.•The proposed model is verified by comparing its results with the measured results.•The sensitivity and robustness analyses of the proposed method are tested.
This article introduces a new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems. The principle target is ...to optimize the generating units' fuel cost under the system constraints. At initial stage, the objective function is solved to find the optimal siting of Distributed Generation (DG) units within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding DG units. The generators' real output power defines the exploration field for the OPF problem. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem. More than one electric grid is tested to check the validity of the proposed algorithm such as the IEEE 14-bus, and 30-bus networks. Simulations included different scenarios are implemented in these two networks. To obtain a realistic result, real daily load curve is considered in this study. The results of simulations are investigated and analyzed. Results confirm the flexibility, validation, and applicability of the introduced SFO-based OPF methodology when compared with the genetic algorithm.
This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search ...(CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems’ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function.
Among the hundreds of reported
species,
(Labill.) DC. is one of the six that grow in Jordan. Many species of this genus are used in folk medicine to treat a variety of ailments and several biological ...and pharmacological activities have been ascribed to their essential oil (EO). For this study, the EO obtained from a specimen of
grown in Jordan was analyzed by GC-MS. Ninety-six compounds were detected, of which oxygenated monoterpenes was the predominant class (47.9%), followed by non-terpene derivatives (27.9%), while sesquiterpenes represented 14.2% of the total composition. The most abundant compound in the EO was 1,8-cineole (21.7%). The cytotoxic activity of the EO was evaluated against three cancer cell lines (MCF7, A2780 and HT29), and one normal fibroblast cell line (MRC5) by MTT assay. Significant growth inhibition was observed in EO-exposed A2780 and HT29 cells (IC
= 12.99 and 14.02 μg/mL, respectively), while MCF7 and MRC5 were less susceptible. The EO induced apoptosis and increased the preG1 events in A2780 cells. 1,8-Cineole, the major constituent of the EO, exhibited submicromolar cytotoxicity against A2780 cells, and was 42 times more selective against MRC5 cells. Its cytotoxicity against A2780 cells was comparable with that of doxorubicin, but 1,8-cineole was more selective for MRC5 normal cells. Interestingly, 1,8-cineole enhanced apoptosis in A2780, and caused a remarkable dose-dependent increase in preG1 events. Thus, 1,8-cineole has demonstrated promising cytotoxic and proapoptotic properties.
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