Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.

The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).

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Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem's complexity, the proposed technique JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This paper addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques. INDEX TERMSDistributed generators, Power losses minimization, switched capacitors, distribution reconfiguration, manta ray foraging algorithm. Nomenclature: Nbn Whole number of the branches in the system OFn Objective function CoV Control variables OT Tie branches which are opened NT Number of branches which could be opened to retain the radial structure of distribution system. Qsc Reactive output power from switching of the capacitors NC Number of the current switched capacitors. Pog Dispatchable output power of DG Ndg Number of located DGs Qsc min Minimum of reactive power resulting from switched capacitors Qsc max Maximum of reactive power resulting from switched capacitors Pog min Minimum of active power resulting from DG Pog max Maximum of active power resulting from DG V Voltage magnitude Ibn Flow of the current in the branches Ibn max Upper limit of the current flow in the branches Pd Active load demand Qd Reactive load demand PRG & PRQ Penetration level which is acceptable from the DGs and CBs Mb Buses number of the system QGsub Substation reactive power

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This article provides an improved Marine Predator Optimization (IMPO) for the optimized performance of combined alternating/direct current (AC/DC) electrical grids. The optimum performance of such AC/DC electrical grids is approached as a multi-objective issue with the goal of reducing the overall generated environmental emissions, fuel costs and the asSoCiated energy losses. The suggested IMPO includes an exterior repository which is meant to preserve nondominated individuals. The fuzzified decision procedure is often used to employed with a view to determining the correct acceptable operational solution for the combined AC/DC electricity grids. The suggested IMPO is created via the MATLAB environment and is employed on an updated standard power system of standard IEEE 57-bus. Besides, a comparative analysis is performed between the proposed IMPO algorithm, particle swarm optimization, bat optimization, dragonfly optimization, crow search optimization, grey wolf optimization, multi-verse optimization and salp swarm optimization. The simulation outputs demonstrate the effectiveness and preponderance of the proposed IMPO with capability in extracting well-diversified Pareto solutions.