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Optimal Power Flow (OPF) problem is one of the most important and widely studied nonlinear optimization problems in power system operation. This study presents the implementation of a new technology based on the hybrid Firefly and krill herd method (FKH), which has been provided and used for OPF problems in power systems. In FKH, an improved formulation of the attractiveness and adjustment of light intensity operator initially employed in FA, named attractiveness and light intensity the update operator (ALIU), is inserted into the KH approach as a local search perform. The FKH is prove with the solving of the OPF problem for various types of single-objective and multi-objective functions such as generation cost, reduced emission, active power losses and voltage deviation which are optimized simultaneously on exam system, viz the IEEE-30 Bus test system, which is used to test and confirm the efficiency of the proposed FKH technique. By comparing with several optimization techniques, the results produced by using the recommended FKH technique are provided in detail. The results obtained in this study appear that the FKH technique can be efficiency used to solve the non-linear and non-convex problems and high performance compared with other optimization methods in the literature. This study can achieve a minimum objective by finding the optimum setting for system control variables.

In the few last decades, Distribution Generation (DG) has drawn a great attention by researchers around the world in the field of Radial Distributed Systems (RDSs). Generally, the optimal placement is based on the maximization of the Voltage Stability Index (VSI) and the optimal sizing is based on the minimization of the Total Active Power Losses (TAPLs). Hence, a Multi-Objective Optimization Problem (MOOP) is proposed to achieve the both mentioned objectives. For this purpose, a new simple optimization algorithm known as Bat Algorithm (BA) based on Weight Sum Method (WSM) has been used to resolve the MOOP. Then, the Fuzzy Based (FB) technique is employed to find the Best Compromise Solution. This paper also provides a comparison between the proposed algorithm and other recently published methods. From the obtained results, the advantage of the proposed algorithm is clearly observed from multiple points of view such as enhancement of Voltage Profile (VP), decreasing of the TAPL, and the maximization of the VSI. The investigations have been carried out on a standard IEEE 12-bus, 33-bus, 69-bus, and 85-bus test feeders.

The optimal power flow (OPF) problem considers as an optimization problem, in which the utility strives to reduce its global costs while pleasing all of its constraints. Therefore, Interior Search Algorithm (ISA) is applied to treat this problem. Where, ISA is a specific type of artificial intelligence and a mathematical programming, not a meta- heuristic algorithm. The effectiveness of this method in solving the OPF problem is evaluated on two test power systems, the IEEE 30-bus and the IEEE 57-bus test systems. For the first example, the ISA-OPF algorithm finds an answer that agrees with published results. For the 57-bus system, the ISA-OPF demonstrates its ability to transact with larger systems. Thus, the ISA-OPF algorithm is shown to be a robust tool to treat this optimization compared with other methods. Moreover, the advantage of ISA is that it has only one parameter of control which makes the simplicity in the main algorithm.

The optimization algorithms in electrical engineering present an important tool for improving the dynamic and static operation of electrical distribution networks (EDN) by supporting the regional operator system for distribution network reconfiguration with the presence of dispersed generation systems based on the PV and wind turbine hybrid system. The short circuit current, in particular of the phase-to-ground type, is the most prevalent and intense on the EDN and affects voltage stability and increases power losses. In this work, we propose a new optimization technique, the hybrid improved whale and sine cosine optimization algorithm, based on the randomly determined distance between each whale’s current position and the best position using probabilistic-based sine and cosine functions, to improve the balance between exploitation and exploration phases. The objectives implemented are to minimize total active power losses and maximize the voltage profile. Two test systems were conducted for this work: overhead and undergrounding 63 bus systems using private data on the algerian electricity and gas distribution company and verifying the effectiveness of the proposed method through its application in the IEEE 33 bus standard distribution system. We then compared the results obtained with the latest research.

The aim of economic dispatch (ED) problem is to provide an efficient utilization of energy resources to produce economic and secure operating conditions for the planning and operation of a power system. ED is formed as a nonlinear optimization problem with conflicting objectives and subjected to both inequality and equality constraints. An efficient improvement of krill herd (KH) algorithm, a powerful metaheuristic method, has been introduced in this paper. The KH algorithm inspired by the Lagrangian and evolutionary behaviour of the krill people in nature, has been investigated to solve ED problem on 6, 13, 20 and 40 generating units. The proposed chaotic krill herd (CKH)) improvement is done by incorporating the chaos approach to KH algorithm for raising the global convergence speed and for enhancing its performance. The elitism scheme serves to save the best krill during the procedure when updating the krill. The results show clearly the superiority of CKH in searching for the best cost value results when compared with well-known metaheuristic search algorithms.

The optimal power flow (OPF) is becoming the most popular problem encountered in studies related to power system analysis. OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. In this paper, inspired by the herding behavior of elephant, a new type of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving OPF problem. EHO has a fast convergence rate due to the use of clan updating operator and separating operator. The elitism scheme is also used to save the best elephant during the process when updating the elephant. Case studies based on standard IEEE 30-bus test system are employed to prove the capability of the proposed EHO algorithm. The results clearly show the superiority of EHO in searching for the better function values than other well-known metaheuristic search algorithms that has been already done.

The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing its performance. The elitism scheme is also serves to save the best cuckoo during amid the procedure when updating the cuckoo. The results show clearly the superiority of CCS in searching for the best function values results when compared with well-known metaheuristic search algorithms.

The economic power dispatch (EPD) problem aims to find the optimal operational settings of power generating units with minimizing the entire production costs of generation and all system constraints are kept into their acceptable limitations. Meanwhile, achieving this economical operation based on the reduction of the fuel cost and fossil fuel fired power plant emission should be as little as possible. Another nonlinear framework, with contradictory objectives, called combined economical emission power dispatch (CEEPD) is considered as the target framework in this study. The CEEPD framework is subjected to a set of operational equality and inequality constraints. In terms of the solution methodology, this paper is focused at developing a novel optimization algorithm called Chaotic Krill Herd Algorithm (CKHA) as an efficient tool for solving non-convex EPD and CEEPD frameworks. The proposed CKHA is tested on several IEEE test systems for refining its convergence and therefore aggregating the overall solution method. The numerical results clearly show the dominance of CKHA in finding the competitive best fitness functions compared with the advanced methods those published in the literature.

The aim of this research study is to describe the hybrid renewable energy resources, the photovoltaic and the wind turbine are utilized to produce AC power for a Sahara Hassi R'Mel region in south of Algeria is optimally designed. Hybrid power generation systems are an operative solution for the variable generated power of renewable energy sources. In the new design, the ability circuit and the surveillance regulation of the presented grid-connected hybrid power system simulation is examined via MATLAB/Simulink. To detect the feasibility of the controlled system, this system is studied under various solar radiation and wind speed profiles. On the basis of the results, good tracking with a high accuracy rate is obtained after using filtering component by enhancing the different topology configurations in the expression of comparison voltage (V), and power (W). Overtime, the overall system efficiency is enhanced compared to the MPPT control system. The obtained simulation results for the incremental conductance PV/Wind MPPT controller have accomplished high effective system achievements. IncCond method is appropriate for working in vastly variable weather conditions with easy design, high tracking velocity, and minimum step count.

The aim of economic dispatch (ED) problem is to provide an efficient utilization of energy resources to produce economic and secure operating conditions for the planning and operation of a power system. ED is formed as a nonlinear optimization problem with conflicting objectives and it is subjected to both inequality and equality constraints. An efficient improvement of firefly algorithm (FA), a powerful metaheuristic method, has been introduced in this paper. FA is a bio-inspired optimization algorithm that is inspired by flashing patterns and behaviour of fireflies in nature, it has been introduced for solving non-convex economic dispatch problem due to valve-point effects. The proposed chaotic firefly algorithm (CFA) improvement is done by incorporating the chaos approach to FA algorithm for raising the global convergence speed and for enhancing its performance. The results show clearly the superiority of CFA in searching for the best cost value results when compared with well-known metaheuristic search algorithms.