Temperature dependent optimal power flow using chaotic whale optimization algorithm

2021 ◽  
Author(s):  
Dharmbir Prasad ◽  
Aparajita Mukherjee ◽  
Vivekananda Mukherjee
Keyword(s):  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Whale Optimization Algorithm ◽  
Temperature Dependent ◽  
Optimal Power ◽  
Whale Optimization

scholarly journals EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2975
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Shokooh Taghian ◽  
Seyedali Mirjalili ◽  
Laith Abualigah ◽  
Mohamed Abd Abd Elaziz ◽  
...  
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Objective Functions ◽  
Multi Objective ◽  
Optimal Power ◽  
Whale Optimization

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

Whale Optimization Algorithm With Wavelet Mutation for the Solution of Optimal Power Flow Problem

2018 ◽  
pp. 500-553 ◽  
Author(s):  
V. Mukherjee ◽  
Aparajita Mukherjee ◽  
Dharmbir Prasad
Keyword(s):  
Power Systems ◽  
Transmission Line ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Whale Optimization Algorithm ◽  
Fuel Cost ◽  
Test Power ◽  
Whale Optimization ◽  
Line Loss

This chapter proposes whale optimization algorithm (WOA) with wavelet mutation (WOA-WM) for solving optimal power flow (OPF) problem. The proposed WOA-WM algorithm of the present work utilizes wavelet theory to enhance the optimizing performance of basic WOA in exploring the solution space more effectively for getting better solution. Both WOA and the proposed WOA-WM algorithms are tested on four test power systems under different objective functions (that reflects either minimization of fuel cost or that of transmission line loss or improvement of voltage profile) for getting the optimal solutions of the OPF problem. For multi-objective problem formulation, fuel cost, transmission line loss, and voltage deviation are minimized simultaneously. The simulation results are compared to those offered by some recently reported algorithms surfaced in various recent literature. The WOA-WM-based results demonstrate convincing features in solving the OPF problem of the undertaken test power systems.

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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