Multi objective functions of constraint optimal power flow based on modified ant colony system optimization technique

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively.

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

Electronics ◽

10.3390/electronics10232975 ◽

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.

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Touring Ant colony Optimization technique for Optimal Power Flow incorporating thyristor controlled series compensator

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) ◽

10.1109/nabic.2009.5393815 ◽

2009 ◽

Cited By ~ 3

Author(s):

S. Sreejith ◽

K. Chandrasekaran ◽

Sishaj P Simon

Keyword(s):

Ant Colony Optimization ◽

Power Flow ◽

Optimal Power Flow ◽

Optimization Technique ◽

Ant Colony ◽

Optimal Power ◽

Thyristor Controlled Series Compensator

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Multi objective optimal power flow using particle swarm optimization technique

2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) ◽

10.1109/scopes.2016.7955644 ◽

2016 ◽

Cited By ~ 1

Author(s):

Debasish Choudhury ◽

Sarmila Patra

Keyword(s):

Particle Swarm Optimization ◽

Power Flow ◽

Optimal Power Flow ◽

Optimization Technique ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective ◽

Optimal Power ◽

Particle Swarm Optimization Technique

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Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

Journal of Engineering ◽

10.31026/j.eng.2018.03.07 ◽

2018 ◽

Vol 24 (3) ◽

pp. 84

Author(s):

Hassan Abdullah Kubba ◽

Mounir Thamer Esmieel

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Power Flow ◽

Optimal Power Flow ◽

Reactive Power ◽

Test System ◽

Multi Objective ◽

Optimal Power ◽

Blending Method ◽

Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.

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