Optimal power flow solutions through multi-objective programming

Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm

“…Generally, achieving optimal or near optimal solution for a specific problem requires multiple trials as well as accurate adjustment of associated parameters. Some of the proposed population-based methods such as MDE (Modified Differential Evolution) algorithm [2] presents algorithm for solving OPF problem with non-convex and non-smooth generator fuel cost, DE (Differential Evolution) [3] with different objective functions that reflect total fuel cost minimization, voltage stability enhancement, and voltage profile improvement, an ISS (Improved Scatter Search) method to deal with multi-objective EED (Environmental/Economic Dispatch) problems [4], which is formulated as a large-scale highly constrained nonlinear multi-objective optimization problem, based on concepts of Pareto dominance and crowding distance and a new scheme for the combination method [5], a new DQLF (Decoupled Quadratic Load Flow) solution with EGA (Enhanced Genetic Algorithm) [6] to solve the OPF problem for simultaneous minimization of fuel cost, loss and voltage stability index, a proposed DPOPF (Distributed and Parallel OPF) algorithm for smart grid with renewable energy sources to minimizing the fuel cost and reduce carbon emission for energy saving in the OPF problem [7]. Also, other heuristic optimization algorithms have been applied such as BBO (Biogeography-Based Optimization) algorithm which is presented to solve OPF problems of a power system with generators that may have either convex or non-convex fuel cost characteristics [8], MSFLA (Modified Shuffle Frog Leaping Algorithm) [9] which solves the multi-objective OPF problem considering economical and emission issues, FPSO (Fuzzy Evolutionary and Particle Swarm Optimization) algorithm [10] which employs the integration of fuzzy systems with PSO (Particle Swarm Optimization) and Genetic Algorithm (EGA) approaches for optimal setting of OPF problem control variables with objective function that reflects total fuel cost minimization with different linear and non-linear constraints, MOHS (Multi-Objective Harmony Search ) algorithm [11] for OPF problem as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation also compared with fast Nondominated Sorting GA (NSGA-II) method, ABC (Artificial Bee Colony) algorithm [12] with different objective functions such as convex and non-convex fuel costs, emission, voltage profile improvement, active power loss, voltage stability enhancement and are chosen for this highly constrained nonlinear non-convex OPF problem, a new and efficient method using hybrid FFA (Firefly Algorithm) [13] for solving EPD (Economic Power Dispatch) problem which is tested and validated on the IEEE 30-bus power system, IPSO (Improved PSO) algorithm [14] for multi-objective OPF problem considering the cost, voltage stability index, emission and power loss, a hybrid algorithm integrating the FCASO (Fuzzy adaptive Chaotic Ant Swarm Optimization) algorithm and the SQP (Sequential Quadratic Programming) techniques, named FCASO-SQP algorithm which is presented for solving t...…”

A novel multi-objective approach based on improved electromagnetism-like algorithm to solve optimal power flow problem considering the detailed model of thermal generators

“…Recently, a vast number of studies have been focused on solving the OPF problem, which can be divided into 2 main categories including mathematical‐based methods and meta‐heuristic methods. Traditional mathematical approaches such as interior point method, benders decomposition, and successive linear programming have been employed to solve the OPF problem. The above‐mentioned methods ignore the physical limitations of generating units and, therefore, suppose that a typical generation unit has a smooth and convex quadratic cost function, which is an oversimplified assumption.…”

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

“…If one solution cannot be improved further over the number limit, and it will be abandoned and a new solution will be generated by Equation (20) to replace the abandoned one.…”

“…The methods for solving the multi-objective optimization problem include the weighting method [20] and fuzzy mathematics method [21]. The weighting coefficients of the weighting method are determined by decision maker preferences or several simulations.…”

“…If a feasible solution cannot be improved by a predetermined number of cycles limit, that shows the fitness value of the solution is a local optimum, then this solution will be abandoned and the a scout bee generates a new solution without any guidance using Equation (20).…”