Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Abstract

This paper proposes a novel technique named modified iteration particle swarm optimization for finding possible optimal solution in the economic load dispatch problem considering non-convex and non-smooth fuel cost function. Many constrains such as ramp rate limits, transmission losses and prohibited operating zones are considered. The valve point effects with nonlinear cost function in non-convex economic dispatch are also considered. Classical particle swarm optimization (CPSO) highly depends on its parameters and often suffers from being involved in local optima. The proposed approach incorporates two new index named "first iteration best" and "second iteration best" in CPSO. It results that searching behavior is enhanced and solution quality is promoted. To ensure the efficiency of the proposed approach, it is applied for four test system including 3, 6, 13 and 15 units. From the simulation results, the capabilities and effectiveness of the proposed approach compared with existing methods is confirmed.

The solution of the economic dispatch problem via an efficient Teaching-Learning-Based Optimization method

“…Table 4 shows the average and best total generation costs provided by the proposed TLBO, as well as by PSO and GA [2], by modified algorithms of PSO [25,26]. According to [2], PSO and GA were run 50 times.…”

Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

“…where n and d are the number of moth and dimension respectively. Both (10) and (11) are assumed to store the corresponding fitness values in an array as expressed below:…”

“…Once the total cost has obtained for respected moth (after updating with the flames such in eqn. (11)(12)(13)(14)), the matrix is sorted where the best solution so far is located at the top while the worst result is located at the bottom of the population matrix. If the updated variables are out of bound from the constraints, they are pegged at the minimum or maximum boundaries so that the result obtained is correct.…”

“…The modification of FA (MFA) based on [5] has been proposed in [6] to solve ED problems with practical constraints. Other algorithms that also have been proposed in literature to solve ED are such as Grey Wolf Optimizer (GWO) [7], Flower Pollination Algorithm (FPA) [8], Whale Optimization Algorithm (WOA) [9], Improve/ modified PSO [10,11] and Hybrid Bee Algorithm with Tabu Search (BA/TS) [12].…”

Equal Incremental Fuel Cost Approach for Multi Area Operation of Power System

“…The list of other techniques is provided in TABLE I. [8] Direct Search Method [13] Heuristic/Bio Inspired Nature Inspired Optimization [5] Flower Pollination [6] Algorithm Hopfield Neural Network [11] Evolutionary programming [12] Hybrid Differential Evolution Particle Swarm Optimizer [9] Nonlinear optimization Neural Network [14] Adaptive Shuffled Frog Leaping Algorithm [15] Modified Iteration Particle Swarm Optimization [16] Multi area with multi fuel options is included into economic dispatch problem in [7]. Prasanna et al [10] incorporated fuzzy logic strategy in Evolutionary programming and Tabu search, to solve security constrained multi area economic dispatch problem.…”