Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

Abstract

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

“…Similarly, if we assume a compulsion time of 9.875 seconds for 40 units, a 42-unit Squirrel Search Algorithm would take longer than 9.875 seconds 30) . Likewise, if 30 units require 130.5 seconds, then a 42-unit Adaptive Differential Evolution-Simulated Annealing would exceed 130.5 seconds 31) . When we estimate that 40 units need 233 seconds, we can infer that a 42-unit Ameliorated Grey Wolf Optimization would require more than 233 seconds 32) .…”

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

“…Physics and biology often motivate these algorithms. Many metaheuristic algorithms have been used to solve ED problems such as Evolutionary Programming (EP) [13], Interior Search Algorithm [14], Genetic Algorithm (GA) [15], Simulated Annealing (SA) [16], Differential Evaluation (DE) [17],Particle Swarm Optimization (PSO) [18], , and Artificial Neural Networks (ANNs) [19],. The disadvantage of EP is that it slowly converges to near-optimal for some problems [20].…”

Energy Sustainability Through Generation Scheduling

“…The economic dispatch is very intricate to resolve because of the frequent varying system demand, huge amount of data and constraints, and the non-linear objective function. Many optimisation approaches such as integer and dynamic programming (Nemati et al, 2018;Wang et al, 2014), Genetic Algorithm (Singh et al, 2014), Simulated Annealing (He et al, 2018), hopfield neural network (Reddy and Momoh, 2015), Particle Swarm Optimization (Chen et al, 2018), Tabu Search Algorithm (Naama et al, 2013), and Grasshopper Optimization Algorithm (Suriya et al, 2018;Karthikeyan et al, 2018) are available in the market; however, each one has its own convenience and constraints.…”