Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

Jagat Kishore Pattanaik,Mousumi Basu,Deba Prasad Dash

doi:10.1186/s43067-019-0001-4

Jagat Kishore Pattanaik, Mousumi Basu + Show 1 more

Open Access

https://doi.org/10.1186/s43067-019-0001-4

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Abstract

This paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

Highlights

Static economic dispatch (SED) allocates the load demand which is constant for a given interval of time, among the online generators economically while satisfying various constraints including static behavior of the generators
This paper investigates the applicability of the following five different artificial intelligent (AI) techniques in dynamic economic dispatch (DED) problem: differential evolution (DE), particle swarm optimization (PSO), evolutionary programming (EP), genetic algorithm (GA), and simulated annealing (SA)
A comparative study is performed for the five AI techniques for solving the dynamic economic dispatch (DED) problem for a ten-unit test system with nonsmooth fuel cost function is used

Summary

Introduction

Static economic dispatch (SED) allocates the load demand which is constant for a given interval of time, among the online generators economically while satisfying various constraints including static behavior of the generators. Since the DED was introduced, several methods [2,3,4,5,6,7,8,9,10,11,12,13] such as Lagrangian relaxation, gradient projection method, dynamic programming, hybrid EP and SQP, hybrid HNN-QP, hybrid differential evolution, etc., have been employed for solving this problem. All of these methods may not be able to find an optimal solution and usually stuck at a local optimum solution

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Results

Conclusion