Abstract

Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

Highlights

  • The results show that Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA) provides efficient and cheap power generation compared to oppositional–invasive weed optimization (OIWO) and other optimization algorithms mentioned in the literature

  • It is concluded that a trial and error method can be used to select a suitable set of parameters in order to optimize an Economic Load Dispatch (ELD) problem

  • The effectiveness of the HGAFSA is demonstrated through the simulation of solutions to a multi-objective ELD problem with higher-order cost functions using 13, 40, 110, 140, 160, and 463 generating unit test systems

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Summary

Introduction

HGAFSA is applied to solve a multi-objective ELD problem considering the effects of multiple fuel cost functions and valve-point loading. The choice of GA and AFSA is based on the following factors: (a) GA is a heuristic technique with a well-defined set of search equations that is effective in solving problems such optimal sizing and location of capacitor banks and distributed generators [15], optimal power flow [16], optimal location of tie and sectionalizing switches in distribution systems, and optimal network expansion [17], and (b) AFSA is a relatively new heuristic technique based on well-refined and sophisticated solution search equations and is widely applied in controller design, optimal PID tuning, and objective function minimization/maximization [18].

Formulation of ELD
The Cost Function
Artificial Fish Swarm Algorithm
Praying
Chasing
Genetic Algorithm
Reproduction
Crossover
Mutation
Formulation of Hybrid Genetic–Artificial Fish Swarm Algorithm
Decoder Function
Encoder Function
Population Update
Performance Validation
Test System 1
Test System 2
Test System 3
Test System 4
Test System 5
Test System 6
Sensitivity Analysis
Findings
Conclusions and Future Directions
Full Text
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