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Solving combined economic emission dispatch model via hybrid differential evaluation and crow search algorithm

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Abstract

One of the major aspects regarding the operation and planning of a power system was to reduce the emission of pollutions and fuel costs in thermal power plants. This issue was resolved as an optimization crisis, where the reduction of emission of pollutions and fuel cost was done and it is known as the combined economic emission dispatch (CEED) problem. Various techniques were introduced for improving the performance of the power plants with respect to algorithm reliability, solution accuracy, global optimality, and convergence speed for resolving the CEED issues. Therefore, this work establishes a CEED approach for the smart grid system and resolves it by exploiting the hybridized concepts of the crow search algorithm (CSA) and differential evolution (DE). The hybridized model of the two well-known schemes is achieved by updating the solutions of both the schemes and merging them with the random searching model. Thus, the new approach is named as hybrid DE and CSA model. The CEED approach is subjected to reduce its cost and therefore, sufficient trade-off between the emission and economic costs could be sustained. The presented hybrid scheme is simulated on 3 diverse bus systems and its performance is evaluated over other state-of-the-art models in terms of CPU time and generation strategy.

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Abbreviations

CEED:

Combined economic emission dispatch

CSA:

Crow search algorithm

DE:

Differential evolution

hDECSA:

Hybrid DE and CSA

PSO:

Particle swarm optimization

GU:

Generating units

LM:

Lagrangian multiplier

FLC:

Fuzzy logic control

ANN:

Artificial neural network

GA:

Genetic algorithm

BA:

Bat algorithm

HD:

High dimensional

FPA:

Flower pollination algorithm

ELD:

Emission load dispatch

VPE:

Valve point effect

MPSO:

Modulated particle swarm optimization

POZ:

Prohibited operating zones

CPU:

Central processing unit

VPE:

Valve point effect

SOA:

Spiral optimization algorithm

AWL:

Avoidance of worst locations

DEED:

Dynamic EED

AWL:

Avoidance of worst locations

GIDN:

Gradually increasing directed neighbourhood

MARL:

Multi-agent reinforcement learning

CNO:

Collective neurodynamic optimization

PNN:

Projection neural network

MG:

Micro grid

MHS:

Modified harmony search

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Correspondence to Gunjan Bhargava.

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Bhargava, G., Yadav, N.K. Solving combined economic emission dispatch model via hybrid differential evaluation and crow search algorithm. Evol. Intel. (2020). https://doi.org/10.1007/s12065-020-00357-0

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Keywords

  • CEED issue
  • Fuel cost
  • Emission of pollutions
  • Hybrid CSA and DE
  • CPU time