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

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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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Fig. 1
Fig. 2



Combined economic emission dispatch


Crow search algorithm


Differential evolution


Hybrid DE and CSA


Particle swarm optimization


Generating units


Lagrangian multiplier


Fuzzy logic control


Artificial neural network


Genetic algorithm


Bat algorithm


High dimensional


Flower pollination algorithm


Emission load dispatch


Valve point effect


Modulated particle swarm optimization


Prohibited operating zones


Central processing unit


Valve point effect


Spiral optimization algorithm


Avoidance of worst locations


Dynamic EED


Avoidance of worst locations


Gradually increasing directed neighbourhood


Multi-agent reinforcement learning


Collective neurodynamic optimization


Projection neural network


Micro grid


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).

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