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The combined economic and emission dispatch problem seeks to determine the optimal output of power generating units to satisfy demand and transmission losses while minimising both generation costs and environmental emissions. To resolve this complex multi-objective optimisation challenge, a meta-heuristic method known as the crow search algorithm has been adapted. Inspired by crow behaviour, the algorithm features a simple design that requires tuning only two adjustable parameters. The method was implemented in MATLAB and evaluated across four distinct benchmark power setups, including a three-generator setup, an IEEE thirty-bus system, and systems with ten and forty thermal generators. Compared against established techniques such as particle swarm optimisation, genetic algorithms, and hybrid genetic algorithms, the crow search algorithm delivered faster computation and more efficient solutions across the tested dispatch scenarios.
Balancing affordable electricity generation with strict environmental emissions targets is an essential operational challenge for power grids. Advanced optimisation algorithms enable operators to calculate generation schedules that simultaneously lower fuel costs and reduce air pollution. Improving the computational speed and efficiency of these calculations helps grid managers manage complex networks more cleanly and economically.
This method is applicable to electrical power systems and grid management software, with utility operators and power dispatchers serving as potential users. Because validation is currently limited to MATLAB simulations across standard test systems with up to forty generators, the technology remains at an early, algorithmic testing stage prior to any real-world utility integration.
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The combined economic and emission dispatch (CEED) problem is a multi-objective non-linear optimization problem with several constraints. Its target is searching for optimum generation outputs of available generating units in a power system to supply the electrical loads and transmission losses at minimum generation costs combined with minimum pollutant emissions. To achieve an optimal solution for this problem, this paper proposes an application of a new meta-heuristic optimizer called crow search algorithm (CSA). CSA is inspired from the intelligent attitude of crows. It is very simple since it has only two adjustable parameters. The CSA is employed and developed in MATLAB for solving the CEED problem. It is applied to four test systems consisting of three thermal generators, the standard IEEE 30-bus model system, ten and forty thermal generators. A comparison between the developed CSA and other optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and hybrid genetic algorithm (HGA) is executed in terms of solution equality and computation efficiency. Simulation results demonstrate clearly the effectiveness of the proposed CSA in solving the CEED problem since its obtained solution is faster and more efficient than that obtained by using other techniques.
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DOI: 10.1109/mepcon.2017.8301166
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