Abstract
Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work.
Highlights
The problem of economically allocating the power production of each generating unit and minimizing the emissions of these units is an ongoing challenge for engineers
The network system of six generator units with several demand loads, as shown in Table 1, was used to solve the Economic Load Dispatch (ELD) problem based on several optimization algorithms, namely, the Chameleon Swarm Algorithm (CSA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA) algorithms
The order of algorithms based on the best cost is CSA, GWO, SCA, and EWA for all demand cases
Summary
The problem of economically allocating the power production of each generating unit and minimizing the emissions of these units is an ongoing challenge for engineers. In [7], authors used Moth Flame Optimization (MFO) for solving the ELD problem considering the valve point effect, wind power, and the load transit conditions. In [8], a novel algorithm considering amalgamation of quantum theory, the Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO) was used for solving the ELD of a power system having photovoltaic generation. In [18], the authors proposed an improved version of Teaching Learning Based Optimization (TLBO) for solving dynamic ELD considering wind resources and load demand uncertainty. A hybrid algorithm considering amalgamation of PSO with DE was proposed for solving ELD with and without the valve point effect in [20]. In [21], authors applied Ant Colony Optimization (ACO) for solving ELD in the case of an IEEE 26 bus test system considering the valve point effect.
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