Abstract
In the present paper, a novel meta-heuristic algorithm, namely quasi-oppositional search-based political optimizer (QOPO), is proposed to solve a non-convex single and bi-objective economic and emission load dispatch problem (EELDP). In the proposed QOPO technique, an opposite estimate candidate solution is performed simultaneously on each candidate solution of the political optimizer to find a better solution of EELDP. In the bi-objective EELDP, QOPSO is applied to simultaneously minimize fuel costs and emissions by considering various constraints such as the valve-point loading effect (VPLE) and generator limits for a generation. The effectiveness of the proposed QOPO technique has been applied on three units, six units, 10-units, 11-units, 13-units, and 40-unit systems by considering the VPLE, transmission line losses, and generator limits. The results obtained using the proposed QOPO are compared with those obtained by other techniques reported in the literature. The relative results divulge that the proposed QOPO technique has a good exploration and exploitation capability to determine the optimal global solution compared to the other methods provided in the literature without violation of any constraints and bounded limits.
Highlights
With the growing demand for power day by day, the cost incurred in generating power, in fossil fuel plants, is very high
The proposed quasi-oppositional searchbased political optimizer (QOPO) technique has been executed with a population size of 64 (the number of parties (8) multiplied by the number of constituencies (8)) and a lambda value of 0.2
It has been found that the cost of saving fuel cost has been increased from 0.01 USD/h to 21.77 USD/h as system size increases from three units to forty units, respectively
Summary
With the growing demand for power day by day, the cost incurred in generating power, in fossil fuel plants, is very high. The objective of the economic load dispatch problem (ELDP) is to schedule the committed power generating units output to meet the required load demand at minimum fuel cost and satisfy all the system and generating unit constraints. Even though the interior point approach is said to be more efficient computationally, in the case of non-linear objective functions, it may offer an infeasible solution due to improper selection of the step size [4]. These conventional techniques require incremental fuel cost curves, which are monotonously increasing/piece-wise linear in nature. To overcome the drawbacks of conventional techniques, various soft computing methods have been suggested in the literature
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
