Abstract
In order to cope with the increasing energy demand, microgrids emerged as a potential solution which allows the designer a lot of flexibility. The optimization of the controller parameters of a microgrid ensures a stable and environment friendly operation. Non-dominated Sorting Sine Cosine Algorithm (NSSCA) is a hybrid of Sine Cosine Algorithm and Non-dominated Sorting technique. This algorithm is applied to optimize the control parameters of a microgrid which incorporates both static and dynamic load. The obtained results are compared with the results of the established Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in order to justify the proposal of the NSSCA. The average time needed to converge in NSSCA is 7.617s whereas NSGA-II requires an average of 10.660s. Moreover, the required number of iterations for NSSCA is 2 which is significantly less in comparison to the 12 iterations in NSGA-II.
Highlights
Renewable energy sources are often integrated in microgrids because they are environmentally friendly and are considered an answer to the fossil fuel scarcity
The present study proposes a new Multiple Objective Optimization (MOO) where Sine Cosine Algorithm (SCA) is combined with Nondominated Sorting technique to form the hybridized Nondominated Sorting Sine Cosine Algorithm (NSSCA)
The non-dominated sorting technique was merged with the Sine Cosine Algorithm (SCA) in order to develop a multi-objective optimization algorithm named NSSCA
Summary
Renewable energy sources are often integrated in microgrids because they are environmentally friendly and are considered an answer to the fossil fuel scarcity. Of a microgrid with a view to enhance its stability, efficiency, and cost effectiveness [3, 4] In this aspect, various optimization algorithms are often adopted because they can often identify the global optimum system and have a better convergence probability [5, 6]. Authors in [10] used the MOO NSGA-II in optimizing the controller parameters but lacked comparison analysis between existing works. In this regard, the present study proposes a new MOO where Sine Cosine Algorithm (SCA) is combined with Nondominated Sorting technique to form the hybridized Nondominated Sorting Sine Cosine Algorithm (NSSCA). In order to establish the efficacy of the designed NSSCA, the results are compared with the ones of the established Non-dominated Sorting Genetic Algorithm (NSGA-II) [12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callMore From: Engineering, Technology & Applied Science Research
Disclaimer: 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.
