Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

• Published in: Journal of Electrical Systems and Information Technology Vol. 7; no. 1; pp. 1 - 26
• Main Authors: Dey, Bishwajit, Bhattacharyya, Biplab, Raj, Saurav, Babu, Rohit
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 02.10.2020; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Alternative energy sources; Crow search algorithm; Demand side management; Distributed generation; Energy management; Energy resources; Energy storage; Engineering; Generators; Grey wolf optimizer; Linear programming; Literature reviews; Load; Mechanical Engineering; Microgrid; Optimization; Optimization techniques; Relaxation time; Renewable resources; Search algorithms; Sine–cosine algorithm; Statistical analysis; Trigonometric functions; Uncertainty; Unit commitment; Wind power; Uncertainty; Crow search algorithm; Microgrid; Grey wolf optimizer; Sine–cosine algorithm
• ISSN: 2314-7172; 2314-7172
• DOI: 10.1186/s43067-020-00023-6

Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.