Multi-objective economic operation of modern power system considering weather variability using adaptive cuckoo search algorithm

• Main Authors: K. V. Kumar Kavuturu, P. V. R. L. Narasimham
• Format: Article
• Language: English
• Published: SpringerOpen 2020-07-01
• Series: Journal of Electrical Systems and Information Technology
• Subjects: Adaptive cuckoo search algorithm; Economic schedule; Optimal unified power flow controller; Photovoltaic generation; Weather variability
• Online Access: http://link.springer.com/article/10.1186/s43067-020-00019-2

Abstract Currently, most of the power systems are being integrated with flexible AC transmission system devices and renewable energy sources for operating with enhanced security margins and balancing the increasing demand cost-effectively. On the other side, the trend of increasing global warming and extremely changing weather conditions is continuing across the world. Under this scenario, it is essential to realize their effect on various power system components and its economic operation. In this paper, the parameters namely resistance of the transmission line/transformer, load and solar photovoltaic generation are modeled considering ambient temperature effect. Later, economic schedule under changing weather conditions is proposed for attaining multi-objectives simultaneously like total operating cost of conventional energy, real power loss, average voltage collapse point indicator index and average voltage deviation index. Also, the dispatchable problems in the transmission system and various practical operating constraints are handled via optimally setting the parameters of optimal unified power flow controller. The optimization problem is solved using adaptive cuckoo search algorithm (ACSA), in which a dynamically increasing switching parameter in a power of three is adopted for adjusting the random walk between local optima and global optima. The superiority of the proposed ACSA in solving the multiobjective, nonlinear complex optimization problem over basic CSA and particle swarm optimization, chicken swarm optimization and flower pollination algorithm is presented by illustrating various case studies on standard IEEE 14, 30 and 118–bus test systems.