A Novel Quasi-Oppositional Learning-Based Chaos-Assisted Sine Cosine Algorithm for Hybrid Energy Integrated Dynamic Economic Emission Dispatch

Abstract

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an significant role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms.

Keywords:

• Chaos
• Dynamic economic emission load dispatch
• Hybrid energy
• Quasi-oppositional learning strategy
• Sine cosine algorithm

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Koustav Dasgupta

Koustav Dasgupta obtained PhD degree in electrical engineering from the Indian Institute of Technology (Indian School of Mines), Dhanbad in 2022. He received his BTech degree in electrical engineering from Mallabhum Institute of Technology, Bankura, India in 2011 and MTech degree in electrical power system from Dr BC Roy Engineering College, Durgapur, India in 2013. His areas of interest are hydrothermal scheduling, renewable energy, economic load dispatch and soft computing techniques. Corresponding author. Email: [email protected]

Provas Kumar Roy

Provas Kumar Roy obtained PhD degree in electrical engineering from the National Institute of Technology, Durgapur in 2011. He received the master's degree in electrical machines, in 2001 from Jadavpur University. He finished his engineering studies in electrical engineering from Regional Engineering College (Presently Known as National Institute of Technology) Durgapur. Presently, he is working as a professor in Department of Electrical Engineering at Kalyani Government Engineering College, West Bengal, India. He was the recipient of the Outstanding Reviewer Award for IJEPES (Elsevier, 2018), EAAI (Elsevier, 2017), Renewable Energy Focus (Elsevier, 2018), ASEJ (Elsevier, 2017). For the second consecutive year, he is named in the world top 2% of Scientists List. He has published more than 190 research papers in National/International Journals and conference and more than 100 Journals published in reputed SCI and Scopus indexed Journals, and more than 10 book chapters and two books of international standard. Eight research scholars obtained their PhD degree under his guidance and 8 students are pursuing their PhD. His research interest includes economic load dispatch, optimal power flow, FACTS, automatic generation control, radial distribution network, power system stabilizer, image processing, machine learning, evolutionary techniques, etc. Email: [email protected]

Vivekananda Mukherjee

Vivekananda Mukherjee was born in 1970 at Raina, Burdwan, West Bengal, India. He received his graduation in electrical engineering and post-graduation in power systems from BE College, Shibpur, Howrah, India and BE College (Deemed University), Shibpur, Howrah, India, respectively. He received his PhD degree from NIT, Durgapur, India. Presently, he is an associate professor in the Department of Electrical Engineering, the Indian School of Mines, Dhanbad, Jharkhand, India. His research interest is application of soft computing intelligence to various fields of power systems. He is a Member of The Institution of Engineers (India). Email: [email protected]