A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems
This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Therm...
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| Vydané v: | Decision analytics journal Ročník 8; s. 100299 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.09.2023
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| ISSN: | 2772-6622, 2772-6622 |
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| Abstract | This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.
•Propose a novel approach to optimize power flow in hybrid energy systems.•Use Newton’s Law of Cooling to model energy transfer in the system.•Apply new non-dominated sorting and crowding distance strategies.•Perform comprehensive tests on a modified IEEE 30-Bus system.•Show improved solutions and strong convergence. |
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| AbstractList | This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.
•Propose a novel approach to optimize power flow in hybrid energy systems.•Use Newton’s Law of Cooling to model energy transfer in the system.•Apply new non-dominated sorting and crowding distance strategies.•Perform comprehensive tests on a modified IEEE 30-Bus system.•Show improved solutions and strong convergence. |
| ArticleNumber | 100299 |
| Author | Chakraborty, Shankar Jangir, Pradeep Kalita, Kanak Pandya, Sundaram Agrawal, Sunilkumar |
| Author_xml | – sequence: 1 givenname: Sunilkumar surname: Agrawal fullname: Agrawal, Sunilkumar email: sunil11187@gmail.com organization: Government Engineering College, Gandhinagar, Gujarat, India – sequence: 2 givenname: Sundaram surname: Pandya fullname: Pandya, Sundaram email: sundarampandya@gmail.com organization: Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, India – sequence: 3 givenname: Pradeep surname: Jangir fullname: Jangir, Pradeep email: pkjmtech@gmail.com organization: Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Sikar, Rajasthan, India – sequence: 4 givenname: Kanak orcidid: 0000-0001-9289-9495 surname: Kalita fullname: Kalita, Kanak email: drkanakkalita@veltech.edu.in organization: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India – sequence: 5 givenname: Shankar surname: Chakraborty fullname: Chakraborty, Shankar email: s_chakraborty00@yahoo.in organization: Department of Production Engineering, Jadavpur University, Kolkata, India |
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| Keywords | Pareto front Multi-objective optimization Optimal power flow Metaheuristics Physics-based algorithm Renewable energy resources |
| Language | English |
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