Multi-objective Artificial Physical Optimization Algorithm for Daily Economic Environmental Dispatch of Hydrothermal Systems

Abstract

This article formulates the daily economic/environmental hydrothermal scheduling problem as a multi-objective optimization problem. By introducing non-dominated sorting and crowding distance, the multi-objective artificial physical optimization algorithm is proposed to solve the daily economic/environmental hydrothermal scheduling problem. To enhance the performance of the proposed algorithm, new velocity update equation, which takes advantage of the individual memory and population information, is utilized. To overcome the drawback of premature convergence, a chaotic mutation is adopted in the multi-objective artificial physical optimization algorithm. Especially for handling the equality constraints of daily economic/environmental hydrothermal scheduling, novel heuristic strategies are developed to repair the infeasible solutions. To demonstrate the effectiveness of the multi-objective artificial physical optimization algorithm for solving daily economic/environmental hydrothermal scheduling, the proposed method is implemented on a hydrothermal system and the numerical results are compared with several optimization approaches. It demonstrates that the proposed multi-objective artificial physical optimization algorithm is competent as an alternative for the daily economic/environmental hydrothermal scheduling problem.

Keywords:

• daily hydrothermal scheduling
• multi-objective optimization
• artificial physical optimization
• chaotic mutation
• power systems
• economic dispatch
• fuel cost
• emission
• handling constraints
• heuristic strategies

Additional information

Notes on contributors

Xiaohui Yuan

Xiaohui Yuan received his Ph.D. in hydropower engineering from Huazhong University of Science and Technology, China, in 2002. He is currently a professor of the School of Hydropower and Information Engineering at Huazhong University of Science and Technology. His research interests include evolutionary computation, complex system modeling and simulation, system optimization, and applications in power systems.

Hao Tian

Hao Tian received his B.E. in the Department of Software Engineering from Jilin University, China, in 2010. He is currently a Ph.D. candidate of the School of Hydropower and Information Engineering at Huazhong University of Science and Technology. His research areas are the multi-objective evolutionary optimization method and its application in hydropower engineering.

Yanbin Yuan

Yanbin Yuan received his Ph.D. in geodetection and information technology from China University of Geosciences (Wuhan) in 2000. He is currently a professor of information systems at Wuhan University of Technology, China. He is a member of International Association for Mathematical Geology (IAMG) and Geographical Society of China (GSC). His research interests include mineral resource prospecting and exploration, geographical information systems, hydrology, and water resources.

Xiaopan Zhang

Xiaopan Zhang received his Ph.D. in system engineering from Huazhong University of Science and Technology, China, in 2007. He is currently an associate professor of geographic information science at Wuhan University of Technology. His research interests include complex system modeling and simulation, system optimization, and geographic information system.