Modified Opposition-Based Particle Swarm Optimization for Combined Economic and Emission Dispatch Problem

Abstract

Modification of the particle swarm optimization (PSO) method is proposed with an opposition-based learning strategy to find the optimal solution to electrical power dispatch problems. The objective of the proposed algorithm is to address the combined economic and emissions dispatch problem (CEED) of thermal power plants. This problem includes constraints such as the valve point effect, prohibited zones of operation, and ramp rate limits. In order to assess its performance, the proposed algorithm is first evaluated using a set of benchmark functions. Later, three thermal generating systems having 6, 10, and 40 units respectively are regarded as the test systems to validate the proposed method. The proposed method is tested, and a comparison of the results are made with popular optimization techniques reported in the literature such as PDE, MODE, NSGA II, and MOSSA. Promising results have been obtained with opposition-based PSO in comparison with their current equivalents. A comparison was made between the fuel cost, emissions, and CPU time of the proposed method with the two other PSO variants: inertia factor PSO (IFPSO) and constriction factor PSO (CFPSO). The results showed a decline in the overall cost by approximately 3.73% and a decrease in CPU time by as much as 2.6 s. Furthermore, the obtained predictions consistently exhibit a high level of accuracy, typically approaching 100%.

Keywords:

• Combined economic and emission dispatch
• opposition-based learning
• penalty factor method
• particle swarm optimization
• valve point loading

DATA AVAILABILITY STATEMENT

The data will be available on request.

DISCLOSURE STATEMENT

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

Additional information

Funding

The authors did not receive support from any organization for the submitted work.

Notes on contributors

Swathy Muraleedharan

Swathy Muraleedharan, completed her masters degree from Anna University, India in Power electronics and drives. Her current research interests include Optimization techniques, Economic load management, Renewable energy and Machine learning techniques in load management. She is a research scholar at Cochin University of Science and Technology.

Chembakathuparambil Ayyappan Babu

Chembakathuparambil Ayyappan Babu, obtained doctoral degree from National Institute of Technology Calicut, India in Power and energy system and is working as the Professor Emeritus, Cochin University of Science and Technology. Research interests include - peak demand management, renewable energy systems, power electronics for RE power integration, electrical safety etc. He has 63 publications and 472 citations.

Ajith Kumar Sasidharanpillai

Ajith Kumar Sasidharanpillai, currently serves as Assistant Professor in the Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Campus. He obtained his Ph.D. from the Department of Aerospace Engineering, Indian Institute of Technology Chennai, India. He has two Postdoc fellowships from Nanyang Technological University (NTU), Singapore and Indian Institute of Technology Chennai. His research interests spread in vast areas; aerodynamics, Computational Fluid Dynamics (CFD), Optimization techniques, Neural Networks, Renewable Energy, Magneto hydro dynamics and Thermal convection problems. He has 33 publications and 64 citations.