Particle swarm optimization with crazy particles for nonconvex economic dispatch |
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| Authors: | Krishna Teerth Chaturvedi Manjaree Pandit Laxmi Srivastava |
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| Affiliation: | 1. Department of Electrical Engineering, UIT, Rajiv Gandhi University of Technology, Bhopal, India;2. Department of Electrical Engineering, M.I.T.S., Gwalior 474 005, M.P., India;1. Cryogenics and Fluids Branch, NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Code 552.0, Greenbelt, MD 20771, USA;2. Aerospace Engineering Department, United States Naval Academy, Annapolis, MD 20412, USA;1. School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, Scotland, UK;2. Health Protection Scotland, Meridian Court, Glasgow, Scotland, UK;3. Edinburgh Royal Infirmary, Edinburgh, Scotland, UK;4. Ninewells Hospital and Medical School, Dundee, Scotland, UK;5. Gartnavel General Hospital, Glasgow, Scotland, UK;6. Glasgow Royal Infirmary, Glasgow, Scotland, UK;7. Crosshouse Hospital, Kilmarnock, Scotland, UK;8. Aberdeen Royal Infirmary, Aberdeen, Scotland, UK;9. Monklands Hospital, Airdrie, Scotland, UK;10. Kirkcaldy Hospital, Kirkcaldy, UK;1. School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China;2. Information and Engineering College of Dalian University, Dalian 116622, China;3. School of Computer, Qingdao Technological University, Qingdao 266033, China;1. Smart Energy Systems Lab., Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran;2. Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran |
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| Abstract: | The paper presents an effective evolutionary method for economic power dispatch. The idea is to allocate power demand to the on-line power generators in such a manner that the cost of operation is minimized. Conventional methods assume quadratic or piecewise quadratic cost curves of power generators but modern generating units have non-linearities which make this assumption inaccurate. Evolutionary optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) are free from convexity assumptions and succeed in achieving near global solutions due to their excellent parallel search capability. But these methods usually tend to converge prematurely to a local minimum solution, particularly when the search space is irregular. To tackle this problem “crazy particles” are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation. The performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior. |
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