Heuristic Optimization Techniques for Determining Optimal Reserve Structure of Power Generating Systems

Yi Ding (), Lalit Goel (), Peng Wang (), Yuanzhang Sun (), Poh Chiang Loh () and Qiuwei Wu ()
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Yi Ding: Technical University of Denmark
Lalit Goel: Nanyang Technological University
Peng Wang: Nanyang Technological University
Yuanzhang Sun: Wuhan University
Poh Chiang Loh: Nanyang Technological University
Qiuwei Wu: Technical University of Denmark

Chapter Chapter 10 in Recent Advances in System Reliability, 2012, pp 145-154 from Springer

Abstract: Abstract Electric power generating systems are typical examples of multi-state systems (MSS). Sufficient reserve is critically important for maintaining generating system reliabilities. The reliability of a system can be increased by increasing the reserve capacity, noting that at the same time the reserve cost of the system will also increase. The reserve structure of a MSS should be determined based on striking a balance between the required reliability and the reserve cost. The objective of reserve management for a MSS is to schedule the reserve at the minimum system reserve cost while maintaining the required level of supply reliability to its customers. In previous research, Genetic Algorithm (GA) has been used to solve most reliability optimization problems. However, the GA is not very computationally efficient in some cases. In this chapter a new heuristic optimization technique—the particle swarm optimization has been used to determine the optimal reserve structure for power generating systems, which can greatly improve the computational efficiency. The computational efficiency and accuracy of the proposed method have been compared with those of the GA technique in the illustrative example.

Keywords: Genetic Algorithm; Particle Swarm Optimization; Reserve Capacity; Power Generate System; Particle Swarm Optimization Method (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-4471-2207-4_10

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DOI: 10.1007/978-1-4471-2207-4_10

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