Pumped-Storage Scheduling Using Evolutionary Particle Swarm Optimization

• Published in: IEEE transactions on energy conversion Vol. 23; no. 1; pp. 294 - 301
• Main Author: Chen, Po-Hung
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Convergence; Costs; Decoding; Encoding; Evolutionary; Evolutionary particle swarm optimization (EPSO); Genetic mutations; hydrothermal coordination; Mathematical models; Mutations; Optimization; Particle swarm optimization; Production; pumped storage (P/S); Reservoirs; Scheduling; Scheduling algorithm; Spinning
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2007.914312

This paper presents new solution algorithms based on an evolutionary particle swarm optimization (EPSO) for solving the pumped-storage (P/S) scheduling problem. The proposed EPSO approach combines a basic particle swarm optimization (PSO) with binary encoding/decoding techniques as well as a mutation operation. The binary encoding/decoding techniques are adopted to model the discrete characteristics of a P/S plant. The mutation operation is applied to accelerate convergence and escape local optimums. The optimal generation schedules for both P/S and thermal units are concurrently obtained within the evolutionary process of a scoring function. Therefore, hydrothermal iteration is no longer needed. The proposed approach is applied with great success to an actual utility system consisting of four P/S units and 34 thermal units. Experimental results indicate the attractive properties of the EPSO approach in a practical application, namely, a highly optimal solution and robust convergence behavior.