Power economic dispatching subject to DG uncertainty via bare-bones PSO

• Published in: Journal of the Chinese Institute of Engineers Vol. 41; no. 6; pp. 503 - 511
• Main Authors: Kang, Qi, Sheng, Wenjia, An, Jing, Han, Jingxiao
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 18.08.2018
• Subjects: bare-bones PSO; distributed generation; Economic dispatch; uncertainty
• ISSN: 0253-3839; 2158-7299
• DOI: 10.1080/02533839.2018.1498025

Distributed generation (DG) has gradually become significant in the power system owing to energy shortages and environmental pollution. However, the traditional structure of power systems is changed with the advent of DG, and the output power of DG has the characteristic of randomness due to relying on renewable energy, which will cause risks in the power system. This paper studies the issue of economic dispatch while not only considering the power system's controllable DG but also taking uncontrollable DG with single or multiple generator failures into account. Firstly, we built the random power output models of distributed generators including wind power, solar power, fuel cell and diesel. Considering random power output of DG, then we present dynamic energy dispatching strategies to keep voltage deviation, active power loss and fuel cost to a minimum. In addition, we presented three improved bare-bones particle swarm optimization (BBPSO+) algorithms to adopt the model presented in the paper. Finally, this paper evaluates the improved method's performance through the IEEE 118-bus system and also compares solution quality and convergence performance of the BBPSO+ with genetic algorithm (GA), PSO, random drift PSO (RDPSO) and BBPSO. The experimental result indicates that in case of considering both controllable and uncontrollable DG, the BBPSO+ algorithm solves problems more effectively and is promising to be applied in a power system.