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Zigzag search for multi-objective optimization considering generation cost and emission

Author

Listed:
  • Zhang, Qiwei
  • Li, Fangxing
  • Wang, Honggang
  • Xue, Yaosuo

Abstract

The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results. In this paper, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. A set of non-dominant solutions can be obtained to form the Pareto front. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system. The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). This demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and efficiency.

Suggested Citation

  • Zhang, Qiwei & Li, Fangxing & Wang, Honggang & Xue, Yaosuo, 2019. "Zigzag search for multi-objective optimization considering generation cost and emission," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315016
    DOI: 10.1016/j.apenergy.2019.113814
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    Cited by:

    1. Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).

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