Open AccessTwo‐stage glowworm swarm optimisation for economical operation of hydropower station
Author(s) -
Wang Xiaoyu,
Yang Kan,
Zhou Xianghua
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2017.0466
Subject(s) - particle swarm optimization , mathematical optimization , genetic algorithm , hydropower , computer science , stage (stratigraphy) , mathematics , engineering , paleontology , electrical engineering , biology
This study proposes a two‐stage glowworm swarm optimisation (GSO) algorithm for the economical operation of the inner plant of a hydropower station. Binary GSO and real‐coded GSO (RCGSO) algorithms are applied with different types of encodings to solve the unit commitment sub‐problem and the economic load distribution (ELD) sub‐problem, respectively. Moreover, an improved dynamic patching mechanism is developed to avoid invalid calculations and enrich the diversity of the solutions. A luciferin transfer mechanism helps the algorithm escape the local optimum and a local research mechanism enhances the diversity of the solution space by selecting from among the derived solutions. The RCGSO algorithm uses a variable‐step mechanism to avoid missing the optimal solution. In comparison with the genetic algorithm and particle swarm optimisation, the RCGSO is significantly robust and provides better solutions to ELD sub‐problems. Numerical simulations exhibited the superiority of the two‐stage GSO algorithm in terms of stably and quickly solving the economical operation problem of hydropower stations.