access icon free Two-stage glowworm swarm optimisation for economical operation of hydropower station

© The Institution of Engineering and Technology
Received 12/08/2017, Accepted 25/03/2018, Revised 22/02/2018, Published 05/04/2018

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.

Inspec keywords: load distribution; hydroelectric power stations; power generation economics; optimisation; numerical analysis

Other keywords: genetic algorithm; economic load distribution; improved dynamic patching mechanism; real-coded glowworm swarm optimisation algorithm; ELD; RCGSO algorithm; particle swarm optimisation; numerical simulation; economical operation; two-stage glowworm swarm optimisation algorithm; variable-step mechanism; hydropower station; luciferin transfer mechanism; two-stage GSO algorithm

Subjects: Hydroelectric power stations and plants; Optimisation techniques; Other numerical methods; Power system management, operation and economics

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