Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework

•Wind and solar models have been added to the thermal model and price penalty unifies conflicting goals.•To solve the dynamic multivariable constrained optimization problem, forward dynamic approach has been implemented.•Spinning reserves control Wind and Solar’s unpredictability. Unit commitment re...

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Vydáno v:Applied energy Ročník 326; s. 120031
Hlavní autoři: Kansal, Veenus, Dhillon, J.S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.11.2022
Témata:
algorithms
Artificial hummingbird algorithm
coal
Coordinated wind-solar-thermal generation
energy
Exploitation
Exploration
hummingbirds
prices
renewable energy sources
Simplex search strategy
statistical analysis
system optimization
wind
Exploration
Artificial hummingbird algorithm
Coordinated wind-solar-thermal generation
Exploitation
Simplex search strategy
ISSN:0306-2619, 1872-9118
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Shrnutí:•Wind and solar models have been added to the thermal model and price penalty unifies conflicting goals.•To solve the dynamic multivariable constrained optimization problem, forward dynamic approach has been implemented.•Spinning reserves control Wind and Solar’s unpredictability. Unit commitment restricts Wind and Solar production.•A novel optimization technique named “ameliorated artificial hummingbird algorithm” has been proposed for scheduling problem.•AAHA employs simplex search, a local search approach, to optimize exploitation and reduce the number of parameters. This paper proposed an optimization technique, namely ameliorated artificial hummingbird algorithm (AAHA), that blends artificial hummingbird algorithm (AHA) with simplex search strategy (SSS) to solve the coordinated wind-solar-thermal generation scheduling problem. The AAHA simulates the foraging behaviour of hummingbirds for food, including guided, territorial, and migration foraging. Guided foraging helps in the higher exploration in the initial stages, and territorial foraging performs the exploitation in its neighbourhood. Migration foraging explores the search space. The SSS enhances the weak territorial and migration foraging of AHA by improving the exploitation mechanism. The proposed method is simple and has less dependency on parameters to adjust. The solar and wind units are committed to ascertaining their share for uninterrupted supply. The price penalty method is applied to unify the emission of gaseous pollutants due to thermal generation with operating costs. To reduce the use of coal, renewable energy sources have been considered in this problem which results in reducing the pollutants’ emissions and saving in fuel costs. To solve the dynamic multivariable constrained optimization problem, the forward approach has been implemented. The performance of the proposed algorithm is tested on different electric test systems, and a statistical test justifies the results.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2022.120031