Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search

doi:10.1007/s42235-024-00591-7

2024-11-16

PRE

Sharaz Ali

Mohammed Azmi Al‐Betar

Mohamed Nasor

Mohammed A. Awadallah *

DOI：10.1007/s42235-024-00591-7

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摘要

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The Unit Commitment Problem (UCP) corresponds to the planning of power generation schedules. The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand, which also minimizes the total operating cost while adhering to different constraints such as power generation limits, unit startup, and shutdown times. In this paper, four different binary variants of the Bald Eagle Search (BES) algorithm, were introduced, which used two variants using S-shape, U-shape, and V-shape transfer functions. In addition, the best-performing variant (using an S-shape transfer function) was selected and improved further by incorporating two binary operators: swap-window and window-mutation. This variation is labeled Improved Binary Bald Eagle Search (IBBESS2). All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-, 10-, 20-, 40-, 60-, 80-, and 100-unit. For comparative evaluation, 34 comparative methods from existing literature were compared, in which IBBESS2 achieved competitive scores against other optimization techniques. In other words, the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-, 40-, 60-, 80-, and 100-unit problems. Furthermore, IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms, especially in large-scale unit commitment problems. The Friedman statistical test further validates the results, where the proposed IBBESS2 is ranked the best. In conclusion, the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

Keyword:

Swarm intelligence

Bald eagle search

Unit commitment problem

Optimization