Komodo Mlipir Algorithm to Solve Generator Scheduling Problems

Aditya, Indra Ardhanayudha; Simaremare, Arionmaro Asi; Raharjo, Jangkung; Suyanto, Suyanto; Wijayanto, Inung

doi:10.1109/ice3is56585.2022.10010294

Cited by 1 publication

(2 citation statements)

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“…While the economic dispatch problem can be viewed as a linear programming challenge, finding the optimal solution is not a straightforward task due to the intricate nature of the variables involved. To address this problem, specific algorithms like Lambda Iteration Methods, Gradient Methods, or artificial methods such as Multi Dimension of Coarse to Fine Search 23) , Large to Small Area Technique 5) , Komodo Mlipir Algorithm 7) , Cuckoo Algorithm, Whale Optimization Algorithm 24) , Particle Swarm Optimization 25) , Cat Swarm Optimization 25) , Ant Colony Optimization 25) , and Artificial Bee Colony Algorithm 25) .…”

Section: Economic Dispatchmentioning

confidence: 99%

“…KMA divides the candidate solutions into three groups based on Komodo individuals: big males, females, and small males. The KMA algorithm begins with the movement of the big male, referred to as high-exploitation low exploration (HILE), followed by females attempting to find solutions by mating with the best big male (highest quality) or through parthenogenesis (exploration) 7) .…”

Section: Introductionmentioning

confidence: 99%

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Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

Rifki Rahman Nur Ikhsan,

Raharjo,

Rahmat

2024

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The economic dispatch process in power markets aims to find the optimal generation schedule based on cost-based bids from generators. The lambda iteration method is commonly used but faces computational challenges for large-scale systems. To address this, a novel approach called the vectorized lambda iteration method is proposed. This method utilizes vectorization techniques to reduce computation time while maintaining accuracy. By parallelizing the computation of schedules and Lagrange multipliers, it achieves faster convergence and enables real-time decision-making. The method improves efficiency, competitiveness, and adaptability in power markets, ensuring reliable operation. Software and hardware optimizations further enhance performance. The results demonstrate a significant reduction in computation time and the lowest cost across three test systems. The vectorized lambda iteration method offers an efficient solution for power system operation and decision-making, with the potential for optimization in practical applications. The proposed vectorized variant that we introduced is capable of reducing computation time by 99% compared to conventional methods, while also achieving a 2% cost reduction.

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