Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Abstract

This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

“…For example, a voltage balancing for DC systems was made in [22] by optimization for a two-phase bipolar DC power system considering power flow, power loss and voltage unbalancing indices. In addition, in [23], a binary optimization has been deployed to solve a unit commitment problem for the short-term operation of systems, including different numbers of units. Furthermore, in [24], a two-stage optimization strategy has been implemented for the placement and sizing of photovoltaic systems.…”

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

“…Kigsirisin and Miyauchi proposed an optimization method derived from the basic MFO called alternative binary MFO (BAMFO) [ 44 ]. They used it to solve unit commitment (UC) optimization problems.…”

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

“… Kigsirisin and Miyauchi (2021) proposed a solution to handle the challenges in unit commitment (UC) using a modified version of the MFO algorithm called alternative binary MFO. However, this approach suffered from a flaw due to a fixed flame technique that caused the algorithm to become trapped at a local optimum.…”