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.

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Monitoring Electrical Load Demand (ELD) is very crucial for plants and manufacturers to observe the electrical consumption and to set the rate of a primary source, the source limit, for supplying electricity to their own electrical system. This study presents Calinski-Harabasz criterion method to find out K value in order to divide ELD of Bangkhen water treatment plant (BKP), the largest water treatment plant in Thailand and SouthEast Asia, as a case study into K groups. ELD of BKP recorded in twelve months is shown and analyzed by this method. Three seasons are done, too. Then, K-means clustering algorithm is applied to find the centroid value, the cluster center, of K group. The maximum cluster center is used to be the source limit to supply electricity to the plant. Moreover, this study evaluates reliability indices to find out the security of the plant from using the source limit. They are shown and discussed, when the peak load demand occurs.