Formulation and application of quantum-inspired tidal firefly technique for multiple-objective mixed cost-effective emission dispatch

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

“…Table 10 provides the outcomes for fuel cost, SO 2 emissions, NO x emissions, CO 2 emissions, and total cost. The total cost solution achieved by the SDO algorithm is optimal compared to recent methods, including LR [42], PSO [43], SA [44], QBA [38], MBO [45], SCA [40], GOA [46], QITFA [47], and CAEO4 [48], as well as the four comparative algorithms, GWO, MFO, TSO, and WOA. The table displays that the SDO algorithm produced the lowest total cost solution for all system demands.…”

“…Its ability to efficiently explore and exploit the search space, avoid local optima, and converge at a fast rate makes it a powerful tool for solving a wide range of optimization problems in various fields. The main contributions of this paper could be summarized as: Proposing a new optimizer called SDO for solving economic emission dispatch problems of power systems considering the price penalty factor and variable load demand levels. Demonstrating the effectiveness of SDO in three different scenarios involving three, five, and six units, respectively, where it outperformed many existing algorithms, including GWO, MFO, TSO, and WOA, as well as other established algorithms such asGA [37], PSO [37], quantum‐inspired PSO (QPSO) [38], the firefly algorithm (FA) [39], sine cosine algorithm (SCA) [40], SA [41], Lagrange's method (LR) [42], PSO [43], SA [44], the quantum‐behaved bat algorithm (QBA) [29], modified biogeography‐based optimization (MBO) [45], the grasshopper optimization algorithm (GOA) [46], quantum‐inspired tidal FA (QITFA) [47], and the 4th chaotic artificial ecosystem‐based optimization (CAEO4) [48]. Providing extensive analysis and comparison of the outcomes obtained from SDO with various established algorithms, showing that SDO consistently delivers better results in terms of both accuracy and efficiency. Implying significant implications for power plant management, as the SDO technique has the potential to optimize power plant management and improve energy efficiency, ultimately leading to resource savings and cost reductions. …”