Solving the optimal power flow problems using the superiority of feasible solutions-moth flame optimizer - Alam, Mohammad Khurshed (2024)
Optimal Power Flow (OPF) represents a formidable optimization challenge in electrical power systems, demanding continual reassessment of novel techniques to effectively address non-convex optimization. The complexity of this task is significantly heightened by the integration of unpredictable and intermittent renewable energy sources, such as wind and solar power, into the electrical grid. This necessitates advanced optimization approaches that can handle the added variability and constraints. Thus, the researchers trying to solve the OPF using metaheuristic approaches. Numerous algorithms have been put forth in the literature to address the aforementioned OPF challenges. Nevertheless, the approach often involves the use of penalty functions to handle constraints, which is known to be tedious and inefficient. By leveraging innovative methods like the Superiority of Feasible solutions (SF) integrated with Moth Flame Optimization (MFO), it is possible to enhance the efficiency and reliability of OPF solutions. These approaches not only address traditional optimization objectives but also adapt to the dynamic nature of modern power systems, ensuring robust and efficient grid management. The integration of Flexible AC Transmission System (FACTS) devices is increasingly prevalent in modern power systems, offering effective solutions to manage escalating demand and alleviate network congestion. The main goal of this study is to use a cuttingedge version of recent metaheuristic algorithm, namely Moth-Flame Optimizer (MFO) algorithm for solving the mentioned OPF problems. The integration of the Superiority of Feasible solutions (SF) with MFO, termed SF-MFO, addresses constraints in the OPF problem, providing an alternative to the penalty function approach. This study includes the integration of wind and solar power and evaluates five objectives: minimizing energy production cost, transmission losses, voltage deviations, emissions, and combined cost and emissions. Simulation results on IEEE-30, 57, and 118 bus systems show that SFMFO achieves energy generation costs of $894.3432, $23,466.41, and $137,094.80 per hour, respectively, representing a reduction of 1.34% to 1.65% compared to other algorithms. Additionally, testing on a modified IEEE 30-bus system for FACTS device allocation demonstrated the algorithm's effectiveness. Therefore, SF-MFO is proposed as a superior alternative for various OPF problems, including future multi-objective challenges.