A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems

Abstract

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.

Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

“…In this particular scenario, the influence of FACTS devices on decreasing power losses within the electrical transmission network is evidently more significant than in other instances. The power loss attained by CGO amounts to 1.761859 MW, which is lower than the values of 2.2278212 MW in RIME, 2.8777 MW in OOA, 1.9416 MW in SMA, 2.482 MW in SCA [21], 1.880 MW in IMO [21], 1.7898 MW in GWO [22], 1.9736 MW in FPA [22], and 2.0420 MW in SCA [22]. As illustrated in Table 12, all algorithms exhibit reduced power losses compared to previous cases.…”

“…Specifically, in the CGO algorithm, the total generation cost decreased to $807.0393/h, which is lower than the costs observed in the RIME algorithm ($808.5072/h), OOA algorithm ($817.0351/h), and SMA algorithm ($807.496018/h). Additionally, the CGO algorithm's cost was lower compared to other algorithms studied, such as MVO [21] ($808.030/h), ALO [21] ($809.449/h), SCA [21] ($818.654/h), CBA [22] ($810.5056/h), FPA [22] ($808.0864/h), and SCA [22] ($815.4325/h), as shown in Figure 12. This indicates that the CGO approach yielded a lower cost compared to the other algorithms.…”

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

“…Other popular multi-objective (MO) Algorithms include MO ant lion optimizer (MOALO) 43 , MO equilibrium optimizer (MOEO) 44 , MO slime mould algorithm (MOSMA) 45 , MO arithmetic optimization algorithm (MOAOA) 46 , non-dominated sorting ions motion algorithm (NSIMO) 47 , MO marine predator algorithm (MOMPA) 48 , multi-objective multi-verse optimization (MOMVO) 49 , non-dominated sorting grey wolf optimizer (NS-GWO) 50 , MO gradient-based optimizer (MOGBO) 51 , MO plasma generation optimizer (MOPGO) 52 , non-dominated sorting Harris hawks optimization (NSHHO) 53 , MO thermal exchange optimization (MOTEO) 54 , decomposition based multi-objective heat transfer search (MOHTS/D) 55 , Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D) 56 , MOGNDO Algorithm 57 , Non-dominated sorting moth flame optimizer (NSMFO) 58 , Non-dominated sorting whale optimization algorithm (NSWOA) 59 , Non-Dominated Sorting Dragonfly Algorithm (NSDA) 60 , a reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation 61 , a many-objective evolutionary algorithm based on hybrid dynamic decomposition 62 and use of two penalty values in multiobjective evolutionary algorithm based on decomposition 63 .…”

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

“…Other popular Multi-Objective (MO) Algorithms include MO ant lion optimizer (MOALO) [ 49 ], MO equilibrium optimizer (MOEO) [ 50 ], MO slime mould algorithm (MOSMA) [ 51 ], MO arithmetic optimization algorithm (MOAOA) [ 52 ], non-dominated sorting ions motion algorithm (NSIMO) [ 53 ], MO evolutionary algorithm based on decomposition (MOEA/D) [ 54 ], Non-dominated sorting genetic algorithm (NSGA-II) [ 55 ], multi-objective multi-verse optimization (MOMVO) [ 56 ], non-dominated sorting grey wolf optimizer (NS-GWO) [ 57 ], MO Gradient-Based Optimizer (MOGBO) [ 58 ], MO plasma generation optimizer (MOPGO) [ 59 ], non-dominated sorting Harris hawks optimization (NSHHO) [ 60 ], MO thermal exchange optimization (MOTEO) [ 61 ], decomposition based multi-objective heat transfer search (MOHTS/D) [ 62 ], Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D) [ 63 ], MOGNDO Algorithm [ 64 ], Non-dominated sorting moth flame optimizer (NSMFO) [ 65 ], Non-dominated sorting whale optimization algorithm (NSWOA) [ 66 ] and Non-Dominated Sorting Dragonfly Algorithm (NSDA) [ 67 ]. We aimed to gauge their capabilities in swiftly converging to the true Pareto optimal front and the distribution of the obtained non-dominated solutions.…”