Developing a Marine Predator Algorithm for Optimal Power Flow Analysis considering Uncertainty of Renewable Energy Sources

Abstract

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate.

Genetically Optimized Pitch Angle Controller of a Wind Turbine with Fuzzy Logic Design Approach

“…A number of metaheuristic optimization techniques could be applied as alternatives to the conventional GA approach employed in this paper. Examples of these approaches are MPA (Marine Predator Algorithm) [52], PSO (Particle Swarm Optimization) [53], GSA (Gravitational Search Algorithm) [54], CS (Cuckoo Search) [55], FA (Firefly Algorithm) [56], CMA-ES (Covariance Matrix Adaptation Evolution Strategy), BO (Bonobo Optimization), BA (Bat Algorithm), BSO (Brain Storming Optimization), and TLBO (Teaching Learning Based Optimization). These approaches, as standard or with modifications, are capable of introducing online (between optimization iterations) optimization parameter updates.…”

“…Ref. [52] presents a MPA implementation on power flow optimization in the field of power generation. The alternative optimization technique and GA with online optimization parameter updates [59] do have the potential of outperforming the conventional GA (GA without parameter updates).…”

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

“…At buses 5 and 11, the two thermal plants have been replaced by two wind power plants, and the thermal plant at bus 13 has also been replaced by a solar PV plant 33 . The IEEE 57-bus system is also reformed by changing the thermal plants at buses 2 and 6 with two wind plants and changing the thermal plant at bus 9 with solar PV plant 52 . The data of the wind and solar plants of the IEEE 30-bus system and the IEEE 57-bus system are provided in Supplementary Material Tables 1 A and 4 A, respectively.…”