Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints

The hybrid model of the power system infrastructure is an essential part of the sophisticated technology of the electrical network. Generally, for the Optimal Power Flow (OPF) problem, the power system with only thermal generators is considered. In traditional OPF problems, the fuel cost required to produce electrical energy is considered, and emissions are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as clean, diversity, and renewability. As a result, RESs are being integrated into the existing electrical grid at an increasing rate. The study in this paper proposes a techno-economic investigation into the single- and multi-objective OPF, coordinating with RESs, such as wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used to predict the required power. A recently reported equilibrium optimizer and its multi-objective version are considered for handling OPF problems. The superior performance of the equilibrium optimizer is further verified with the results of both single- and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the suggested algorithm can find better optimal solutions in a smaller number of generations (iterations) with faster convergence and well distributed optimal Pareto front for multi-objective problems. The results are verified by employing an IEEE-30 bus hybrid power network, and performance comparisons are made among well-established algorithms. Simulation findings show that the suggested algorithm can achieve a reasonable compromise solution for different objectives.

Automated summary

This study proposes a techno-economic investigation into single- and multi-objective optimal power flow problems, coordinating with renewable energy sources such as wind, PV, and small hydropower. Different probability density functions are used to predict the required power, and a recently reported equilibrium optimizer is considered for handling the problems. The results show that the suggested algorithm can find better optimal solutions with faster convergence and well distributed optimal Pareto front for multi-objective problems.