Day-ahead Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Harris Hawk Optimization

The increasing integration of renewable energy sources (RESs), such as wind and solar power, contributes to the escalating unpredictability of electrical power systems. Ac- knowledging and accommodating the stochastic nature of these sources is paramount for accurate conclusions in power system analyses. Given the inadequacy of probability density functions in addressing uncertainty in Day-Ahead Optimal Power Flow (DA-OPF), this study employs a scenario-generating method via Monte Carlo simulations. Furthermore, it applies the Harris Hawks Optimization (HHO) technique to optimize the day-ahead operation of a power system integrating stochastic wind and solar energy sources, focusing on minimizing operational costs. The analysis encompasses both standard and modified IEEE 30-bus configurations. The findings illustrate the efficacy and efficiency of the proposed algorithm in tackling the intricate challenge posed by RESs uncertainty. Through the amalgamation of scenario generation techniques and optimization algorithms, this study presents a viable approach to manage the inherent complexity of power networks incorporating stochastic RESs.