Disturbed optimal power flow with renewable source and static synchronous compensator

As the share of renewable energy sources increases in modern power systems, the inherent variability of these sources leads to more significant fluctuations in power load. This increased variability introduces additionalchallenges for the stability and reliability of the system. Therefore, to better model real-world power systems, this paper proposes the bus-level disturbed optimal power flow (D-OPF) problem, considering both renewable energy sources and Static Synchronous Compensators (STATCOMs). In addition, to address the uncertainties introduced by renewable energy sources and load fluctuations, this paper proposes an Enhanced Quadratic Interpolation Optimization (EQIO) algorithm. The EQIO algorithm integrates Tent chaotic mapping, Survival-of-the-Fittest selection, and dynamic opposition-based learning to improve convergence and solution accuracy under uncertain conditions. The effectiveness of the proposed EQIO algorithm is validated on the CEC2017 benchmark functions and further tested on the IEEE 30-bus and 118-bus systems under disturbed scenarios. Experimental results show that EQIO achieves Friedman Ranks of 1.1750 and 1.0733 for the 30-bus and 118-bus systems, respectively, and obtains the optimal solution in 90.08 % of all disturbed scenario tests. These outcomes demonstrate the superiority of EQIO over other algorithms in solving the D-OPF problem.