Optimal Power Flow in Hybrid Wind-PV-V2G Systems With Dynamic Load Demand Using a Hybrid MRFO-AHA Algorithm

The energy produced from various sources in modern power systems needs to be optimally planned for scheduling and managing the power system under specific conditions. In recent times, the world's growing population, increasing energy needs, technological advancements, environmental concerns, and global climate change have led to a rise in the demand for electric energy. One significant solution to meeting this energy demand is the use of renewable energy sources (RESs) in power systems. However, integrating RESs has made the construction of power systems more complex. Moreover, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The optimal power flow (OPF) is a challenging problem when dealing with integrating renewable energy sources (RESs) and plug-in electric vehicles (PEVs) into modern power systems. In this paper, we address the OPF problem that arises from power systems with a high penetration of controllable renewable sources and PEVs. We simulate uncertain solar irradiance, wind speed, and PEV behavior using Weibull, lognormal, and normal probability distribution functions (PDFs) respectively. The proposed stochastic OPF problem, which incorporates controllable renewable sources and Vehicle-to-Grid (V2G) technology, is addressed using the hybrid metaheuristic optimization algorithm (AHMRFO). The AHMRFO hybrid algorithm combines the exploratory power of the manta ray foraging optimization (MRFO) with the exploiting strength of artificial hummingbird algorithm (AHA) resulting in a more balanced, robust, and efficient optimization tool. It can effectively handle complex problems, avoid premature convergence, and provide high-quality solutions across a wide range of optimization challenges. Initial evaluations for the proposed algorithm are executed on 7 Congress on Evolutionary Computation (CEC) test functions and its obtained results was compared with recent optimization algorithms such as original MRFO, AHA, supply demand-based optimization (SDO), hunter prey optimization (HPO), weighted mean of vectors (INFO), and northern goshawk optimization (NGO). Moreover, simulations carried out on different test systems demonstrate the effectiveness and efficiency of the proposed AHMRFO algorithm when compared to other algorithms such as the MRFO and AHA. The AHMRFO technique proves to be highly effective, yielding the lowest fitness values of 781.5836 $/h and 808.3611 $/h in the first two scenarios for the modified IEEE 30-bus system with RES and it has the minimum fitness values of 790.2783 $/h in their respective scenarios for the modified IEEE 30-bus system with stochastic Vehicle-to-grid and RES. These results highlight the precision and strength of AHMRFO technique in effectively addressing various instances of the OPF problem. Additionally, the Wilcoxon signed-rank test has been employed to demonstrate the superiority, effectiveness, and robustness of the proposed AHMRFO algorithm.