Stochastic Optimal Power Flow Integrating With Renewable Energy Resources and V2G Uncertainty Considering Time-Varying Demand: Hybrid GTO-MRFO Algorithm

Abstract

Over the past ten years, global electricity networks have undergone rapid advancements, primarily driven by the widespread adoption of renewable energy resources (RES). While these sources have accompanied in a gathering of advantages, such as cost-effective operation of wind and solar photovoltaic (PV) installations and the mitigation of environmental hazards linked with traditional power sources, they have also introduced a host of challenges to planning and operation of power systems. Also, 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 standard optimal power flow (OPF) problem, which is naturally nonlinear, has become even more complex with the addition of renewable energy sources and plug-in electric vehicles along with traditional thermal power generators. This increased complexity comes from the unpredictable and intermittent nature of these new resources. Monte Carlo techniques are used to estimate the production costs of renewable energy sources and plug-in electric vehicles (PEVs) and analyze their viability. The uncertainty of the renewable sources and PEVs is modeled using Weibull, lognormal, and normal probability distribution functions (PDFs). The comprehensive OPF, incorporating renewable energy components and PEVs, is cast as a single objective problem encompassing various objectives, such as decreasing fuel costs, total emissions, voltage deviations, and real transmission losses. This research shares a common objective by introducing a novel hybrid metaheuristic optimization algorithm (MRGTO) to address the OPF challenge. Additionally, the study explores the impact of renewable energy resources and Vehicle-to-Grid (V2G) on the stochastic OPF problem. The MRGTO employs artificial gorilla troops optimizer (GTO) with manta ray foraging optimization (MRFO) algorithm to achieve the optimal results for the OPF problem with stochastic RES and V2G. The developed technique is expected to increase the solution accuracy through increasing the solution diversity through an optimization procedure. Initial assessments are executed on benchmark functions. After that, a combined model of wind and PV-integrated IEEE 30-bus system are executed by the proposed MRGTO algorithm and other well-known optimization algorithms. Additionally, the effectiveness of the proposed method is assessed using the IEEE 30-bus test system under different scenarios. The evaluations have shown the proposed MRGTO technique to be superior at attainment a best solution for the OPF problem considering stochastic wind and PV power and V2G. Moreover, the obtained solutions confirmed the MRGTO technique would be more effective in optimization with quicker convergence rate, and higher convergence precision. After testing, the effectiveness of the MRGTO algorithm has been found to be much robust than the conventional artificial gorilla troops optimizer, manta ray foraging optimization, and other well-known published heuristics, metaheuristics, and hybrid optimization techniques.INDEX TERMS Optimal power flow, plug-in electric vehicle, renewable energy sources, V2G, artificial gorilla troops optimizer, manta ray foraging optimization.

Keywords

• Optimal power flow
• plug-in electric vehicle
• renewable energy sources
• V2G
• artificial gorilla troops optimizer
• manta ray foraging optimization