الفهرس 
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Abstract
The penetration level of Renewable Energy Resources (RESs) in the electrical power system is increasing as they provide a cleaner and a cheaper alternative as compared to conventional electricity generators. The main challenge to the spread of these RESs is that they are not dispatchable due to their stochastic nature. Hence, their coincidence with demand is not guaranteed, and this affects system reliability.
The main aim of this research is to apply two innovative metaheuristic optimization algorithms, the Artificial Gorilla Troops Optimization (AGTO) and the Giant Trevally Optimizer (GTO) to hybrid power systems including photovoltaic (PV) and wind energy (WE) sources to resolve the Probabilistic Optimal Power Flow (POPF) problem by integrating renewable energy sources (RESs), while simultaneously addressing the inherent uncertainties and costs associated with them.
The study primarily addresses two challenges: extracting the most accurate output power from PV and wind energy while navigating the uncertainties of RESs, and incorporating the costs of RESs, including direct, reserve, and penalty costs, into the objective cost function. The first challenge is managed by applying Monte Carlo Simulation (MCS), supplemented by the K-Means Clustering strategy and the Elbow technique. The second challenge is managed by applying the concept of probability.
The proposed algorithms’ effectiveness is validated through their application to classical Optimum Power Flow (OPF) problems (with no RES) for IEEE 30-bus and 118-bus systems, and their results are compared with those from established algorithms. Upon confirmation of their efficacy, the study explores various scenarios of standalone and combined PV and WE sources, as well as fixed and variable loads.
Two main scenarios are explored, both incorporating the inherent uncertainties of RESs: First, integrating RESs into the system without considering their costs and solving the Probabilistic Optimal Power Flow (POPF) problem using the AGTO algorithm. Second, integrating RESs while factoring in their costs and solving the POPF using the GTO algorithm. Findings indicate that although integrating RESs without considering their costs reduces total operational cost, a more accurate reflection of system dynamics is achieved by considering the costs of RESs. Even though the inclusion of these costs slightly increases the total generation cost, it remains significantly lower than scenarios disregarding these costs.
In summary, the research emphasizes the importance of accounting for RES costs, offering a more accurate representation of real.world system dynamics and promoting informed decision.making processes.