Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Abstract

In this study, the Giant Trevally Optimizer (GTO) is employed to solve the probabilistic optimum power flow (P-OPF) issue, considering Renewable Energy Source (RES) uncertainties, achieving notable cost reduction. The objective function is established to minimize the overall generation cost, including the RES cost, which significantly surpassing existing solutions. The uncertain nature of the RES is represented through the employment of a Monte Carlo Simulation (MCS), strengthened by the K-means Clustering approach and the Elbow technique. Various cases are investigated, including various combinations of PV systems, WE systems, and both fixed and fluctuating loads. The study demonstrates that while considering the costs of solar, wind, or both might slightly increase the total generation cost, the cumulative generation cost remains significantly less than the scenario that does not consider the cost of RESs. The superior outcomes presented in this research underline the importance of considering RES costs, providing a more accurate representation of real-world system dynamics and enabling more effective decision making.

Novel Fractional Order and Stochastic Formulations for the Precise Prediction of Commercial Photovoltaic Curves

“…Finally, adding fractional operators and stochastic white noise results in more realistic I-V curves tangible to the actual datasheet curves. Future works will introduce the application of the proposed PV modeling in power systems [49][50][51][52][53].…”

An Active Distribution Grid Exceedance Testing and Risk-Planning Simulation Based on Carbon Capture and Multisource Data from the Power Internet of Things

“…The mode of operation of the gas boiler can be modeled as (15) where P g GB (t) is the natural gas power input into the gas boiler in time period t; P h GB (t) is the thermal energy output from the gas boiler in time period t; R h GB is the thermal energy conversion rate of the gas boiler; P g,max GB and P g,min GB are the upper and lower limits of the natural gas power input into the gas boiler, respectively; and ∆P g,max GB and ∆P g,min GB are the upper and lower limits of the creep of the gas boiler, respectively.…”

“…References [13,14] analyzed the accuracy of Monte Carlo methods based on specific scenarios. References [15,16] considered the correlation between wind farms that are close together when analyzing the probabilistic power flow operation characteristics of power systems. Reference [17] illustrated that the semi-invariant method can be used to effectively resolve the probability distribution of different state quantities.…”

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

“…Numerical results showed that BPNN outperformed conventional algorithms in terms of speed. Apart from ANNs, there are various ML algorithms indirectly used for solving the OPF problem, such as support vector machine (SVM) [34,35], random forest [36,37], K-means [38,39], and others.…”