Modeling of transmission capacity in reserve market considering the penetration of renewable resources

Aazami, Rahmat; Iranmehr, Hamid; Tavoosi, Jafar; Mohammadzadeh, Ardashir; Sabzalian, Mohammad Hosein; Javadi, Mohammad Sadegh

doi:10.1016/j.ijepes.2022.108708

Cited by 1 publication

(1 citation statement)

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“…The system operator is assisted in determining operating reserve requirements for daily and intraday markets by the novel reserve management tool proposed in the aforementioned study [19]. A planning model for the utilization of transmission line emergency capacity in the energy system reserve market with renewable energy sources was described by Rahmat et al To achieve this, they first discussed the impacts of running a transmission line at a power higher than its nominal power, quantified the life reductions brought on by running transmission lines in these circumstances, and then calculated the cost based on the rate of life decrease [20]. The challenge of optimal energy and reserve planning in a power system dominated by renewable energy under uncertain and intermittent energy power output correlations was studied by Zhang et al To minimize the total cost, they developed a distributionally robust chance constraint model [21].…”

Section: Introductionmentioning

confidence: 99%

Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources

Atiç,

Izgi

2024

Sustainability

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Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is developed with a new method that takes into consideration the power generated from IRESs and the consumed power (CP) estimation errors. In the proposed method, CP, generated wind power (GWP), and generated solar power (GSP) predictions made with machine learning methods are included in the EPNS formulation. The most accurate prediction results were obtained with the Multi Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms used for prediction, and these results were compared. Using different forecasting methods, the relation between forecast accuracy, reserve requirement, and total cost was examined. Reliability, smart reserve planning (SRP), and total cost analysis for power systems were carried out with the CNN algorithm, which provides the most successful prediction result among the prediction algorithms used. The effect of increasing the limit EPNS value allowed by the power system operator, that is, reducing the system reliability, on the reserve requirement and total cost has been revealed. This study provides a useful proposal for the integration of IRESs, such as solar and wind power plants, into power systems.

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Section: Introductionmentioning

confidence: 99%