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TRANSMISSION RESERVE DESIGN IN ELECTRICITY MARKETS CONSIDERING RAMPING CONSTRAINTS AND LOAD UNCERTAINTY
Title
TRANSMISSION RESERVE DESIGN IN ELECTRICITY MARKETS CONSIDERING RAMPING CONSTRAINTS AND LOAD UNCERTAINTY
Creator
Xiao, Xuli
Date
2016, 2016-12
Description
With increasing penetration of renewable energy, uncertainty challenges ISOs to keep power balance in real-time. As ramping issues draw public...
Show moreWith increasing penetration of renewable energy, uncertainty challenges ISOs to keep power balance in real-time. As ramping issues draw public attention, many ISOs have instituted flexible ramping products to ensure ramping reserve at generation side. However, not all the ramping reserves are deliverable when a transmission line is already congested. In the real-time market, if an uncertain load estimation is known at peak time t+10mins previously, SCUC/SCED is able to spare transmission reserve by changing the dispatch at time t with additional uncertain load constraints at t. To spare transmission reserve under uncertainty, this research proposes an uncertain load estimation to generate an estimated uncertain load and uncertainty constraints at t+10 in SCUC/SCED: with the help of a stochastic optimization model, uncertainties are quantified as a random actual load y and utilized in a modified stochastic model for undeliverable ramping reserve issues; once the optimal total system generation x is obtained, treated as an estimated uncertain load, uncertainty constraints are added at t+10mins in SCUC/SCED to obtain a secure dispatch at t. Therefore, transmission ramping reserve is ensured by a change in dispatch at t. Numerical results show that this design enhances the economy and scalability of power systems. In addition, scalability analysis proves it works for any scale of power systems with multiple local peak loads.
M.S. in Electrical Engineering, December 2016
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Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
Title
Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
Creator
Ahmed, Niyaz Afnan
Date
2024
Description
The performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the...
Show moreThe performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies.
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