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Energies 2016, 9(12), 1040; doi:10.3390/en9121040

A Flexible Ramping Capacity Model for Generation Scheduling with High Levels of Wind Energy Penetration

1
Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Department of Statistics Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 10 October 2016 / Revised: 19 November 2016 / Accepted: 5 December 2016 / Published: 11 December 2016
(This article belongs to the Special Issue Wind Turbine 2017)
View Full-Text   |   Download PDF [2664 KB, uploaded 11 December 2016]   |  

Abstract

The penetration level of renewable generation has increased significantly in recent years, which has led to operational concerns associated with the system ramping capability. Here, we propose the flexible ramping capacity (FRC) model, which considers the practical ramping capability of generation resources as well as the uncertainty in net load. The FRC model also incorporates the demand curve of the ramping capacity, which represents the hourly economic value of the ramping capacity. The model is formulated mathematically using ramp constraints, which are incorporated into unit commitment (UC) and economic dispatch (ED) processes. Simulations are carried out using a 10-unit system to compare the FRC model with conventional methods. We show that the FRC method can improve reliability and reduce expected operating costs. The simulation results also show that, by using the FRC model, system reliability can be satisfied at high wind power generation levels while achieving economic efficiency. View Full-Text
Keywords: renewable generation; flexible ramping capacity; variability; uncertainty; demand curve renewable generation; flexible ramping capacity; variability; uncertainty; demand curve
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Kwon, H.; Park, J.-K.; Kim, D.; Yi, J.; Park, H. A Flexible Ramping Capacity Model for Generation Scheduling with High Levels of Wind Energy Penetration. Energies 2016, 9, 1040.

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