2499. Empirical Parameter Estimation for Power Plants using Open Data
Invited abstract in session MB-44: Inverse Design Modelling of Energy Systems, stream Energy Economics & Management.
Monday, 10:30-12:00Room: Newlyn 1.01
Authors (first author is the speaker)
| 1. | Jie Xu
|
| House of Energy Markets and Finance, University of Duisburg-Essen | |
| 2. | Philipp Castro
|
| House of Energy Markets and Finance, University Duisburg-Essen | |
| 3. | Maike Spilger
|
| Universität Duisburg-Essen | |
| 4. | Christoph Weber
|
| University Duisburg-Essen | |
| 5. | Jonathan Berrisch
|
| House of Energy Markets and Finance, University of Duisburg-Essen | |
| 6. | Florian Ziel
|
| House of Energy Markets and Finance, University of Duisburg-Essen |
Abstract
Dispatchable power plants offer the flexibility needed to accommodate intermittent renewable generation and variations in demand. Energy system models typically make use of generic engineering estimates of the key power plant parameters. In our research we make use of publicly available data, like hourly unit-level generation time series from the ENTSO-E Transparency Platform, to obtain empirically validated estimates for the parameters that describe the efficiency and flexibility of thermal power plants to respond to residual load variations.
The proposed method complements the generation data with market data, notably day-ahead prices, to identify the economic incentives and operational strategies of power plant operators and the underlying power plant parameters. We focus on the minimum stable operation limit as well as the efficiency at both this lower operation limit and the nameplate capacity. Both are key parameters in typical unit commitment and dispatch models for power plant operation. To identify the operational status, we make use of clustering techniques. We furthermore consider the complications arising from temperature-dependency of parameters, provision of ancillary services, CHP and retrofits. The empirical application focuses on gas-fired power plants in Germany and the Netherlands.
The developed methods can be extended to further parameters including start-up costs and ramping rates and may be embedded in a general inverse optimization approach.
Keywords
- Analytics and Data Science
- Electricity Markets
Status: accepted
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