Open AccessProbabilistic power forecast of renewable distributed generation for provision of control reserve using vine copulas
Author(s) -
Möws Stefan,
Scheffer Volker,
Becker Christian
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.1172
Subject(s) - vine copula , probabilistic logic , reliability (semiconductor) , copula (linguistics) , computer science , renewable energy , wind power , solar power , reliability engineering , econometrics , power (physics) , mathematics , engineering , physics , quantum mechanics , artificial intelligence , electrical engineering
This work focuses on calculating the amount of control reserve, which can be provided by a pool of renewable power plants on the next day. The power forecast of wind and solar power plants depends on the weather forecast, which always contains errors. A merger of individual plants at different locations is advantageous in order to reduce the overall forecast error. Still, the amount of control reserve needs to be determined with a high level of reliability. For the calculation, a probabilistic approach based on historical and current weather data is chosen. In order to model the spatial dependencies of the forecast errors between individual plants, R‐vine copulas are used. In the copula theory, R‐vine copulas provide high accuracy in modelling the dependency of stochastic variables. The methodology is validated and compared to three alternative approaches with a use case of 32 wind and solar plants. The calculated amount of control reserve provided and the achieved reliability proves to be superior to alternative approaches. Additionally, the required reliability level is varied to investigate the impact on the amount of control reserve, which can be offered with the pool.