Journal article
Distributed Reconciliation in Day-Ahead Wind Power Forecasting
University of Pisa1
Energy Analytics and Markets, Center for Electric Power and Energy, Centers, Technical University of Denmark2
Center for Electric Power and Energy, Centers, Technical University of Denmark3
Department of Electrical Engineering, Technical University of Denmark4
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark5
With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts for a set of wind farms is equal to the forecast made directly for the power generation of that portfolio.
This forecast reconciliation problem has attracted increased attention in the energy forecasting literature over the last few years. Here, we review the state of the art and its applicability to day-ahead forecasting of wind power generation, in the context of spatial reconciliation. After gathering some observations on the properties of the game-theoretical optimal projection reconciliation approach, we propose to readily rethink it in a distributed setup by using the Alternating Direction Method of Multipliers (ADMM).
Three case studies are considered for illustrating the interest and performance of the approach, based on simulated data, the National Renewable Energy Labaratory (NREL) Wind Toolkit dataset, and a dataset for a number of geographically distributed wind farms in Sardinia, Italy.
Language: | English |
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Publisher: | MDPI AG |
Year: | 2019 |
Pages: | 1112 |
ISSN: | 19961073 |
Types: | Journal article |
DOI: | 10.3390/en12061112 |
ORCIDs: | Pinson, Pierre |