期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 12, 期 1, 页码 533-542出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2020.3009615
关键词
Forecasting; Distributed databases; Data privacy; Electricity supply industry; Power measurement; Predictive models; Wind forecasting; Collaborative forecasting; data marketplace; data pricing; renewable energy; electricity market
资金
- Smart4RES project (European Union's Horizon 2020) [864337]
- Portuguese funding agency, FCT (Fundacao para a Ciencia e a Tecnologia) [PD/BD/128189/2016]
- POCH (Operational Program of Human Capital)
- EU
- Fundação para a Ciência e a Tecnologia [PD/BD/128189/2016] Funding Source: FCT
The study proposes a data marketplace to incentivize collaboration between different data owners for sharing renewable energy forecast data, which is shown to increase revenue and reduce system imbalance costs.
Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve renewable energy sources (RES) forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.
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