Open AccessDay‐ahead wind power ramp forecasting using an image‐based similarity search strategy
Author(s) -
Cao Bo,
Chang Liuchen,
Gong Xun,
Pijnenburg Petrus,
Levy Thomas,
Kilpatrick Ryan
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12595
Subject(s) - wind power , wind power forecasting , probabilistic forecasting , electric power system , wind speed , probabilistic logic , computer science , meteorology , reliability engineering , power (physics) , engineering , artificial intelligence , physics , quantum mechanics , electrical engineering
With the increase in penetration of wind generation on interconnected power systems, the importance of wind power ramp forecasting has continuously grown. Large power ramps caused by sudden weather changes raise more concerns due to their significant impact on the power system economics and stability. Correct wind power ramp forecasts can help the system operators and utility companies to tradeoff the risks when scheduling wind energy in the electricity market. In this paper, a day‐ahead wind power ramp forecasting algorithm is developed to provide probabilistic ramp forecasts for look‐ahead times up to 48 h using hourly wind speed forecasts from Environment Canada High Resolution Deterministic Prediction System (HRDPS). An image‐based similarity search strategy has been designed to build a direct link between the wind speed forecasts and the wind power ramp prediction, thus reducing the impact of the uncertainty from both the power production forecast model and the ramp identification process on the forecasting accuracy. A performance assessment and validation of the proposed ramp event forecasting method is conducted by using the forecast and operation data from six investigated wind farms across Canada.