Reducing balancing cost of a wind power plant by deep learning in market data: A case study for Turkey


APPLIED ENERGY, vol.289, 2021 (Journal Indexed in SCI) identifier identifier


By the liberalization of energy markets, renewable energy producers are increasingly selling their electricity in the day-ahead market. However, day-ahead forecasts of wind generators are not sufficiently accurate and therefore they are exposed to an imbalance cost due to the incorrect offerings. Although extensive and detailed market data are constantly publicized by the market operator, historical market data are not utilized effectively to reduce this cost. The present study initially casts the imbalance cost reducing problem as a binary classification problem and constructs a framework that consists of a long short term memory autoencoder and a blend of advanced classifiers. Then, the method extracts information from the market data if the day-ahead or imbalance price will be higher at a given hour of the next day. Using this information, auxiliary algorithms alter existing production forecasts and prevents abrupt rises in the imbalance cost. Extensive tests throughout a year show that the strategy performs reliably well and it has provided between 6.258% and 11.195% decrease in the balancing cost for four tested wind power plants.