Absorption of PV Power Prediction Errors with Headroom Control by Statistical, Machine Learning and Combined Models

Owing to the mass penetration of renewable energy (RE) in Japan, the replacement reserve for feed-in tariff (RR-FIT) was introduced in 2021. To promote the further introduction of RE, we attempted to provide reserve power value to the balancing market by controlling the headroom of photovoltaic (PV) power with a PV power plant as a research subject. At the planning stage, the information available is the predicted value, and PV prediction error always occur on the day. Therefore, in this research, we proposed an algorithm to determine the absorption of prediction errors headroom in PV output, thereby maximally reducing negative imbalance. In addition, we developed a statistical model, SVR model, and combined models to calculate and compare the number of imbalance spots and amount of imbalance. Accordingly, the proposed methods can significantly reduce imbalance compared to planning with predicted values, without setting the headroom of prediction errors.