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Development of an incentive model for renewable energy resources using forecasting accuracy in South Korea

This study presents an incentive model for a renewable energy source (RES) supplier, based on forecasting accuracy. The incentive model imposes responsibility of forecasting to RES suppliers by providing an additional revenue or penalty, according to forecasting accuracy. For this, a profit analysis reflecting the current environment was performed. The structure of the incentive model is designed based on the results of profit analysis. Subsequently, the price parameter was determined using an artificially generated forecasting error of the RES. In addition, the utilization of energy storage system (ESS) is analyzed, including the operation algorithm and optimal sizing, to verify the utility of the ESS when the incentive model is applied. In the case study, the actual price data and RES data from South Korea were used. The results show that the incentive model can provide a high level of profit or penalty for the RES supplier, according to the forecasting accuracy. It is also shown that the optimal capacity of the ESS based on econometrics can be reduced by more than 50% compared with the current environment, thereby reducing the burden on the ESS investment of RES suppliers.