Machine Learning-Based Day-Ahead Prediction of Price-Setting Scheduled Energy in the Korean Electricity Trading Mechanism

Power generation companies, which participate in the electricity trading mechanism, need to determine optimal capacity bidding strategy to maximize their operational profit in Korea. Since the Price-setting Power Generation Schedule determines the profit of power generators, it is important to predict Price-setting Scheduled Energy during the trading day before the bidding phase. We propose a methodology for predicting the Pricing-setting Scheduled Energy from the power exchange without optimizing it. Instead of simulating the planning process, machine learning algorithms are applied and compared in the process of predicting the Pricing-setting Scheduled Energy. The input variables consist of seasonal and price information including calendar, fuel cost, and system marginal price. Three categories of machine learning (ML) algorithms including single, bagging and boosting approaches are implemented and tested to compare their performances. The computational experiments show that ML algorithms with price variable are shown to be better in terms of the considered measures. In addition, boosting approach is more effective than single and bagging approaches.

Automated summary

Power generation companies in Korea aim to maximize operational profit by determining optimal capacity bidding strategy. This study proposes a machine learning methodology for predicting Pricing-setting Scheduled Energy, utilizing seasonal and price information. The experimentation shows that machine learning algorithms with price variable are more effective, with boosting approach outperforming single and bagging approaches.