Unified carbon emissions and market prices forecasts of the power grid

Carbon emissions and market prices forecasts of the power grid are of great importance for all electricity traders and consumers. Both forecasts enable flexible demand scheduling, ensuring sustainability and cost-efficiency. Many studies show the benefits of using both forecasts independently but not in combination, which remains an unexplored problem. The latest state-of-the-art techniques involve advanced statistical and machine learning algorithms leveraging seasonal patterns and exogenous forecasts. However, most of the reported studies deal only with problems of modeling and feature engineering, neglecting the forecast and model errors, which accumulate within the time-evolving power grid. This research aims to tackle these issues by introducing a versatile framework for short-term probabilistic forecasting of unified carbon emissions and market prices for electricity intra-day market participants. The approach utilizes the Hidden Markov Model for predictive estimation to determine the future energy mix of the country’s power grid. Furthermore, a novel optimization-based strategy, Moving Horizon Predictive Correction, is proposed to enhance the estimated energy mix performance, minimizing forecast and model errors. Subsequently, two separate recurrent neural networks are trained to provide probabilistic forecasts of carbon emissions and market prices, accounting for the stochastic dynamic of the power grid. A comparative analysis examines six case studies from various European countries and compares them with state-of-the-art forecasting methods. The results indicate that the proposed method can improve the qualitative measures by up to 53% for carbon emissions and up to 18% for market prices forecasts. Besides improving traditional point predictions, methods show significant increases in the quality of prediction intervals. Further application of the proposed forecasts is employed for a flexible 4-hour electricity consumption schedule. This showcases the usage of the proposed forecasts to find the best possible trade-off for low carbon emission, cost-effective electricity consumption time slots.