Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration. (1st March 2023)

• Record Type: Journal Article
• Authors: Rubasinghe, Osaka; Zhang, Tingze; Zhang, Xinan; Choi, San Shing; Chau, Tat Kei; Chow, Yau; Fernando, Tyrone; Iu, Herbert Ho-Ching
• Is Part Of: Applied energy. Volume 333(2023)
• Publication Date: 2023-03-01
• Subjects: Day-ahead net load forecasting -- Grid system-wide level forecasting -- Valley and peak load forecasting -- Empirical mode decomposition -- Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise -- Long Short-Term Memory; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.120641
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: The increasing penetration of photovoltaic has been reshaping the electricity net load curve, which has a significant impact on power system operation and short-term dispatch scheduling. Accurate short-term net load forecasting is essential to ensure reliable and economical operations of a power system. Nonetheless, most of the existing net load forecasting approaches are mostly focused on net load forecasting at household, distribution or microgrid level, but not at grid system-wide level. They also suffer from low accuracy due to the presence of uncertainties on high-frequency fluctuations in the net load. This paper proposes a new improved two-stage net load forecasting method at grid system-wide level. Firstly, it contributes to eliminate the high-frequency components with insignificant amount of energy from the original net load by using the "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-ICEEMDAN" technique. Then, net load decomposition outcomes form the inputs of a computationally efficient and accurate "Long Short-Term Memory-LSTM" network algorithm to produce an accurate day-ahead forecasting, which lays out the foundation of day-ahead power dispatch scheduling. The superiority of the suggested algorithm was confirmed by comparing the obtained results against five other algorithms that use different empirical based decomposition techniques along with Back Propagation (BP) or LSTM. Statistical metrics, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were computed to show the model accuracy. The validity of the proposed method is verified by the net load data of "South West Interconnected System" power network in Western Australia, which refers to the total demand on the conventional generators made up of the consumers' actual demand plus system losses, minus the solar power harnessed by the rooftop PV panels installed within the grid system. Achieving a very high day-ahead net load forecasting accuracy of 96.67% confirms our hypothesis on ICEEMDAN's capability to decompose the net load carefully into different meaningful components. Highlights: Research fills the gap of net load forecasting in grid system-wide level. Challenging nature in forecasting weather parameters and PV power gen is avoided. Obtained significant improvement in valley and peak net load forecasting up to 98%. Australian data that has the highest per capita PV installations in the world used.