Affiliation:
1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. China Electric Power Research Institute, Nanjing 210003, China
Abstract
Distributed resources at a grid’s end cannot upload operational power data to local centers due to data transmission and privacy issues. This leaves the centers with incomplete information, thus impacting decision making. This paper presents a virtual aggregation-based model for such scenarios. We define four virtual aggregate types based on resource response characteristics. Using characteristic coefficients, we identify these aggregates’ categories and proportions from bus power. To address blind source separation in single-channel power signals, we apply the Ensemble Empirical Mode Decomposition-Fast Independent Component Analysis (EEMD-FastICA) method. This helps extract and analyze bus power, thereby deriving power curves for different aggregates. Moreover, we use a graph convolutional network to explore how factors like date, time, weather, and pricing intertwine with aggregate power. We develop a predictive model with an advanced SpatioTemporal Graph Convolutional Network (STGCN), thus facilitating proactive power forecasting for virtual aggregates. Case studies show our method’s efficacy in extracting power curves under limited information, with the STGCN ensuring accurate, forward-looking predictions.
Funder
Science and Technology Program of State Grid
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference23 articles.
1. (2022, November 28). Electrification is the Key to Building a Zero Carbon Energy System. Available online: http://paper.people.com.cn/zgnyb/html/2022-11/28/content_25952672.htm.
2. Overview of Energy Low carbonization Technology under the Background of Carbon Neutrality and Its Inspiration for the Development of New Power Systems;Huang;Proc. CSEE,2021
3. Energy Internet: Concepts, Architecture, and Frontier Prospects;Sun;Autom. Electr. Power Syst.,2015
4. Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization;Zhao;IEEE Trans. Ind. Inform.,2012
5. Wu, C.Y., Jiang, P., Gu, W., and Sun, Y. (October, January 28). Day-ahead optimal dispatch with CHP and wind turbines based on room temperature control. Proceedings of the 2016 IEEE International Conference on Power System Technology (POWERCON), Wollongong, NSW, Australia.