Editorial
Evaluation and Planning Method for Renewable Energy Carrying Capacity of Novel Power Systems Based on Deep Spatiotemporal Feature Learning and Inter-Provincial Mutual Assistance Synergy
@ARTICLE{10.4108/ew.15077, author={Sudan Lai and Guilian Wu and Shiyuan Ni and Lu Tang and JInlin Liao}, title={Evaluation and Planning Method for Renewable Energy Carrying Capacity of Novel Power Systems Based on Deep Spatiotemporal Feature Learning and Inter-Provincial Mutual Assistance Synergy}, journal={EAI Endorsed Transactions on Energy Web}, volume={13}, number={1}, publisher={EAI}, journal_a={EW}, year={2026}, month={10}, keywords={Novel Power System, Renewable Energy Carrying Capacity, Spatial-Temporal Graph Convolutional Network, Nonlinear Coupling Features, Two-Stage Collaborative Planning}, doi={10.4108/ew.15077} }- Sudan Lai
Guilian Wu
Shiyuan Ni
Lu Tang
JInlin Liao
Year: 2026
Evaluation and Planning Method for Renewable Energy Carrying Capacity of Novel Power Systems Based on Deep Spatiotemporal Feature Learning and Inter-Provincial Mutual Assistance Synergy
EW
EAI
DOI: 10.4108/ew.15077
Abstract
INTRODUCTION: With the in-depth advancement of novel power system construction, the randomness and volatility brought by high-proportion renewable energy grid connections pose severe challenges to long-term planning. Traditional methods struggle with extracting complex non-linear features from massive data and face limited flexible resources within a single region. OBJECTIVES: This paper aims to propose a dynamic evaluation and collaborative planning method driven by deep spatiotemporal feature learning and inter-provincial power mutual assistance to accurately assess and expand the overall accommodation space. METHODS: Techniques derived from deep learning and graph neural networks are employed to conduct this research. Based on multi-source heterogeneous power grid operation measurement data, a spatial-temporal graph convolutional network (STGCN) model is constructed. Unlike traditional noise reduction methods, this model actively observes and compensates for the complex nonlinear coupling terms among source-grid-load. Subsequently, a two-stage collaborative planning model is established, utilizing the dynamic carrying boundaries output by the STGCN for grid expansion and operation simulation. RESULTS: The main results obtained in this paper are the following. Simulations on a certain provincial grid in China demonstrate that the STGCN model significantly improves computational efficiency and accuracy in extracting spatial-temporal features. Furthermore, the inter-provincial mutual assistance mechanism effectively optimizes resource allocation under the boundaries provided by the deep learning model. CONCLUSION: These are the conclusions of this paper: The integration of deep spatiotemporal learning with cross-regional mechanisms provides a highly accurate, data-driven computational framework for renewable energy carrying capacity evaluation and grid planning.
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