TY  - EJOU
AU  - Ji, Shijie 
AU  - Lei, Shuya 
AU  - Ren, Lan 
AU  - Wei, Renjie 
AU  - Yang, Ning 
AU  - Wang, Yishen 

TI  - Heterogeneous Graph Neural Network–Assisted Joint Clearing for Intra- and Inter-Provincial Electricity Spot Markets
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - With the continuous growth of the scale of inter-provincial electricity transactions, the intra-provincial independent clearing model has gradually revealed limitations in terms of resource collaborative optimization and regional supply-demand balance, and the coordination mechanism between the two-tier markets of inter-provincial and intra-provincial levels urgently needs to be improved. To address this problem, this paper constructs a bi-level joint clearing model for the intra-provincial and inter-provincial electricity spot markets, which includes three stages: intra-provincial clearing, inter-provincial entity screening and inter-provincial joint clearing. The model aims to effectively connect the results of intra-provincial transactions with inter-provincial resource allocation through a hierarchical coordination mechanism, so as to realize the orderly linkage and global optimization of the two-tier markets. To improve the solution efficiency of large-scale mixed integer linear programming (MILP), this paper proposes an auxiliary solution method based on heterogeneous graph neural networks (HGNN). Through the heterogeneous graph modeling with multi-type nodes and multi-relation structures, the method realizes the expression of coupling information among the generation side, demand side, provinces and tie-lines, and performs pre-identification of integer variables to generate high-quality initial solutions, thereby improving the solution efficiency of MILP. Case study results show that, compared with traditional methods, the proposed method significantly reduces the solution time while maintaining the optimality of clearing results, effectively promotes the efficient allocation of inter-provincial electricity resources, and exhibits good real-time performance and scalability in a large-scale market environment.
KW  - Electricity spot market; joint clearing; heterogeneous graph neural network; mixed-integer programming

DO  - 10.32604/ee.2026.075094