Background: China's power resources are unevenly distributed in geography, and the
supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize
the allocation of power resources through cross-provincial and cross-regional power trading.
Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization
models to achieve optimal allocation of electricity and power resources cross-provincial and
cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm
loads, while multi-agent technology is used to report the power trading price.
Results: Cross-provincial and cross-regional power trading become a network flow problem, through
which we can find the optimized complete trading paths.
Conclusion: Numerical case study results have verified the efficiency of the proposed method in
optimizing power allocation across provinces and regions.