@article{Zhang2026, 
author = {Jingxuan Zhang and Xinyue Chang and Hongbin Sun and Yixun Xue and Zhongkai Yi and Zening Li},
title = {Stochastic Planning Considering Uncertainties of Renewable Energy Seasonal Correlation and Sub-DR for Smart Distribution Network},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {3},
pages = {1252-1263},
keywords = {Demand response, renewable energy correlation, source-load uncertainty},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2023.05630},
doi = {10.17775/CSEEJPES.2023.05630},
abstract = {At present, a high proportion of renewable energy is integrated into smart distribution networks. The power outputs of renewable energy, such as wind and photovoltaic energy, are featured by randomness and uncertainties. In addition, different types of loads have different demand response characteristics with significant uncertainty. These parameters pose great challenges to the safety and reliability of the smart distribution network in the planning stage. Therefore, a renewable energy seasonal correlation model is proposed to exploit the complementary seasonal characteristics between wind and photovoltaic power outputs, thereby reducing the uncertainty of renewable energy. Moreover, a sub-DR model considering the uncertainties of user willingness for different types of loads is modelled, and an industrial load demand response pricing mechanism considering the interaction between supply and demand is developed. Finally, a stochastic planning model, which incorporates system operation considerations, is established to achieve synergistic coordination among generation, load, and storage in the smart distribution network. The numerical examples show that the proposed method can effectively alleviate the randomness and uncertainties of renewable energy power output in the IEEE 33-bus system and IEEE 123-bus system and ensure the stable operation of the system.}
}