Stochastic planning considering the uncertainties of renewable energy seasonal correlation and Sub-DR for smart distribution network

At present, a high proportion of renewable energy is connected to a smart distribution network. The power outputs of renewable energy, such as wind and photovoltaic energy, is featured by randomness and uncertainties. In addition, different types of loads have different demand response characteristics and strong 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 seasonal complementary characteristics between wind and photovoltaic power outputs to reduce the uncertainty of renewable energy. Moreover, a sub-DR model considering the uncertainties of user willingness for different types of loads is modeled, and an industrial load demand response pricing mechanism considering the interaction between supply and demand is developed. Finally, a stochastic planning model considering system operation is established to achieve source-load storage synergy 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.