A Data-driven Robust Planning Method for Integrated Energy System Considering Uncertainty of Renewable Energy

Because renewable energy is highly random and intermittent, the large-scale grid connection of renewable energy grid will seriously affect the safeness and invulnerability of the power system. The development of integrated energy system (IES) can solve this problem. With the goal of minimizing investment and operating costs, a data-driven scenario-based robust planning method for IES considering the uncertainty of renewable energy is proposed. The proposed model is solved by column and constraint generation (CC&G) algorithm. The results show that the data-driven robust model can provide flexible decision-making schemes based on a larger confidence level when the information is limited and improve the system economy.