An Information Gap Decision Theory-based Bi-level Robust Model for Low-carbon Transition Planning of Distribution Network Considering Multi-source Uncertainties

The low-carbon transition of power networks is an important part of achieving dual carbon goals. Current approaches focus on deploying renewable generators (RGs) or retrofitting fossil fuel generators with the emerging carbon capture, utilization, and storage (CCUS). A potential contradiction lies between them. For the higher penetration of renewable energy, the fewer emissions there will be emitted by traditional units. Additionally, the multi-source uncertainties from technologies, markets, and operation conditions bring unforeseen impacts on the total transition costs. This paper focuses on the robust capacity allocation model of different low-carbon equipment under multi-source uncertainties. It proposes an information gap decision theory (IGDT) based bi-level robust model for the low-carbon transition problem of distribution networks to size RGs and CCUS in a coordinated way. The multi-source uncertainties are categorized based on their time scales and modeled by the envelope bound uncertainty model and uncertainty budget model. A nested Column-and-Constraint Generation (C&CG) enhanced multi-objective Harris Hawk Optimization (MOHHO) algorithm is proposed to solve the formulated multi-objective bi-level mixed integer optimization. The simulation result indicates that compared with deterministic planning, the proposed model can cope with multi-source uncertainties with a 20% controllable increase in total cost budget and a 3.69% slight decrease in emission reduction.