Probabilistic integrated framework for AC/DC transmission congestion management considering system expansion, demand response, and renewable energy sources and load uncertainties

摘要

Congestion management (CM) is one of the most crucial tasks in power system operation and planning, which has become more challenging in recent years due to the growth of renewable energy sources (RESs) and flexible loads. This paper presents an integrated framework that simultaneously employs different methods, including re-scheduling, transmission expansion, and demand response programs (DRPs), to manage the AC/DC transmission congestion. The uncertainty associated with remote wind/solar farms and load demand is taken into account and is modelled using the probabilistic point estimate method. To provide a comprehensive analysis, three different management approaches taking both planning and operation phases into account are considered in this study. In the context of CM, the first management approach considered is to minimize the overall system cost including both investment and operational costs (cost-efficient approach). The second approach is to minimize the overall active power losses (energy-efficient approach). The last one is to make a trade-off between these two approaches (cost-/energy-efficient approach) by simultaneously minimizing system investment cost and operational loss as a multi-objective optimization problem. The effectiveness of the proposed framework is evaluated on IEEE two-area RTS-96 (MRTS) network using an AC/DC power flow tool.