Abstract:

To meet the new demands of high-quality development of distribution networks and enhance their capacity to accommodate large-scale distributed generation and electric vehicle (EV) loads, this paper proposes a robust joint planning method for soft open points (SOP) and distributed energy storage systems (DESS) in AC/DC hybrid distribution networks, with consideration of EV demand response. Firstly, to address source-load uncertainty, typical and extreme daily operation scenarios are extracted using K-means clustering, and a scenario probability uncertainty set is constructed with l₁-norm and infinity-norm constraints to adjust the model’s conservativeness. And then, the response behaviors of EV users to real-time price are characterized by a demand price elasticity coefficient. A two-stage robust optimization model is formulated to minimize the annual total cost, and the second-order cone relaxation and McCormick envelopes are used to convexify the model. Scenario probability variables are expanded in binary form to enable worst-case scenario search within the uncertainty set. Candidate SOP locations are extended based on network partitioning. The model is solved efficiently by applying duality theory and the inexact column-and-constraint generation (i-C&CG) algorithm. Finally, the effectiveness of the proposed model in supporting voltage, ensuring renewable energy accommodation, and reducing losses is verified in a 69-bus system.

Key words: AC/DC hybrid distribution network, electric vehicle, soft open point, energy storage system, two-stage robust optimization