Day-Ahead Joint Chance-Constrained Optimal Scheduling of the Distribution Network With Electric Vehicle Aggregators

Directly integrating many electric vehicles (EVs) into a distribution network significantly increases the system management complexity. Effective aggregation of EVs is essential to efficiently manage the distribution network. This paper introduces an improved polytope-based aggregation method that accounts for the physical characteristics of EVs. Compared to the conventional polytope-based method, this approach notably reduces violations of physical constraints and achieves more accurate energy-level calculations. Next, a joint chance-constrained day-ahead optimal scheduling model of the distribution network is established. To solve this model, joint chance constraints (JCCs) are decomposed into individual chance constraints (ICCs) with risk probabilities as variables. Then, these ICCs are transformed into linear forms using the Gaussian Mixture Model (GMM) and the piece-wise linearization method. Finally, a novel iterative method is used to refine Boole’s inequality. The proposed iterative method achieves better economic performance than the existing method while maintaining the system’s risk probability. The effectiveness of the proposed method is validated through numerical case studies based on real datasets in IEEE 33-bus and 141-bus distribution networks.