Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties

This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

Automated summary

This paper proposes a stochastic programming method for coordinated operation of distributed energy resources in the unbalanced active distribution network with diverse correlated uncertainties. The method models the three-phase branch flow to characterize the unbalanced nature, schedules DER for three phases, and derives a realistic DER allocation. It co-optimizes active and reactive power resources for voltage regulation and power loss reduction. The proposed method effectively reduces the system cost and co-optimizes the active and reactive power.