Risk-Averse Coordinated Operation of Distributed Energy Resources in Active Distribution Networks Considering Load and Renewable Uncertainty

This paper presents a risk-averse information-gap decision theory (IGDT)-based day-ahead scheduling framework for active distribution networks with high penetration of inverter-interfaced resources. The proposed day-ahead strategy coordinates active and reactive power scheduling in an active distribution network comprising renewable generation, diesel units, demand-side management, electric vehicle charging stations, and energy-storage-equipped soft open points. The corresponding deterministic operating condition is then used as the reference state for uncertainty analysis. The scheduling problem is formulated as a mixed-integer nonlinear programming (MINLP) model considering network operating constraints and voltage-dependent load characteristics. Uncertainty associated with load demand and renewable generation is addressed using the IGDT risk-averse approach to quantify admissible uncertainty. The proposed methodology is implemented on a modified IEEE 33 bus distribution system considering deterministic operation, load-demand uncertainty, renewable-generation uncertainty, and simultaneous uncertainty in both load demand and renewable generation. The optimization model is developed in GAMS and solved using the DICOPT solver. The simulation results demonstrate the capability of the proposed framework to accommodate simultaneous load-demand and renewable-generation uncertainty within a predefined operating-cost threshold while maintaining secure network operation.