Efficient Risk Analysis of DG Investment in ADNs

Active Distribution Networks (ADNs) are deregulated medium-voltage systems operated by Distribution System Operators (DSOs) that supply local demand while integrating Distributed Generators (DGs). Owing to renewable intermittency and relatively low load aggregation, DG investment planning in ADNs is highly sensitive to uncertainty. This paper proposes a novel framework for risk-aware DG investment based on Adaptive Robust Optimization (ARO) combined with Benders decomposition. The resulting formulation efficiently identifies worst-case realizations of uncertainties and adjusts long-term investment decisions accordingly. Its multi-year structure enables the assessment of key financial indicators, including Net Present Cost (NPC) and payback period, while accommodating non-convex binary constraints to accurately represent DG operational characteristics. The methodology is validated on the radial IEEE 33-bus system. Numerical results demonstrate its effectiveness in performing adaptive risk analysis and delivering computationally tractable, economically sound investment strategies under uncertainty.