AI & ML
Risk Assessment of Fair Multi-Period Dynamic Operating Envelopes under Uncertainty
arXiv eess.SY0 views
arXiv:2610.42014v1 Announce Type: new Abstract: Dynamic operating envelopes (DOEs) can allocate time-varying export capacity to distributed energy resources (DERs), and while they are not intended to make operations immune to voltage violations, they should guide operations to mostly remain inside expected values under changing regimes and residual forecast errors. However, although recent literature proposed many different techniques for implementing DOEs, there is sparse discussion of analyzing and quantifying the risk of using more or less conservative DOEs under uncertain operating regimes. Having this gap in mind, this paper develops a risk-oriented extension of a previously established fair distributed multi-period optimal power flow (OPF) framework, where fair or technical DOEs are fixed as first-stage decisions and the OPF leverages battery energy storage and renewable curtailment as recourse under regime uncertainties. An independent nonlinear AC power flow then computes a resulting ensemble, which is summarized by (i) voltage-violation probability, severity, and a persistence metric for consecutive out-of-band hours, (ii) curtailment and Jain and Gini indices, and (iii) model deviations. Different probability situations are assessed, ranging from uniform, sunny-heavy, night-peak-heavy, and adversarial, which separate the envelope design from the assumed operating distribution. An optional outer loop allows for fine-tuning a fairness--efficiency parameter under explicit violation and persistence budgets. The combination of the outer loop and the overall framework, which quantifies how often and how severely operating limits are exceeded, creates an auditable methodology mixing equitable export allocation, distributed recourse, and AC risk assessment for active distribution network operators, and this framework is assessed using up to 1000 Monte Carlo scenarios per mixture and DOE design.
Source:https://arxiv.org/abs/2610.42014PreviousEpidemic Forecasting and Behavioral Response Estimation with a Behavior-Coupled SEIR ModelNextA Neural Network Approach towards Real-Time Knot Point Density Estimates for the Nonlinear Quadrotor
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