Robust Flexibility Provision from DERs: A Network-Constrained Multi-Step Optimization Approach
Abstract
This paper proposes a network-constrained multi-step optimization approach for robust flexibility provision from distributed energy resources (DERs) under photovoltaic (PV) generation uncertainty. The proposed method optimally schedules day-ahead battery operations based on PV output predictions and confidence intervals, while strictly satisfying network constraints. To ensure operational feasibility, the AC power flow is modeled using DistFlow equations. To bridge the gap between computational tractability and exact AC feasibility, a multi-step solution strategy is introduced. First, a linearized DistFlow (LinDistFlow) model is employed within a column-and-constraint generation algorithm to efficiently identify the worst-case scenario. Subsequently, the robust day-ahead battery schedule is determined by solving a second-order cone (SOC)-relaxed DistFlow model under the identified scenario. Finally, a post-processing exact recovery step is executed by solving the original non-convex DistFlow equations under the fixed battery schedule and the worst-case scenario. This crucial step compensates for approximation errors introduced by the LinDistFlow and SOC models, significantly enhancing practical operational AC feasibility under the identified critical condition. Extensive case studies on the IEEE 33-bus test system verify the effectiveness of the proposed multi-step approach in balancing computational efficiency and robust flexibility provision.