A Hybrid Robust Optimization Model for Day-Ahead Management of Active Distribution Networks

In this paper, a hybrid day-ahead robust optimization model is presented for the optimal operation of active distribution networks subject to real-time operation. Maintaining the convex structure of the problem with load flow equations is the most important goal in how to robust modeling of uncertainties. For this purpose, the combination of the robust optimization with the worst case realization and risk-averse model of information-gap decision theory has been applied for real-time uncertainties modeling. The first approach is used for the uncertainty modeling of the real-time market price and the latter is used for modeling of the loads and renewable generations uncertainties. A new and more accurate formulation is presented for modeling of the day-ahead planning in the presence of uncertain real-time operation based on two-stage optimization of the benders decomposition. The day-ahead optimization is formulated in the first stage as a deterministic mixed integer linear programming. Initial dispatch of the generators and power exchange with the day-ahead market are determined in the first stage. At the second stage, the real-time optimization has been placed with the aim of redispatch of the generators and power exchange with the real-time market in the presence of the uncertainties and network constraints.