Robust optimization of the active distribution network involving risk assessment

Abstract

Inherent dynamic constraints of distributed generations (DGs) and the correlation between injected variables bring great challenges to distribution network operation. In order to improve the degree of coupling and interconnection coordination between different energy devices, improve the ability of the distribution network to cope with the uncertainty of DGs, achieve low-carbon operation, and improve the environmental friendliness of distribution network operation, this article proposes a robust optimization approach involving risk assessment. The semi-invariant method and scene clustering are used to deal with the uncertainty of DGs and load, thus formulating a robust optimization model for distribution network distribution based on risk indices. To address the time-varying constraints of energy storage systems (ESSs) and gas turbines, a two-stage box-based decomposition model is established. Dynamic constraints are included in the first stage to constrain the operating state and operating domain of the unit and ESSs. In the second stage, the multi-timescale optimization problem is transformed into multiple single-timescale optimization problems, which are solved by the column and constraint generation (C&CG) algorithm to improve the solution efficiency. The feasibility of the comprehensive optimization model based on dynamic reconfiguration and distributed robust optimization (DRO) is demonstrated with the PG&E 69 bus system.