Chance-Constraint Volt-VAR Optimization in PV-Penetrated Distribution Networks

Increasing the levels of renewable energy penetration in distribution network is challenging for operations due to their intermittent nature. To ensure secure and optimum operation of the grid, new tools to deal with uncertainties are required. Volt/VAR optimization (VVO) is one of the primary functions in distribution networks trying to optimize operation of the networks while respecting all operational and security constraints. In this paper, a second-order cone power flow (SOC-PF) model combined with chance constraint optimization is used to formulate the VVO to deal with uncertainties introduced by loads and stochastic distributed energy resources (DERs), such as photovoltaic (PV) generations. The performance of the analytical reformulation of the chance-constraint VVO problem is verified using the modified 33-bus distribution system with added PV units, energy storage systems, and capacitor banks. The results denonstrate the effectiveness of the proposed method in terms of decreasing the total operation cost of the network including loss, voltage deviation, and reactive power.

Automated summary

Increasing renewable energy penetration in distribution networks is challenging due to their intermittent nature. New tools are needed to deal with uncertainties and optimize grid operation. This paper proposes a second-order cone power flow model combined with chance constraint optimization for volt/VAR optimization in distribution networks, considering uncertainties introduced by loads and distributed energy resources. Experimental results on a modified 33-bus distribution system demonstrate the effectiveness of the proposed method in reducing total operation cost.