Risk-Averse Scheduling via Conservation Voltage Reduction in Unbalanced Distribution Feeders

The increasing penetration of solar photovoltaics (PVs) generation in distribution grids necessitates the need for optimal operation and scheduling of active/reactive resources for regulating the voltage along distribution feeders and reducing power consumption. In this paper, a mixed integer linear programming (MILP) risk-averse stochastic optimization model is proposed to co-optimize the traditional switching of capacitor banks and transformer tap along with PV and energy storage system (ESS) inverters. In day-ahead (DA) stage, traditional devices and purchased power of DA are scheduled. In real-time stage, the fast response inverters, ESS, and real-time market ensure sufficient active and reactive power support for distribution grids considering the conservation voltage reduction (CVR) plan. The CVR is integrated with the framework to reduce the energy consumption of voltage dependent loads by operating the grid close to the lower acceptable voltage ranges. The uncertainty of sudden changes in PV generation is represented by a Gaussian Mixture model (GMM). The generated uncertainty scenarios are reduced using an unsupervised fuzzy k-means method. Finally, the effectiveness of the proposed framework is verified using a modified version of the unbalanced IEEE 33-node system.

Automated summary

This paper proposes a MILP risk-averse stochastic optimization model to co-optimize traditional devices and solar photovoltaics along with energy storage systems in distribution grids. The Conservation Voltage Reduction (CVR) is integrated into the framework to reduce load energy consumption. Gaussian Mixture Model (GMM) and unsupervised fuzzy k-means method are used to handle uncertainty in PV generation.