Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

TL;DR

This paper develops a stochastic optimal power flow model that integrates network reconfiguration to effectively manage congestion and facilitate renewable energy integration, reducing curtailment and congestion costs.

Contribution

It introduces a novel stochastic OPF framework incorporating network reconfiguration, addressing renewable uncertainty and congestion management simultaneously.

Findings

Network reconfiguration relieves transmission congestion effectively.

Renewable curtailment is significantly reduced with the proposed scheme.

Contingency-case congestion costs are substantially lowered.

Abstract

There has been a significant growth of variable renewable generation in the power grid today. However, the industry still uses deterministic optimization to model and solve the optimal power flow (OPF) problem for real-time generation dispatch that ignores the uncertainty associated with intermittent renewable power. Thus, it is necessary to study stochastic OPF (SOPF) that can better handle uncertainty since SOPF is able to consider the probabilistic forecasting information of intermittent renewables. Transmission network congestion is one of the main reasons for renewable energy curtailment. Prior efforts in the literature show that utilizing transmission network reconfiguration can relieve congestion and resolve congestion-induced issues. This paper enhances SOPF by incorporating network reconfiguration into the dispatch model. Numerical simulations show that renewable curtailment can be avoided with the proposed network reconfiguration scheme that relieves transmission congestion in post-contingency situations. It is also shown that network reconfiguration can substantially reduce congestion cost, especially the contingency-case congestion cost.