Zonal congestion management mixing large battery storage systems and generation curtailment

TL;DR

This paper proposes a combined local congestion management approach using large battery storage systems and renewable energy curtailment, employing Model Predictive Control to optimize system stability in high-renewable zones.

Contribution

It introduces a novel model integrating batteries and curtailment for congestion management and an MPC-based energy management approach for improved system control.

Findings

Simulation results demonstrate effective congestion mitigation.

The approach enhances system stability in renewable-rich zones.

Parameter sensitivity analysis informs optimal design choices.

Abstract

The French transmission system operator (RTE) needs to face a significant congestion increase in specific zones of the electrical network due to high integration of renewable energies. Network reconfiguration and renewable energy curtailment are currently employed to manage congestion and guarantee the system security and stability. In sensitive zones, however, stronger levers need to be developed. Large battery storage systems are receiving an increasing interest for their potential in congestion management. In this paper, a model for local congestion management mixing batteries and renewable generation curtailment is developed. Subsequently, an energy management approach relying on the principles of Model Predictive Control is presented. Results of simulations on RTE data sets are presented for the analysis of the degrees of freedom and sensitive parameters of the design.