Co-Optimizing Operating Cost and Renewable Energy Curtailment in D-FACTS Allocation

Modern energy grids have become extremely complex systems, requiring more variable and active flow control. As a remedy to this, Distributed Flexible AC Transmission Systems (D-FACTS) are cost-efficient devices used to mitigate power flow congestion and integrate renewable energies. The objective of this study is then to propose an efficient multiple objective evolutionary algorithm to solve a stochastic model for D-FACTS allocation which aims to optimize both the utilization of renewable resources and the total operating costs of the grid. The model was implemented on a modified RTS-96 test system, and the results show that optimally allocating D-FACTS modules using the proposed model can significantly reduce power system operating costs and improve the integration of renewable energy sources. Additionally, the case study shows that the model is very computationally efficient.