Risk Segmentation And Portfolio Analysis For Pareto Dominance In High Renewable Penetration And Storage Reserves
This project will apply advanced techniques from financial engineering and risk management to analyze the risk of power systems on both the asset and system level. At the asset level (Thrust 1), the project will introduce a risk segmentation method called tranching to assess risk profiles based on the variability of the renewable resource, storage units, and locational specifications. Risk scores will be developed which are adaptive to system level feedback. At the system level (Thrust 2), novel copula-based probabilistic risk models will be developed to assess the correlation between different asset tranches. The ultimate goal of this project from NC State is to determine asset suitability for different types of contracts.
Sponsor
Rensselaer Polytechnic Institute
The grant—running from August 1, 2020 to July 31, 2023—is for a total of $290,604.
Principle Investigators
Zeljko Pantic
Aranya Chakrabortty
More Details
The proposed project will apply risk segmentation, adaptive credit scoring and network-based portfolio analysis techniques from financial engineering and risk management for risk analytics of power systems at both asset and system levels. At the asset level (Thrust 1), the project will introduce risk segmentation of an asset’s throughput by applying tranching similar to collateralized debt obligations. The risk-free to most risky tranches will be assessed for their risk profile in terms of risk scores taking into account the variability of the renewable resource (wind or solar), presence of storage units or services that they may be equipped/associated with, and the asset’s locational specification. This risk scoring will be designed to be adaptive based on system level (Thrust 2) feedback at different contractual time-scales, starting from sub-seconds to tens of minutes, to determine asset suitability as an energy, regulation, spin, non-spin, or replacement reserve. Novel copula-based probabilistic risk models will be developed for the joint correlation structures between different contract tranches of assets for asset and system level risk assessment.