Risk segmentation and Portfolio Analysis for Pareto Dominance in High Renewable Penetration and Storage Reserves
Rensselaer Polytechnic Institute (RPI) will develop market mechanism and risk assessment techniques to support cost-effective and risk-informed integration of renewable energy resources into the grid. The RPI team will holistically apply risk segmentation (tranching), credit scoring, and portfolio analysis techniques from financial engineering and risk management for risk analytics of asset and system operation. The team will analyze the system-wide risk in meeting an increasingly stochastic demand with supply at different time scales using network-based portfolio analysis which seek Pareto-dominant solutions to match risk appetite. Adaptive risk scoring and tranching techniques will determine the type of services for which generation and storage assets are most suitable. The project will consider potential instability risks in real-time operations due to fluctuations in the intermittent resources, establish bid curves for renewable resources based on uncertainties in energy forecast, and assess system level risk from higher renewable energy utilization using appropriately developed metrics.
PERFORM projects will design methods and risk scores to clearly communicate the physical delivery risk of an energy asset’s offer and design grid management systems that organically capture uncertainty. These management systems will evaluate and hedge the system risk position to meet or exceed a baseline system risk index. This pursuit will achieve the following area impacts:
Optimal utilization of renewable and clean resources for all grid services improves grid reliability, reduces energy imports, and provides a sustainable path to energy independence.
When low- or zero-emission assets provide all grid products and services, grid operations are no longer reliant on legacy, carbon-heavy centralized generation assets, which enables the grid to absorb more clean resources.