Proactive Operations and Investment Planning via Stochastic Optimization to Enhance Power Systems Extreme Weather Resilience
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Purdue Univ., West Lafayette, IN (United States). Davidson School of Chemical Engineering
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
We present novel stochastic optimization models to improve power systems resilience to extreme weather events. We consider proactive redispatch, transmission line hardening, and transmission line capacity increases as alternatives for mitigating expected load shed due to extreme weather. Our model is based on linearized or "DC" optimal power flow, similar to models in widespread use by independent system operators (ISOs) and regional transmission operators (RTOs). Our computational experiments indicate that proactive redispatch alone can reduce the expected load shed by as much as 25% relative to standard economic dispatch. This resiliency enhancement strategy requires no capital investments and is implementable by ISOs and RTOs solely through operational adjustments. We additionally demonstrate that transmission line hardening and increases in transmission capacity can, in limited quantities, be effective strategies to further enhance power grid resiliency, although at significant capital investment cost. We perform a cross validation analysis to demonstrate the robustness of proposed recommendations. Our proposed model can be augmented to incorporate a variety of other operational and investment resilience strategies, or combination of such strategies.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity Delivery and Energy Reliability (OE); Dept. of Homeland Security (DHS) (United States)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1492392
- Report Number(s):
- SAND--2018-9647J; 667558
- Journal Information:
- Optimization Online Repository, Journal Name: Optimization Online Repository Vol. 2018; ISSN 9999-0042
- Publisher:
- Mathematical Optimization Society
- Country of Publication:
- United States
- Language:
- English
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