Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

Technical Report · Mon Jun 13 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1873393· OSTI ID:1873393

Hobbs, Benjamin F ^[1]; Wang, Yijiao ^[2]; Xu, Qingyu ^[2]; Zhang, Shu ^[2]; Hamann, Hendrik F. ^[3]; Zhang, Rui ^[3]; Siebenschuh, Carlo ^[3]; Zhang, Jie ^[4]; Li, Binghui ^[4]; He, Li ^[4]; Krishnan, Venkat ^[5]; Fang, Xin ^[5]; Spyrou, Evangelia ^[5]; Krad, Ibrahim ^[5]; Edwards, Paul ^[5]; Sky, Haiku ^[5]

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1. Johns Hopkins Univ., Baltimore, MD (United States); The Johns Hopkins Universiity
2. Johns Hopkins Univ., Baltimore, MD (United States)
3. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
4. Univ. of Texas at Dallas, Richardson, TX (United States)
5. National Renewable Energy Lab. (NREL), Golden, CO (United States)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

• Research Organization: Johns Hopkins Univ., Baltimore, MD (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: EE0008215
• OSTI ID: 1873393
• Report Number(s): 1
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
29 ENERGY PLANNING, POLICY, AND ECONOMY
97 MATHEMATICS AND COMPUTING
California Independent System Operator
Mid-Continent Independent System Operator
flexible ramp product requirements
frequency regulation
machine learning
netload ramp
probabilistic solar power forecasts
production cost
reliability
visualization