Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

Journal Article · Thu Sep 15 04:00:00 EDT 2022 · Solar Energy Advances

DOI:https://doi.org/10.1016/j.seja.2022.100024· OSTI ID:1991262

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

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1. Johns Hopkins University, Baltimore, MD (United States); Market Surveillance Committee of the California Independent System Operator, Folsom, CA (United States); Johns Hopkins University
2. University of Texas at Dallas, Richardson, TX (United States)
3. IBM Thomas J. Watson Research Center, Yorktown Heights, NY (United States)
4. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
5. Johns Hopkins University, Baltimore, MD (United States)

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast 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. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $$\$$100$ million per year for a CAISO-size system).

• Research Organization: Johns Hopkins University, Baltimore, MD (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: EE0008215
• OSTI ID: 1991262
• Journal Information: Solar Energy Advances, Journal Name: Solar Energy Advances Vol. 2; ISSN 2667-1131
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
29 ENERGY PLANNING, POLICY, AND ECONOMY
97 MATHEMATICS AND COMPUTING
California
Flexible ramp product requirements
Machine learning
Probabilistic solar power forecasts
Production cost
Reliability