Wind-Power Scenarios for Stochastic Unit Commitment: The Cost of Uncertainty B.A. Rachunok 1,2 A. Staid 2 J.P. Watson 2 D. Yang 3,2 D. Woodruff 3 Power System Overview Zhou, Yifan, et al. "Modeling and Optimization of Multitype Power Sources Stochastic Unit Commitment Using Interval Number Programming." Journal of Energy Engineering 143.5 (2017): 04017036. • Independent System Operators: balance bulk power load with supply • Supply comes from power generation • Load (demand) comes from residential and commercial power use 1.Scenarios for tomorrow are generated using tomorrow’s forecast 2.Scenarios are used to optimally dispatch power generation 3.Dispatching is evaluated using tomorrow’s actual wind 4.Bulk power costs calculated 5.Renewable curtailment calculated Scenario Evaluation We want to use scenarios to represent tomorrow’s supply uncertainty. How? Representing Uncertainty Planning for Tomorrow’s Power Needs • Load is relatively easy to predict • Day-ahead renewable forecasts are uncertain • Over/underestimating supply is costly Tomorrow’s Demand ✅ Tomorrow’s Supply " vs Predicting Nitsche, Sabrina, et al. "Improving Wind Power Prediction Intervals Using Vendor- Supplied Probabilistic Forecast Information." IEEE Power and Energy Society General Meeting (PES). (1) (2) (3) (1) Random Variate Sampling (Wang 2008) • Generates a fixed number of scenarios • Scenarios distributed normally • Centered around forecast • Highly dependent on forecast quality (2) Quantile Regression (Pinson 2007) • Estimates quantiles of errors • Generates scenarios for fixed quantiles • No one scenario ‘looks’ realistic (3) Epi-splines (Staid 2017) • Smoothed partition scenarios • Scenarios all have common points • Highly flexible in smoothness and partitions • Width adjusted probabilistically • Scenarios pass for reasonable • Ramps are consistent with actual wind Results (1) (2) (3) (1) (2) (3) • Epi-splines have low load shedding • Also low total costs • Minimal scenarios needed à simpler optimization • Scenarios plausible to operators + decision makers • Balance minimal load shedding and curtailment Scenarios are tested using real data in a power dispatching simulation Simulation procedure 1 Purdue University School of Industrial Engineering 2 Sandia National labs 3 University of California Davis Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE- NA0003525.

(1) (2) (3) - Purdue Universitybrachuno/old/coda.pdfStochastic Unit Commitment Using Interval Number Programming."Journal of Energy Engineering143.5 (2017): ... power generation 3.Dispatching

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Page 1: (1) (2) (3) - Purdue Universitybrachuno/old/coda.pdfStochastic Unit Commitment Using Interval Number Programming."Journal of Energy Engineering143.5 (2017): ... power generation 3.Dispatching

Wind-Power Scenarios for Stochastic Unit Commitment: The Cost of UncertaintyB.A. Rachunok 1,2 A. Staid 2 J.P. Watson 2 D. Yang 3,2 D. Woodruff 3

Power System Overview

Zhou, Yifan, et al. "Modeling and Optimization of Multitype Power Sources Stochastic Unit Commitment Using Interval Number Programming." Journal of Energy Engineering 143.5 (2017): 04017036.

• Independent System Operators: balance bulk power load with supply

• Supply comes from power generation• Load (demand) comes from residential and

commercial power use

1.Scenarios for tomorrow are generated using tomorrow’s forecast

2.Scenarios are used to optimally dispatch power generation

3.Dispatching is evaluated using tomorrow’s actual wind

4.Bulk power costs calculated5.Renewable curtailment calculated

Scenario EvaluationWe want to use scenarios to represent tomorrow’s supply uncertainty. How?

Representing Uncertainty

Planning for Tomorrow’s Power Needs

• Load is relatively easy to predict• Day-ahead renewable forecasts are uncertain• Over/underestimating supply is costly

Tomorrow’s Demand ✅

Tomorrow’s Supply "

PredictingNitsche, Sabrina, et al. "Improving Wind Power Prediction Intervals Using Vendor-Supplied Probabilistic Forecast Information." IEEE Power and Energy Society General Meeting (PES).

(1) (2) (3)

(1) Random Variate Sampling (Wang 2008)• Generates a fixed number of scenarios• Scenarios distributed normally• Centered around forecast• Highly dependent on forecast quality

(2) Quantile Regression (Pinson 2007)• Estimates quantiles of errors• Generates scenarios for fixed quantiles• No one scenario ‘looks’ realistic

(3) Epi-splines (Staid 2017)• Smoothed partition scenarios• Scenarios all have common points• Highly flexible in smoothness and partitions• Width adjusted probabilistically• Scenarios pass for reasonable• Ramps are consistent with actual wind

Results

(1)

(2)

(3)

(1)(2)(3)

• Epi-splines have low load shedding• Also low total costs• Minimal scenarios needed à simpler optimization• Scenarios plausible to operators + decision makers• Balance minimal load shedding and curtailment

Scenarios are tested using real data in a power dispatching simulation

Simulation procedure

1 Purdue University School of Industrial Engineering 2 Sandia National labs 3 University of California Davis

Sandia National Laboratories is a multimissionlaboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.