Publications

Bynum, M.L., Hackebeil, G., Hart, W.E., Laird, C., Nicholson, B.L., Siirola, J.D., Watson, J., Woodruff, D.L., & Woodruff, D.L. (2020). Pyomo - Optimization Modeling in Python 3rd Ed. https://www.osti.gov/biblio/1771935

Rachunok, B., Staid, A., Watson, J.P., Woodruff, D.L., & Woodruff, D.L. (2020). Assessment of wind power scenario creation methods for stochastic power systems operations. Applied Energy, 268(C). 10.1016/j.apenergy.2020.114986

Woodruff, D.L., Staid, A., Nicholson, B.L., Klise, K.A., & Klise, K.A. (2018). PARMEST: PARAMETER ESTIMATION VIA PYOMO [Conference Poster]. https://www.osti.gov/biblio/1761797

Rachunok, B., Staid, A., Watson, J., Woodruff, D.L., Yang, D., & Yang, D. (2018). Stochastic unit commitment performance considering monte carlo wind power scenarios [Conference Poster]. 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings. 10.1109/PMAPS.2018.8440563

Klise, K.A., Laird, C., Nicholson, B.L., Staid, A., Woodruff, D.L., & Woodruff, D.L. (2018). Parameter Estimation Uncertainty Quantification and Scenarios [Conference Poster]. https://www.osti.gov/biblio/1529782

Bynum, M.L., Hart, W.E., Siirola, J.D., Nicholson, B.L., Laird, C., Woodruff, D.L., Watson, J., & Watson, J. (2018). Pyomo Workshop Summer 2018 [Presentation]. https://www.osti.gov/biblio/1525944

Watson, J., Staid, A., Rachunok, B., Woodruff, D.L., Yang, D., & Yang, D. (2018). Stochastic Unit Commitment Performance Considering Monte Carlo Wind Power Scenarios [Conference Poster]. https://www.osti.gov/biblio/1525666

Watson, J., Woodruff, D.L., Deride Silva, J.A., Slevogt, G., Silva-Monroy, C., & Silva-Monroy, C. (2017). Constructing probabilistic scenarios for wide-area solar power generation. Solar Energy, 160. 10.1016/j.solener.2017.11.067

Watson, J., Staid, A., Woodruff, D.L., Winner, S., Nitsche, S., Silva-Monroy, C., & Silva-Monroy, C. (2017). Improving Wind Power Prediction Intervals Using Vendor-Supplied Probabilistic Forecast Information [Conference Poster]. https://www.osti.gov/biblio/1506929

Staid, A., Watson, J., Woodruff, D.L., Rachunok, B., & Rachunok, B. (2017). Assessment of Wind Power Scenario Generation Methods for Stochastic Unit Commitment [Conference Poster]. https://www.osti.gov/biblio/1513611

Staid, A., Watson, J., Woodruff, D.L., Wets, R., & Wets, R. (2017). Generating Short-Term Probabilistic Wind Power Scenarios via Non-Parametric Forecast Error Density Estimators [Presentation]. https://www.osti.gov/biblio/1426421

Hart, W.E., Laird, C., Watson, J., Woodruff, D.L., Hackebeil, G., Nicholson, B.L., Siirola, J.D., & Siirola, J.D. (2017). Pyomo - Optimization Modeling in Python 2nd Ed. https://www.osti.gov/biblio/1368754

Watson, J., Woodruff, D.L., Barnett, J., & Barnett, J. (2017). BBPH: Using progressive hedging within branch and bound to solve multi-stage stochastic mixed integer programs. Operations Research Letters, 45(1), pp. 34-39. 10.1016/j.orl.2016.11.006

Silva-Monroy, C.A., Watson, J., Staid, A., Woodruff, D.L., & Woodruff, D.L. (2016). Generating (Day-ahead) Probabilistic Scenarios for Solar Power Production [Conference Poster]. https://www.osti.gov/biblio/1398364

Siirola, J.D., Hart, W.E., Laird, C., Nicholson, B.L., Watson, J., Woodruff, D.L., & Woodruff, D.L. (2016). New developments in Pyomo [Conference Poster]. https://www.osti.gov/biblio/1374789

Siirola, J.D., Watson, J., Woodruff, D.L., & Woodruff, D.L. (2016). Accelerating and automatic tuning for Progressive Hedging [Conference Poster]. https://www.osti.gov/biblio/1372209

Watson, J., Woodruff, D.L., & Woodruff, D.L. (2015). Stochas(c Unit Commitment at Scale: Cost Savings Analysis for ISO-‐NE Jean-‐Paul Watson Sandia [Presentation]. https://www.osti.gov/biblio/1576126

Siirola, J.D., Watson, J., Woodruff, D.L., & Woodruff, D.L. (2015). Monitoring and Accelerating Progressive Hedging with Cross-scenario Information [Conference Poster]. https://www.osti.gov/biblio/1262942