Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP

References

• Al-Yahyai, S., Y. Charabi, and A. Gastli. 2010. “Review of the Use of Numerical Weather Prediction (NWP) Models for Wind Energy Assessment.” Renewable and Sustainable Energy Reviews 14: 3192–3198.10.1016/j.rser.2010.07.001
   Web of Science ®Google Scholar
• Antunes, C. H., A. G. Martins, and I. S. Brito. 2004. “A Multiple Objective Mixed Integer Linear Programming Model for Power Generation Expansion Planning.” Energy 29: 613–627.10.1016/j.energy.2003.10.012
   Web of Science ®Google Scholar
• Biel, Konstantin, and Christoph H. Glock. 2016. “Systematic Literature Review of Decision Support Models for Energy-efficient Production Planning.” Computers & Industrial Engineering 101: 243–259. doi:10.1016/j.cie.2016.08.021.
   Web of Science ®Google Scholar
• Bludszuweit, H., J. A. Dominguez-Navarro, and A. Llombart. 2008. “Statistical Analysis of Wind Power Forecast Error.” IEEE Transactions on Power Systems 23: 983–991.10.1109/TPWRS.2008.922526
   Web of Science ®Google Scholar
• Brett, A. C., and S. E. Tuller. 1991. “The Autocorrelation of Hourly Wind Speed Observations.” Journal of Applied Meteorology 30: 823–833.10.1175/1520-0450(1991)030<0823:TAOHWS>2.0.CO;2
   Google Scholar
• Brown, B. G., R. W. Katz, and A. H. Murphy. 1984. “Time Series Models to Simulate and Forecast Wind Speed and Wind Power.” Journal of Climate and Applied Meteorology 23: 1184–1195.10.1175/1520-0450(1984)023<1184:TSMTSA>2.0.CO;2
   Google Scholar
• Burton, T., D. Sharpe, N. Jenkins, and E. Bossanyi. 2011. Wind Energy Handbook. 2nd ed. Chicester: Wiley.10.1002/9781119992714
   Google Scholar
• Carta, José Antonio, and Dunia Mentado. 2007. “A Continuous Bivariate Model for Wind Power Density and Wind Turbine Energy Output Estimations.” Energy Conversion and Management 48 (2): 420–432. doi:10.1016/j.enconman.2006.06.019.
   Web of Science ®Google Scholar
• Corotis, R. B., A. B. Sigl, and M. P. Cohen. 1977. “Variance Analysis of Wind Characteristics for Energy Conversion.” Journal of Applied Meteorology 16: 1149–1157.10.1175/1520-0450(1977)016<1149:VAOWCF>2.0.CO;2
   Google Scholar
• Ehrgott, M. 2005. Multicriteria Optimization. 2nd ed. Berlin Heidelberg: Springer.
   Google Scholar
• Enercon GmbH. 2015. Enercon Product Overview. Accessed July 6, 2016. https://www.enercon.de/fileadmin/Redakteur/Medien-Portal/broschueren/pdf/en/ENERCON_Produkt_en_06_2015.pdf
   Google Scholar
• European Parliament and Council. 2009. “Energy Efficiency Directive (2009/28/EC).” Official Journal of the European Union, 52, L 140/16–L 140/62.
   Google Scholar
• Fang, K., N. Uhan, F. Zhao, and J. W. Sutherland. 2011. “A New Approach to Scheduling in Manufacturing for Power Consumption and Carbon Footprint Reduction.” Journal of Manufacturing Systems 30: 234–240.10.1016/j.jmsy.2011.08.004
   Web of Science ®Google Scholar
• Foley, A. M., P. G. Leahy, A. Marvuglia, and E. J. McKeogh. 2012. “Current Methods and Advances in Forecasting of Wind Power Generation.” Renewable Energy 37: 1–8.10.1016/j.renene.2011.05.033
   Web of Science ®Google Scholar
• Gröwe-Kuska, N., H. Heitsch, and W. Römisch. 2003. “Scenario Reduction and Scenario Tree Construction for Power Management Problems.” Power Tech Conference Proceedings, 2003 IEEE Bologna, June 23–26.
   Google Scholar
• Hake, J.-F., W. Fischer, S. Venghaus, and C. Weckenbrock. 2015. “The German Energiewende – History and Status Quo.” Energy 92 (Part 3): 532–546.
   Web of Science ®Google Scholar
• Hocaoğlu, F. O. 2010. “Novel Analytical Hourly Solar Radiation Models for Photovoltaic Based System Sizing Algorithms.” Energy Conversion and Management 51: 2921–2929.10.1016/j.enconman.2010.06.033
   Web of Science ®Google Scholar
• Iversen, E. B., J. M. Morales, J. K. Møller, and H. Madsen. 2016. “Short-term Probabilistic Forecasting of Wind Speed Using Stochastic Differential Equations.” International Journal of Forecasting 32: 981–990.10.1016/j.ijforecast.2015.03.001
   Web of Science ®Google Scholar
• Kim, I. Y., and O. L. Weck. 2005. “Adaptive Weighted Sum Method for Multiobjective Optimization: A New Method for Pareto Front Generation.” Structural and Multidisciplinary Optimization 31: 105–116.
   Web of Science ®Google Scholar
• Lantz, E., B. Sigrin, M. Gleason, R. Preus, and I. Baring-Gould. 2016. Assessing the Future of Distributed Wind: Opportunities for behind-the-meter Projects. Golden, CO: National Renewable Energy Laboratory.10.2172/1333625
   Google Scholar
• Lei, D., Y. Zheng, and X. Guo. 2017. “A Shuffled Frog-leaping Algorithm for Flexible Job Shop Scheduling with the Consideration of Energy Consumption.” International Journal of Production Research 55 (11): 3126–3140.10.1080/00207543.2016.1262082
   Web of Science ®Google Scholar
• Li, J., F. Lan, and H. Wei. 2016. “A Scenario Optimal Reduction Method for Wind Power Time Series.” IEEE Transactions on Power Systems 31: 1657–1658.10.1109/TPWRS.2015.2412687
   Web of Science ®Google Scholar
• Liu, C.-H. 2016. “Mathematical Programming Formulations for Single-machine Scheduling Problems While Considering Renewable Energy Uncertainty.” International Journal of Production Research 54: 1122–1133.10.1080/00207543.2015.1048380
   Web of Science ®Google Scholar
• Liu, P.-L., and A. Der Kiureghian. 1986. “Multivariate Distribution Models with Prescribed Marginals and Covariances.” Probabilistic Engineering Mechanics 1: 105–112.10.1016/0266-8920(86)90033-0
   Google Scholar
• Liu, G.-S., H.-D. Yang, and M.-B. Cheng. 2017. “A Three-stage Decomposition Approach for Energy-aware Scheduling with Processing-time-dependent Product Quality.” International Journal of Production Research 55 (11): 3073–3091.10.1080/00207543.2016.1241446
   Web of Science ®Google Scholar
• Luo, H., B. Du, G. Q. Huang, H. Chen, and X. Li. 2013. “Hybrid Flow Shop Scheduling Considering Machine Electricity Consumption Cost.” International Journal of Production Economics 146: 423–439.10.1016/j.ijpe.2013.01.028
   Web of Science ®Google Scholar
• Ma, X.-Y., Y.-Z. Sun, and H.-L. Fang. 2013. “Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability.” IEEE Transactions on Sustainable Energy 4: 894–904.10.1109/TSTE.2013.2256807
   Web of Science ®Google Scholar
• May, G., B. Stahl, M. Taisch, and V. Prabhu. 2015. “Multi-objective Genetic Algorithm for Energy-Efficient Job Shop Scheduling.” International Journal of Production Research 53 (23): 7071–7089.10.1080/00207543.2015.1005248
   Web of Science ®Google Scholar
• McCrone, A., U. Moslener, E. Usher, C. Grüning, and V. Sonntag-O’Brian. 2015. Global Trends in Renewable Energy Investment 2015. Frankfurt: Frankfurt School UNEP Collaborating Centre for Climate and Sustainable Energy Finance.
   Google Scholar
• Moon, J.-Y., and J. Park. 2014. “Smart Production Scheduling with Time-dependent and Machine-dependent Electricity Cost by Considering Distributed Energy Resources and Energy Storage.” International Journal of Production Research 52: 3922–3939.10.1080/00207543.2013.860251
   Web of Science ®Google Scholar
• Moon, J.-Y., K. Shin, and J. Park. 2013. “Optimization of Production Scheduling with Time-dependent and Machine-dependent Electricity Cost for Industrial Energy Efficiency.” The International Journal of Advanced Manufacturing Technology 68: 523–535.10.1007/s00170-013-4749-8
   Web of Science ®Google Scholar
• Morales, J. M., R. Mínguez, and A. J. Conejo. 2010. “A Methodology to Generate Statistically Dependent Wind Speed Scenarios.” Applied Energy 87: 843–855.10.1016/j.apenergy.2009.09.022
   Web of Science ®Google Scholar
• Mouzon, G., M. B. Yildirim, and J. Twomey. 2007. “Operational Methods for Minimization of Energy Consumption of Manufacturing Equipment.” International Journal of Production Research 45: 4247–4271.10.1080/00207540701450013
   Web of Science ®Google Scholar
• Pacific Gas and Electric Company. 2016a. Current Electric Rates for Industrial Customers (E-20 Secondary Firm). Accessed July 6. https://www.pge.com/tariffs/electric.shtml#INDUSTRIAL
   Google Scholar
• Pacific Gas and Electric Company. 2016b. ReMAT Feed-in Tariff (Senate Bill 32). Accessed July 6. https://www.pge.com/en_US/for-our-business-partners/floating-pages/remat-feed-in-tariff/remat-feed-in-tariff.page
   Google Scholar
• Pinedo, M. L. 2012. Scheduling: Theory, Algorithms, and Systems. 4th ed. New York: Springer.10.1007/978-1-4614-2361-4
   Google Scholar
• Pinson, P., H. Madsen, H. A. Nielsen, G. Papaefthymiou, and B. Klöckl. 2009. “From Probabilistic Forecasts to Statistical Scenarios of Short-term Wind Power Production.” Wind Energy 12: 51–62.10.1002/we.v12:1
   Web of Science ®Google Scholar
• Sharma, K. C., P. Jain, and R. Bhakar. 2013. “Wind Power Scenario Generation and Reduction in Stochastic Programming Framework.” Electric Power Components and Systems 41: 271–285.10.1080/15325008.2012.742942
   Web of Science ®Google Scholar
• Sharma, A., F. Zhao, and J. W. Sutherland. 2015. “Econological Scheduling of a Manufacturing Enterprise Operating under a Time-of-use Electricity Tariff.” Journal of Cleaner Production 108: 256–270.10.1016/j.jclepro.2015.06.002
   Web of Science ®Google Scholar
• Tognetti, A., P. T. Grosse-Ruyken, and S. M. Wagner. 2015. Green Supply Chain Network Optimization and the Trade-off between Environmental and Economic Objectives. International Journal of Production Economics 170 (Part B): 385–392.10.1016/j.ijpe.2015.05.012
   Web of Science ®Google Scholar
• U.S. Department of Energy. 2015. Revolution … Now: The Future Arrives for Five Clean Energy Technologies – 2015 Update. Washington, DC.
   Google Scholar
• U.S. Energy Information Administration. 2015. Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2015. Washington, DC.
   Google Scholar
• Wang, Y., and L. Li. 2013. “Time-of-use Based Electricity Demand Response for Sustainable Manufacturing Systems.” Energy 63: 233–244.10.1016/j.energy.2013.10.011
   Web of Science ®Google Scholar
• Wang, Y., and L. Li. 2015. “Time-of-use Electricity Pricing for Industrial Customers: A Survey of U.S. Utilities.” Applied Energy 149: 89–103.10.1016/j.apenergy.2015.03.118
   Web of Science ®Google Scholar
• Zhang, H., F. Zhao, K. Fang, and J. W. Sutherland. 2014. “Energy-Conscious Flow Shop Scheduling under Time-of-use Electricity Tariffs.” CIRP Annals – Manufacturing Technology 63: 37–40.10.1016/j.cirp.2014.03.011
   Web of Science ®Google Scholar