Inexact Credibility-Constrained Programming Approach for Electricity Planning in Ontario, Canada
Publication: Journal of Energy Engineering
Volume 147, Issue 4
Abstract
Under current changing climatic conditions, there has been a growing interest in green energy to mitigate carbon emissions. As such, proper management and planning of electricity are essential to mitigating climate change. This paper presents a hybrid inexact credibility constraint programming (ICCP) model for planning and optimization in the electricity sector for Ontario, Canada. The model considers the costs and emissions of electricity generated from six sources over a planning horizon of , minimizing system cost while meeting provincial emission goals. The ICCP method addresses uncertainties by transforming fuzzy variables into crisp equivalents with credibility levels, allowing decision-makers to address uncertainties in planning by tackling uncertainties as intervals through an interactive two-step algorithm. This model was applied to electricity planning in Ontario to address uncertainties due to demand predictions, technological advancements, and shifting energy consumption. The cap-and-trade program was compared to the federal carbon pricing backstop program, and the results over the planning horizon were similar. Through this model, expansion options to address future demands were also compared to minimize emissions while meeting electricity demand.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request:
•
Historical and current electricity supply and demand; and
•
Electrical power generation expansion cost and supply increase.
Acknowledgments
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
References
Burda, C., and R. Peters. 2008. Plugging Ontario into a green future: A renewable is doable action plan. Toronto: Pembina Institute.
Cleave, E., M. Vecchio, D. Spilsbury, and G. Arku. 2019. “Manufacturing change and policy response in the contemporary economic landscape: How cities in Ontario, Canada, understand and plan for manufacturing.” Reg. Stud. Reg. Sci. 6 (1): 469–495. https://doi.org/10.1080/21681376.2019.1668292.
Fan, Y. R., and G. H. Huang. 2012. “A robust two-step method for solving interval linear programming problems within an environmental management context.” J. Environ. Inf. 19 (1): 1–9. https://doi.org/10.3808/jei.201200203.
Government of Ontario. 2016. “Ontario’s five year climate change action plan.” Accessed April 18, 2019. http://www.applications.ene.gov.on.ca/ccap/products/CCAP_ENGLISH.pdf.
Government of Ontario. 2017. “Ontario’s long term energy plan 2017: Delivering fairness and choice.” Accessed April 18, 2019. https://www.ontario.ca/document/2017-long-term-energy-plan.
Hajimiragha, A., M. W. Fowler, and C. A. Canizares. 2009. “Hydrogen economy transition in Ontario–Canada considering the electricity grid constraints.” Int. J. Hydrog. Energy 34 (13): 5275–5293. https://doi.org/10.1016/j.ijhydene.2009.04.063.
Hashim, H., P. Douglas, A. Elkamel, and E. Croiset. 2005. “Optimization model for energy planning with CO2 emission considerations.” Ind. Eng. Chem. Res. 44 (4): 879–890. https://doi.org/10.1021/ie049766o.
Heath, G. A., P. O’Donoughue, D. J. Arent, and M. Bazilian. 2014. “Harmonization of initial estimates of shale gas life cycle greenhouse gas emissions for electric power generation.” Proc. National Acad. Sci. 111 (31): E3167–E3176. https://doi.org/10.1073/pnas.1309334111.
Hu, Q., G. Huang, Y. Cai, and Y. Huang. 2011. “Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties.” Appl. Energy 88 (12): 4642–4654. https://doi.org/10.1016/j.apenergy.2011.06.004.
Huang, G. H., B. B. Baetz, and G. G. Patry. 1995. “Grey integer programming: An application to waste management planning under uncertainty.” Eur. J. Oper. Res. 83 (3): 594–620. https://doi.org/10.1016/0377-2217(94)00093-R.
Huang, G. H., and D. P. Loucks. 2000. “An inexact two-stage stochastic programming model for water resources management under uncertainty.” Civ. Eng. Syst. 17 (2): 95–118. https://doi.org/10.1080/02630250008970277.
Huang, X. 2006. “Credibility-based chance-constrained integer programming models for capital budgeting with fuzzy parameters.” Inf. Sci. 176 (18): 2698–2712. https://doi.org/10.1016/j.ins.2005.11.012.
Independent Electricity System Operator. 2016. “Ontario planning outlook 2016. IESO.” Accessed March 20, 2019. http://ieso.ca/en/sector-participants/planning-and-forecasting/ontario-planning-outlook.
Independent Electricity System Operator. 2018. “Year end data.” Accessed April 18, 2019. http://www.ieso.ca/en/Corporate-IESO/Media/Year-End-Data.
Independent Electricity System Operator. 2019. “Planning to meet the needs of Ontario’s electricity system.” Accessed April 10, 2019. https://www.ieso.ca/media/Files/IESO/Document-Library/regional-planning/Forums/2019/East-Ontario-Forum-Planning-to-Meet-Needs-of-Ontarios-Electricity-System.
Institute for Energy Research. 2009. Levelized cost of new electricity generating technologies. Houston: Institute for Energy Research.
International Panel on Climate Change Working Group III. 2011. IPCC special report on renewable energy sources and climate change mitigation. Geneva: International Panel on Climate Change.
Jami, A. A., and P. R. Walsh. 2014. “The role of public participation in identifying stakeholder synergies in wind power project development: The case study of Ontario, Canada.” Renew. Energy 68: 194–202. https://doi.org/10.1016/j.renene.2014.02.004.
Ji, L., G. H. Huang, D. X. Niu, Y. P. Cai, and J. G. Yin. 2020. “A stochastic optimization model for carbon-emission reduction investment and sustainable energy planning under cost-risk control.” J. Environ. Inf. 36 (2): 107–118.
Jones, W. D. 2007. “Ontario quits coal.” IEEE Spectr. 44 (3): 12–13. https://doi.org/10.1109/MSPEC.2007.323418.
Li, L., L. Lei, M. S. Zheng, A. G. L. Borthwick, and J. R. Ni. 2020. “Stochastic evolutionary-based optimization for rapid diagnosis and energy-saving in pilot-and full-scale carrousel oxidation ditches.” J. Environ. Inf. 35 (1): 81–93.
Li, Y. P., G. H. Huang, L. Cui, and J. Liu. 2019a. “Mathematical modeling for identifying cost-effective policy of municipal solid waste management under uncertainty.” J. Environ. Inf. 34 (1): 55–67. https://doi.org/10.3808/jei.201900417.
Li, Z., G. Huang, Y. Zhang, and Y. Li. 2013. “Inexact two-stage stochastic credibility constrained programming for water quality management.” Resour. Conserv. Recycl. 73: 122–132. https://doi.org/10.1016/j.resconrec.2013.01.008.
Li, Z., G. H. Huang, L. Guo, Y. R. Fan, and J. P. Chen. 2019b. “A fuzzy gradient chance-constrained evacuation model for managing risks of nuclear power plants under multiple uncertainties.” J. Environ. Inf. 33 (2): 129–138. https://doi.org/10.3808/jei.201500315.
Lin, Q. G., and G. H. Huang. 2011. “Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—A case study for the Province of Ontario, Canada.” Clim. Change 104 (2): 353–378. https://doi.org/10.1007/s10584-009-9795-8.
Liu, B. 2006. “A survey of credibility theory.” Fuzzy Optim. Decis. Making 5 (4): 387–408. https://doi.org/10.1007/s10700-006-0016-x.
McPherson, M., and B. Karney. 2017. “A scenario based approach to designing electricity grids with high variable renewable energy penetrations in Ontario, Canada: Development and application of the SILVER model.” Energy 138: 185–196. https://doi.org/10.1016/j.energy.2017.07.027.
Mirzaesmaeeli, H., A. Elkamel, P. L. Douglas, E. Croiset, and M. Gupta. 2010. “A multi-period optimization model for energy planning with emission consideration.” J. Environ. Manage. 91 (5): 1063–1070. https://doi.org/10.1016/j.jenvman.2009.11.009.
National Energy Board. 2017. “Canada’s renewable power landscape 2017—Energy market analysis.” Accessed March 15, 2018. https://www.neb-one.gc.ca/nrg/sttstc/lctrct/rprt/2017cndrnwblpwr/ghgmssn-eng.html.
Raadal, H. L., L. Gagnon, I. S. Modahl, and O. J. Hanssen. 2011. “Life cycle greenhouse gas (GHG) emissions from the generation of wind and hydro power.” Renewable Sustainable Energy Rev. 15 (7): 3417–3422. https://doi.org/10.1016/j.rser.2011.05.001.
Richardson, D. B., and L. D. Harvey. 2015. “Optimizing renewable energy, demand response and energy storage to replace conventional fuels in Ontario, Canada.” Energy 93: 1447–1455. https://doi.org/10.1016/j.energy.2015.10.025.
Stokes, L. C. 2013. “The politics of renewable energy policies: The case of feed-in tariffs in Ontario, Canada.” Energy Policy 56: 490–500. https://doi.org/10.1016/j.enpol.2013.01.009.
Su, W., Z. Yuan, and M. Y. Chow. 2010. Microgrid planning and operation: Solar energy and wind energy. In Proc., IEEE PES GeneralMeeting, 1–7. New York: IEEE.
US Energy Information Administration. 2019. “Annual energy outlook 2019 with projections to 2050.” Accessed September 2, 2018. https://www.eia.gov/outlooks/aeo/.
Warner, E. S., and G. A. Heath. 2012. “Life cycle greenhouse gas emissions of nuclear electricity generation.” Supplement, J. Ind. Ecol. 16 (S1): S73–S92. https://doi.org/10.1111/j.1530-9290.2012.00472.x.
Winfield, M., R. B. Gibson, T. Markvart, K. Gaudreau, and J. Taylor. 2010. “Implications of sustainability assessment for electricity system design: The case of the Ontario power authority’s integrated power system plan.” Energy Policy 38 (8): 4115–4126. https://doi.org/10.1016/j.enpol.2010.03.038.
Yatchew, A., and A. Baziliauskas. 2011. “Ontario feed-in-tariff programs.” Energy Policy 39 (7): 3885–3893. https://doi.org/10.1016/j.enpol.2011.01.033.
Zeng, X. T., Y. P. Li, W. Huang, X. Chen, and A. M. Bao. 2014. “Two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) for planning water resources management.” Eng. Appl. Artif. Intell. 35: 164–175. https://doi.org/10.1016/j.engappai.2014.06.021.
Zhang, C., and P. Guo. 2017. “A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty.” J. Hydrol. 553: 735–749. https://doi.org/10.1016/j.jhydrol.2017.08.008.
Zhang, Y., and G. Huang. 2010. “Fuzzy robust credibility-constrained programming for environmental management and planning.” J. Air Waste Manage. Assoc. 60 (6): 711–721. https://doi.org/10.3155/1047-3289.60.6.711.
Zhang, Y. M., G. Huang, H. W. Lu, and L. He. 2015. “Planning of water resources management and pollution control for Heshui River watershed, China: A full credibility-constrained programming approach.” Sci. Total Environ. 524: 280–289. https://doi.org/10.1016/j.scitotenv.2015.03.032.
Zhang, Y. M., and G. H. Huang. 2011a. “Inexact credibility constrained programming for environmental system management.” Resour. Conserv. Recycl. 55 (4): 441–447. https://doi.org/10.1016/j.resconrec.2010.11.007.
Zhang, Y. M., and G. H. Huang. 2011b. “Optimal water resource planning under fixed budget by interval-parameter credibility constrained programming.” Eng. Optim. 43 (8): 879–889. https://doi.org/10.1080/0305215X.2010.522709.
Zheng, Y., and B. Liu. 2006. “Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm.” Appl. Math. Comput. 176 (2): 673–683. https://doi.org/10.1016/j.amc.2005.10.013.
Zhu, Y., Y. P. Li, G. H. Huang, Y. R. Fan, and S. Nie. 2015. “A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty.” Energy 88: 636–649. https://doi.org/10.1016/j.energy.2015.05.106.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 147 • Issue 4 • August 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 6, 2020
Accepted: Dec 30, 2020
Published online: Apr 29, 2021
Published in print: Aug 1, 2021
Discussion open until: Sep 29, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.