Planning of electric power generation systems under multiple uncertainties and constraint-violation levels

Journal of Environmental Informatics

Volumn 23, Issue 1, 2014, Pages 55-64

  Hu, Q ^a,b   Huang, G H ^a   Cai, Y P ^c,d   Sun, W ^d  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b Beijing Building Technology Development Co Ltd   (China)

^c BEIJING NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF REGINA   (Canada)

Author keywords

Constraint violation; Electric power generation system; Emission mitigation; Lower side attainment degree; Multiple system uncertainties

Indexed keywords

ELECTRICITY GENERATION; EMISSION; ENERGY RESOURCE; FUZZY MATHEMATICS; MITIGATION; POWER GENERATION; UNCERTAINTY ANALYSIS;

EID: 84896918756     PISSN: 17262135     EISSN: 16848799     Source Type: Journal    
DOI: 10.3808/jei.201400257     Document Type: Article

References (26)

1. Cai, Y.P., Huang, G.H., and Tan, Q. (2009a). An inexact optimization model for regional energy systems planning in the mixed stochastic and fuzzy environment. Int. J. Energy Res., 33(5), 443-468. http://dx.doi.org/10.1002/er.1483
2. Cai, Y.P., Huang, G.H., Yang, Z.F., Lin, Q.G., and Tan, Q. (2009b). Community-scale renewable energy systems planning under uncertainty - an interval chance-constraint programming approach. Renew. Sustain. Energy Rev., 13(4), 721-735. http://dx.doi.org/10.1016/j.rser.2008.01.008
3. Cai, Y.P., Huang, G.H., Yang, Z.F., and Tan, Q. (2009c). Identification of optimal strategies for energy management systems planning under multiple uncertainties. Appl. Energy, 86(4), 480-495. http://dx. doi.org/10.1016/j.apenergy.2008.09.025
4. Cai, Y.P., Huang, G.H., Tan, Q., and Yang, Z.F. (2009d). Planning of community-scale renewable energy management systems in a mixed stochastic and fuzzy environment. Renewable Energy, 34(7), 1833-1847. http://dx.doi.org/10.1016/j.renene.2008.11.024
5. Carrión, M., Philpott, A.B., Conejo, A.J., and Arroyo, J.M. (2007). A stochastic programming approach to electric energy procurement for large consumers. IEEE Trans. Power Syst., 22(2): 744-754. http://dx.doi.org/10.1109/TPWRS.2007.895164
6. Chou, S.Y., Lin, J.S.J., and Julian, P. (2009). A note on Solving linear programming problems under fuzziness and randomness environment using attainment values. Inf. Sci., 179 (23), 4083-4088. http://dx. doi.org/10.1016/j.ins.2009.08.013
7. Gjorgiev, B., Cepin, M., and Iliev, A. (2010). Optimal generation schedule of power system considering nuclear power plant with application of genetic algorithms. Proceedings of the International Conference Nuclear Energy for New Europer, Portoroz, Slovenia, 704, 1-8.
8. Gjorgiev, B., and Cepin, M. (2013). A multi-objective optimization based solution for the combined economic-environmental power dispatch problem. Eng. Appl. Artif. Intel., 26(1), 417-429. http://dx. doi.org/10.1016/j.engappai.2012.03.002
9. Guo, L., Li, Y.P., Huang, G.H., Wang, X.W., and Dai, C. (2012). Development of an interval-based evacuation management model in response to nuclear-power plant accident. J. Environ. Inf., 20(2), 58-66. http://dx.doi.org/10.3808/jei.201200220
10. Hu, Q., Huang, G.H., Liu, Z.F., Fan, Y.R., and Li, W. (2012), Inexact fuzzy two-stage programming for water resources management in an environment of fuzziness and randomness. Stochastic Environ. Res. Risk Assess., 26(2), 261-280. http://dx.doi.org/10.1007/s00477 - 011- 0503-7
11. Huang, G.H. (1998). A hydrid inexact-stochastic water management model. Eur. J. Oper. Res., 107, 137-158. http://dx.doi.org/10.1016/ S0377-2217(97)00144-6
12. Huang, G.H., Niu, Y.T., Lin, Q.G., Zhang, X.X., and Yang, Y.P. (2011). An interval-parameter chance-constraint mixed-integer programming for energy systems planning under uncertainty. Energy Source Part B Econ. Plann. Policy, 6(2), 192-205. http://dx.doi.org/10.1080/15567240903485931
13. Jana, C., and Chattopadhyay, R.N. (2004). Block level energy planning for domestic lighting - a multi-objective fuzzy linear programming approach. Energy, 29(11), 1819-1829. http://dx.doi. org/10.1016/ j.energy.2004.03.095
14. Khan, M.J., and Iqbal, M.T. (2005). Pre-feasibility study of standalone hybrid energy systems for applications in New-foundland. Renewable Energy, 30(6), 835-854. http://dx.doi.org/10.1016/j. renene.2004.09.001
15. Li, Y.F., Huang, G.H., Li, Y.P., Xu, Y., and Chen, W.T. (2009). Regional-scale electric power system planning under uncertainty - a multistage interval-stochastic integer linear programming approach. Energy Policy, 38(1), 475-490. http://dx.doi.org/10.1016/j.enpol.2009.09.038
16. Li, Y.F., Li, Y.P., Huang, G.H., and Chen, X. (2010). Energy and environmental systems planning under uncertainty-an inexact fuzzy-stochastic programming approach. Appl. Energy, 87(10), 3189-3211. http://dx.doi.org/10.1016/j.apenergy.2010.02.030
17. Liu, Y., Huang, G.H., Cai, Y.P., Cheng, G.H., Niu, Y.T., and An, K. (2009). Development of an inexact optimization model for coupled coal and power management in North China. Energy Policy, 37 (11), 4345-4363. http://dx.doi.org/10.1016/j.enpol.2009.05.050
18. Mavrotas, G., Diakoulaki, D., Florios, K., and Georgiou, P. (2008). A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: the case of a hospital in Athens. Energy Policy, 36(7), 2415-2429. http://dx.doi.org/10.1016/j.enpol.2008.01.011
19. Sadeghi, M., and Hosseini, H.M. (2006) Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs). Energy policy, 34, 993-1003. http://dx.doi.org/10.1016/j.enpol.2004.09.005
20. Shen, Y.S., Tan, Z.F, Shen, X.L., Bai, J.J., Li, Q.Z., and Wang, S. (2012). Study of energy saving and emission reduction based on the OLAP multi-indicator relational model. J. Envrion. Inf., 20(2), 115-122. http://dx.doi.org/10.3808/jei.201200225
21. Tan, Q., Huang, G.H., and Cai, Y.P. (2010a). Identification of optimal plans for municipal solid waste management in an environment of fuzziness and two-layer randomness. Stochastic Environ. Res. Risk Assess., 24, 147-164. http://dx.doi.org/10.1007/s00477-009-0307-1
22. Tan, Q., Huang, G.H., and Cai, Y.P. (2010b). A superiority-inferioritybased inexact fuzzy stochastic programming approach for solid waste management under uncertainty. Environ. Model. Assess., 15 (5), 381-396. http://dx.doi.org/10.1007/s10666-009-9214-6
23. Tan, Q., Huang, G.H., and Cai, Y.P. (2010c). Waste management with recourse: An inexact dynamic programming model containing fuzzy boundary intervals in objectives and constraints. J. Environ. Manage., 91(9), 1898-1913. http://dx.doi.org/10.1016/j.jenvman. 2010.04.005
24. Van Hop, N. (2007). Solving linear programming problems under fuzziness and randomness environment using attainment values. Inf. Sci., 177(14), 2971-2984. http://dx.doi.org/10.1016/j.ins.2007. 01.032
25. Weber, C., Meiborn, P., Barth, R., and Brand, H. (2009). WILMAR: A stochastic programming tool to analyze the large-scale integration of wind energy. Optimization in Energy Industry, Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-540-88965-6_19
26. Zimmerman, H.J. (2001). Fuzzy set theory - and its application. 4th ed. Netherlands: Springer. http://dx.doi.org/10.1007/978-94-010- 0646-0