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Integer programming with random-boundary intervals for planning municipal power systems

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  • Cao, M.F.
  • Huang, G.H.
  • Lin, Q.G.

Abstract

Uncertainty attached to municipal power systems has long been crucial considerations for the related planners. Such an uncertainty could be expressed as random-boundary intervals (RBIs). In this study, an integer programming with random-boundary intervals (IPRBI) approach was developed for municipal electricity-supply management under uncertainty. A concept of random-boundary interval (RBI) was introduced to reflect dual uncertainties that exist in many system components. A solution method named two-boundary approach (TBA) was proposed to solve the IPRBI model. A case study was provided for demonstrating applicability of the developed method. The results indicated that the RBI and integer-interval concepts were effective in dealing with highly uncertain parameters. The IPRBI method solutions could be used for generating efficient electricity-supply schemes under various complexities. They can also be used for analyzing tradeoffs between system cost and electricity-shortage risk. Compared with the existing methods, IPRBI was advantageous in reflecting the complexities of system uncertainties that were presented as RBIs, integer-intervals and intervals.

Suggested Citation

  • Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:8:p:2506-2516
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    3. Li, G.C. & Huang, G.H. & Lin, Q.G. & Zhang, X.D. & Tan, Q. & Chen, Y.M., 2011. "Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management," Energy, Elsevier, vol. 36(5), pages 3388-3398.
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    5. Wang, Xingwei & Cai, Yanpeng & Chen, Jiajun & Dai, Chao, 2013. "A grey-forecasting interval-parameter mixed-integer programming approach for integrated electric-environmental management–A case study of Beijing," Energy, Elsevier, vol. 63(C), pages 334-344.
    6. Tan, Raymond R., 2011. "A general source-sink model with inoperability constraints for robust energy sector planning," Applied Energy, Elsevier, vol. 88(11), pages 3759-3764.
    7. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    8. Li, Mo & Fu, Qiang & Singh, Vijay P. & Liu, Dong & Gong, Xinglong, 2020. "Risk-based agricultural water allocation under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 233(C).

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