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Demand Response Optimization Based on Building’s Characteristics

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Optimization in the Real World

Part of the book series: Mathematics for Industry ((MFI,volume 13))

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Abstract

Demand response (DR) is one of the technologies that targets for power control based on the cooperation between power suppliers and consumers. In the case of buildings’ power control, it is important for buildings to achieve buildings’ power reduction target more correctly under individual buildings’ less burden. We suppose the framework of buildings’ aggregator that collects the information for power reduction (NEGAWATT information) and requests each building to reduce demand to achieve buildings’ power reduction target efficiently. In the framework, we first collect the NEGAWATT information based on buildings’ characteristics, and then we make the demand response plans (DR plans) that meet the requirements of buildings. In this paper, we focus on the DR optimization techniques based on NEGAWATT information, and show two simulation results, one of which shows the aggregation effect in a simple simulation, the other of which shows the multiple scenario methods to deal with the DR optimization under uncertainty. These simulations show that DR utilizing NEGAWATT information is more efficient for demand-supply balance than conventional DR methods.

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Notes

  1. 1.

    Some parts of this article, some diagrams and some tables have already been published in [2, 3]. Copyright @ 2014 Information Processing Society of Japan and Copyright @ 2013 Toshiba Corporation.

References

  1. Japan Smart City Portal: http://jscp.nepc.or.jp/en/. Accessed on 9 Jan 2015

  2. Otsuki, T.: Negawatt planning based on consumer’s characteristics under uncertainty. Trans. Math. Model. Appl. Inf. Process. Soc. Jpn. (IPSJ-TOM), 7(1), 44–52 (2014)

    Google Scholar 

  3. Otsuki, T., Aisu, H., Iino, Y.: DR optimization based on building’s characteristics under uncertainty. Toshiba Rev. 68(7), 23–26 (2013)

    Google Scholar 

  4. Goldman, C., Reid, M., Levy, R., Silverstein, A.: Coordination of energy efficiency and demand response, LBNL-3044E, Jan 2010

    Google Scholar 

  5. Carlson, B., Chen, Y., Hong, M.: MISO unlocks billions in savings through the application of operations research for energy and ancillary services markets. Interfaces 42(1), 58–73 (2012). Jan

    Article  Google Scholar 

  6. Schisler, K., et al.: The role of demand response in ancillary services markets. In: Transmission and Distribution Exposition Conference: 2008 IEEE PES Powering Toward the Future, PIMS 2008. No. 4517087 (2008)

    Google Scholar 

  7. Bard, J.F.: Short-term scheduling of thermal-electric generators using Lagrangian relaxations. Oper. Res. 36, 756–766 (1988)

    Article  MATH  Google Scholar 

  8. Muckstadt, J.A., Koenig, S.A.: An application of Lagrangian relaxation to scheduling in power-generation systems. Oper. Res. 25, 387–403 (1977)

    Article  MATH  Google Scholar 

  9. Tokoro, K., Masuda, Y., Nishino, H.: A planning method using genetic algorithm for large scale unit commitment problem. Central Research Institute of Electric Power Industry. Report No. R04018, Jan 2006

    Google Scholar 

  10. Shiina, T., Birge, J.R.: Multistage stochastic programming model for electric power capacity expansion problem. Jpn. J. Ind. Appl. Math. 20, 379–397 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  11. Energy saving standard format for small and medium-sized business: http://www.meti.go.jp/earthquake/electricity_supply/0513_electricity_supply_02_07.pdf. Accessed on 9 Jan 2015

  12. Energy white paper 2010: http://www.enecho.meti.go.jp/about/whitepaper/2010pdf/. Accessed on 9 Jan 2015

  13. Birge, J.R.: Stochastic programming computation and applications. INFORMS J. Comput. 9, 111–133 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  14. Van Slyke, R.M., Wets, R.: L-shaped linear programs with applications to optimal control and stochastic programming. SIAM J. Appl. Math. 17(4), 638–663 (1969)

    Article  MATH  MathSciNet  Google Scholar 

  15. Plambeck, E.L., Fu, B.R., Robinson, S.M., Suri, R.: Sample path optimization of convex stochastic performance functions. Math. Program. 75, 137–176 (1996)

    Google Scholar 

  16. Rockafellar, R.T., Wets, R.J.B.: Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16(1), 119–147 (1991)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgments

This research is partially supported by the national project, the Next-generation Energy and Social System Demonstration Project, initiated by METI.

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Correspondence to Tomoshi Otsuki .

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Otsuki, T. (2016). Demand Response Optimization Based on Building’s Characteristics. In: Fujisawa, K., Shinano, Y., Waki, H. (eds) Optimization in the Real World. Mathematics for Industry, vol 13. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55420-2_10

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  • DOI: https://doi.org/10.1007/978-4-431-55420-2_10

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  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55419-6

  • Online ISBN: 978-4-431-55420-2

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