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Modeling demand response and economic impact of advanced and smart metering

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Abstract

Advanced metering constitutes an essential component of communications between electricity suppliers and consumers. It may be possible to augment demand response by coupling Advanced Metering Infrastructure (AMI) with advanced implementations in homes. In this paper, we quantify the economic impact of such advanced implementations and show that these technologies promise to add value for both major stakeholders—electricity suppliers and the consumers. In particular, we present a comparative study by evaluating the adoption of Basic-AMI versus one that incorporates automation devices enabling communication and control. We present a linear programming modeling framework to capture demand response behavior while reflecting regulatory policies to ensure lower critical peak prices. Based on this framework, we present a case study for the state of Ohio, although the general nature of the analysis is applicable to other states as well. Our results show that dynamic pricing coupled with AMI—in particular, its advanced implementations—provides significant incentives to the utility, regulatory commission and the consumer.

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Notes

  1. PROMOD is a widely used proprietary simulator available from Ventyx, Inc.

  2. Generation costs account for about two-thirds of the cost of electricity.

  3. PJM and MISO Fact Sheets from http://www.pjm.com and http://www.misoenergy.org.

  4. Brattle Group, Memorandum to PUCO, 2010.

  5. CPP is used as an acronym for critical peak price.

  6. Load cost refers to the cost of supplying energy demand.

  7. The peak or maximum LMP is maximum of the four hour block averages.

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Acknowledgments

We thank the Public Utility Commission of Ohio (PUCO) for funding this research, and are grateful to the PUCO staff, particularly, Dan Johnson, Hisham Choueiki and David Wang for extensive discussions throughout the duration of the project. The software and data support provided by Ventyx is also deeply appreciated. We thank Binyuan Chen for his contribution. The authors acknowledge that the research reported in this paper was partially funded by NSF under Grant No. CMMI 0900070.

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Authors and Affiliations

  1. Data Driven Decisions Lab, Integrated Systems Engineering Department, The Ohio State University, Columbus, OH, 43210, USA

    Praneeth Aketi

  2. Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, 90089, USA

    Suvrajeet Sen

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  1. Praneeth Aketi
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  2. Suvrajeet Sen
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Correspondence to Praneeth Aketi.

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Aketi, P., Sen, S. Modeling demand response and economic impact of advanced and smart metering. Energy Syst 5, 583–606 (2014). https://doi.org/10.1007/s12667-013-0113-1

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