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Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol

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  • Eissa, M.M.

Abstract

An incentive demand response program with two tools is proposed in this paper to help the customers to participate in the program. The first tool is the use of advanced metering infrastructure (AMI) with two-way communication system that carries significant implications for customer service, privacy and consumer protection policies. The proposed demand response program with AMI, based on real time signals, can be an effective method for utilities to manage system peaks by controlling customer loads with renewable sources. Widespread use of program with AMI will be supported by reliable and flexible ECHONET Lite Specification (ELS) communication protocol, as the second tool, that would encourage consumers to participate and reduce their energy use at peak times. ECHONET Lite, next-generation industrial and household level protocol system, is designed to integrate many devices and systems. Developing commercial energy management system (CEMS) based on advanced smart metering and ECHONET Lite protocol can help in getting the balance between the load and resources at the customers’ premises. CEMS can manage appliances and renewable resource devices at the consumers’ premises with the premise that the consumers should manage the power consumption by themselves to balance and also reduce the energy costs. The developed management system is based on a diffusion model for load balancing. The automated model of gaseous diffusion is applied as new load balancing model between the load and sources. The model helps getting an automatic balancing between the demand and resources at the customers’ premises. The proposed demand-response with real-time pricing and hourly pricing offers consumers choice and transparency, and promotes efficiency. The provided scheme provides tools that can assist the customers’ premises to participate in demand response programs.

Suggested Citation

  • Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:273-292
    DOI: 10.1016/j.apenergy.2018.11.083
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    References listed on IDEAS

    as
    1. He, Xiaoping & Reiner, David, 2016. "Electricity demand and basic needs: Empirical evidence from China's households," Energy Policy, Elsevier, vol. 90(C), pages 212-221.
    2. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    3. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    4. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    5. El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
    6. Kwag, Hyung-Geun & Kim, Jin-O, 2012. "Optimal combined scheduling of generation and demand response with demand resource constraints," Applied Energy, Elsevier, vol. 96(C), pages 161-170.
    7. Oseni, Musiliu O., 2015. "Assessing the consumers’ willingness to adopt a prepayment metering system in Nigeria," Energy Policy, Elsevier, vol. 86(C), pages 154-165.
    8. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    9. Garvey, S.D. & Eames, P.C. & Wang, J.H. & Pimm, A.J. & Waterson, M. & MacKay, R.S. & Giulietti, M. & Flatley, L.C. & Thomson, M. & Barton, J. & Evans, D.J. & Busby, J. & Garvey, J.E., 2015. "On generation-integrated energy storage," Energy Policy, Elsevier, vol. 86(C), pages 544-551.
    10. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    11. Elwell, Clifford A. & Biddulph, Phillip & Lowe, Robert & Oreszczyn, Tadj, 2015. "Determining the impact of regulatory policy on UK gas use using Bayesian analysis on publicly available data," Energy Policy, Elsevier, vol. 86(C), pages 770-783.
    12. David S. Loughran and Jonathan Kulick, 2004. "Demand-Side Management and Energy Efficiency in the United States," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 19-44.
    13. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    14. Liao, Hua & Du, Yun-Fei & Huang, Zhimin & Wei, Yi-Ming, 2016. "Measuring energy economic efficiency: A mathematical programming approach," Applied Energy, Elsevier, vol. 179(C), pages 479-487.
    15. Zúñiga, K.V. & Castilla, I. & Aguilar, R.M., 2014. "Using fuzzy logic to model the behavior of residential electrical utility customers," Applied Energy, Elsevier, vol. 115(C), pages 384-393.
    16. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
    17. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    Full references (including those not matched with items on IDEAS)

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