IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v236y2019icp273-292.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918317999
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.11.083?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sooyoung Jung & Yong Tae Yoon & Jun-Ho Huh, 2020. "An Efficient Micro Grid Optimization Theory," Mathematics, MDPI, vol. 8(4), pages 1-21, April.
    2. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Lynch, Muireann Á. & Nolan, Sheila & Devine, Mel T. & O’Malley, Mark, 2019. "The impacts of demand response participation in capacity markets," Applied Energy, Elsevier, vol. 250(C), pages 444-451.
    4. Bhattacharjee, Somudeep & Nandi, Champa, 2021. "Design of a voting based smart energy management system of the renewable energy based hybrid energy system for a small community," Energy, Elsevier, vol. 214(C).
    5. Luís Sousa Rodrigues & Daniel Lemos Marques & Jorge Augusto Ferreira & Vítor António Ferreira Costa & Nelson Dias Martins & Fernando José Neto Da Silva, 2022. "The Load Shifting Potential of Domestic Refrigerators in Smart Grids: A Comprehensive Review," Energies, MDPI, vol. 15(20), pages 1-36, October.
    6. Luo, Xiaoyuan & Wang, Xinyu & Zhang, Mingyue & Guan, Xinping, 2019. "Distributed detection and isolation of bias injection attack in smart energy grid via interval observer," Applied Energy, Elsevier, vol. 256(C).
    7. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2023. "A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 348(C).
    8. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    9. Roksana Yasmin & B. M. Ruhul Amin & Rakibuzzaman Shah & Andrew Barton, 2024. "A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy," Sustainability, MDPI, vol. 16(2), pages 1-41, January.
    10. Christodoulos Spagkakas & Dimitrios Stimoniaris & Dimitrios Tsiamitros, 2023. "Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm," Energies, MDPI, vol. 16(19), pages 1-17, September.
    11. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    12. Yao Wang & Yan Lu & Liwei Ju & Ting Wang & Qingkun Tan & Jiawei Wang & Zhongfu Tan, 2019. "A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism," Energies, MDPI, vol. 12(3), pages 1-28, January.
    13. Herie Park, 2020. "Human Comfort-Based-Home Energy Management for Demand Response Participation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    14. Zheng, Junjie & Lai, Chun Sing & Yuan, Haoliang & Dong, Zhao Yang & Meng, Ke & Lai, Loi Lei, 2020. "Electricity plan recommender system with electrical instruction-based recovery," Energy, Elsevier, vol. 203(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
    3. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    4. Arasteh, Hamidreza & Sepasian, Mohammad Sadegh & Vahidinasab, Vahid, 2016. "An aggregated model for coordinated planning and reconfiguration of electric distribution networks," Energy, Elsevier, vol. 94(C), pages 786-798.
    5. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    6. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.
    7. Chris Johnathon & Ashish Prakash Agalgaonkar & Joel Kennedy & Chayne Planiden, 2021. "Analyzing Electricity Markets with Increasing Penetration of Large-Scale Renewable Power Generation," Energies, MDPI, vol. 14(22), pages 1-15, November.
    8. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    9. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    10. Yu Huang & Shuqin Li & Peng Ding & Yan Zhang & Kai Yang & Weiting Zhang, 2019. "Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program," Energies, MDPI, vol. 12(20), pages 1-21, October.
    11. 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.
    12. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    13. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    14. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    15. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    16. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    17. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    18. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    19. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
    20. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:236:y:2019:i:c:p:273-292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.