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System behaviour modelling for demand response provision in a smart grid

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  • Dave, Saraansh
  • Sooriyabandara, Mahesh
  • Yearworth, Mike

Abstract

While pilot projects in the smart grid domain have abounded through public and private efforts, there is still uncertainty in identifying effective business models for the smart grid. In this paper we take the view of a new entrant in this market acting as a third party provider of demand response. New entrants are a key player in emerging technological domains but simulation and policy analysis from this perspective have not been forthcoming. We present a novel approach for evaluating business models within a regulatory context and avoid committing to specific technical solutions but instead embark on a parameter exploration through simple yet insightful agent-based models. Our simulations analyse the impact of system performance by three key variables; participant population size, household flexibility in terms of the maximum number of DR events allowed and size of load shifting/shedding available. The simulations indicate that benefits of avoided capital investment leads to valuing a participating household at approximately £1800 over a 20 year period. These results show how mandated infrastructure influenced by policy can affect the value proposition of a demand response service and provide a useful reference for system level parameter requirements. With weak business models, policy decisions can be crucial in providing the impetus needed to spur growth in this market.

Suggested Citation

  • Dave, Saraansh & Sooriyabandara, Mahesh & Yearworth, Mike, 2013. "System behaviour modelling for demand response provision in a smart grid," Energy Policy, Elsevier, vol. 61(C), pages 172-181.
  • Handle: RePEc:eee:enepol:v:61:y:2013:i:c:p:172-181
    DOI: 10.1016/j.enpol.2013.05.098
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    References listed on IDEAS

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    Cited by:

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    2. Ute Paukstadt & Jörg Becker, 2021. "Uncovering the business value of the internet of things in the energy domain – a review of smart energy business models," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 51-66, March.
    3. Chen Wang & Kaile Zhou & Lanlan Li & Shanlin Yang, 2018. "Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1309-1327, February.
    4. 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.
    5. Andrew Blohm & Jaden Crawford & Steven A. Gabriel, 2021. "Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study," Energies, MDPI, vol. 14(4), pages 1-16, February.
    6. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    7. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    8. Niesten, Eva & Alkemade, Floortje, 2016. "How is value created and captured in smart grids? A review of the literature and an analysis of pilot projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 629-638.
    9. Laugs, Gideon A.H. & Benders, René M.J. & Moll, Henri C., 2020. "Balancing responsibilities: Effects of growth of variable renewable energy, storage, and undue grid interaction," Energy Policy, Elsevier, vol. 139(C).
    10. Drysdale, Brian & Wu, Jianzhong & Jenkins, Nick, 2015. "Flexible demand in the GB domestic electricity sector in 2030," Applied Energy, Elsevier, vol. 139(C), pages 281-290.

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