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Dynamic pricing for responsive demand to increase distribution network efficiency

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  • Gu, Chenghong
  • Yan, Xiaohe
  • Yan, Zhang
  • Li, Furong

Abstract

This paper designs a novel dynamic tariff scheme for demand response (DR) by considering networks costs through balancing the trade-off between network investment costs and congestion costs. The objective is to actively engage customers in network planning and operation for reducing network costs and finally their electricity bills. System congestion costs are quantified according to generation and load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might face the same energy signals but they are differentiated by network cost signals. Once customers conduct DR during system congested periods, the smaller savings from investment and congestion cost are considered as the economic singles for rewarding the response. The innovation is that the method translates network congestion/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. A typical UK distribution network is utilised to illustrate the new approach and results show that derived economic signals can effectively benefit end customers for reducing system congestion costs and deferring required network investment.

Suggested Citation

  • Gu, Chenghong & Yan, Xiaohe & Yan, Zhang & Li, Furong, 2017. "Dynamic pricing for responsive demand to increase distribution network efficiency," Applied Energy, Elsevier, vol. 205(C), pages 236-243.
  • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:236-243
    DOI: 10.1016/j.apenergy.2017.07.102
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    1. Valenzuela, Jorge & Thimmapuram, Prakash R. & Kim, Jinho, 2012. "Modeling and simulation of consumer response to dynamic pricing with enabled technologies," Applied Energy, Elsevier, vol. 96(C), pages 122-132.
    2. Kim, Youngjin & Norford, Leslie K., 2017. "Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation," Applied Energy, Elsevier, vol. 193(C), pages 308-324.
    3. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    4. Kane, Laura & Ault, Graham, 2014. "A review and analysis of renewable energy curtailment schemes and Principles of Access: Transitioning towards business as usual," Energy Policy, Elsevier, vol. 72(C), pages 67-77.
    5. Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
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