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The effect of rate design on power distribution reliability considering adoption of distributed energy resources

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  • Maheshwari, Aditya
  • Heleno, Miguel
  • Ludkovski, Michael

Abstract

Electricity rates are a main driver for adoption of Distributed Energy Resources (DERs) by private consumers. In turn, DERs are a major component of the reliability of energy access in the long run. Defining reliability indices in a paradigm where energy is generated both behind and in front of the meter is part of an ongoing discussion about the future role of utilities and system operators with many regulatory implications. This paper contributes to that discussion by analyzing the effect of rate design on the long term reliability indices of power distribution. A methodology to quantify this effect is proposed and a case study involving photovoltaic (PV) and storage technology adoption in California is presented. Several numerical simulations illustrate how electricity rates affect the grid reliability by altering dispatch and adoption of the DERs. We further document that the impact of rate design on reliability can be very different from the perspective of the utility versus that of the consumers. Our model affirms the positive connection between investments in DERs and the grid reliability and provides an additional tool to policy-makers for improving the reliability of the grid in the long term.

Suggested Citation

  • Maheshwari, Aditya & Heleno, Miguel & Ludkovski, Michael, 2020. "The effect of rate design on power distribution reliability considering adoption of distributed energy resources," Applied Energy, Elsevier, vol. 268(C).
  • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304761
    DOI: 10.1016/j.apenergy.2020.114964
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    References listed on IDEAS

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    1. Chesser, Michael & Hanly, Jim & Cassells, Damien & Apergis, Nicholas, 2018. "The positive feedback cycle in the electricity market: Residential solar PV adoption, electricity demand and prices," Energy Policy, Elsevier, vol. 122(C), pages 36-44.
    2. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    3. Sioshansi, Ramteen, 2016. "Retail electricity tariff and mechanism design to incentivize distributed renewable generation," Energy Policy, Elsevier, vol. 95(C), pages 498-508.
    4. Candas, Soner & Siala, Kais & Hamacher, Thomas, 2019. "Sociodynamic modeling of small-scale PV adoption and insights on future expansion without feed-in tariffs," Energy Policy, Elsevier, vol. 125(C), pages 521-536.
    5. Cai, Desmond W.H. & Adlakha, Sachin & Low, Steven H. & De Martini, Paul & Mani Chandy, K., 2013. "Impact of residential PV adoption on Retail Electricity Rates," Energy Policy, Elsevier, vol. 62(C), pages 830-843.
    6. Cardoso, G. & Stadler, M. & Bozchalui, M.C. & Sharma, R. & Marnay, C. & Barbosa-Póvoa, A. & Ferrão, P., 2014. "Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules," Energy, Elsevier, vol. 64(C), pages 17-30.
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    Cited by:

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    3. Rossi, Federico & Heleno, Miguel & Basosi, Riccardo & Sinicropi, Adalgisa, 2021. "LCA driven solar compensation mechanism for Renewable Energy Communities: the Italian case," Energy, Elsevier, vol. 235(C).
    4. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).
    5. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    6. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2021. "Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach," Applied Energy, Elsevier, vol. 299(C).
    7. Heleno, Miguel & Sehloff, David & Coelho, Antonio & Valenzuela, Alan, 2020. "Probabilistic impact of electricity tariffs on distribution grids considering adoption of solar and storage technologies," Applied Energy, Elsevier, vol. 279(C).

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