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Data-driven planning of distributed energy resources amidst socio-technical complexities

Author

Listed:
  • Rishee K. Jain

    (Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2))

  • Junjie Qin

    (Institute for Computational and Mathematical Engineering, Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2))

  • Ram Rajagopal

    (Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2))

Abstract

New distributed energy resources (DER) are rapidly replacing centralized power generation due to their environmental, economic and resiliency benefits. Previous analyses of DER systems have been limited in their ability to account for socio-technical complexities, such as intermittent supply, heterogeneous demand and balance-of-system cost dynamics. Here we develop ReMatch, an interdisciplinary modelling framework, spanning engineering, consumer behaviour and data science, and apply it to 10,000 consumers in California, USA. Our results show that deploying DER would yield nearly a 50% reduction in the levelized cost of electricity (LCOE) over the status quo even after accounting for socio-technical complexities. We abstract a detailed matching of consumers to DER infrastructure from our results and discuss how this matching can facilitate the development of smart and targeted renewable energy policies, programmes and incentives. Our findings point to the large-scale economic and technical feasibility of DER and underscore the pertinent role DER can play in achieving sustainable energy goals.

Suggested Citation

  • Rishee K. Jain & Junjie Qin & Ram Rajagopal, 2017. "Data-driven planning of distributed energy resources amidst socio-technical complexities," Nature Energy, Nature, vol. 2(8), pages 1-11, August.
    • Handle: RePEc:nat:natene:v:2:y:2017:i:8:d:10.1038_nenergy.2017.112
    DOI: 10.1038/nenergy.2017.112
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    Citations

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    Cited by:

    1. Jonathan Roth & Jayashree Chadalawada & Rishee K. Jain & Clayton Miller, 2021. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Jiao, Zihao & Ran, Lun & Zhang, Yanzi & Ren, Yaping, 2021. "Robust vehicle-to-grid power dispatching operations amid sociotechnical complexities," Applied Energy, Elsevier, vol. 281(C).
    3. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    4. Chen, Xiao & Zanocco, Chad & Flora, June & Rajagopal, Ram, 2022. "Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation," Applied Energy, Elsevier, vol. 318(C).
    5. Chad Zanocco & Tao Sun & Gregory Stelmach & June Flora & Ram Rajagopal & Hilary Boudet, 2022. "Assessing Californians’ awareness of their daily electricity use patterns," Nature Energy, Nature, vol. 7(12), pages 1191-1199, December.
    6. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    7. Outcault, Sarah & Sanguinetti, Angela & Nelson, Leslie, 2022. "Technology characteristics that influence adoption of residential distributed energy resources: Adapting Rogers’ framework," Energy Policy, Elsevier, vol. 168(C).
    8. Ran, Cuiling & Zhang, Yanzi & Yin, Ying, 2021. "Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits," Applied Energy, Elsevier, vol. 292(C).
    9. Arnulf Grubler & Charlie Wilson & Nuno Bento & Benigna Boza-Kiss & Volker Krey & David L. McCollum & Narasimha D. Rao & Keywan Riahi & Joeri Rogelj & Simon Stercke & Jonathan Cullen & Stefan Frank & O, 2018. "A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies," Nature Energy, Nature, vol. 3(6), pages 515-527, June.
    10. Ahl, Amanda & Goto, Mika & Yarime, Masaru & Tanaka, Kenji & Sagawa, Daishi, 2022. "Challenges and opportunities of blockchain energy applications: Interrelatedness among technological, economic, social, environmental, and institutional dimensions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    11. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    12. Li, Haoran & Zhang, Chenghui & Sun, Bo, 2022. "Deep integration planning of sustainable energies in district energy system and distributed energy station," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    13. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings," Applied Energy, Elsevier, vol. 228(C), pages 2165-2175.
    14. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    15. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).

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