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

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.

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Figure 1: Schematic of the ReMatch modelling framework.
Figure 2: Bottom-up infrastructure analysis and consumer behaviour characterization.
Figure 3: Simplified visual depiction of our proposed heuristic forward stepwise algorithm.
Figure 4: Energy dispatched by DER and consumer type.
Figure 5: Energy utilization in time for different consumer types.
Figure 6: Matching of DER infrastructure to consumers.

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References

  1. Jia, L. & Tong, L. Renewables and storage in distribution systems: centralized vs. decentralized integration. IEEE J. Sel. Areas Commun. 34, 665–674 (2016).

    Article  Google Scholar 

  2. Bronski, P. et al. The Economics of Grid Defection: When and Where Distributed Generation Plust Storage Competes with Traditional Utility Service (Rocky Mountain Institute, 2014); https://rmi.org/insights/reports/economics-grid-defection

    Google Scholar 

  3. Akorede, M. F., Hizam, H. & Pouresmaeil, E. Distributed energy resources and benefits to the environment. Renew. Sustain. Energy Rev. 14, 724–734 (2010).

    Article  Google Scholar 

  4. Zangeneh, A., Jadid, S. & Rahimi-Kian, A. A fuzzy environmental-technical-economic model for distributed generation planning. Energy 36, 3437–3445 (2011).

    Article  Google Scholar 

  5. Hernandez, R. R., Hoffacker, M. K. & Field, C. B. Efficient use of land to meet sustainable energy needs. Nat. Clim. Change 5, 353–358 (2015).

    Article  Google Scholar 

  6. Paliwal, P., Patidar, N. P. & Nema, R. K. Planning of grid integrated distributed generators: a review of technology, objectives and techniques. Renew. Sustain. Energy Rev. 40, 557–570 (2014).

    Article  Google Scholar 

  7. Poudineh, R. & Jamasb, T. Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement. Energy Policy 67, 222–231 (2014).

    Article  Google Scholar 

  8. Borges, C. L. T. & Falcao, D. M. Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int. J. Electr. Power Energy Syst. 28, 413–420 (2006).

    Article  Google Scholar 

  9. Halu, A., Scala, A., Khiyami, A. & González, M. C. Data-driven modeling of solar-powered urban microgrids. Sci. Adv. 2, e1500700 (2016).

    Article  Google Scholar 

  10. Atwa, Y. M., El-Saadany, E. F. & Guise, A. C. Supply adequacy assessment of distribution system including wind-based DG during different modes of operation. IEEE Trans. Power Syst. 25, 78–86 (2010).

    Article  Google Scholar 

  11. Gilman, P. & Lilienthal, P. Micropower system modeling with HOMER. Integr. Altern. Sources Energy 15, 379–418 (2006).

    Google Scholar 

  12. Aman, M. M., Jasmon, G. B., Bakar, a. H. a. & Mokhlis, H. A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm. Energy 66, 202–215 (2014).

    Article  Google Scholar 

  13. Foster, J. D., Berry, A. M., Boland, N. & Waterer, H. Comparison of mixed-integer programming and genetic algorithm methods for distributed generation planning. IEEE Trans. Power Syst. 29, 833–843 (2014).

    Article  Google Scholar 

  14. Cai, Y. P., Huang, G. H., Yang, Z. F., Lin, Q. G. & Tan, Q. Community-scale renewable energy systems planning under uncertainty—an interval chance-constrained programming approach. Renew. Sustain. Energy Rev. 13, 721–735 (2009).

    Article  Google Scholar 

  15. Kwac, J., Flora, J. & Rajagopal, R. Household energy consumption segmentation using hourly data. IEEE Trans. Smart Grid 5, 420–430 (2014).

    Article  Google Scholar 

  16. Chu, S. & Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 488, 294–303 (2012).

    Article  Google Scholar 

  17. Stadler, M., Groissbock, M., Cardoso, G. & Marnay, C. Optimizing distributed energy resources and building retrofits with the strategic DER-CAModel. Appl. Energy 132, 557–567 (2014).

    Article  Google Scholar 

  18. Khodaei, A., Bahramirad, S. & Shahidehpour, M. Microgrid planning under uncertainty. IEEE Trans. Power Syst. 30, 2417–2425 (2015).

    Article  Google Scholar 

  19. Zou, K., Agalgaonkar, A. P., Muttaqi, K. M. & Perera, S. Distribution system planning with incorporating DG reactive capability and system uncertainties. IEEE Trans. Sustain. Energy 3, 112–123 (2012).

    Article  Google Scholar 

  20. Huang, Z., Yu, H., Peng, Z. & Zhao, M. Methods and tools for community energy planning: a review. Renew. Sustain. Energy Rev. 42, 1335–1348 (2015).

    Article  Google Scholar 

  21. Mena, R., Hennebel, M., Li, Y.-F., Ruiz, C. & Zio, E. A risk-based simulation and multi-objective optimization framework for the integration of distributed renewable generation and storage. Renew. Sustain. Energy Rev. 37, 778–793 (2014).

    Article  Google Scholar 

  22. Zhou, Z. et al. A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl. Energy 103, 135–144 (2013).

    Article  Google Scholar 

  23. State of California Assembly Bill No. 327 Ch. 611 (2013); https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201320140AB327

  24. Friedman, B., Ardani, K., Feldman, D., Citron, R. & Margolis, R. Benchmarking Non-Hardware Balance-of-System (Soft) Costs for U.S. Photovoltaic Systems, Using a Bottom-Up Approach and Installer Survey? 2nd edn (National Renewable Energy Laboratory and Rocky Mountain Institute, 2013).

    Book  Google Scholar 

  25. Borgeson, S., Flora, J. A., Kwac, J., Tan, C.-W. & Rajagopal, R. in Learning from Hourly Household Energy Consumption: Extracting, Visualizing and Interpreting Household Smart Meter Data in Design, User Experience, and Usability: Interactive Experience Design Lecture Notes in Computer Science Vol. 9188, 337–345 (Springer International Publishing, 2015).

    Google Scholar 

  26. Erdinc, O., Paterakis, N. G., Pappi, I. N., Bakirtzis, A. G. & Catalao, J. P. S. A new perspective for sizing of distributed generation and energy storage for smart households under demand response. Appl. Energy 143, 26–37 (2015).

    Article  Google Scholar 

  27. Lopez-Pena, A., Perez-Arriaga, I. & Linares, P. Renewables vs. energy efficiency: the cost of carbon emissions reduction in Spain. Energy Policy 50, 659–668 (2012).

    Article  Google Scholar 

  28. Martins, V. F. & Borges, C. L. T. Active distribution network integrated planning incorporating distributed generation and load response uncertainties. IEEE Trans. Power Syst. 26, 2164–2172 (2011).

    Article  Google Scholar 

  29. Dupacova, J., Consigli, G. & Wallace, S. Scenarios for multistage stochastic programs. Ann. Oper. Res. 100, 25–53 (2000).

    Article  MathSciNet  Google Scholar 

  30. Pflug, G. C. Scenario tree generation for multiperiod financial optimization by optimal discretization. Math. Program. 89, 251–271 (2001).

    Article  MathSciNet  Google Scholar 

  31. Shapiro, A. et al. Lectures on Stochastic Programming: Modeling and Theory Vol. 16 (SIAM, 2014).

    MATH  Google Scholar 

  32. Hung, D. Q., Mithulananthan, N. & Bansal, R. C. An optimal investment planning framework for multiple distributed generation units in industrial distribution systems. Appl. Energy 124, 62–72 (2014).

    Article  Google Scholar 

  33. Branker, K., Pathak, M. J. M. & Pearce, J. M. A review of solar photovoltaic levelized cost of electricity. Renew. Sustain. Energy Rev. 15, 4470–4482 (2011).

    Article  Google Scholar 

  34. Average Monthly Bill-Residential (United States Energy Information Administration, 2014).

  35. Distributed Generation Renewable Energy Estimate of Costs (National Renewable Energy Laboratory, 2013); http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html

  36. Parag, Y. & Sovacool, B. K. Electricity market design for the prosumer era. Nat. Energy 1, 16032 (2016).

    Article  Google Scholar 

  37. Villaraigosa, A. R., Sivaram, V. & Nichols, R. Powering Los Angeles with renewable energy. Nat. Clim. Change 3, 771–775 (2013).

    Article  Google Scholar 

  38. Crawley, D. B. et al. EnergyPlus: creating a new-generation building energy simulation program. Energy Build. 33, 319–331 (2001).

    Article  Google Scholar 

  39. Darling, S. B., You, F., Veselka, T. & Velosa, A. Assumptions and the levelized cost of energy for photovoltaics. Energy Environ. Sci. 4, 3133–3139 (2011).

    Article  Google Scholar 

  40. Loveday, E. Solarcity Reveals Installed Pricing for Tesla Powerwall (Motorsport Network, 2015); http://insideevs.com/solarcity-reveals-installed-pricing-for-tesla-powerwall

    Google Scholar 

  41. National Residential Efficiency Database (National Renewable Energy Laboratory, 2014); http://www.nrel.gov/ap/retrofits

Download references

Acknowledgements

This material is based upon the work supported in part by the Stanford Precourt Institute for Energy, a Terman Faculty Fellowship (R.K.J.), a Powell Foundation Fellowship (R.R.) and the US National Science Foundation under a SEES Fellows award (1461549) and a CAREER award (1554178). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors

Contributions

R.K.J., J.Q. and R.R. conceived and designed the research and modelling framework. R.K.J. and J.Q. implemented the framework and analysed the results. R.K.J., J.Q. and R.R. prepared the manuscript. R.R. provided institutional and material support for the research.

Corresponding authors

Correspondence to Rishee K. Jain or Ram Rajagopal.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Notes 1–2, Supplementary Figures 1–5, Supplementary Tables 1–7 and Supplementary References. (PDF 521 kb)

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Jain, R., Qin, J. & Rajagopal, R. Data-driven planning of distributed energy resources amidst socio-technical complexities. Nat Energy 2, 17112 (2017). https://doi.org/10.1038/nenergy.2017.112

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