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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Jia, L. & Tong, L. Renewables and storage in distribution systems: centralized vs. decentralized integration. IEEE J. Sel. Areas Commun. 34, 665–674 (2016).
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
Akorede, M. F., Hizam, H. & Pouresmaeil, E. Distributed energy resources and benefits to the environment. Renew. Sustain. Energy Rev. 14, 724–734 (2010).
Zangeneh, A., Jadid, S. & Rahimi-Kian, A. A fuzzy environmental-technical-economic model for distributed generation planning. Energy 36, 3437–3445 (2011).
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).
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).
Poudineh, R. & Jamasb, T. Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement. Energy Policy 67, 222–231 (2014).
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).
Halu, A., Scala, A., Khiyami, A. & González, M. C. Data-driven modeling of solar-powered urban microgrids. Sci. Adv. 2, e1500700 (2016).
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).
Gilman, P. & Lilienthal, P. Micropower system modeling with HOMER. Integr. Altern. Sources Energy 15, 379–418 (2006).
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).
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).
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).
Kwac, J., Flora, J. & Rajagopal, R. Household energy consumption segmentation using hourly data. IEEE Trans. Smart Grid 5, 420–430 (2014).
Chu, S. & Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 488, 294–303 (2012).
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).
Khodaei, A., Bahramirad, S. & Shahidehpour, M. Microgrid planning under uncertainty. IEEE Trans. Power Syst. 30, 2417–2425 (2015).
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).
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).
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).
Zhou, Z. et al. A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl. Energy 103, 135–144 (2013).
State of California Assembly Bill No. 327 Ch. 611 (2013); https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201320140AB327
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).
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).
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).
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).
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).
Dupacova, J., Consigli, G. & Wallace, S. Scenarios for multistage stochastic programs. Ann. Oper. Res. 100, 25–53 (2000).
Pflug, G. C. Scenario tree generation for multiperiod financial optimization by optimal discretization. Math. Program. 89, 251–271 (2001).
Shapiro, A. et al. Lectures on Stochastic Programming: Modeling and Theory Vol. 16 (SIAM, 2014).
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).
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).
Average Monthly Bill-Residential (United States Energy Information Administration, 2014).
Distributed Generation Renewable Energy Estimate of Costs (National Renewable Energy Laboratory, 2013); http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html
Parag, Y. & Sovacool, B. K. Electricity market design for the prosumer era. Nat. Energy 1, 16032 (2016).
Villaraigosa, A. R., Sivaram, V. & Nichols, R. Powering Los Angeles with renewable energy. Nat. Clim. Change 3, 771–775 (2013).
Crawley, D. B. et al. EnergyPlus: creating a new-generation building energy simulation program. Energy Build. 33, 319–331 (2001).
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).
Loveday, E. Solarcity Reveals Installed Pricing for Tesla Powerwall (Motorsport Network, 2015); http://insideevs.com/solarcity-reveals-installed-pricing-for-tesla-powerwall
National Residential Efficiency Database (National Renewable Energy Laboratory, 2014); http://www.nrel.gov/ap/retrofits
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.
Author information
Authors and Affiliations
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
Ethics declarations
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)
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/nenergy.2017.112
This article is cited by
-
Realizing the full potential of behavioural science for climate change mitigation
Nature Climate Change (2024)
-
Assessing Californians’ awareness of their daily electricity use patterns
Nature Energy (2022)
-
Recent Advances in the Unconventional Design of Electrochemical Energy Storage and Conversion Devices
Electrochemical Energy Reviews (2022)
-
Caring for the environment: measuring the dynamic impact of remittances and FDI on CO2 emissions in China
Environmental Science and Pollution Research (2022)
-
A comprehensive review of planning, modeling, optimization, and control of distributed energy systems
Carbon Neutrality (2022)