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Intelligent Electrification as an Enabler of Clean Energy and Decarbonization

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Abstract

Purpose of Review

Electrification efforts will change electric demand patterns, but must be made beneficial to the deployment of renewable generation. To ensure this, we need intelligent coordination of millions of resulting distributed energy resources (DERs). We provide an overview of challenges and opportunities associated with intelligent electrification as a means to enable decarbonization and clean energy.

Summary

Intelligent electrification can bring value to the grid and consumers, but depends on its implementation and cyber-physical coordination architecture to manage consumer quality of service (QoS), grid services, and grid reliability. We also review and discuss challenges with getting intelligent electrification efforts to scale.

Recent Findings

We find that many methods already exist for coordinating DERs to deliver valuable grid services, but that practical implementation barriers exist regarding feedback control, integrating grid data, and deploying intelligent electrification at scale. In addition, accurately characterizing and maximizing the available flexibility of a fleet of DERs is an open technical problem.

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Notes

  1. Herein, we use the FERC Order No. 2222 definition of a DER [7], which is broad and includes BTM loads: “DERs are small-scale power generation or storage technologies (typically from 1 to 10,000 kW) that can provide an alternative to or an enhancement of the traditional electric power system. These can be located on an electric utility’s distribution system, a subsystem of the utility’s distribution system or behind a customer meter. They may include electric storage, intermittent generation, distributed generation, demand response, energy efficiency, thermal storage or electric vehicles and their charging equipment.”

  2. It would be prudent to mention that utility-scale energy storage can generally deliver a wider variety of grid services than thermal peaker plants to support frequency and voltage stability, energy arbitrage, and black start capabilities. However, this manuscript will mainly focus on grid services related to active power and frequency control capabilities as their incentives are well-defined.

  3. Note that the $/kW-year values provided for wholesale grid services are representative of price-taking, marginal values from [5, 19, 26, 36] within a 2030 and 2040 time-frame.

References

Papers of particular interest, published recently, have been highlighted as: $\bullet $ Of importance

  1. Jenkins JD, Luke M, Thernstrom S. Getting to zero carbon emissions in the electric power sector. Joule. 2018;2(12):2498–510.

    Article  Google Scholar 

  2. Clarke L, Wei YM, Navarro ADLV, Garg A, Hahmann A, Khennas S, Azevedo I, Löschel A, Singh A, Steg L, Strbac G, Wada K (2022) Energy systems. In: Shukla P, Skea J, Slade R, Khourdajie AA, van Diemen R, McCollum D, Pathak M, Some S, Vyas P, Fradera R, Belkacemi M, Hasija A, Lisboa G, Luz S, Malley J (eds) Climate change 2022: mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, book section 6, 10.1017/9781009157926.008, https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter06.pdf.

  3. Creutzig F, Roy J, Devine-Wright P, Díaz-José J, Geels F, Grubler A, Maizi N, Masanet E, Mulugetta Y, Onyige C, Perkins P, Sanches-Pereira A, Weber E (2022) Demand, services and social aspects of mitigation. In: Shukla P, Skea J, Slade R, Khourdajie AA, van Diemen R, McCollum D, Pathak M, Some S, Vyas P, Fradera R, Belkacemi M, Hasija A, Lisboa G, Luz S, Malley J (eds) Climate change 2022: mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, book section 5, 10.1017/9781009157926.007, https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter05.pdf.

  4. Hledik R. The hidden battery. Brattle Group: Tech. rep; 2016.

    Google Scholar 

  5. Hledik R, Faruqui A, Lee T, Hingham J. The national potential for load flexibility. Brattle Group: Tech. rep; 2019.

    Google Scholar 

  6. Langevin J, Harris CB, Satre-Meloy A, Chandra-Putra H, Speake A, Present E, Adhikari R, Wilson EJ, Satchwell AJ. US building energy efficiency and flexibility as an electric grid resource. Joule. 2021;5(8):2102–28.

    Article  Google Scholar 

  7. Zhou E, Hurlbut D, Xu K (2021) A primer on FERC order no. 2222: insights for international power systems. Tech. Rep. NREL-TP-5C00-80166, 1823766, MainId:42369, NREL, 10.2172/1823766, https://www.osti.gov/servlets/purl/1823766/.

  8. Schweppe FC, Tabors RD, Kirtley JL, Outhred HR, Pickel FH, Cox AJ (1980) Homeostatic utility control. IEEE Transactions on Power Apparatus and Systems PAS-99(3):1151–1163.

  9. Morgan MG, Talukdar SN. Electric power load management: some technical, economic, regulatory and social issues. Proceedings of the IEEE. 1979;67(2):241–312.

    Article  Google Scholar 

  10. Oren SS (2013) A historical perspective and business model for load response aggregation based on priority service. In: 46th Hawaii International Conference on System Sciences, IEEE, pp 2206–2214, 10.1109/HICSS.2013.39, http://ieeexplore.ieee.org/document/6480109/.

  11. Microsoft Research (2019) Manufacturing trends report. Tech. rep., Microsoft, https://info.microsoft.com/rs/157-GQE-382/images/EN-US-CNTNT-Report-2019-Manufacturing-Trends.pdf.

  12. Nethercutt EJ. Demand flexibility within a performance-based regulatory framework. National Association of Regulatory Utility Commissioners, Washington, D.C.: Tech. rep; 2023.

    Google Scholar 

  13. Australian Energy Market Operator (2021) Batteries explainer: big batteries. https://aemo.com.au/en/learn/energy-explained/energy-101/energy-explained-big-batteries.

  14. US Energy Information Administration (2022) U.S. battery storage capacity will increase significantly by 2025. https://www.eia.gov/todayinenergy/detail.php?id=54939#.

  15. Clean Energy Council (2021) Battery storage: the new clean peaker. Tech. rep., CEC, Australia, https://assets.cleanenergycouncil.org.au/documents/resources/reports/battery-storage-the-new-clean-peaker.pdf.

  16. Cole W, Frazier AW, Augustine C (2021) Cost projections for utility-scale battery storage: 2021 update. Renewable Energy.

  17. Cammardella N, Mathias J, Kiener M, Busic A, Meyn S (2018) Balancing California’s grid without batteries. In: IEEE Conference on Decision and Control, pp 7314–7321, publisher: IEEE.

  18. Brehm K, Dyson M, McEvoy A, Usry C (2023) Virtual power plants, real benefits. Tech. rep., RMI, https://rmi.org/insight/virtual-power-plants-real-benefits/.

  19. Hledik R, Peters K. Real reliability: the value of virtual power. Brattle Group: Tech. rep; 2023.

    Google Scholar 

  20. Almassalkhi M, Frolik J, Hines P. Packetizing the power grid: the rules of the internet can also balance electricity supply and demand. IEEE Spectrum. 2022;59(2):42–7. https://doi.org/10.1109/MSPEC.2022.9706403.

    Article  Google Scholar 

  21. St John J (2023) Inside the software running Sunrun’s home solar-battery fleets. Canary Media https://www.canarymedia.com/articles/grid-edge/inside-the-software-running-sunruns-home-solar-battery-fleets.

  22. Tesla (2023) Join Tesla & PG &E to Create a Virtual Power Plant. https://www.tesla.com/support/energy/powerwall/own/tesla-pge-virtual-power-plant-pilot.

  23. FERC (2011) Demand response compensation in organized wholesale energy markets.

  24. FERC (2018) Electric storage participation in markets operated by regional transmission organizations and independent system operators.

  25. FERC (2020) Participation of distributed energy resource aggregations in markets operated by regional transmission organizations and independent system operators.

  26. Dyson M, Mandel J, et a (2015) The economics of demand flexibility. Tech. rep., RMI, http://www.rmi.org/electricity_demand_ flexibility.

  27. Lo H, Blumsack S, Hines P, Meyn S. Electricity rates for the zero marginal cost grid. The Electricity Journal. 2019;32(3):39–43.

    Article  Google Scholar 

  28. Milovanoff A, Posen ID, MacLean HL. Electrification of light-duty vehicle fleet alone will not meet mitigation targets. Nature Climate Change. 2020;10(12):1102–7.

    Article  Google Scholar 

  29. US Energy Information Administration (2023) Batteries explainer: big batteries. https://www.eia.gov/electricity/.

  30. Lorenczik S, Zavala PB, Fraser P (2022) Electricity market report. Tech. rep., International Energy Agency, https://www.iea.org/reports/electricity-market-report-december-2020.

  31. Kintner-Meyer M, Davis S, Sridhar S, Bhatnagar D, Mahserejian S, Ghosal M (2020) Electric vehicles at scale–phase I analysis: high EV adoption impacts on the western US power grid. Tech. Rep. PNNL-29894, Pacific Northwest National Laboratory (PNNL).

  32. TORNBJERG J (2022) Jule-blackout varsler hårdere belastning af elnettet. Intelligent Energi https://ienergi.dk/nyheder/jule-blackout-varsler-haardere-belastning-elnettet.

  33. \(\bullet \) Han C, Witthaut D, Gorjão LR, Böttcher PC (2022) Collective effects and synchronization of demand in real-time demand response. Journal of Physics: Complexity 3(2):025002. https://doi.org/10.1088/2632-072X/ac6477, https://iopscience.iop.org/article/10.1088/2632-072X/ac6477. This paper examines the role pricing strategies in bringing about instability in a fleet od DERs. It takes an interesting (statistical) physics angle to illustrate stability and dynamics complexities.

  34. Kundu S, Hiskens IA. Nonlinear dynamics of hysteresis-based load controls. IFAC Proceedings Volumes. 2014;47(3):5419–25.

    Article  Google Scholar 

  35. Nazir MS, Hiskens I. Analysis of synchronization in load ensembles. Electric Power Systems Research. 2021;190: 106779.

    Article  Google Scholar 

  36. Bolun Xu, Dvorkin Y, Kirschen DS, Silva-Monroy CA, Watson JP (2016) A comparison of policies on the participation of storage in U.S. frequency regulation markets. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), IEEE, Boston, MA, USA, pp 1–5, 10.1109/PESGM.2016.7741531, http://ieeexplore.ieee.org/document/7741531/.

  37. Brahma S, Ossareh H, Almassalkhi MR (2022b) Statistical modeling and forecasting of automatic generation control signals. In: IREP Symposium - Bulk Power System Dynamics and Control.

  38. Brahma S, A Khurram, Ossareh H, Almassalkhi M (2022a) Optimal frequency regulation using packetized energy management. IEEE Transactions on Power Systems.

  39. EnergyHub (2023) A better way to evaluate the carbon impact of virtual power plants. https://www.energyhub.com/blog/a-better-way-to-evaluate-the-carbon-impact-of-virtual-power-plants/?utm_content=245975845 &utm_medium=social &utm_source=linkedin &hss_channel=lcp-595749.

  40. Zhou E, Mai T (2021) Electrification futures study: operational analysis of U.S. power systems with increased electrification and demand-side flexibility. Tech. Rep. NREL/TP-6A20-79094, NREL, https://doi.org/10.2172/1785329.

  41. Alstone P, et al (2017) 2025 California demand response potential study - charting California’s demand response future. Tech. Rep. LBNL-2001113, LBNL.

  42. LeBel M, Weston F, Sandoval R (2020) Demand charges: what are they good for? Tech. rep., REGULATORY ASSISTANCE PROJECT (RAP), https://www.raponline.org/wp-content/uploads/2020/11/rap-lebel-weston-sandoval-demand-charges-what-are-they-good-for-2020-november.pdf.

  43. GMP (2020) Green mountain power corporation energy storage system service. https://greenmountainpower.com/wp-content/uploads/2020/06/GMP-Powerwall-Program-Tariff-and-Lease-2020.pdf.

  44. Davis R (2023) What is Tesla’s virtual power plant? https://www.lastbulb.com/virtual-power-plant.

  45. Taft J (2016) Grid architecture 2. Tech. Rep. PNL-24044 2, PNNL.

  46. Kristov L, De Martini P, Taft JD. A tale of two visions: designing a decentralized transactive electric system. IEEE Power and Energy Magazine. 2016;14(3):63–9.

    Article  Google Scholar 

  47. \(\bullet \) Taft J, Ogle J (2021) Grid architecture guidance specification for FAST-DERMS. Tech. Rep. PNNL-31172, PNNL. The PNNL report provides an updated and modern view on DER control architectures, including coordination, communication, and intelligence. Furthermore, it encompasses various transmission and distribution and aggregator interface considerations.

  48. Mathieu JL, Koch S, Callaway DS. State estimation and control of electric loads to manage real-time energy imbalance. IEEE Transactions on Power Systems. 2013;28(1):430–40.

    Article  Google Scholar 

  49. Meyn SP, Barooah P, Busic A, Chen Y, Ehren J. Ancillary service to the grid using intelligent deferrable loads. IEEE Transactions on Automatic Control. 2015;60(11):2847–62.

    Article  MathSciNet  Google Scholar 

  50. Tindemans SH, Trovato V, Strbac G. Decentralized control of thermostatic loads for flexible demand response. IEEE Transactions on Control Systems Technology. 2015;23(5):1685–700.

    Article  Google Scholar 

  51. Frolik J (2004) QoS control for random access wireless sensor networks. In: Proc. 2004 Wireless Communications and Networking Conference (WCNC04), Greenville, SC.

  52. Frolik J, Hines P (2012) Random access, electric vehicle charge management. In: 1st IEEE International Electric Vehicle Conference. (IEVC), Greenville.

  53. Zhang B, Baillieul J. Control and communication protocols based on packetized direct load control in smart building microgrids. Proceedings of the IEEE. 2016;104(4):837–57.

    Article  Google Scholar 

  54. Espinosa LAD, Almassalkhi M. A packetized energy management macromodel with quality of service guarantees for demand-side resources. IEEE Trans Power Syst. 2020;35(5):3660–70.

    Article  Google Scholar 

  55. Nandanoori SP, Kundu S, Vrabie D, Kalsi K, Lian J (2018) Prioritized threshold allocation for distributed frequency response. In: 2018 IEEE Conference on Control Technology and Applications (CCTA), IEEE, pp 237–244

  56. Botkin-Levy M, Engelmann A, Mühlpfordt T, Faulwasser T, Almassalkhi MR. Distributed control of charging for electric vehicle fleets under dynamic transformer ratings. IEEE Transactions on Control Systems Technology. 2022;30(4):1578–94. https://doi.org/10.1109/TCST.2021.3120494.

    Article  Google Scholar 

  57. Office of Cybersecurity, Energy Security, and Emergency Response (2022) Cybersecurity considerations for distributed energy resources on the U.S. electric grid. Tech. rep., U.S. Department of Energy.

  58. Ross SC, Vuylsteke G, Mathieu JL. Effects of load-based frequency regulation on distribution network operation. IEEE Transactions on Power Systems. 2019;34(2):1569–78.

    Article  Google Scholar 

  59. Taheri S, Jalali M, Kekatos V, Tong L. Fast probabilistic hosting capacity analysis for active distribution systems. IEEE Transactions on Smart Grid. 2021;12(3):2000–12. https://doi.org/10.1109/TSG.2020.3038651.

    Article  Google Scholar 

  60. Molzahn D, Roald LA (2019) Grid-aware versus grid-agnostic distribution system control: a method for certifying engineering constraint satisfaction. In: Hawaii International Conference on System Sciences.

  61. Almassalkhi M, Brahma S, Nazir N, Ossareh H, Racherla P, Kundu S, Nandanoori SP, Ramachandran T, Singhal A, Gayme D, Ji C, Mallada E, Shen Y, You P, Anand D. Hierarchical, grid-aware, and economically optimal coordination of distributed energy resources in realistic distribution systems. Energies. 2020;13(23):6399.

    Article  Google Scholar 

  62. Blair B, Fitzgerald G, Dougherty C. The state of managed charging in 2021. Smart Electric Power Alliance, Washington, D.C.: Tech. rep; 2021.

    Google Scholar 

  63. Khan A, Paudyal S, Almassalkhi M (2022) Performance evaluation of network-admissible demand dispatch in multi-phase distribution grids. In: IREP Symposium - Bulk Power System Dynamics and Control.

  64. Ross SC, Ozay N, Mathieu JL (2019a) Coordination between an aggregator and distribution operator to achieve network-aware load control. In: 2019 IEEE Milan PowerTech, IEEE, Milan, Italy, pp 1–6, 10.1109/PTC.2019.8810635, https://ieeexplore.ieee.org/document/8810635/.

  65. Jang S, Ozay N, Mathieu JL (2022) Data-driven estimation of probabilistic constraints for network-safe distributed energy resource control. In: 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, Monticello, IL, USA, pp 1–8, 10.1109/Allerton49937.2022.9929351, https://ieeexplore.ieee.org/document/9929351/.

  66. N Nazir, Almassalkhi M (2019) Convex inner approximation of the feeder hosting capacity limits on dispatchable demand. In: IEEE Conference on Decision and Control, Nice, France.

  67. Liu MZ, Ochoa LN, Riaz S, Mancarella P, Ting T, San J, Theunissen J. Grid and market services from the edge: using operating envelopes to unlock network-aware bottom-up flexibility. IEEE Power and Energy Magazine. 2021;19(4):52–62. https://doi.org/10.1109/MPE.2021.3072819.

    Article  Google Scholar 

  68. Nazir N, Almassalkhi M. Grid-aware aggregation and realtime disaggregation of distributed energy resources in radial networks. IEEE Transactions on Power Systems. 2022;37(3):1706–17. https://doi.org/10.1109/TPWRS.2021.3121215.

    Article  Google Scholar 

  69. N Nazir, Almassalkhi M (2022) Market mechanism to enable grid-aware dispatch of aggregators in radial distribution networks. In: IREP Symposium - Bulk Power System Dynamics and Control.

  70. \(\bullet \) Riaz S, Mancarella P (2021) Modelling and characterisation of flexibility from distributed energy resources. IEEE transactions on power systems 37(1):38–50. The paper takes an interesting systematic view of flexibility (power, duration, and responsiveness) from DERs and considers both flexibility at one time (static) and over a time horizon (dynamic). In addition, the paper considers a costs of (achieving) flexibility as much as flexibility itself, which represent an interesting coupling between available flexibility and any potential incentives to perform.

  71. Hao H, Sanandaji BM, Poolla K, Vincent TL. Aggregate flexibility of thermostatically controlled loads. IEEE Transactions on Power Systems. 2015;30(1):189–98. https://doi.org/10.1109/TPWRS.2014.2328865.

    Article  Google Scholar 

  72. Bhattacharya A, Hansen J, Kalsi K, Lian J, Nandanoori SP, Reeve H, Adetola V, Lin F, Leichtman T, Gourisetti SN, et al. (2019) Incentive-based control and coordination of distributed energy resources. Tech. Rep. DOE-PNNL-28724, Pacific Northwest National Lab.(PNNL), Richland, WA (United States).

  73. Hughes JT, Domínguez-García AD, Poolla K. Identification of virtual battery models for flexible loads. IEEE Transactions on Power Systems. 2016;31(6):4660–9. https://doi.org/10.1109/TPWRS.2015.2505645.

    Article  Google Scholar 

  74. Chakraborty I, Nandanoori SP, Kundu S (2018) Virtual battery parameter identification using transfer learning based stacked autoencoder. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp 1269–1274.

  75. Matar M, Mavalizadeh H, Brahma S, Almassalkhi MR, Wshah S. Learning the state-of-charge of heterogeneous fleets of distributed energy resources with temporal residual networks. Journal of Energy Storage. 2023;70: 107979.

    Article  Google Scholar 

  76. Zhao L, Zhang W, Hao H, Kalsi K. A geometric approach to aggregate flexibility modeling of thermostatically controlled loads. IEEE Transactions on Power Systems. 2017;32(6):4721–31.

    Article  Google Scholar 

  77. Kundu S, Kalsi K, Backhaus S (2018b) Approximating flexibility in distributed energy resources: a geometric approach. In: Power Systems Computations Conference, Dublin, Ireland, http://arxiv.org/abs/1803.06921, arXiv: 1803.06921.

  78. Kundu S, Chandan V, Kalsi K (2018a) Scalable computation of 2D-Minkowski sum of arbitrary non-convex domains: modeling flexibility in energy resources. In: Hawaii International Conference on System Sciences, http://arxiv.org/abs/1809.04265, arXiv: 1809.04265.

  79. Nazir MS, Hiskens IA, Bernstein A, Dall’Anese E (2018) Inner approximation of Minkowski sums: a union-based approach and applications to aggregated energy resources. In: 2018 IEEE Conference on Decision and Control (CDC), IEEE, pp 5708–5715.

  80. Cui B, Zamzam A, Bernstein A (2021) Network-cognizant time-coupled aggregate flexibility of distribution systems under uncertainties. In: 2021 American Control Conference (ACC), IEEE, pp 4178–4183.

  81. Chakraborty I, Nandanoori SP, Kundu S, Kalsi K (2020) Stochastic virtual battery modeling of uncertain electrical loads using variational autoencoder. In: 2020 American Control Conference (ACC), IEEE, pp 1305–1310.

  82. Qi N, Pinson P, Almassalkhi MR, Cheng L, Zhuang Y (2023) Chance-constrained generic energy storage operations under decision-dependent uncertainty. IEEE Transactions on Sustainable Energy pp 1–14. https://doi.org/10.1109/TSTE.2023.3262135.

  83. Jennifer Pattison Tuohy (2023) What matters about Matter, the new smart home standard. https://www.theverge.com/22832127/matter-smart-home-products-thread-wifi-explainer.

  84. Chan K, Kim Y, Jo JY (2022) DER communication networks and their security issues. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, pp 0785–0790.

  85. Singh R, Hines PD, Howerter SE, Reilly JT (2022) Beyond DERMS: demonstration of automated grid services, mode transition, and resilience. Tech. rep., Argonne National Lab.(ANL), Argonne, IL (United States).

  86. Gopstein A, Nguyen C, O’Fallon C, Hastings N, Wollman D (2021) NIST framework and roadmap for smart grid interoperability standards, Release 4.0. Tech. rep., National Institute of Standards and Technology, https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1108r4.pdf.

  87. Wu D, Hao H, Fu T, Kalsi K (2018) Regional assessment of virtual battery potential from building loads. In: 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T &D), IEEE, pp 1–5.

  88. Lomas N (2023) Lun is in a mad rush to help heat-pump installers decarbonize homes. TechCrunch https://techcrunch.com/2023/03/22/lun-seed/.

  89. Mohan N, Robbins B, Raju S, Maiden Mueller D, Airineni MR, Guggilam S, Venkatraman D (2023) Electric power engineering - a national imperative! A Report on NSF-NAE Workshop. Tech. rep., University of Minnesota.

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Acknowledgements

The authors would like thank Paul Hines (of EnergyHub), Cyril Brunner (of Vermont Electric Cooperative), and Dan Kopin (of VELCO) for sharing their feedback and many insightful discussions.

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Authors and Affiliations

  1. University of Vermont, Burlington, VT, USA

    Mads R. Almassalkhi & Soumya Kundu

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    Mads R. Almassalkhi & Soumya Kundu

  3. Washington State University-Tri Cities, Richland, WA, USA

    Soumya Kundu

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M. R. Almassalkhi graciously acknowledges funding from NSF Award ECCS-2047306.

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Almassalkhi, M.R., Kundu, S. Intelligent Electrification as an Enabler of Clean Energy and Decarbonization. Curr Sustainable Renewable Energy Rep 10, 183–196 (2023). https://doi.org/10.1007/s40518-023-00228-z

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