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Challenges and prospects for negawatt trading in light of recent technological developments

Abstract

With the advancement of the smart grid, the current energy system is moving towards a future where people can buy what they need, can sell when they have excess and can trade the right of buying to other proactive consumers (prosumers). Although the first two schemes already exist in the market, selling the right of buying — also known as negawatt trading — is something that is yet to be implemented. Here we review the challenges and prospects of negawatt trading in light of recent technological advancements. Through reviewing a number of emerging technologies, we show that the necessary methodologies that are needed to establish negawatt trading as a feasible energy management scheme in the smart grid are already available. Grid interactive buildings and distributed ledger technologies, for instance, can ensure active participation and fair pricing. However, some additional challenges need to be addressed for fully functional negawatt trading mechanisms in today’s energy market.

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Fig. 1: Challenges of negawatt trading.
Fig. 2: Market for peer-to-peer negawatt sharing.
Fig. 3: IoT for prosumers decision-making.

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References

  1. Egli, F., Steffen, B. & Schnidt, T. A dynamic analysis of financing conditions for renewable energy technologies. Nat. Energy 3, 1084–1092 (2018).

    Google Scholar 

  2. Thomas, L., Zhou, Y., Long, C., Wu, J. & Jenkins, N. A general form of smart contract for decentralized energy systems management. Nat. Energy 4, 140–149 (2019).

    Google Scholar 

  3. Morstyn, T., Farrell, N., Darby, S. J. & Mcculloch, M. D. Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants. Nat. Energy 3, 94–101 (2018).

    Google Scholar 

  4. Jing, Z., Pipattanasomporn, M. & Rahman, S. Blockchain-based negawatt trading platform: conceptual architecture and case studies. In Proc. IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia) 68–73 (IEEE, 2019).

  5. Lovins, A. B. Saving gigabucks with negawatts. Public Utilities Fortnightly 115, 19–26 (1985). Pioneer paper on negawatt trading.

    Google Scholar 

  6. Lovins, A. B. The Negawatt Revolution — Solving the CO2 Problem Keynote address at the Green Energy Conference, Montreal, Canada (CCNR, 1989); http://www.ccnr.org/amory.html

  7. Krarti, M. in Optimal Design and Retrofit of Energy Efficient Buildings, Communities, and Urban Centers (ed. Krarti, M.) Ch. 4, 189–245 (Butterworth-Heinemann, 2018).

  8. Tushar, W., Saha, T. K., Yuen, C., Smith, D. & Poor, H. V. Peer-to-peer trading in electricity networks: an overview. IEEE Trans. Smart Grid 11, 3185–3200 (2020). A comprehensive overview of peer-to-peer energy (watt) sharing in electricity networks.

    Google Scholar 

  9. Guerrero, J., Chapman, A. C. & Verbič, G. Decentralized P2P energy trading under network constraints in a low-voltage network. IEEE Trans. Smart Grid 10, 5163–5173 (2019).

    Google Scholar 

  10. Baroche, T., Pinson, P., Latimier, R. L. G. & Ahmed, H. B. Exogenous cost allocation in peer-to-peer electricity markets. IEEE Trans. Power Syst. 34, 2553–2564 (2019).

    Google Scholar 

  11. Rogers, E. A. & Junga, E. Intelligent Efficiency Technology and Market Assessment Technical Report IE1701 (American Council for an Energy-Efficient Economy, 2017); https://www.aceee.org/sites/default/files/publications/researchreports/ie1701.pdf

  12. Yan, X., Ozturk, Y., Hu, Z. & Song, Y. A review on price-driven residential demand response. Renew. Sustain. Energy Rev. 96, 411–419 (2018).

    Google Scholar 

  13. Hannan, M. A. et al. A review of Internet of energy based building energy management systems: issues and recommendations. IEEE Access 6, 38997–39014 (2018).

    Google Scholar 

  14. Sousa, T. et al. Peer-to-peer and community-based markets: a comprehensive review. Renew. Sustain. Energy Rev. 104, 367–378 (2019).

    Google Scholar 

  15. Annunziata, E., Rizzi, F. & Frey, M. Enhancing energy efficiency in public buildings: the role of local energy audit programmes. Energy Policy 69, 364–373 (2014).

    Google Scholar 

  16. Blanchet, T. Struggle over energy transition in Berlin: How do grassroots initiatives affect local energy policy-making? Energy Policy 78, 246–254 (2015).

    Google Scholar 

  17. Honda, K., Kusakiyo, K., Matsuzawa, S., Kosakada, M. & Miyazaki, Y. Experiences of demand response in Yokohama demonstration project. CIRED Open Access Proc. J. 2017, 1759–1762 (2017).

    Google Scholar 

  18. AEMO and arena demand response trial to provide 200 megawatts of emergency reserves for extreme peaks. AEMO (11 October 2017); https://arena.gov.au/news/aemo-arena-demand-response/.

  19. Currie, G., Evans, R., Duffield, C. & Mareels, I. Policy options to regulate PV in low voltage grids—Australian case with international implications. Technol. Econ. Smart Grids Sustain. Energy 4, 10 (2019).

    Google Scholar 

  20. Rosenow, J. & Thomas, S. Rewarding energy efficiency for energy system services through markets: opportunities and challenges in Europe. Zenodo https://doi.org/10.5281/zenodo.3634842 (2020).

  21. Tushar, W. et al. Internet of Things for green building management: disruptive innovations through low-cost sensor technology and artificial intelligence. IEEE Signal Process. Mag. 35, 100–110 (2018).

    Google Scholar 

  22. Tushar, W. et al. Exploiting design thinking to improve energy efficiency of buildings. Energy 197, 117141:1–117141:16 (2020). A novel application of design innovation for improving energy efficiency.

    Google Scholar 

  23. Wang, Y., Chen, Q., Hong, T. & Kang, C. Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans. Smart Grid 10, 3125–3148 (2019).

    Google Scholar 

  24. Zhang, K. et al. Security and privacy in smart city applications: challenges and solutions. IEEE Commun. Mag. 55, 122–129 (2017).

    Google Scholar 

  25. Anderson, A. Climate change education for mitigation and adaptation. J. Educ. Sustain. Dev. 6, 191–206 (2012).

    Google Scholar 

  26. Dowd, A., Ashworth, P., Carr-Cornish & Stenner, K. Energymark: Empowering individual Australians to reduce their energy consumption. Energy Policy 51, 264–276 (2012).

    Google Scholar 

  27. Schwartz, S. H. Words, deeds and the perception of consequences and responsibility in action situations. J. Pers. Soc. Psychol. 10, 232–242 (1968).

    Google Scholar 

  28. Tajfel, H. & Turner, J. C. in Psychology of Intergroup Relations (eds Worchel, S. & Austin, W. G.) 7–24 (Nelson-Hall, 1986).

  29. Siegrist, M. The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk Anal. 20, 195–203 (2000).

    Google Scholar 

  30. Huijts, N. M. A., Molin, E. J. E. & Steg, L. Psychological factors influencing sustainable energy technology acceptance: a review-based comprehensive framework. Renew. Sustain. Energy Rev. 16, 525–531 (2012).

    Google Scholar 

  31. Hargreaves, T. & Middlemiss, L. The importance of social relations in shaping energy demand. Nat. Energy 5, 195–201 (2020). This study identifies three types of social relation that substantially influence energy demand.

    Google Scholar 

  32. Jogunola, O. et al. Comparative analysis of P2P architecture for energy trading and sharing. Energies 11, 62:1–62:20 (2018).

    Google Scholar 

  33. Pató, Z., Baker, P. & Rosenow, J. Performance-based Regulation: Aligning Incentives with Clean Energy Outcomes (The Regulatory Assistance Project, 2019); https://www.raponline.org/wp-content/uploads/2019/06/rap-zp-pb-jr-performance-based-regulation-2019-june2.pdf

  34. Perry, C., Bastian, H. & York, D. Grid-interactive Efficient Building Utility Programs: State of the Market Technical Report (American Council for an Energy-Efficient Economy, 2019); https://www.aceee.org/sites/default/files/gebs-103019.pdf

  35. Neukomm, M., Nubbe, V. & Fares, R. Grid-interactive Efficient Buildings: Overview Technical Report (Office of Energy Efficiency and Renewable Energy, US Department of Energy, 2019); https://www.energy.gov/sites/prod/files/2019/04/f61/bto-geb_overview-4.15.19.pdf

  36. Ul Hassan, N., Yuen, C. & Niyato, D. Blockchain technologies for smart energy systems: fundamentals, challenges, and solutions. IEEE Ind. Electron. Mag. 13, 106–118 (2019).

    Google Scholar 

  37. Zia, M. F. et al. Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis. IEEE Access 8, 19410–19432 (2020).

    Google Scholar 

  38. Burger, C., Kuhlmann, A., Richard, P. & Weinmann, J. Blockchain in the Energy Transition. A Survey among Decision-Makers in the German Energy Industry Technical Report (dena, German Energy Agency, 2016); https://www.esmt.org/system/files_force/dena_esmt_studie_blockchain_english.pdf?download=1

  39. Andoni, M. et al. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 100, 143–174 (2019).

    Google Scholar 

  40. Schollmeier, R. A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications. In Proc. International Conference on Peer-to-Peer Computing 101–102 (IEEE, 2001).

  41. Si, F. et al. Cost-efficient multi-energy management with flexible complementarity strategy for energy internet. Appl. Energy 231, 803–815 (2018).

    Google Scholar 

  42. Li, Y., Yang, W., He, P., Chen, C. & Wang, X. Design and management of a distributed hybrid energy system through smart contract and blockchain. Appl. Energy 248, 390–405 (2019).

    Google Scholar 

  43. Kirchhoff, H. & Strunz, K. Key drivers for successful development of peer-to-peer microgrids for swarm electrification. Appl. Energy 244, 46–62 (2019).

    Google Scholar 

  44. Morstyn, T. & McCulloch, M. Multi-class energy management for peer-to-peer energy trading driven by prosumer preferences. IEEE Trans. Power Syst. 34, 4005–4014 (2019).

    Google Scholar 

  45. Moret, F., Baroche, T., Sorin, E. & Pinson, P. Negotiation algorithms for peer-to-peer electricity markets: computational properties. In Proc. Power Systems Computation Conference (PSCC) 1–7 (IEEE, 2018).

  46. Noor, S., Yang, W., Guo, M., Dam, K. H. & Wang, X. Energy demand side management within micro-grid networks enhanced by blockchain. Appl. Energy 228, 1385–1398 (2018).

    Google Scholar 

  47. Abrishambaf, O., Lezama, F., Faria, P. & Vale, Z. Towards transactive energy systems: an analysis on current trends. Energy Strat. Rev. 26, 100418:1–100418:17 (2019). Comprehensive review of the transactive energy system.

    Google Scholar 

  48. Sorin, E., Bobo, L. & Pinson, P. Consensus-based approach to peer-to-peer electricity markets with product differentiation. IEEE Trans. Power Syst. 34, 994–1004 (2019).

    Google Scholar 

  49. Tushar, W. et al. Energy storage sharing in smart grid: a modified auction-based approach. IEEE Trans. Smart Grid 7, 1462–1475 (2016). A new approach to storage sharing in an interactive energy network.

    Google Scholar 

  50. Liu, N. et al. Online energy sharing for nanogrid clusters: a Lyapunov optimization approach. IEEE Trans. Smart Grid 9, 4624–4636 (2018).

    Google Scholar 

  51. Sachs, J. et al. Adaptive 5G low-latency communication for tactile internet services. Proc. IEEE 107, 325–349 (2019).

    Google Scholar 

  52. Viswanath, S. K. et al. System design of the internet of things for residential smart grid. IEEE Wirel. Commun. 23, 90–98 (2016).

    Google Scholar 

  53. Goldie-Scot, L. A behind the scenes take on lithium-ion battery prices. BloombergNEF https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/ (2019).

  54. Tushar, W. et al. Transforming energy networks via peer-to-peer energy trading: the potential of game-theoretic approaches. IEEE Signal Process. Mag. 35, 90–111 (2018).

    Google Scholar 

  55. Wolske, K. S., Gillingham, K. T. & Schultz, P. W. Peer influence on household energy behaviours. Nat. Energy 5, 202–212 (2020).

    Google Scholar 

  56. Frederiks, E. R., Stenner, K. & Hobman, E. V. Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain. Energy Rev. 41, 1385–1394 (2015).

    Google Scholar 

  57. Saad, W., Glass, A. L., Mandayam, N. B. & Poor, H. V. Toward a consumer-centric grid: a behavioral perspective. Proc. IEEE 104, 865–882 (2016).

    Google Scholar 

  58. Tiefenbeck, V., Worner, A., Schob, S., Fleisch, E. & Staake, T. Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives. Nat. Energy 4, 35–41 (2018). This paper discusses the importance of real-time feedback on energy savings.

    Google Scholar 

  59. Boudet, H. et al. Effects of a behaviour change intervention for Girl Scouts on child and parent energy-saving behaviours. Nat. Energy 1, 16091 (2016).

    Google Scholar 

  60. White, L. V. & Sintov, N. D. Inaccurate consumer perceptions of monetary savings in a demand-side response programme predict programme acceptance. Nat. Energy 3, 1101–1108 (2018).

    Google Scholar 

  61. Başar, T. & Olsder, G. J. Dynamic Noncooperative Game Theory (Academic Press, 1995).

  62. Saad, W., Zhu Han, Poor, H. V. & Başar, T. A noncooperative game for double auction-based energy trading between phevs and distribution grids. In Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm) 267–272 (IEEE, 2011).

  63. Long, C., Wu, J., Zhou, Y. & Jenkins, N. Peer-to-peer energy sharing through a two-stage aggregated battery control in a community microgrid. Appl. Energy 226, 261–276 (2018).

    Google Scholar 

  64. Nguyen, S., Peng, W., Sokolowski, P., Alahakoon, D. & Yu, X. Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading. Appl. Energy 228, 2567–2580 (2018).

    Google Scholar 

  65. Lüth, A., Zepter, J. M., del Granado, P. C. & Egging, R. Local electricity market designs for peer-to-peer trading: the role of battery flexibility. Appl. Energy 229, 1233–1243 (2018).

    Google Scholar 

  66. Vázquez-Canteli, J. R. & Nagy, Z. Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl. Energy 235, 1072–1089 (2019).

    Google Scholar 

  67. Konstantakopoulos, I. C. et al. A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure. Appl. Energy 237, 810–821 (2019).

    Google Scholar 

  68. Reynolds, J., Rezgui, Y., Kwan, A. & Piriou, S. A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control. Energy 151, 729–739 (2018).

    Google Scholar 

  69. Okawa, Y. & Namerikawa, T. Distributed optimal power management via negawatt trading in real-time electricity market. IEEE Trans. Smart Grid 8, 3009–3019 (2017). A demonstration of how negawatt trading can be performed for power management in a real-time energy market.

    Google Scholar 

  70. Tushar, W. et al. A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid. Appl. Energy 243, 10–20 (2019). Application of motivational psychology in peer-to-peer energy sharing.

  71. Fairley, P. Blockchain world — feeding the blockchain beast if bitcoin ever does go mainstream, the electricity needed to sustain it will be enormous. IEEE Spectr. 54, 36–59 (2017).

    Google Scholar 

  72. Li, W. et al. Data driven electricity management for residential air conditioning systems: an experimental approach. IEEE Trans. Emerg. Top. Comput. 7, 380–391 (2019).

    Google Scholar 

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Acknowledgements

This work was supported in part by the Advance Queensland Industry Research Fellowship AQRF11016-17RD2, in part by the University of Queensland Solar (UQ Solar; solar-energy.uq.edu.au), in part by the SUTD-MIT International Design Centre (IDC; idc.sutd.edu.sg) and in part by the US National Science Foundation under grants DMS-1736417 and ECCS-1824710.

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Correspondence to Wayes Tushar.

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Tushar, W., Saha, T.K., Yuen, C. et al. Challenges and prospects for negawatt trading in light of recent technological developments. Nat Energy 5, 834–841 (2020). https://doi.org/10.1038/s41560-020-0671-0

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