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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References
Egli, F., Steffen, B. & Schnidt, T. A dynamic analysis of financing conditions for renewable energy technologies. Nat. Energy 3, 1084–1092 (2018).
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).
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).
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).
Lovins, A. B. Saving gigabucks with negawatts. Public Utilities Fortnightly 115, 19–26 (1985). Pioneer paper on negawatt trading.
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
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).
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.
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).
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).
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
Yan, X., Ozturk, Y., Hu, Z. & Song, Y. A review on price-driven residential demand response. Renew. Sustain. Energy Rev. 96, 411–419 (2018).
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).
Sousa, T. et al. Peer-to-peer and community-based markets: a comprehensive review. Renew. Sustain. Energy Rev. 104, 367–378 (2019).
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).
Blanchet, T. Struggle over energy transition in Berlin: How do grassroots initiatives affect local energy policy-making? Energy Policy 78, 246–254 (2015).
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).
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/.
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).
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).
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).
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.
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).
Zhang, K. et al. Security and privacy in smart city applications: challenges and solutions. IEEE Commun. Mag. 55, 122–129 (2017).
Anderson, A. Climate change education for mitigation and adaptation. J. Educ. Sustain. Dev. 6, 191–206 (2012).
Dowd, A., Ashworth, P., Carr-Cornish & Stenner, K. Energymark: Empowering individual Australians to reduce their energy consumption. Energy Policy 51, 264–276 (2012).
Schwartz, S. H. Words, deeds and the perception of consequences and responsibility in action situations. J. Pers. Soc. Psychol. 10, 232–242 (1968).
Tajfel, H. & Turner, J. C. in Psychology of Intergroup Relations (eds Worchel, S. & Austin, W. G.) 7–24 (Nelson-Hall, 1986).
Siegrist, M. The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk Anal. 20, 195–203 (2000).
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).
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.
Jogunola, O. et al. Comparative analysis of P2P architecture for energy trading and sharing. Energies 11, 62:1–62:20 (2018).
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
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
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
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).
Zia, M. F. et al. Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis. IEEE Access 8, 19410–19432 (2020).
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
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).
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).
Si, F. et al. Cost-efficient multi-energy management with flexible complementarity strategy for energy internet. Appl. Energy 231, 803–815 (2018).
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).
Kirchhoff, H. & Strunz, K. Key drivers for successful development of peer-to-peer microgrids for swarm electrification. Appl. Energy 244, 46–62 (2019).
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).
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).
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).
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.
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).
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.
Liu, N. et al. Online energy sharing for nanogrid clusters: a Lyapunov optimization approach. IEEE Trans. Smart Grid 9, 4624–4636 (2018).
Sachs, J. et al. Adaptive 5G low-latency communication for tactile internet services. Proc. IEEE 107, 325–349 (2019).
Viswanath, S. K. et al. System design of the internet of things for residential smart grid. IEEE Wirel. Commun. 23, 90–98 (2016).
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).
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).
Wolske, K. S., Gillingham, K. T. & Schultz, P. W. Peer influence on household energy behaviours. Nat. Energy 5, 202–212 (2020).
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).
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).
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.
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).
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).
Başar, T. & Olsder, G. J. Dynamic Noncooperative Game Theory (Academic Press, 1995).
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).
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).
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).
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).
Vázquez-Canteli, J. R. & Nagy, Z. Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl. Energy 235, 1072–1089 (2019).
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).
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).
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.
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.
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).
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).
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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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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DOI: https://doi.org/10.1038/s41560-020-0671-0