Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants

Abstract

Power networks are undergoing a fundamental transition, with traditionally passive consumers becoming ‘prosumers’ — proactive consumers with distributed energy resources, actively managing their consumption, production and storage of energy. A key question that remains unresolved is: how can we incentivize coordination between vast numbers of distributed energy resources, each with different owners and characteristics? Virtual power plants and peer-to-peer (P2P) energy trading offer different sources of value to prosumers and the power network, and have been proposed as different potential structures for future prosumer electricity markets. In this Perspective, we argue they can be combined to capture the benefits of both. We thus propose the concept of the federated power plant, a virtual power plant formed through P2P transactions between self-organizing prosumers. This addresses social, institutional and economic issues faced by top-down strategies for coordinating virtual power plants, while unlocking additional value for P2P energy trading.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Multi-class P2P energy trading.
Fig. 2: Combining the energy transactions of a P2P platform and a VPP.

References

  1. 1.

    World Energy Trilemma Index 2016 (World Energy Council, 2016).

  2. 2.

    Arent, D. J., Wise, A. & Gelman, R. The status and prospects of renewable energy for combating global warming. Energy Econ. 33, 584–593 (2011).

    Article  Google Scholar 

  3. 3.

    Han, D.-M. & Lim, J.-H. Smart home energy management system using IEEE 802.15.4 and ZigBee. IEEE Trans. Consum. Electron. 56, 1403–1410 (2010).

    Article  Google Scholar 

  4. 4.

    Dimeas, A. et al. Smart houses in the smart grid: developing an interactive network. IEEE Electrification Mag. 2, 81–93 (2014).

    Article  Google Scholar 

  5. 5.

    Schleicher-Tappeser, R. How renewables will change electricity markets in the next five years. Energy Policy 48, 64–75 (2012).

    Article  Google Scholar 

  6. 6.

    Darby, S. J. & McKenna, E. Social implications of residential demand response in cool temperate climates. Energy Policy 49, 759–769 (2012).

    Article  Google Scholar 

  7. 7.

    Wilson, R. Architecture of Power Markets. Econometrica 70, 1299–1340 (2002).

    Article  Google Scholar 

  8. 8.

    Cai, D. W. H., Adlakha, S., Low, S. H., De Martini, P. & Mani Chandy, K. Impact of residential PV adoption on retail electricity rates. Energy Policy 62, 830–843 (2013).

    Article  Google Scholar 

  9. 9.

    Bronski. P. et al. The Economics of Grid Defection (Rocky Mountain Institute, 2014).

  10. 10.

    Pudjianto, D. et al. Value of integrating distributed energy resources in the UK electricity system. In IEEE PES General Meeting (IEEE, 2010); http://doi.org/dw29mq

  11. 11.

    Pudjianto, D., Ramsay, C. & Strbac, G. Virtual power plant and system integration of distributed energy resources. Renew. Power Generation IET 1, 10–16 (2007).

    Article  Google Scholar 

  12. 12.

    Rahimi, F. & Ipakchi, A. Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 1, 82–88 (2010).

    Article  Google Scholar 

  13. 13.

    Callaway, D. S. & Hiskens, I. A. Achieving controllability of electric loads. Proc. IEEE 99, 184–199 (2011).

    Article  Google Scholar 

  14. 14.

    Heussen, K., You, S., Biegel, B., Hansen, L. H. & Andersen, K. B. Indirect control for demand side management — A conceptual introduction. In IEEE PES Innov. Smart Grid Technol. Conf. Eu. (IEEE, 2012).

  15. 15.

    Good, N., Ellis, K. A. & Mancarella, P. Review and classification of barriers and enablers of demand response in the smart grid. Renew. Sustain. Energy Rev. 72, 57–72 (2016).

    Article  Google Scholar 

  16. 16.

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

    Article  Google Scholar 

  17. 17.

    Hagiu, A. & Wright, J. Multi-sided platforms. Int. J. Ind. Organ. 43, 162–174 (2015).

    Article  Google Scholar 

  18. 18.

    Ochoa, L. N., Pilo, F., Keane, A., Cuffe, P. & Pisano, G. Embracing an adaptable, flexible posture: ensuring that future European distribution networks are ready for more active roles. IEEE Power Energy Mag. 14, 16–28 (2016).

    Article  Google Scholar 

  19. 19.

    Gill, S., Kockar, I. & Ault, G. W. Dynamic optimal power flow for active distribution networks. IEEE Trans. Power Syst. 29, 121–131 (2014).

    Article  Google Scholar 

  20. 20.

    Pudjianto, D., Ramsay, C. & Strbac, G. Microgrids and virtual power plants: concepts to support the integration of distributed energy resources. Proc. Inst. Mech. Eng. J. Power Energy 222, 731–741 (2008).

    Article  Google Scholar 

  21. 21.

    Lasseter, R. H. MicroGrids. In 2002 IEEE Power Engineering Soc. Winter Meeting. (Cat. No. 02CH37309) 1, 305–308 (2002).

  22. 22.

    Mandelli, S., Barbieri, J., Mereu, R. & Colombo, E. Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review. Renew. Sustain. Energy Rev. 58, 1621–1646 (2016).

    Article  Google Scholar 

  23. 23.

    Morstyn, T., Hredzak, B. & Agelidis, V. G. Control strategies for microgrids with distributed energy storage systems: An overview. IEEE Trans. Smart Grid http://doi.org/chmz (2016).

  24. 24.

    Mnatsakanyan, A. & Kennedy, S. W. A novel demand response model with an application for a virtual power plant. IEEE Trans. Smart Grid 6, 230–237 (2015).

    Article  Google Scholar 

  25. 25.

    Darby, S. J. Load management at home: advantages and drawbacks of some ‘active demand side’ options. J. Power Energy 227, 9–17 (2012).

    Article  Google Scholar 

  26. 26.

    Defeuilley, C. Retail competition in electricity markets. Energy Policy 37, 377–386 (2009).

    Article  Google Scholar 

  27. 27.

    Einav, L., Farronato, C. & Levin, J. Peer-to-peer markets. Ann. Rev. Econ. 8, 615–635 (2016).

    Article  Google Scholar 

  28. 28.

    Boait, P. J., Snape, J. R., Darby, S. J., Hamilton, J. & Morris, R. J. R. Making legacy thermal storage heating fit for the smart grid. Energy Build. 138, 630–640 (2017).

    Article  Google Scholar 

  29. 29.

    Von Appen, J., Stetz, T., Braun, M. & Schmiegel, A. Local voltage control strategies for PV storage systems in distribution grids. IEEE Trans. Smart Grid 5, 1002–1009 (2014).

    Article  Google Scholar 

  30. 30.

    Steinheimer, M., Trick, U. & Ruhrig, P. Energy communities in smart markets for optimisation of peer-to-peer interconnected smart homes. In Proc. 2012 8th Int. Symp. Commun. Systems Networks Digital Signal Processing http://doi.org/chm2 (2012).

  31. 31.

    Sevlian, R. A. & Rajagopal, R. A model for the effect of aggregation on short term load forecasting. In 2014 IEEE PES General Meeting Conf. Exposition http://doi.org/chwx (IEEE, 2014).

  32. 32.

    Hart, E. K., Stoutenburg, E. D. & Jacobson, M. Z. The potential of intermittent renewables to meet electric power demand: current methods and emerging analytical techniques. In Proc. IEEE 100, 322–334 (2012).

  33. 33.

    Baeyens, E., Bitar, E. Y., Khargonekar, P. P. & Poolla, K. Coalitional aggregation of wind power. In IEEE Trans. Power Syst. 28, 3774–3784 (2013).

  34. 34.

    Yang, Y., Solgaard, H. S. & Haider, W. Value seeking, price sensitive, or green? Analyzing preference heterogeneity among residential energy consumers in Denmark. Energy Res. Soc. Sci. 6, 15–28 (2015).

    Article  Google Scholar 

  35. 35.

    Da Silva, P. G., Karnouskos, S. & Ilic, D. A survey towards understanding residential prosumers in smart grid neighbourhoods. In IEEE PES Innov. Smart Grid Technol. Eur. http://doi.org/chm3 (2012).

  36. 36.

    Current Practices in Consumer-Driven Renewable Electricity Markets (European Consumer Organisation, 2016).

  37. 37.

    Dagher, L., Bird, L. & Heeter, J. Residential green power demand in the United States. Renew. Energy 114, 1062–1068 (2017).

    Article  Google Scholar 

  38. 38.

    Fleiner, T., Janko, Z., Tamura, A. & Teytelboym, A. Trading networks with bilateral contracts. In Proc. Third Conf. Auctions Market Mech. Applications http://doi.org/chm9 (2016).

  39. 39.

    Lee, W., Xiang, L., Schober, R. & Wong, V. W. S. Direct electricity trading in smart grid: A coalitional game analysis. IEEE J. Sel. Areas Commun. 32, 1398–1411 (2014).

    Article  Google Scholar 

  40. 40.

    Hatfield, J. W. & Kominers, S. D. Contract design and stability in many-to-many matching. Games Econ. Behav. 101, 1–34 (2016).

    MathSciNet  MATH  Google Scholar 

  41. 41.

    Mapping of TSOs’ and DSOs’ Roles and Responsibilities Related to Market Design to Enable Energy Services (PÖYRY, 2015).

  42. 42.

    McKenna, E., Richardson, I. & Thomson, M. Smart meter data: Balancing consumer privacy concerns with legitimate applications. Energy Policy 41, 807–814 (2012).

    Article  Google Scholar 

  43. 43.

    Papadaskalopoulos, D., Strbac, G., Mancarella, P., Aunedi, M. & Stanojevic, V. Decentralized participation of flexible demand in electricity markets — Part II: Application with electric vehicles and heat pump systems. IEEE Trans. Power Syst. 28, 3667–3674 (2013).

    Article  Google Scholar 

  44. 44.

    Kraning, M., Chu, E., Lavaei, J. & Boyd, S. Dynamic network energy management via proximal message passing. Found. Trends Optim. 1, 70–122 (2014).

    Google Scholar 

  45. 45.

    Morstyn, T., Hredzak, B. & Agelidis, V. Network topology independent multi-agent dynamic optimal power flow for microgrids with distributed energy storage systems. In IEEE Trans. Smart Grid http://doi.org/chm4 (2016).

  46. 46.

    Grünewald, P., McKenna, E. & Thomson, M. Keep it simple: time-of-use tariffs in high-wind scenarios. IET Renew. Power Gener. 9, 176–183 (2015).

    Article  Google Scholar 

  47. 47.

    Jia, L. & Tong, L. Dynamic pricing and distributed energy management for demand response. IEEE Trans. Smart Grid 7, 1128–1136 (2016).

    Article  Google Scholar 

  48. 48.

    Sotkiewicz, P. M. & Vignolo, J. M. Nodal pricing for distribution networks: Efficient pricing for efficiency enhancing DG. IEEE Trans. Power Syst. 21, 1013–1014 (2006).

    Article  Google Scholar 

  49. 49.

    Roozbehani, M., Dahleh, M. A. & Mitter, S. K. Volatility of power grids under real-time pricing. IEEE Trans. Power Syst. 27, 1926–1940 (2012).

    Article  Google Scholar 

  50. 50.

    Papadaskalopoulos, D. & Strbac, G. Nonlinear and randomized pricing for distributed management of flexible loads. IEEE Trans. Smart Grid 7, 1137–1146 (2016).

    Article  Google Scholar 

  51. 51.

    Margellos, K. & Oren, S. Capacity controlled demand side management: A stochastic pricing analysis. IEEE Trans. Power Syst. 31, 706–717 (2016).

    Article  Google Scholar 

  52. 52.

    Fell, M. J., Shipworth, D., Huebner, G. M. & Elwell, C. A. Public acceptability of domestic demand-side response in Great Britain: The role of automation and direct load control. Energy Res. Soc. Sci. 9, 72–84 (2015).

    Article  Google Scholar 

  53. 53.

    Joskow, P. L. Why Do We Need Electricity Retailers?; Or, Can You Get it Cheaper Wholesale? (Centre for Energy and Environmental Policy Research, 2000).

  54. 54.

    Alvaro-Hermana, R., Fraile-Ardanuy, J., Zufiria, P. J., Knapen, L. & Janssens, D. Peer to peer energy trading with electric vehicles. IEEE Intell. Transp. Syst. Mag. 8, 33–44 (2016).

    Article  Google Scholar 

  55. 55.

    Smith, A., Hargreaves, T., Hielscher, S., Martiskainen, M. & Seyfang, G. Making the most of community energies: Three perspectives on grassroots innovation. Environ. Plan. A 48, 407–432 (2016).

    Article  Google Scholar 

  56. 56.

    Robert, F. C., Ramanathan, U., Mukundan, Durga, P. & Mohan, R. When academia meets rural India: Lessons learnt from a MicroGrid implementation. In 2016 IEEE Global Humanitarian Technol. Confer. http://doi.org/chm5 (2016).

  57. 57.

    Zhumabekuly Aitzhan, N. & Svetinovic, D. Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams. IEEE Trans. Dependable Secure Computing PP, 1 (2016).

  58. 58.

    Blockchain — an Opportunity for Energy Producers and Consumers? (PwC Global Power & Utilities, 2016).

Download references

Acknowledgements

The authors are appreciative for the support of the Oxford Martin Programme on Integrating Renewable Energy and the Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/N03466X/1, Peer-to-Peer Energy Trading and Sharing — 3M (Multi-times, Multi-scales, Multi-qualities).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Thomas Morstyn.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Morstyn, T., Farrell, N., Darby, S.J. et al. Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants. Nat Energy 3, 94–101 (2018). https://doi.org/10.1038/s41560-017-0075-y

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing