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.
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World Energy Trilemma Index 2016 (World Energy Council, 2016).
Arent, D. J., Wise, A. & Gelman, R. The status and prospects of renewable energy for combating global warming. Energy Econ. 33, 584–593 (2011).
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).
Dimeas, A. et al. Smart houses in the smart grid: developing an interactive network. IEEE Electrification Mag. 2, 81–93 (2014).
Schleicher-Tappeser, R. How renewables will change electricity markets in the next five years. Energy Policy 48, 64–75 (2012).
Darby, S. J. & McKenna, E. Social implications of residential demand response in cool temperate climates. Energy Policy 49, 759–769 (2012).
Wilson, R. Architecture of Power Markets. Econometrica 70, 1299–1340 (2002).
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).
Bronski. P. et al. The Economics of Grid Defection (Rocky Mountain Institute, 2014).
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
Pudjianto, D., Ramsay, C. & Strbac, G. Virtual power plant and system integration of distributed energy resources. Renew. Power Generation IET 1, 10–16 (2007).
Rahimi, F. & Ipakchi, A. Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 1, 82–88 (2010).
Callaway, D. S. & Hiskens, I. A. Achieving controllability of electric loads. Proc. IEEE 99, 184–199 (2011).
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).
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).
Parag, Y. & Sovacool, B. K. Electricity market design for the prosumer era. Nat. Energy 1, 16032 (2016).
Hagiu, A. & Wright, J. Multi-sided platforms. Int. J. Ind. Organ. 43, 162–174 (2015).
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).
Gill, S., Kockar, I. & Ault, G. W. Dynamic optimal power flow for active distribution networks. IEEE Trans. Power Syst. 29, 121–131 (2014).
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).
Lasseter, R. H. MicroGrids. In 2002 IEEE Power Engineering Soc. Winter Meeting. (Cat. No. 02CH37309) 1, 305–308 (2002).
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).
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).
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).
Darby, S. J. Load management at home: advantages and drawbacks of some ‘active demand side’ options. J. Power Energy 227, 9–17 (2012).
Defeuilley, C. Retail competition in electricity markets. Energy Policy 37, 377–386 (2009).
Einav, L., Farronato, C. & Levin, J. Peer-to-peer markets. Ann. Rev. Econ. 8, 615–635 (2016).
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).
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).
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).
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).
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).
Baeyens, E., Bitar, E. Y., Khargonekar, P. P. & Poolla, K. Coalitional aggregation of wind power. In IEEE Trans. Power Syst. 28, 3774–3784 (2013).
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).
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).
Current Practices in Consumer-Driven Renewable Electricity Markets (European Consumer Organisation, 2016).
Dagher, L., Bird, L. & Heeter, J. Residential green power demand in the United States. Renew. Energy 114, 1062–1068 (2017).
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).
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).
Hatfield, J. W. & Kominers, S. D. Contract design and stability in many-to-many matching. Games Econ. Behav. 101, 1–34 (2016).
Mapping of TSOs’ and DSOs’ Roles and Responsibilities Related to Market Design to Enable Energy Services (PÖYRY, 2015).
McKenna, E., Richardson, I. & Thomson, M. Smart meter data: Balancing consumer privacy concerns with legitimate applications. Energy Policy 41, 807–814 (2012).
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).
Kraning, M., Chu, E., Lavaei, J. & Boyd, S. Dynamic network energy management via proximal message passing. Found. Trends Optim. 1, 70–122 (2014).
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).
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).
Jia, L. & Tong, L. Dynamic pricing and distributed energy management for demand response. IEEE Trans. Smart Grid 7, 1128–1136 (2016).
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).
Roozbehani, M., Dahleh, M. A. & Mitter, S. K. Volatility of power grids under real-time pricing. IEEE Trans. Power Syst. 27, 1926–1940 (2012).
Papadaskalopoulos, D. & Strbac, G. Nonlinear and randomized pricing for distributed management of flexible loads. IEEE Trans. Smart Grid 7, 1137–1146 (2016).
Margellos, K. & Oren, S. Capacity controlled demand side management: A stochastic pricing analysis. IEEE Trans. Power Syst. 31, 706–717 (2016).
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).
Joskow, P. L. Why Do We Need Electricity Retailers?; Or, Can You Get it Cheaper Wholesale? (Centre for Energy and Environmental Policy Research, 2000).
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).
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).
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).
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).
Blockchain — an Opportunity for Energy Producers and Consumers? (PwC Global Power & Utilities, 2016).
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).
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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
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