Abstract
Power systems are undergoing a fundamental transition as energy resources are increasingly distributed through all layers of the electricity grid. Local energy markets offer opportunities for value co-creation through the coordination of these resources, with benefits at the individual, community and societal levels. This chapter presents an overview of the main active players in local energy markets, their objectives, and the ways in which they can participate. We then present a systematic taxonomy of coordination strategies for these players with heterogeneous objectives and constraints. The coordination mechanisms can be classified in terms of the level of agency of participating players, the information communication structure, and whether the local energy market represents a competitive or cooperative game. A numerical case study is then conducted to illustrate the value obtained by active players through competition and cooperation. Finally, we discuss avenues forward to help integrate and coordinate the active players in local energy markets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Interquartile range with no or limited overshoot (high confidence).
- 2.
Note that the level at which a controlled unit is defined may be a household, a building, a neighbourhood, etc. Each coordination level can be nested; i.e., an aggregator may perform direct control of units downstream and trade in the wholesale market in a mediated competition upstream.
- 3.
Utility was defined by Jeremy Bentham as “that property in any object, whereby it tends to produce benefit, advantage, pleasure, good, or happiness (all this in the present case comes to the same thing) or (what comes again to the same thing) to prevent the happening of mischief, pain, evil, or unhappiness to the party whose interest is considered” (Bentham 1879). A utility function, in turn, is an economist’s convenient representation of an individual’s preferences that permits mathematical analysis (Hashimzade et al. 2017).
References
Abbas AO, Chowdhury BH (2021) Using customer-side resources for market-based transmission and distribution level grid services—a review. Int J Electr Power Ener Syst 125(May 2020):106480. https://doi.org/10.1016/j.ijepes.2020.106480
Ableitner L (2019) Quartierstrom. Implementation of a real world prosumer centric local energy market in Walenstadt, Switzerland. arXiv:1905.07242
Abrishambaf O, Lezama F, Faria P, Vale Z (2019) Towards transactive energy systems: an analysis on current trends. Ener Strateg Rev 26:100418. https://doi.org/10.1016/j.esr.2019.100418
Andrianesis P, Caramanis MC (2019) Optimal grid—distributed energy resource coordination: distribution locational marginal costs and hierarchical decomposition. In: 2019 57th annual Allerton conference on communication, control, and computing, Allerton, pp 318–325. https://doi.org/10.1109/ALLERTON.2019.8919689
Apostolopoulou D, Bahramirad S, Khodaei A (2016) The interface of power: moving toward distribution system operators. IEEE Power Ener Mag 46–51
Arblaster M (2018) Economic regulation of air traffic management: principles and approaches. In: Arblaster M (ed) Air traffic management. Elsevier, pp 143–172. https://doi.org/10.1016/B978-0-12-811118-5.00007-2. https://www.sciencedirect.com/science/article/pii/B9780128111185000072
Arlt M-L, Chassin DP, Kiesling LL (2021) Opening up transactive systems: Introducing tess and specification in a field deployment. Energies 14(13). https://doi.org/10.3390/en14133970. https://www.mdpi.com/1996-1073/14/13/3970
Babar M, Nguyen PH, Cuk V, Kamphuis IG, Bongaerts M, Hanzelka Z (2018) The evaluation of agile demand response: an applied methodology. IEEE Trans Smart Grid 9(6):6118–6127. https://doi.org/10.1109/TSG.2017.2703643
Bandeiras F, Pinheiro E, Gomes M, Coelho P, Fernandes J (2020) Review of the cooperation and operation of microgrid clusters. Renew Sustain Ener Rev 133(August):110311. https://doi.org/10.1016/j.rser.2020.110311
Behrangrad M (2015) A review of demand side management business models in the electricity market. Renew Sustain Ener Rev 47:270–283. https://doi.org/10.1016/j.rser.2015.03.033
Bentham J (1879) An introduction to the principles of morals and legislation. Clarendon Press, Oxford
Black J, Hashimzade N, Myles G (2012) A dictionary of economics. Oxford University Press. https://doi.org/10.1093/acref/9780199696321.001.0001
Blasch J, Filippini M, Kumar N (2019) Boundedly rational consumers, energy and investment literacy, and the display of information on household appliances. Resour Ener Econ 56:39–58
Cao J (2019) Deep reinforcement learning based energy storage arbitrage with accurate lithium-ion battery degradation model. IEEE Trans Smart Grid 14(8):1–9
Cao J, Crozier C, McCulloch M, Fan Z (2019) Optimal design and operation of a low carbon community based multi-energy systems considering EV integration. IEEE Trans Sustain Ener 10(3):1217–1226. https://doi.org/10.1109/TSTE.2018.2864123
Charbonnier F, Morstyn T, McCulloch MD (2022) Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility. Appl Ener 314:118825
Charbonnier F, Morstyn T, McCulloch M (2022) Coordination of resources at the edge of the electricity grid: systematic review and taxonomy. Appl Ener
Charles River Associates, An assessment of the economic value of demand-side participation in the Balancing Mechanism and an evaluation of options to improve access (2017)
Chen T, Su W (2019) Indirect customer-to-customer energy trading with reinforcement learning. IEEE Trans Smart Grid 10(4):4338–4348. https://doi.org/10.1109/TSG.2018.2857449
Claessens BJ, Vandael S, Ruelens F, De Craemer K, Beusen B (2013) Peak shaving of a heterogeneous cluster of residential flexibility carriers using reinforcement learning. In: 2013 4th IEEE/PES innovative smart grid technologies Europe. ISGT Europe 2013, pp 1–5. https://doi.org/10.1109/ISGTEurope.2013.6695254
Coffrin C, Van Hentenryck P, Bent R (2012) Approximating line losses and apparent power in AC power flow linearizations. In: IEEE power and energy society general meeting, pp 1–8. https://doi.org/10.1109/PESGM.2012.6345342
Council of European Energy Regulators, Regulatory aspects of self- consumption and energy communities CEER report, Tech. Rep. (2019). https://www.ceer.eu/documents/104400/-/-/8ee38e61-a802-bd6f-db27-4fb61aa6eb6a
Creamer E, Eadson W, Pinker A, Tingey M, Markantoni M, Foden M, Speight TB, Barnacle ML (2018) Community energy?: Entanglements of community, state, and private sector. Geogr Compass 12(7):1–16. https://doi.org/10.1111/gec3.12378
Crozier C, Apostolopoulou D, McCulloch M (2018) Mitigating the impact of personal vehicle electrification: a power generation perspective. Ener Policy 118(2013):474–481. https://doi.org/10.1016/j.enpol.2018.03.056
Dalamagkidis K, Kolokotsa D, Kalaitzakis K, Stavrakakis GS (2007) Reinforcement learning for energy conservation and comfort in buildings. Building Environ 42(7):2686–2698. https://doi.org/10.1016/j.buildenv.2006.07.010
Darby SJ (2019) Smart and sustainable, fast and slow. In: Eceee summer study proceedings 2019-June, pp 939–948
Darby SJ (2020) Demand response and smart technology in theory and practice: customer experiences and system actors. Ener Policy 143(April):111573. https://doi.org/10.1016/j.enpol.2020.111573
Dauer D, Flath CM, Ströhle P, Weinhardt C (2013) Market-based EV charging coordination. In: Proceedings—2013 IEEE/WIC/ACM international conference on intelligent agent technology, IAT 2013 2, pp 102–107. https://doi.org/10.1109/WI-IAT.2013.97
Department for Business Energy and Industrial Strategy, Energy consumption in the UK (2020)
Department for Transport, National Travel Survey 2002-2017 (2019). http://doi.org/10.5255/UKDA-SN-5340-10
Di Silvestre ML, Gallo P, Ippolito MG, Sanseverino ER, Zizzo G (2018) A technical approach to the energy blockchain in microgrids. IEEE Trans Ind Inform 14(11):4792–4803. https://doi.org/10.1109/TII.2018.2806357
Dietz T (2015) Altruism, self-interest, and energy consumption. PNAS 112(6):1654–1655. https://doi.org/10.1073/pnas.1423686112
Dudjak V, Neves D, Alskaif T, Khadem S, Pena-bello A, Saggese P, Bowler B, Andoni M, Bertolini M, Zhou Y, Lormeteau B, Mustafa MA, Wang Y, Francis C, Zobiri F, Parra D, Papaemmanouil A (2021) Impact of local energy markets integration in power systems layer: a comprehensive review. Appl Ener 301(March):117434. https://doi.org/10.1016/j.apenergy.2021.117434
Dufo-López R, Lujano-Rojas JM, Bernal-Agustín JL (2014) Comparison of different lead-acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems. Appl Ener 115:242–253
Dusparic I (2013) Multi-agent residential demand response based on load forecasting. In: 2013 1st IEEE conference on technologies for sustainability, SusTech 2013, pp 90–96. https://doi.org/10.1109/SusTech.2013.6617303
Dusparic I, Maximizing renewable energy use with decentralized residential demand response. In: 2015 IEEE 1st international smart cities conference, ISC2 2015. https://doi.org/10.1109/ISC2.2015.7366212
Eid C, Codani P, Perez Y, Reneses J, Hakvoort R (2016) Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design. Renew Sustain Ener Rev 64:237–247. https://doi.org/10.1016/j.rser.2016.06.008
Elder GH (1994) Time, human agency, and social change: perspectives on the life course. Soc Psychol Q 57(1):4–15. http://www.jstor.org/stable/2786971
Energy Systems Catapult (2019) The policy and regulatory context for new Local Energy Markets. Technical Report, August, Energy Systems Catapult
European Commission (2016) An EU strategy on heating and cooling, Technical report. https://ec.europa.eu/energy/sites/ener/files/documents/1_EN_ACT_part1_v14.pdf
Farhi E, Werning I (2019) Monetary policy, bounded rationality, and incomplete markets. Am Econ Rev 109(11):3887–3928
Fazal R, Solanki J, Solanki SK (2012) Demand response using multi-agent system. In: 2012 North American power symposium, NAPS 2012. https://doi.org/10.1109/NAPS.2012.6336401
Fleiner T, Janko Z, Tamura A, Teytelboym A (2015) Trading networks with bilateral contracts. In: EAI endorsed transactions on serious games, pp 1–39. https://doi.org/10.4108/eai.8-8-2015.2260329
Fortenbacher P, Mathieu JL, Andersson G (2017) Modeling and optimal operation of distributed battery storage in low voltage grids. IEEE Trans Power Syst 32(6):4340–4350. arXiv:1603.06468. https://doi.org/10.1109/TPWRS.2017.2682339
Frederiks ER, Stenner K, Hobman EV (2015) Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew Sustain Ener Rev 41:1385–1394
Gilbert A, Bazilian MD, Gross S (2021) The emerging global natural gas market and the energy crisis of 2021–2022, Technical Report, Dec 2021, Brookings
Guerrero J, Chapman AC, Verbic G (2018) Decentralized P2P energy trading under network constraints in a low-voltage network. IEEE Trans Smart Grid 1–10. arXiv:1809.06976. https://doi.org/10.1109/TSG.2018.2878445
Guerrero J, Gebbran D, Mhanna S, Chapman AC, Verbi\(\check{c}\) G (2020) Towards a transactive energy system for integration of distributed energy resources: home energy management, distributed optimal power flow, and peer-to-peer energy trading. Renew Sustain Energ Revi 132
Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Ener Rev 59:166–178. https://doi.org/10.1016/j.rser.2016.01.016
Han L, Morstyn T, McCulloch M (2019) Incentivizing prosumer coalitions with energy management using cooperative game theory. IEEE Trans Power Syst 34(1):303–313. https://doi.org/10.1109/TPWRS.2018.2858540
Hao H, Sanandaji BM, Poolla K, Vincent T (2015) Aggregate flexibility of thermostatically controlled loads. IEEE Trans Power Syst 30(1):189–198. https://doi.org/10.1109/TPWRS.2014.2328865
Hashimzade N, Myles G, Black J (2017) Utility function
Hayes BP, Thakur S, Breslin JG (2020) Co-simulation of electricity distribution networks and peer to peer energy trading platforms. Int J Electr Power Ener Syst 115(May 2019):105419. https://doi.org/10.1016/j.ijepes.2019.105419
Herbert S (1982) Models of bounded rationality. MIT Press, Mass, London, Cambridge
Heussen K, Koch S, Ulbig A, Andersson G (2010) Energy storage in power system operation: the power nodes modeling framework. In: IEEE PES innovative smart grid technologies conference Europe, ISGT Europe, pp 1–8. https://doi.org/10.1109/ISGTEUROPE.2010.5638865
Hurtado LA, Mocanu E, Nguyen PH, Gibescu M, Kamphuis RI (2018) Enabling cooperative behavior for building demand response based on extended joint action learning. IEEE Trans Ind Inform 14(1):127–136. https://doi.org/10.1109/TII.2017.2753408
Inês C, Guilherme PL, Esther M-G, Swantje G, Stephen H, Lars H (2020) Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Ener Policy 138:111212. https://doi.org/10.1016/j.enpol.2019.111212. www.sciencedirect.com/science/article/pii/S0301421519307943
ISO (2007) Calculation of energy use for space heating and cooling ISO/FDIS 13790:2007(E)
Jamasb T, Pollitt M (2007) Incentive regulation of electricity distribution networks: lessons of experience from Britain. Ener Policy 35(12):6163–6187. https://doi.org/10.1016/j.enpol.2007.06.022
Ji C, You P, Pivo EJ, Shen Y, Gayme DF, Mallada E (2019) Optimal coordination of distribution system resources under uncertainty for joint energy and ancillary service market participation
Kandasamy NK, Tseng K, Soong B (2017) A virtual storage capacity using demand response management to overcome intermittency of solar pv generation. IET Renew Power Gener 11(09 2017). https://doi.org/10.1049/iet-rpg.2017.0036
Khorasany M, Mishra Y, Ledwich G (2020) A decentralized bilateral energy trading system for peer-to-peer electricity markets. IEEE Trans Ind Electron 67(6):4646–4657. https://doi.org/10.1109/TIE.2019.2931229
Kim JG, Lee B (2020) Automatic P2P energy trading model based on reinforcement learning using long short-term delayed reward. Energies 13(20). https://doi.org/10.3390/en13205359
Kim B, Zhang Y, Van Der Schaar M, Lee J (2016) Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans Smart Grid 7(5):2187–2198
Kok K, Widergren S (2016) A society of devices: integrating intelligent distributed resources with transactive energy. IEEE Power Ener Mag 14(3):34–45. https://doi.org/10.1109/MPE.2016.2524962
Léautier T-O (2019) Imperfect markets and imperfect regulation: an introduction to the microeconomics and political economy of power markets. MIT Press
Lee JW, Lee DH (2011) Residential electricity load scheduling for multi-class appliances with Time-of-Use pricing. In: 2011 IEEE GLOBECOM workshops. GC Wkshps, pp 1194–1198. https://doi.org/10.1109/GLOCOMW.2011.6162370
Li Z, Kang J, Yu R, Ye D, Deng Q, Zhang Y (2018) Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans Ind Inform 14(8):3690–3700, cited by 388. https://doi.org/10.1109/TII.2017.2786307. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039778045 &doi=10.1109%2fTII.2017.2786307 &partnerID=40 &md5=72ec5d315d1ddcf4cae831e0632d11cb
Lu R, Hong SH (2019) Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Appl Ener 236(December 2018):937–949. https://doi.org/10.1016/j.apenergy.2018.12.061
Maharjan S, Zhu Q, Zhang Y, Gjessing S, Basar T (2013) Dependable demand response management in the smart grid: a stackelberg game approach. IEEE Trans Smart Grid 4(1):120–132. https://doi.org/10.1109/TSG.2012.2223766
Mai TT, Nguyen PH, Tran QT, Cagnano A, De Carne G, Amirat Y, Le AT, De Tuglie E (2021) An overview of grid-edge control with the digital transformation. Electr Eng 103(4):1989–2007
Marinescu A, Dusparic I, Clarke S (2017) Prediction-based multi-agent reinforcement learning in inherently non-stationary environments. ACM Trans Auton Adapt Syst 12(2). https://doi.org/10.1145/3070861
Masson-Delmotte V (2018) Global warming of 1.5C. An IPCC special report on the impacts of global warming of 1.5C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change
Mayr E, Zhang Z, Iftikhar B (2022) Gone bust? The crisis in Britain’s energy supply market, Technical report, FTI
McKenna E, Thomson M (2016) High-resolution stochastic integrated thermal-electrical domestic demand model. Appl Ener 165:445–461
Meng L, Sanseverino ER, Luna A, Dragicevic T, Vasquez JC, Guerrero JM (2016) Microgrid supervisory controllers and energy management systems: a literature review. Renew Sustain Ener Rev 60:1263–1273
Moon N, Rodgers D, Mchugh S (2015) Energy market investigation—a report for the competition and markets authority by GfK NOP, Technical Report
Moret F, Pinson P (2019) Energy collectives: a community and fairness based approach to future electricity markets. IEEE Trans Power Syst 34(5):3994–4004. https://doi.org/10.1109/TPWRS.2018.2808961
Morstyn T, Farrell N, Darby SJ, McCulloch MD (2018a) Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants. Nat Ener 3(2):94–101. https://doi.org/10.1038/s41560-017-0075-y
Morstyn T, Hredzak B, Agelidis V (2018b) Control strategies for microgrids with distributed energy storage systems: an overview. IEEE Trans Smart Grid 9(4):3652–3666. https://doi.org/10.1109/TSG.2016.2637958
Morstyn T, Hredzak B, Aguilera R, Agelidis V (2018c) Model predictive control for distributed microgrid battery energy storage systems. IEEE Trans Control Syst Technol 26(3):1107–1114. arXiv:1702.04699. https://doi.org/10.1109/TCST.2017.2699159
Morstyn T, McCulloch M (2019) Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences. IEEE Trans Power Syst 34(5):4005–4014. https://doi.org/10.1109/TPWRS.2018.2834472
Morstyn T, Mcculloch M (2020) Peer-to-Peer energy trading. In: Analytics for the sharing economy: mathematics engineering and business perspectives (March). https://doi.org/10.1007/978-3-030-35032-1
Morstyn T, Teytelboym A, Hepburn C, McCulloch M (2020) Integrating P2P energy trading with probabilistic distribution locational marginal pricing. IEEE Trans Smart Grid 11(4):3095–3106. https://doi.org/10.1109/TSG.2019.2963238
Morstyn T, Teytelboym A, McCulloch M (2019a) Bilateral contract networks for peer-to-peer energy trading. IEEE Trans Smart Grid 10(2):2026–2035. https://doi.org/10.1109/TSG.2017.2786668
Morstyn T, Teytelboym A, McCulloch M (2019b) Designing decentralized markets for distribution system flexibility. IEEE Trans Power Syst 34(3):1–12. https://doi.org/10.1109/TPWRS.2018.2886244
Muratori M (2018) Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat Ener 3(3):193–201. https://doi.org/10.1038/s41560-017-0074-z
Nicol S, Roys M, Ormandy D, Ezratty V (2015) The cost of poor housing in the European Union, Technical report, BRE. https://www.bre.co.uk/filelibrary/Briefing papers/92993_BRE_Poor-Housing_in_-Europe.pdf
Niella T, Stier-Moses N, Sigman M (2016) Nudging cooperation in a crowd experiment. PLOS ONE 11(1):1–20
Nolan JM, Schultz PW, Cialdini RB, Goldstein NJ, Griskevicius V (2008) Normative social influence is underdetected. Person Soc Psychol Bull 34(7):913–923. https://doi.org/10.1177/0146167208316691
Octopus Energy (2019) Octopus energy API
Ofgem PC (2016) Aggregators–barriers and external impacts. Technical Report, May, OFGEM
O’Neill D, Levorato M, Goldsmith A, Mitra U (2010) Residential demand response using reinforcement learning. In: 2010 First IEEE international conference on smart grid communications, pp 409–414. https://doi.org/10.1109/smartgrid.2010.5622078
Origami Energy, Value chain for flexibility providers, Technical report, Local Energy Oxfordshire (LEO) (2021). https://project-leo.co.uk/wp-content/uploads/2021/06/LEO-D2.8-Value-Chain-for-Flexibility-Providers-v2.1-LEO-cover.pdf
Parry M (2007) Climate change 2007: impacts, adaptation and vulnerability. Published for the Intergovernmental Panel on Climate Change [by] Cambridge University Press, Cambridge
Pumphrey K, Walker S, Andoni M, Robu V (2020) Green hope or red herring? Examining consumer perceptions of peer-to-peer energy trading in the United Kingdom. Ener Res Soc Sci 68(Sept 2019):101603. https://doi.org/10.1016/j.erss.2020.101603
Römer B, Reichhart P, Kranz J, Picot A (2012) The role of smart metering and decentralized electricity storage for smart grids: the importance of positive externalities. Ener policy 50:486–495
Rottondi C, Verticale G (2017) A privacy-friendly gaming framework in smart electricity and water grids 5:14221–14233
Rozada S, Apostolopoulou D, Alonso E (2020) Load frequency control: a deep multi-agent reinforcement learning approach. In: IEEE power and energy society general meeting 2020-Aug, pp 0–4. https://doi.org/10.1109/PESGM41954.2020.9281614
Samadi P, Mohsenian-Rad H, Wong VWS, Schober R (2013) Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans Smart Grid 4(2):1007–1016. https://doi.org/10.1109/TSG.2012.2234769
Savelli I, Morstyn T (2021) Better together: harnessing social relationships in smart energy communities. Ener Res Soc Sci 78:102125
Schellenberg C, Lohan J, Dimache L (2020) Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage. Renew Sustain Ener Rev 131(June):109966
Siano P (2014) Demand response and smart grids-A survey. Renew Sustain Ener Rev 30:461–478. https://doi.org/10.1016/j.rser.2013.10.022
Sousa T, Soares T, Pinson P, Moret F, Baroche T, Sorin E (2019) Peer-to-peer and community-based markets: a comprehensive review. Renew Sustaina Ener Rev 104:367–378. arXiv:1810.09859. https://doi.org/10.1016/j.rser.2019.01.036
Stadler M, Krause W, Sonnenschein M, Vogel U (2007) The adaptive fridge—comparing different control schemes for enhancing load shifting of electricity demand. Environ Protect 199–206
Sun Y, Somani A, Carroll T (2015) Learning based bidding strategy for HVAC systems in double auction retail energy markets. In: Proceedings of the American control conference 2015-July, pp 2912–2917. https://doi.org/10.1109/ACC.2015.7171177
Tayab UB, Roslan MAB, Hwai LJ, Kashif M (2017) A review of droop control techniques for microgrid. Renew Sustain Ener Rev 76(March):717–727. https://doi.org/10.1016/j.rser.2017.03.028
Taylor A (2014) Accelerating learning in multi-objective systems through transfer learning. In: Proceedings of the international joint conference on neural networks, pp 2298–2305. https://doi.org/10.1109/IJCNN.2014.6889438
The European Parliament, The Council of the European, Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity and amending Directive 2012/27/EU (2019)
Tindemans S, Trovato V, Strbac G (2015) Decentralized control of thermostatic loads for flexible demand response. IEEE Trans Control Syst Technol 23(5):1685–1700. https://doi.org/10.1109/TCST.2014.2381163
Tushar W (2019) A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid. Appl Ener 243(November 2018):10–20. https://doi.org/10.1016/j.apenergy.2019.03.111
Tushar W, Saha T, Yuen C, Morstyn T, Nahid-Al-Masood, Poor H, Bean R (2020) Grid influenced peer-to-peer energy trading. IEEE Trans Smart Grid 11(2):1407–1418. arXiv:1908.09449. https://doi.org/10.1109/TSG.2019.2937981
Vayá MG, Roselló LB, Andersson G (2014) Optimal bidding of plug-in electric vehicles in a market-based control setup. In: Proceedings—2014 power systems computation conference, PSCC 2014. https://doi.org/10.1109/PSCC.2014.7038108
Vázquez-Canteli J, Nagy Z (2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Ener 235(Oct 2018):1072–1089. https://doi.org/10.1016/j.apenergy.2018.11.002
Vespermann N, Hamacher T, Kazempour J, Member S (2021) Risk trading in energy communities 12(2):1249–1263
Vivid Economics, Imperial College London, Accelerated electrification and the GB electricity system, report prepared for Committee on Climate Change, pp 1–79 (2019). https://www.theccc.org.uk/wp-content/uploads/2019/05/CCC-Accelerated-Electrification-Vivid-Economics-Imperial-1.pdf
Wang H, Zhang B (2018) Energy storage arbitrage in real-time markets via reinforcement learning. In: IEEE power and energy society general meeting, vol 2018, pp 1–11. arXiv:1711.03127. https://doi.org/10.1109/PESGM.2018.8586321
Wardle R (2014a) Dataset (TC1a): basic profiling of domestic smart meter customers
Wardle R (2014b), Dataset (TC5): Enhanced profiling of domestic customers with solar photovoltaics (PV)
Wen Z, O’Neill D, Maei H (2015) Optimal demand response using device-based reinforcement learning. IEEE Trans Smart Grid 6(5):2312–2324. https://doi.org/10.1109/TSG.2015.2396993
Wilson R (2002) Architecture of power markets. Econometrica 70(4):1299–1340. https://doi.org/10.1111/1468-0262.00334
Wooldridge M (2002) Intelligent agents: the key concepts. Springer, Berlin, Heidelberg
Wuester H, Lee JJ, Lumijarvi A (2016) Unlocking renewable energy investment: the role of risk mitigation and structured finance. Technical report IRENA
Wu F, Varaiya P (1999) Coordinated multilateral trades for electric power networks: theory and implementation. Int J Electr Power Ener Syst 21:75–102. https://doi.org/10.1049/cp:19951190
Yang H, Zhang M, Lai M (2011) Complex dynamics of cournot game with bounded rationality in an oligopolistic electricity market. Optim Eng 12(4):559–582
Yang L, Nagy Z, Goffin P, Schlueter A (2015) Reinforcement learning for optimal control of low exergy buildings. Appl Ener 156:577–586. https://doi.org/10.1016/j.apenergy.2015.07.050
Yao J (2017) Cybersecurity of demand side management in the smart electricity grid: Privacy protection, battery capacity sharing and power grid under attack, PhD thesis, copyright—Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works. https://www.proquest.com/dissertations-theses/cybersecurity-demand-side-management-smart/docview/1957432786/se-2?accountid=13042. Accessed 20 May 2021
Ye Y, Qiu D, Sun M, Papadaskalopoulos D, Strbac G (2020) Deep reinforcement learning for strategic bidding in electricity markets. IEEE Trans Smart Grid 11(2):1343–1355. https://doi.org/10.1109/TSG.2019.2936142
Zhang X, Bao T, Yu T, Yang B, Han C (2017) Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid. Energy 133:348–365. https://doi.org/10.1016/j.energy.2017.05.114
Zhang Z, Li R, Li F (2020) A novel peer-to-peer local electricity market for joint trading of energy and uncertainty. IEEE Trans Smart Grid 11(2):1205–1215. https://doi.org/10.1109/TSG.2019.2933574
Zhao J, Lu J, Lo KL (2017) A transmission congestion cost allocation method in bilateral trading electricity market. Ener Power Eng 09(04):240–249. https://doi.org/10.4236/epe.2017.94b029
Zhu M (2014) Distributed demand response algorithms against semi-honest adversaries. In: IEEE power and energy society general meeting, Oct 2014, pp 0–4. https://doi.org/10.1109/PESGM.2014.6939191
Acknowledgements
This work was supported by the Saven European Scholarship and by the UK Research and Innovation and the Engineering and Physical Sciences Research Council (award references EP/S000887/1, EP/S031901/1, and EP/T028564/1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Charbonnier, F., Morstyn, T., McCulloch, M. (2023). Active Players in Local Energy Markets. In: Shafie-khah, M., Gazafroudi, A.S. (eds) Trading in Local Energy Markets and Energy Communities. Lecture Notes in Energy, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-031-21402-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-21402-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21401-1
Online ISBN: 978-3-031-21402-8
eBook Packages: EnergyEnergy (R0)