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Applying responsible algorithm design to neighbourhood-scale batteries in Australia

Abstract

The digital energy era presents at least three systemic concerns to the design and operation of algorithms: bias of considerations towards the easily quantifiable; inhibition of explainability; and undermining of trust and inclusion, as well as energy users’ autonomy and control. Here we examine these tensions through an interdisciplinary study that reveals the diversity of possible algorithms and their accompanying material effects, focused on neighbourhood-scale batteries (NSBs) in Australia. We conducted qualitative research with energy sector professionals and citizens to understand the range of perceived benefits and risks of NSBs and the algorithms that drive their behaviour. Issues raised by stakeholders were integrated into NSB optimization algorithms whose effects on NSB owners and customers were quantified through techno-economic modelling. Our results show the allocation of benefits and risks vary considerably between different algorithm designs. This indicates a need to improve energy algorithm governance, enabling accountability and responsiveness across the design and use of algorithms so that the digitization of energy technology does not lead to adverse public outcomes.

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Fig. 1: Risks and benefits of NSB.
Fig. 2: Layout of neighbourhood energy system and neighbourhood net load.
Fig. 3: Simulation of NSB operating under financial optimization.
Fig. 4: Simulation of NSB operating under carbon emission reduction optimization and cooptimization.
Fig. 5: Simulation of NSB operating under self-sufficiency optimization and timer function.
Fig. 6: Comparison of the performance of the six algorithms against five metrics.

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Data availability

The NextGen electricity data used in this study are available from the ACT Government through the website www.energydata.act.gov.au/pubhome. The qualitative data are not available due to abiding by ethics requirements to ensure anonymity of participants.

Code availability

We carried out quantitative analysis using our open-source software packages available at https://github.com/bsgip/c3x-data and https://github.com/bsgip/c3x-enomo. The script used to calculate presented results is available at https://github.com/bsgip/2021-responsible_algorithm_NSB.

References

  1. Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447 (2019).

    Article  Google Scholar 

  2. Gianfrancesco, M. A., Tamang, S., Yazdany, J. & Schmajuk, G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern. Med. 178, 1544–1547 (2018).

    Article  Google Scholar 

  3. Cowgill, B. Bias and productivity in humans and algorithms. Columbia Business School Research Paper https://doi.org/10.2139/ssrn.3584916 (2019).

  4. Raghavan, M., Barocas, S., Kleinberg, J. & Levy, K. in Proc. 2020 Conference on Fairness, Accountability, and Transparency 469–481 (Association for Computing Machinery, 2020); https://doi.org/10.1145/3351095.3372828

  5. Joh, E. E. Feeding the machine: Policing, Crime Data, & Algorithms Symposium: big data, national security, and the Fourth Amendment. William Mary Bill. Rights J. 26, 287–302 (2017).

    Google Scholar 

  6. Carlson, A. M. The need for transparency in the age of predictive sentencing algorithms notes. Iowa Law Rev. 103, 303–330 (2017).

    Google Scholar 

  7. Starbird, K. Disinformation’s spread: bots, trolls and all of us. Nature 571, 449–449 (2019).

    Article  Google Scholar 

  8. Crawford, K. & Paglen, T. Excavating AI: The politics of images in machine learning training sets. AI Soc. https://doi.org/10.1007/s00146-021-01162-8 (2021).

  9. Cleveland, F. M. Cyber security issues for advanced metering infrastructure (AMI). in Proc. 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century 1–5 (IEEE, 2008); https://doi.org/10.1109/PES.2008.4596535

  10. Brewster, T. Hundreds of Tesla Powerwalls exposed to potential password hacks via Google—don’t let one be yours. Forbes (17 November 2020); https://www.forbes.com/sites/thomasbrewster/2020/11/17/hundreds-of-tesla-powerwalls-exposed-to-potential-password-hacks-via-google---dont-let-one-be-yours/?sh=221f81843735

  11. Boyarskaya, M., Olteanu, A. & Crawford, K. Overcoming failures of imagination in AI infused system development and deployment. Preprint at https://arxiv.org/abs/2011.13416 (2020).

  12. Kloppenburg, S. & Boekelo, M. Digital platforms and the future of energy provisioning: promises and perils for the next phase of the energy transition. Energy Res. Soc. Sci. 49, 68–73 (2019).

    Article  Google Scholar 

  13. Ransan-Cooper, H., Lovell, H., Watson, P., Harwood, A. & Hann, V. Frustration, confusion and excitement: mixed emotional responses to new household solar-battery systems in Australia. Energy Res. Soc. Sci. 70, 101656 (2020).

    Article  Google Scholar 

  14. Chandrashekeran, S. From responsibilization to responsiveness through metrics: smart meter deployment in Australia. Geoforum 116, 110–118 (2020).

    Article  Google Scholar 

  15. Arghandeh, R., Woyak, J., Onen, A., Jung, J. & Broadwater, R. P. Economic optimal operation of Community Energy Storage systems in competitive energy markets. Appl. Energy 135, 71–80 (2014).

    Article  Google Scholar 

  16. Mediwaththe, C. P., Shaw, M., Halgamuge, S., Smith, D. B. & Scott, P. An incentive-compatible energy trading framework for neighborhood area networks with shared energy storage. IEEE Trans. Sustain. Energy 11, 467–476 (2020).

    Article  Google Scholar 

  17. Parra, D., Norman, S. A., Walker, G. S. & Gillott, M. Optimum community energy storage system for demand load shifting. Appl. Energy 174, 130–143 (2016).

    Article  Google Scholar 

  18. Norbu, S., Couraud, B., Robu, V., Andoni, M. & Flynn, D. Modelling the redistribution of benefits from joint investments in community energy projects. Appl. Energy 287, 116575 (2021).

    Article  Google Scholar 

  19. Mediwaththe, C. P. & Blackhall, L. Network-aware demand-side management framework with a community energy storage system considering voltage constraints. IEEE Trans. Power Syst. 36, 1229–1238 (2020).

  20. Lee, J., Bérard, J.-P., Razeghi, G. & Samuelsen, S. Maximizing PV hosting capacity of distribution feeder microgrid. Appl. Energy 261, 114400 (2020).

    Article  Google Scholar 

  21. Grünewald, P. H., Cockerill, T. T., Contestabile, M. & Pearson, P. J. G. The socio-technical transition of distributed electricity storage into future networks—system value and stakeholder views. Energy Policy 50, 449–457 (2012).

    Article  Google Scholar 

  22. Gaede, J. & Rowlands, I. H. The value of multiple perspectives. Energy Res. Soc. Sci. 48, 262–268 (2019).

    Article  Google Scholar 

  23. Müller, S. C. & Welpe, I. M. Sharing electricity storage at the community level: an empirical analysis of potential business models and barriers. Energy Policy 118, 492–503 (2018).

    Article  Google Scholar 

  24. Koirala, B. P., van Oost, E. & van der Windt, H. Community energy storage: a responsible innovation towards a sustainable energy system? Appl. Energy 231, 570–585 (2018).

    Article  Google Scholar 

  25. Hoffmann, E. & Mohaupt, F. Joint storage: a mixed-method analysis of consumer perspectives on community energy storage in Germany. Energies 13, 3025 (2020).

    Article  Google Scholar 

  26. Gährs, S. & Knoefel, J. Stakeholder demands and regulatory framework for community energy storage with a focus on Germany. Energy Policy 144, 111678 (2020).

    Article  Google Scholar 

  27. Ambrosio-Albalá, P., Upham, P. & Bale, C. S. E. Purely ornamental? Public perceptions of distributed energy storage in the United Kingdom. Energy Res. Soc. Sci. 48, 139–150 (2019).

    Article  Google Scholar 

  28. Neyland, D. Bearing accountable witness to the ethical algorithmic system. Sci. Technol. Hum. Values 41, 50–76 (2016).

    Article  Google Scholar 

  29. Stilgoe, J., Owen, R. & Macnaghten, P. Developing a framework for responsible innovation. Res. Policy 42, 1568–1580 (2013).

    Article  Google Scholar 

  30. Jenkins, K. E. H., Spruit, S., Milchram, C., Höffken, J. & Taebi, B. Synthesizing value sensitive design, responsible research and innovation, and energy justice: a conceptual review. Energy Res. Soc. Sci. 69, 101727 (2020).

    Article  Google Scholar 

  31. Cohen, J. J. et al. Tackling the challenge of interdisciplinary energy research: a research toolkit. Energy Res. Soc. Sci. 74, 101966 (2021).

    Article  Google Scholar 

  32. Byrd, J. Chart of the day: something has gone terribly wrong with electricity prices. ABC News (2018); https://www.abc.net.au/news/2018-07-18/electricity-price-rises-chart-of-the-day/9985300

  33. Postcode data for small-scale installations (Australian Government Clean Energy Regulator, 2021); http://www.cleanenergyregulator.gov.au/RET/Forms-and-resources/Postcode-data-for-small-scale-installations

  34. Balmer, A. S. et al. Five rules of thumb for post-ELSI interdisciplinary collaborations. J. Responsible Innov. 3, 73–80 (2016).

    Article  Google Scholar 

  35. Shaw, M. et al. The NextGen Energy Storage trial in the ACT, Australia. in Proc. Tenth ACM International Conference on Future Energy Systems 439–442 (Association for Computing Machinery, 2019); https://doi.org/10.1145/3307772.3331017

  36. Distributed Energy Resources Integration—Updating Regulatory Arrangements (Australian Energy Market Commission, 2020); https://www.aemc.gov.au/sites/default/files/documents/consultation_paper_-_der_integration_-_updating_regulatory_arrangements.pdf

  37. Future Network Strategy: 2020-2025 Regulatory Proposal Transforming our Network and Services to Meet Customers’ Future Energy Needs (South Australia Power Networks, 2017).

  38. Schram, W. L., AlSkaif, T., Lampropoulos, I., Henein, S. & van Sark, W. G. J. H. M. On the trade-off between environmental and economic objectives in community energy storage operational optimization. IEEE Trans. Sustain. Energy 11, 2653–2661 (2020).

    Article  Google Scholar 

  39. Scott, P., Gordon, D., Franklin, E., Jones, L. & Thiébaux, S. Network-aware coordination of residential distributed energy resources. IEEE Trans. Smart Grid 10, 6528–6537 (2019).

    Article  Google Scholar 

  40. Wasiak, I., Pawelek, R. & Mienski, R. Energy storage application in low-voltage microgrids for energy management and power quality improvement. IET Gener. Transm. Amp Distrib. 8, 463–472 (2013).

    Article  Google Scholar 

  41. McConnell, D. & Sandiford, M. Winds of Change: An Analysis of Recent Changes in the South Australian Electricity Market (Univ. Melbourne, 2016); https://doi.org/10.4225/49/57A0A5C1373F9

  42. Saltelli, A. et al. Five ways to ensure that models serve society: a manifesto. Nature 582, 482–484 (2020).

    Article  Google Scholar 

  43. Davis, J. L. How Artifacts Afford: The Power and Politics of Everyday Things (MIT Press, 2020).

  44. Morley, J., Floridi, L., Kinsey, L. & Elhalal, A. From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Sci. Eng. Ethics 26, 2141–2168 (2020).

    Article  Google Scholar 

  45. Castelvecchi, D. Prestigious AI meeting takes steps to improve ethics of research. Nature 589, 12–13 (2020).

    Article  Google Scholar 

  46. The Ethics of Artificial Intelligence: Issues and Initiatives (European Parliament: Directorate General for Parliamentary Research Services, Publications Office, 2020).

  47. Kalkbrenner, B. J. Residential vs. community battery storage systems—consumer preferences in Germany. Energy Policy 129, 1355–1363 (2019).

    Article  Google Scholar 

  48. Williams, B. A., Brooks, C. F. & Shmargad, Y. How algorithms discriminate based on data they lack: challenges, solutions, and policy implications. J. Inf. Policy 8, 78–115 (2018).

    Article  Google Scholar 

  49. Celermajer, D. et al. Multispecies justice: theories, challenges, and a research agenda for environmental politics. Environ. Polit. 1–2, 119–140 (2020).

    Google Scholar 

  50. Gonzalez‐Ricoy, I. & Rey, F. Enfranchising the future: climate justice and the representation of future generations. WIREs Clim. Change 10, e598 (2019).

    Article  Google Scholar 

  51. Powerbank community battery storage. Western Power https://www.westernpower.com.au/our-energy-evolution/projects-and-trials/powerbank-community-battery-storage/ (2021)

  52. Wolsink, M. Social acceptance, lost objects, and obsession with the ‘public’—the pressing need for enhanced conceptual and methodological rigor. Energy Res. Soc. Sci. 48, 269–276 (2019).

    Article  Google Scholar 

  53. Boudet, H. S. Public perceptions of and responses to new energy technologies. Nat. Energy 4, 446–455 (2019).

    Article  Google Scholar 

  54. Batel, S. & Devine-Wright, P. Towards a better understanding of people’s responses to renewable energy technologies: insights from social representations theory. Public Underst. Sci. 24, 311–325 (2015).

    Article  Google Scholar 

  55. Devine-Wright, P. et al. A conceptual framework for understanding the social acceptance of energy infrastructure: insights from energy storage. Energy Policy 107, 27–31 (2017).

    Article  Google Scholar 

  56. Stephenson, J. et al. The energy cultures framework: Exploring the role of norms, practices and material culture in shaping energy behaviour in New Zealand. Energy Res. Soc. Sci. 7, 117–123 (2015).

    Article  Google Scholar 

  57. Sovacool, B. K., Axsen, J. & Sorrell, S. Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design. Spec. Issue Probl. Methods Clim. Energy Res. 45, 12–42 (2018).

    Google Scholar 

  58. Bogner, A., Littig, B. & Menz, W. in The SAGE Handbook of Qualitative Data Collection (ed. Flick, U.) (SAGE, 2018).

  59. Cameron, J. in Qualitative Research Methods in Human Geography (ed. Hay, I.) (Oxford Univ. Press, 2005).

  60. Parker, A. & Tritter, J. Focus group method and methodology: current practice and recent debate. Int. J. Res. Method Educ. 29, 23–37 (2006).

    Article  Google Scholar 

  61. Layder, D. Sociological Practice (SAGE Publications Ltd, 1998); https://doi.org/10.4135/9781849209946

  62. Greenberg, M. R. Energy policy and research: the underappreciation of trust. Energy Res. Soc. Sci. 1, 152–160 (2014).

    Article  Google Scholar 

  63. Nicholls, L., Strengers, Y. & Sadowski, J. Social impacts and control in the smart home. Nat. Energy 5, 180–182 (2020).

    Article  Google Scholar 

  64. Spies-Butcher, B. & Stebbing, A. Climate change and the welfare state? Exploring Australian attitudes to climate and social policy. J. Sociol. 52, 741–758 (2016).

    Article  Google Scholar 

  65. Haines, F. & McConnell, D. Environmental norms and electricity supply: an analysis of normative change and household solar PV in Australia. Environ. Sociol. 2, 155–165 (2016).

    Article  Google Scholar 

  66. Demski, C., Thomas, G., Becker, S., Evensen, D. & Pidgeon, N. Acceptance of energy transitions and policies: public conceptualisations of energy as a need and basic right in the United Kingdom. Energy Res. Soc. Sci. 48, 33–45 (2019).

    Article  Google Scholar 

  67. Cope, M. in Qualitative Research Methods in Human Geography (ed. Hay, I.) (Oxford Univ. Press, 2005).

  68. Next Generation Energy Storage Data Platform (EPSDD, Australian Capital Territory Government, 2021).

  69. National Electricity Market Dashboard (Autralian Energy Market Operator, 2020); https://aemo.com.au/en/energy-systems/electricity/national-electricity-market-nem/data-nem

  70. C3x-Enomo (ENergy Output Model Optimiser) v 1.0 (GitHub, 2021); https://github.com/bsgip/c3x-enomo

  71. BSGIP: battery storage and grid integration program (GitHub, 2020); www.github.com/bsgip

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Acknowledgements

We thank K. Lucas-Healey, H. Temby, A. Mackenzie and J. Davis for thoughtful discussions, as well as for C. Tidemann, K. Golson and A. Dwyer as facilitators for the social research. We are grateful for the generosity of the participants who contributed to the qualitative research activity. We also thank the ANU Battery Storage and Grid Integration Program software development team for their support. All authors received funding from the Australian Renewable Energy Agency under the Distributed Energy Resources Program.

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Contributions

H.R.-C. conceived the study, collected the qualitative data, performed the social science analysis and wrote the article. B.C.P.S. conceived the study, wrote the quantitative analysis software, performed the quantitative analysis and wrote the article. M.S. conceived the study and edited the article. L.B. conceived the study, wrote the quantitative analysis software and edited the article.

Corresponding authors

Correspondence to Hedda Ransan-Cooper or Björn C. P. Sturmberg.

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Ethics statement

All the research conducted in this paper complies with Australian ethical regulations. The quantitative household energy data were provided by the Australian Capital Territory government, made available for the purposes of research by participating households as a requirement of their program participation (data have been anonymized). The social research activity was approved by the Australian National University’s human ethics committee (approval number 2019/241). As part of this process, all participants provided informed written consent. Only the citizen participants in the research (not the energy sector professionals) were provided with a AU$50 voucher for their participation as a way to mitigate the risk of only topic enthusiasts volunteering their time.

Competing interests

The authors declare no competing interests.

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Peer review information Nature Energy thanks Tarek AlSkaif, Christine Milchram and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Notes, Methods, Table 1 and Figs. 1–4.

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Ransan-Cooper, H., Sturmberg, B.C.P., Shaw, M.E. et al. Applying responsible algorithm design to neighbourhood-scale batteries in Australia. Nat Energy 6, 815–823 (2021). https://doi.org/10.1038/s41560-021-00868-9

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