Algorithms are playing ever more pervasive and critical roles in the operation of the electricity system as it becomes digitised and decentralised. The design of these algorithms – considering the values and biases they encode – has to date received scant attention by energy researchers and developers. We believe this to be an important omission given recent controversies regarding the reach of algorithmic authority in ever more areas including healthcare1,2, hiring practices3,4, law enforcement5,6 and new media’s role in social and political life7 which have exposed deep ethical shortcomings that challenge the “arc of inevitability” of technical solutions8.
Existing discussions of algorithms in the energy field have tended to focus on questions of data privacy and the cybersecurity of smart meters and in-home devices9,10. While important, we argue that these overlook at least three systemic concerns. The first is that algorithms tend to narrow considerations to factors that offer plentiful, easily quantified data, such as financial values, excluding public values that may be harder to quantify. Such omissions narrow the conception of energy and have the potential to introduce structural biases11. Secondly, the challenging explainability of algorithms exacerbates accountability concerns and distrust of the energy system, and risks creating hidden outcomes11 such as new forms of wealth transfer. Finally, algorithmic automation could reduce citizens’ autonomy over household energy technologies because of the sophisticated nature of the technology, and reliance on third parties to manage data and provide control systems12. Reduced control of household energy technology, particularly in the case for solar PV and batteries, represents a reshaping of citizens’ relationships with the energy system, representing new risks and potential accountability gaps13,14.
This paper examines these three potential concerns associated with algorithm design in the energy system through the case study of neighbourhood-scale batteries (NSBs). These are 0.1-5 megawatt (MW) batteries, located close to customers in the distribution network. They provide an ideal case study of the multitude of issues associated with energy algorithms because their actions are primarily determined by algorithmic control systems (as opposed to the kinetic laws governing traditional generators), their physical presence in neighbourhood streetscapes raises citizens’ attention and engagement through familiar planning processes, and the energy they store can be viewed as a collective resource – particularly when it comes from local rooftop solar generation – which raises questions of resource sharing.
Existing studies of NSBs have focused on the development of algorithms that optimise NSB operations for techno-economic objectives, such as maximising owner profits15, minimising customers collective costs16,17, managing voltage18, and maximising decarbonisation19,20. These studies typically disregard potential limitations on access to consumer data and presuppose clear – and in our view narrowly defined – customer desires. Social science studies of NSBs meanwhile have primarily focussed on key stakeholders’ views on NSB’s integration with21, or disruption of22, existing energy systems. Others have examined the specific regulatory barriers to business models23 and the need for new institutional arrangements24, but citizens’ perceptions of NSBs themselves have largely been missing25 26. The few existing studies on citizens’ perception consider views on risks and benefits, but not how these would become institutionalised via algorithmic design and governance25 26.
This paper addresses an existing research gap in how to reflect public concerns and values in the algorithm dictating NSB operation ‘upstream’ in the development process (i.e., before the technology is rolled out at any significant scale)27 7. Even while interdisciplinary research is often championed in energy research, to our knowledge, no work has been published that attempts to integrate social values - through deliberation with citizens - in energy algorithm design. In line with an established framework for responsible research, our aim is to demonstrate how researchers can anticipate future harms (and benefits) of new energy algorithms by including citizens perspectives in the development of algorithms. As we demonstrate, algorithms could be developed in responses to issues raised by citizens28. While this will not be an exhaustive account of how to make NSB algorithms design ‘responsible’, we aim to provide a novel demonstration of algorithm design that uncovers a range of important and usually neglected ethical and normative considerations.
Our research design was an iterative collaboration between a social researcher and developers of optimisation algorithms. Our social research involved interviews and focus group discussions (FGD) with energy sector professionals and citizens from diverse backgrounds that explored participants’ views about the potential benefits, risks, and governance considerations of NSBs. This is the first study to consider the general public and energy sector professionals' views of NSBs side by side. From the qualitative research we identified a range of objectives that NSBs may be operated in pursuit of and encoded these objectives into optimisation algorithms. The algorithms were then applied to real world customer electricity data to quantify their techno-economic impacts on stakeholders and the grid. This approach allowed us to uncover the systemic issues in algorithm design that are typically overlooked by algorithm researchers.
This process revealed that both energy sector participants and citizens shared several hopes and fears, but that there were also important divergences. It demonstrated how chosen values could be encoded into algorithms and thereby prioritised over other values that would either require conflicting battery actions or are simply challenging to quantify. Our findings emphasise the need for digital energy technologies to be developed through an “algorithmic accountability in action”29 approach that aligns the behaviour of these technologies with public values. We argue that energy researchers need to work collaboratively across disciplines to develop algorithms in line with evolving public understandings of a sustainable energy system.
Stakeholder identified benefits and risks
The Australian electricity system was largely disaggregated and privatised in the 1990s and since 2008, electricity prices have increased at more than four times the Consumer Price Index30. These high electricity prices, together with concerns about climate change, have driven record installations of rooftop solar, with one in five households now having solar31, as well as generally making electricity a topic of significant public and political engagement, including the emergence of advocacy groups like “solar citizens”32. It is increasingly unfeasible for technocrats – accustomed to managing centralised generation - to develop policies without public scrutiny in what has now become a decentralised energy system.
Our study took place in Australia with research activity in five states. We began with interviews and FGDs with energy sector professionals and (separately) citizens, whose voices then informed our algorithm designs. As shown in Fig. 1, these stakeholders identified a wide range of benefits and risks from NSBs. Some of these influence the design of NSB optimisation algorithms in direct and straightforward ways (indicated in bold in Fig. 1). These principally economic and technical benefits tended to be prioritised by energy professionals from networks and retailers. Consumer advocates and government representatives complemented these issues with less easily quantified values, such as the potential for NSB to build trust in the energy sector. Citizens identified many of the same benefits as energy professionals (see overlap in Fig. 1) but with a clear difference in emphasis. Citizens particularly valued the capacity for NSBs to increase the volume of renewables in the energy system. Several participants also raised the potential for neighbourhood batteries to be a community asset that would increase energy[1] [2] stated that:
...what would be really interesting for me is a community owned shared battery so that it’s not by some company but it’s actually the community that feels an ownership of it and is therefore more connected to it.
Both energy sector professionals and citizens raised the potential for a range of risks or harms. Interestingly, energy sector professionals raised fewer risks, and no unique risks that had not been raised by citizens. These differences would be important to consider in NSB governance and social acceptance for proposed developments. The range and potential conflicts between these values highlights the difficulty of distilling diverse issues into rigid algorithms. And because only a subset of the benefits and risks can be directly addressed through algorithm design, measuring the ‘success’ of NSBs by these metrics alone could miss important aspects of public value.
Having heard stakeholders’ concerns – about less quantifiable values being neglected, and being excluded from the decision-making of algorithm priorities – and their desires for active involvement and understanding of NSB actions, we developed three algorithms that embody these values and allow us to explore the material impacts of design choices.
Encoding values in NSB algorithms
The first two algorithms we developed utilised purely financial metrics. Such metrics are easily quantified and are therefore the most commonly used. They are however often blind to broader social goals. The third algorithm operates on physical power flow metrics, which lends itself to targeting local energy self-sufficiency.
Each algorithm was applied to a scenario consisting of a neighbourhood of 100 households and a 500kW:1000kWh NSB connected to a large upstream power grid (Fig. 2(a)). Each household has a solar system (on average 6kW in capacity) and has a unique power demand and solar generation profile, which are taken from the NextGen Battery Trial in Canberra, Australia’s capital city34. We present results for the summer month of January, with further months shown in the Supplementary Materials. The net load for the 100 households over a representative two-day period is shown in Fig. 2(b), where negative values indicate power exported from the local grid to the upstream grid.
Financial objectives
Exploring financial flows allowed us to understand whether citizens’ concerns that the NSB could be operated in a way that did not spread benefits to local communities could become a real possibility. We first considered two scenarios in which NSB algorithms are designed to pursue one of two financial objectives raised by FGD participants, either:
- to maximise the profit for the NSB owner (labelled max-profit), or
- to minimise the net electricity costs for all households and the NSB (labelled min-cost).
In all scenarios we use the electricity spot price from the South Australian region of the Australian National Electricity Market (shown in Fig. 3(a)). This price signal is only modestly correlated to the net demand of the 100 customers due to their residential usage patterns and 100% penetration of solar. Additionally, we apply a network charge of $0.15/kWh for energy that flows between the local neighbourhood and the upstream grid and a discount network charge of $0.075/kWh for energy that flows between households or between households and the NSB (see Fig. 3(b) and Fig. 2(a)). Such pricing structures are under active consideration in Australia35 and are crucial for the NSB algorithm to prioritise servicing the neighbourhood over the upstream grid. While these conditions represent a specific context that gives rise to its own set of issues, they serve as an illustration of the types of issues that may arise from new energy algorithms, which will likely include trade-offs not covered here.
The charge/discharge actions of the NSB under the max-profit and min-cost algorithms are shown in Figs. 3(c)-(d). The differences between these behaviours are striking. The max-profit algorithm produces far fewer charge/discharge actions, with only the extreme price peaks on the second day being sufficiently lucrative to warrant discharging (in the process creating a massive export of power that is cut off on the scale of Fig. 3 but is shown in Fig. 5). The min-cost algorithm meanwhile charges substantially from the local solar generation on the first day (benefiting from the reduced network charges) and discharges throughout the moderately high prices during the second evening. It also charges a little from solar on the second day and discharges a little on the first evening but substantially less so.
The differences in algorithm actions are especially significant given that the two adjacent days have similar demand and solar generation and only modestly different price profiles. While the behaviour can be explained – the algorithm is co-optimising NSB revenue from price arbitrage with reducing households bills by providing increased amounts of local energy that incur reduced network charges – it is difficult to do so to a lay audience. Such behaviour could thus reinforce knowledge barriers and undermine transparency and trust in the energy system – issues that were identified as major concerns by FGD stakeholders.
The financial impacts of the two algorithms are as-expected: the max-profit algorithm creates the greatest benefit for the NSB (blue striped bars in Fig. 4), while the min-cost algorithm saves households the most (green square hatches in Fig. 4). This highlights how decisions of techno-economic algorithm design are strongly linked to issues such as ownership, inequality and profit extraction. Citizens’ in FGD were highly attuned to this, and substantive parts of the discussion explored these issues in detail. Furthermore, these questions are connected to the issue of trust and explainability, as both financial approaches may claim to produce public benefit through improved market efficiencies and lower market prices, these claims are unlikely to be accepted in the absence of trust and simple and transparent evidence. As one participant said:
… we need to understand the concept of what is abuse of their powers and what isn’t abuse. And of course, us on the street, we don’t know how these people could abuse [their power] if we gave them control.
In this sense the min-cost algorithm is advantageous in presenting savings directly to customers.
Non-financial objectives
The next type of algorithm we developed is not concerned with finances but rather on the power flows between the neighbourhood and the upstream grid. The algorithm’s objective is to minimise these flows to flatten the demand curve and maximise neighbourhood self-sufficiency. The actions of this algorithm, shown in Fig. 3(e), are more regular – and intuitive – than the other algorithms: the NSB charges when there is excess local solar and discharges this energy evenly throughout the evening. The algorithm addresses the FGD desires for self-sufficiency and autonomy and is relatively explainable – matching citizens'’ expectations of batteries smoothing demand. It also minimises households’ electricity costs (yellow diagonal hatches in Fig. 4), however does so at a significant financial cost to the NSB owner (Fig. 4).
The effect of all three algorithms on the net load of the neighbourhood is shown in Fig. 5. The self-sufficiency algorithm is seen to minimise and smooth the load profile significantly, whereas the financial algorithms have variable impact on the excess solar generation and demand peaks and create a new, very large export peak – discharging at the NSB’s full power capacity of 500kW – during the price peak on the second day. This exemplifies how narrowly designed algorithms, such as those preoccupied with financial markets, can pose significant risks to the secure operation of the energy system36 by ignoring physical objectives (as well social ones). Such risks occur whenever markets fail to fully describe systems or spilt system control variables across multiple markets with separate price signals that algorithms must prioritise between.
Lastly, we quantify the impacts of the different algorithms on the grid across the whole month by plotting the power flow between the neighbourhood and the upstream grid at each time interval in Fig. 6. As expected, the self-sufficiency algorithm provides the greatest reduction in power flows into and out of the neighbourhood – best delivering citizens’ stated expectations for NSB behaviour and desires for autonomy. The figure also showcases the aforementioned susceptibility of financially oriented algorithms to drive increased imports and exports (including extreme peaks) in pursuit of arbitrage.
The self-sufficiency algorithm is but one of any number of potential non-financial algorithms. Some technical objectives, such as voltage or frequency management, are well studied in the literature37,38, while there are many more, such as prioritising transparency, that are deserving of future research. We note that while algorithms may allow multiple objectives to be combined and co-optimised (we have not done so in the interest of clarity), such co-optimisation still requires design choices on how each objective is prioritised and traded-off relative to others. This point emerged in a conversation between an energy professional representing low-income people and a network professional:
Terry: … there’s multiple functions at different times and value streams to extract here, depending what AEMO [the market operator] wants at the time and the local network wants at the time to what I want as a consumer.
Julia: And you can stack but you can’t stack everything. There are some services you can’t stack together so you have to have that understanding of what you can combine.
These tensions, uncovered in the FGD and demonstrated in the modelled scenarios, therefore remain as inherent questions to be mediated through socio-political processes of algorithmic governance – they cannot be solved through techno-economic advancements.
[1] This pro-renewable energy stance is consistent with the findings of successive surveys in Australia33.
[2] Naomi [pseydonym], FGD Noosa, December 2019.