Research Proposal – Renewable Microgrids for Remote Australian Communities

Introduction

Several hundred small settlements across remote and very remote Australia sit outside the interconnected electricity markets administered by the Australian Energy Market Operator, and are supplied instead by standalone power systems that still depend heavily on diesel generation (AEMO 2024). These systems keep the lights on, but at a delivered energy cost among the highest in the country and an emissions intensity well above the grid average. Solar-battery-diesel hybrid microgrids offer a credible path to cheaper, cleaner and more reliable supply, yet their technical case has proven necessary but not sufficient: systems perform as modelled only where ownership, financing, tariff and maintenance arrangements are controlled by the communities they serve. This proposal outlines an 18-month mixed methods study pairing techno-economic simulation of representative remote sites with community-governed stakeholder research, so that the engineering optimum and the conditions for its success are examined together.

Background

Diesel dependence in off-grid Australia

Remote area power supply in Australia has historically been built around the diesel generator. The Australian Statistical Geography Standard classifies much of the Northern Territory, northern Queensland, the Kimberley and the interior as remote or very remote (ABS 2023), and many communities there are served by regional utilities running standalone diesel systems rather than by any national market. Fuel is trucked or barged over long distances on seasonal roads, so the delivered price of a litre can sit well above the metropolitan pump price, and a single wet season road closure can threaten weeks of supply.

Cost, reliability and emissions

Three pressures follow. Cost comes first: because fuel dominates operating expense, the levelised cost of energy in remote settlements is high and tracks the global oil price, passing a volatile and uncontrollable burden to small customer bases and the governments that subsidise them. Reliability is the second, as ageing gensets, deferred maintenance and fuel interruptions cause outages whose consequences are severe where refrigeration, medical equipment and communications depend on continuous supply. Emissions are the third, since diesel generation is carbon intensive and continued reliance runs counter to national decarbonisation commitments and to the reductions that mechanisms administered by the Clean Energy Regulator encourage (Clean Energy Regulator 2023).

Solar resource and the Bushlight legacy

These pressures coincide with an exceptional solar resource, since irradiance across northern and central Australia is among the highest of any inhabited region, so photovoltaic generation paired with battery storage can displace much of the diesel while improving supply quality. The opportunity is not new. The Bushlight program, delivered through the Centre for Appropriate Technology in the 2000s, installed renewable systems in remote Indigenous communities and paired the hardware with community energy planning, demand-side education and locally understood tariffs (Centre for Appropriate Technology 2013). Its lesson, that durable outcomes came from the social design as much as the panels, remains relevant, and the Australian Renewable Energy Agency has since funded further remote microgrid pilots whose knowledge sharing informs practice (ARENA 2023).

Problem Statement

The difficulty is that the two bodies of knowledge required to act well on this opportunity are rarely combined. Techno-economic studies can specify, for a site, the solar and storage capacity that minimises lifecycle cost at an acceptable reliability, but typically treat the community as a fixed load rather than an owner, operator and decision maker. Social science and policy studies describe the arrangements that make community energy endure, but seldom connect them to a costed configuration for a specific setting. The result, visible in the record of remote deployments, is systems optimal on paper that underperformed once questions of ownership, maintenance, tariffs and operational control went unresolved. Little Australian evidence couples site-level optimisation with governance evidence generated under community control.

Aim and Research Questions

This study aims to evaluate the techno-economic performance of solar-battery-diesel hybrid microgrids for representative remote Australian settlements, and to identify the governance, ownership and financing conditions that utilities and community organisations regard as necessary for those systems to remain reliable and to deliver benefit locally. Three research questions follow.

1. RQ1: For representative remote load profiles and solar resources, what combination of photovoltaic and battery capacity minimises net present cost and levelised cost of energy while meeting a defined reliability standard, and how much diesel and emissions does it displace?
2. RQ2: How sensitive are the optimal configuration and its economics to the delivered diesel price, to capital costs and to the chosen reliability target?
3. RQ3: What ownership, maintenance, tariff and decision-making arrangements do utilities and community organisations identify as necessary for hybrid microgrids to be reliable and sustained beyond the initial capital grant?

Literature Review

Solar-battery-diesel hybrids

The technical literature on hybrid mini-grids is now mature. Adding photovoltaic generation to a diesel system reduces fuel use in proportion to the solar energy captured, while battery storage lifts the achievable renewable fraction considerably by shifting surplus midday generation to evening load and letting the engine switch off rather than idle inefficiently. Techno-economic assessments consistently find that storage is what turns a fuel-saving retrofit into genuine diesel displacement, though its contribution is bounded by capital cost and by the reliability standard the system must meet (Hossain, Chakrabortty & Ryan 2021). Renewable fractions above two thirds are attainable for typical remote loads, but the marginal cost of each further increment rises steeply near full renewable supply.

Techno-economic modelling

Optimisation is generally conducted through simulation tools that search combinations of generation and storage to minimise a cost objective subject to a reliability constraint. The dominant metrics are net present cost, which sums all discounted lifecycle costs, and the levelised cost of energy, a constant price per unit served. Hossain, Chakrabortty and Ryan (2021) show that the optimum is highly site specific and sensitive to input assumptions, particularly the delivered fuel price and the capital cost of storage, which makes structured sensitivity analysis essential. International evidence on mini-grid electrification reaches a parallel conclusion from the policy side: technical viability is necessary, but tariff design, subsidy structure and operational responsibility determine whether systems are sustained (Bhattacharyya & Palit 2016).

Community energy governance and energy justice

The governance literature supplies what the technical studies omit. The energy justice framework distinguishes distributional justice, concerning who bears costs and receives benefits, procedural justice, concerning who participates in decisions, and recognition justice, concerning whose knowledge and priorities are respected (Sovacool & Dworkin 2015). Australian work shows that community ownership is a spectrum, and that projects endure where communities hold genuine authority rather than mere consultation (Hicks & Ison 2018). In the remote Indigenous context, research documents how tenure on Aboriginal-owned land, prepayment metering and the distribution of benefits shape whether renewable investment advances or undermines local self-determination (Riley 2021). Read with the Bushlight experience, this reframes governance from a delivery detail into a determinant of technical performance.

Synthesis and gap

The techno-economic and governance literatures are each strong but seldom integrated, and Australian studies pairing a costed configuration for a defined remote setting with governance evidence generated under community control are scarce. This proposal addresses that gap by designing the two strands to inform one another, and by treating community governance as part of the method rather than as context.

Conceptual Framework

The study is framed as a socio-technical system, illustrated in Figure 1, in which a technical layer and a governance layer jointly determine the energy service a community receives. The technical layer combines solar photovoltaic generation, battery storage and diesel backup through a control and dispatch function that meets the load at least cost. The governance and financing layer, comprising community ownership, utility operation and maintenance, and financing and tariff arrangements, conditions how the technical layer is sized, operated and paid for, and how its benefits are shared. It draws on the energy justice concepts of distribution, procedure and recognition (Sovacool & Dworkin 2015) and the community energy ownership spectrum (Hicks & Ison 2018).

Research Design and Methodology

Overall design

The study adopts a mixed methods design with two strands analysed separately and then integrated. The first is a quantitative techno-economic simulation of representative remote sites, answering RQ1 and RQ2; the second is a qualitative study of governance conditions through stakeholder interviews under community governance, answering RQ3. Integration occurs at interpretation through a joint display that sets each technical result against the governance evidence bearing on its feasibility.

Strand one: techno-economic simulation

Three to four representative sites will be defined as synthetic profiles rather than named communities, each specified by a climate zone, remoteness class and load size, so that no actual settlement is modelled or identified without its consent. Load, solar resource and cost parameters will be assembled from published sources and calibrated against microgrid pilot reporting (ARENA 2023). Each site will be modelled in an industry-standard hybrid optimisation tool that searches combinations of photovoltaic and battery capacity to minimise net present cost subject to the reliability constraint. Table 1 sets out the input parameters and outcome measures.

Table 1: Simulation input parameters and outcome measures for the techno-economic strand

To illustrate the calculations the model will perform, the carbon intensity of diesel-only supply follows from the specific fuel use and the emission factor:

carbon intensity = 0.28 litres per kWh x 2.68 kg per litre = 0.75 kg CO2-e per kWh.

For a site drawing 2,500 kWh per day, annual demand is 2,500 x 365 = 912,500 kWh. Meeting this with diesel alone would need about 912,500 x 0.28 = 255,500 litres of fuel and release roughly 255,500 x 2.68 = 684,740 kg, about 685 tonnes, of carbon dioxide equivalent each year. A hybrid delivering a renewable fraction of 0.65 would leave 0.35 x 912,500 = 319,375 kWh to the engine, cutting fuel to about 89,400 litres and emissions to roughly 240 tonnes, an avoided 445 tonnes annually.

The levelised cost of energy is the constant price per unit that recovers all lifecycle costs, LCOE = (annualised capital + annual operating cost + annual fuel cost) divided by annual energy served. Capital is annualised with the capital recovery factor, CRF = i(1+i)^n divided by ((1+i)^n minus 1). At a discount rate of 0.07 over 25 years, (1.07)^25 = 5.43, so CRF = (0.07 x 5.43) / (5.43 – 1) = 0.086. Applied to an illustrative hybrid capital cost of A$2.6 million, annualised capital is about A$223,000; with operating cost of A$100,000 and fuel of about A$161,000 (89,400 litres at A$1.80), the illustrative hybrid levelised cost is (223,000 + 100,000 + 161,000) / 912,500 = A$0.53 per kWh, against roughly A$0.73 per kWh for diesel-only supply. These figures show the method and expected order of magnitude rather than results; establishing each site’s optimum is the task of the simulation, after which a structured sensitivity analysis varies the diesel price, capital costs and reliability target to answer RQ2.

Strand two: stakeholder interviews under community governance

The governance strand will use semi-structured interviews with approximately 18 to 24 participants across three groups: engineers and managers in the regional utilities that operate remote standalone systems; representatives of community organisations and community-controlled bodies; and sector participants such as microgrid program staff and financing bodies. Where the research engages First Nations communities or their lands, it will proceed only through a community-controlled reference and governance group with authority over scope, recruitment, interpretation and outputs, on the basis of free, prior and informed consent. This makes the work community-led rather than extractive, and gives the group standing power to amend the questions or halt the research. Interviews will follow the manner participants prefer, including a yarning approach where culturally appropriate, and be analysed thematically.

Analysis and integration

Simulation outputs will be compared across sites and sensitivity scenarios to identify robust configurations and the conditions under which the economics turn. Transcripts will be analysed thematically, using a coding frame drawn from the energy justice and community energy literature and open codes for unanticipated themes, with interpretation verified by the governance group before any finding is settled. Integration occurs through a joint display in which each techno-economic result sits beside the governance evidence that supports, qualifies or contradicts its feasibility, so a configuration optimal in the model is reported with the conditions its success would require.

Ethical Considerations

National Statement and consent

The study will be submitted to the university Human Research Ethics Committee and conducted in accordance with the National Statement on Ethical Conduct in Human Research (NHMRC 2023). Participation by all interviewees will be voluntary and based on written informed consent, with freedom to decline any question or withdraw without consequence, and organisational participants will be de-identified to prevent reputational harm in a small sector.

AIATSIS Code where First Nations communities are involved

Because remote energy in Australia is closely tied to Aboriginal and Torres Strait Islander lands and communities, the design is governed, wherever those communities are involved, by the AIATSIS Code of Ethics for Aboriginal and Torres Strait Islander Research and its principles of Indigenous self-determination, leadership, impact and value, and accountability (AIATSIS 2020). Self-determination is enacted through the governance group’s authority over the research, and modelling synthetic representative sites rather than naming or studying specific communities is a deliberate protection against extractive practice. No community will be identified, and no site-specific engagement will occur without that community’s consent expressed through its own governance.

Indigenous data sovereignty

Data practices give effect to the principle that Indigenous data are subject to Indigenous governance (Kukutai & Taylor 2016). Any data generated with a First Nations community will be stored as the governance group directs, will not be deposited with external repositories without written authorisation, and will remain subject to the group’s authority over its use and interpretation. Benefit is planned at the design stage through a plain-language community report and the return of findings in a usable form.

Researcher positionality

I write as a postgraduate researcher in engineering and public policy at an Australian university, and I do not speak for the communities whose energy systems this study concerns. Where the research engages First Nations communities, I accept the governance group’s decisions as binding, including a decision to change or stop the work, and I report the community’s interpretation as the finding where it differs from my own.

Project Timeline

Table 2 sets out the 18-month schedule. The phases overlap so that simulation results inform the interview prompts, and each phase closes with a governance or verification checkpoint.

Table 2: Project timeline across 18 months, with governance and verification checkpoints

Significance

The study offers three contributions. Methodologically, it demonstrates a workable way to integrate techno-economic optimisation with governance evidence generated under community control, addressing a gap that has limited both literatures for remote energy decisions. For policy and practice, the costed configurations and their governance conditions bear directly on how the Australian Renewable Energy Agency targets microgrid investment (ARENA 2023), how the First Nations Clean Energy Strategy is implemented (DCCEEW 2024), and the co-investment pathways available through bodies such as Indigenous Business Australia. By quantifying diesel displacement and avoided emissions alongside cost, it also shows where incentives administered by the Clean Energy Regulator can best support remote decarbonisation. For communities and utilities, it produces evidence for reliable, affordable and lower-emission supply under arrangements that keep decisions and benefits local, extending the lessons of the Bushlight era.

Limitations

Three limitations are acknowledged. First, the representative sites are synthetic, so the findings are transferable insights rather than designs for any particular community, and real deployment would require a full site survey and locally measured data. Second, the simulation depends on its input assumptions, and costs in this sector are moving quickly; the sensitivity analysis mitigates this by reporting how conclusions change across plausible ranges rather than resting on a single estimate. Third, an 18-month study can capture the conditions stakeholders consider necessary for reliability but cannot observe long-run outcomes directly, so the governance findings are reported as expert and community judgement rather than measured performance.

Conclusion

Remote and off-grid communities in Australia carry the highest energy costs and heaviest emissions intensity in the country while enjoying one of the world’s best solar resources, an inversion that hybrid microgrids are well placed to correct. The record of past deployments shows, however, that the engineering optimum holds only where ownership, financing, maintenance and tariff arrangements are designed with the communities concerned. This proposal sets out an 18-month study examining the two together: a techno-economic simulation of representative sites quantifying cost, reliability and emissions, and a community-governed study of the conditions under which those gains are sustained. By integrating the technical and the social rather than treating either as context, it aims to provide an evidence base for reliable, affordable and low-emission energy for remote Australia on terms its communities control.

References

Australian Bureau of Statistics 2023, Australian Statistical Geography Standard (ASGS) Edition 3: remoteness areas, ABS, Canberra.

Australian Energy Market Operator 2024, Integrated system plan for the National Electricity Market, AEMO, Melbourne.

Australian Institute of Aboriginal and Torres Strait Islander Studies 2020, AIATSIS code of ethics for Aboriginal and Torres Strait Islander research, AIATSIS, Canberra.

Australian Renewable Energy Agency 2023, Remote and regional community microgrids: knowledge sharing report, ARENA, Canberra.

Bhattacharyya, SC & Palit, D 2016, ‘Mini-grid based off-grid electrification to enhance electricity access in developing countries: what policies may be required?’, Energy Policy, vol. 94, pp. 166-178.

Centre for Appropriate Technology 2013, Bushlight: community energy planning and renewable power in remote Indigenous communities, CAT, Alice Springs.

Clean Energy Regulator 2023, Small-scale renewable energy scheme: administrative report, Clean Energy Regulator, Canberra.

Department of Climate Change, Energy, the Environment and Water 2024, First Nations clean energy strategy 2024-2030, Commonwealth of Australia, Canberra.

Hicks, J & Ison, N 2018, ‘An exploration of the boundaries of community energy in Australia’, Energy Policy, vol. 113, pp. 523-534.

Hossain, MA, Chakrabortty, RK & Ryan, MJ 2021, ‘Optimal sizing and techno-economic assessment of solar-battery-diesel microgrids for remote communities’, Renewable and Sustainable Energy Reviews, vol. 145, pp. 111-129.

Kukutai, T & Taylor, J (eds) 2016, Indigenous data sovereignty: toward an agenda, ANU Press, Canberra.

National Health and Medical Research Council 2023, National statement on ethical conduct in human research, NHMRC, Canberra.

Riley, B 2021, Renewable energy, Aboriginal-owned land and community electrification in remote Australia (CAEPR Working Paper No. 142/2021), Centre for Aboriginal Economic Policy Research, Australian National University, Canberra.

Sovacool, BK & Dworkin, MH 2015, ‘Energy justice: conceptual insights and practical applications’, Applied Energy, vol. 142, pp. 435-444.