The electrified depot will run as a test bed for other fleet-owners, with the ANU working on a platform that will interpret data for use as a planning tool.
The lumbering, growling buses that prowl Sydney’s Inner West have slowly been joined by silent electric versions over the past year, but the pace of change is about to speed up as the Leichhardt bus depot and the fleet housed there are electrified.
Full story in EcoGeneration https://www.ecogeneration.com.au/sydney-bus-depot-transitions-to-electric-with-40-e-buses-36-chargers-387kw-rooftop-pv-and-2-5mw-storage/
A new pilot project set to drive down emissions in public transport and heavy transport has today been announced. The $36 million project will consist of Australia’s largest electric bus fleet (40 buses), charging infrastructure and a retrofitted bus depot in Leichhardt, Sydney.
The ANU Battery Storage and Grid Integration Program is playing a supporting role in this multi-partnered project, collaborating with energy consultancy, Zenobe, and electricity transmission network operator, Transgrid. BSGIP will leverage the data produced in this next generation electric bus depot trial in a project entitled RouteZero.
Our new paper in Nature Energy asks fundamental questions of what values and biases algorithms are encoding into our digital energy system.
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.
Full text available here https://rdcu.be/cpu0G