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The shift from large-scale centralised energy systems to smaller scale decentralised systems based on Distributed Energy Resources (DER) is likely to cause a sector-wide replacement of current electricity management practices and business models—creating a new energy paradigm. If handled well, such a transition will not be inherently disruptive; however, it can cause major disruption if long-held views and assumptions are not rapidly reconsidered and renewed, and new supporting structures are not swiftly put in place—hence, if disruption is experienced it will be due to a lack of strategic responses rather than the nature of the technology. This paper clarifies the nature of DERs and outlines key issues and opportunities associated with a range of associated service configurations and business models. The paper outlines key factors affecting the viability of such approaches and identifies leverage points for accelerating uptake. The paper concludes by considering how shifting landscape factors and related opportunities in the coming decades will shape the transition to a decentralised energy system. This paper contains findings from research performed at the Renewable, Affordable, Clean Energy Cooperative Research Centre (RACE CRC) in Australia. [ABSTRACT FROM AUTHOR]

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements. [ABSTRACT FROM AUTHOR]