35 research outputs found
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Optimising VRE plant capacity in Renewable Energy Zones
Australia’s National Electricity Market experienced significant growth in variable renewable energy (VRE) investment commitments over the period 2016-2021. A subset of projects experienced material entry frictions which stemmed from inadequate network hosting capacity. In this article we examine the development of non-regulated Renewable Energy Zones (REZ) as a means by which to help guide forward market commitments and produce greater coordination between generation and transmission plant investments. Using an optimisation model comprising 1500MW of transmission network infrastructure, we explore various definitions of a ‘fully subscribed REZ’ given the portfolio benefits associated with complementary wind and solar plant in Southern Queensland. We also examine the conditions by which various proponents would sponsor a non-regulated REZ. When maximising output forms the objective function, full subscription is achieved by developing ~3400MW of solar and wind in roughly equal proportions, accepting that some level of curtailment is an economic result. Conversely, full subscription in which the combined cost of the REZ and VRE plant is minimised is achieved at ~1800MW of VRE. If maximising net cashflows forms the objective function, VRE plant development is complicated by the dynamic nature of spot prices. Specifically, in early stages of VRE development solar is preferred but as its market share rises and value of output falls, wind investments dominate holding technology costs constant
Competing Sound Sources Reveal Spatial Effects in Cortical Processing
Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source
A Biologically Plausible Computational Model for Auditory Object Recognition
Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate