Tidal energy in Australia – Assessing resource and feasibility to Australia’s future energy mix

This paper presents an overview and progress of a recently commenced three year project funded by the Australian Renewable Energy National Agency led by the Australian Maritime College, (University of Tasmania), in partnership with CSIRO and University of Queensland. The project has a strong industry support (OpenHydro Ltd, Atlantis Resources Limited, MAKO Tidal Turbines Ltd, Spiral Energy Corporation Ltd and BioPower Systems Ltd) and aims at assessing the technical and economic feasibility of tidal energy in Australia, based on the best understanding of resource achievable. The project consists of three interlinked components to support the emerging tidal energy sector. Component 1 will deliver a National Australian high-resolution tidal resource assessment; in Component 2, case studies at two promising locations for energy extraction will be carried out; lastly, Component 3 will deliver technological and economic feasibility assessment for tidal energy integration to Australia’s electricity infrastructure. The outcomes of this project will provide considerable benefit to the emerging tidal energy industry, the strategic-level decision makers of the Australian energy sector, and the management of Australian marine resources by helping them to understand the resource, risks and opportunities available

Loitering with intent-Catching the outlier vessels at sea.

Illegal, Unreported and Unregulated (IUU) fishing activities pose one of the most significant threats to sustainable fisheries worldwide. Identifying illegal behaviour, specifically fishing and at-sea transhipment, continues to be a fundamental hurdle in combating IUU fishing. Here, we explore the use of spatial statistical methods to identify vessels behaving anomalously, in particular with regard to loitering, using the Indonesian Exclusive Economic Zone (EEZ) and surrounding waters as a case-study. Using Automatic Identification System (AIS) for vessel tracking, we applied Generalized Additive Models to capture both the temporal and spatial nature of loitering behaviour. We identified three statistically anomalous loitering behaviours (based on time, speed and distance) and applied the models to 2700 vessels in the region. We were able to rank vessels for individual and joint probability of atypical behaviour, providing a hierarchical list of vessels engaging in anomalous behaviour. While identification of irregular behaviour does not mean vessels are definitely engaging in illegal activities, this statistical modelling approach can be used to prioritise the allocation of enforcement resources and assist authorities under the United Nations Food and Agricultural Organization Port State Measures Agreement for management and enforcement of IUU fishing associated activities

Detecting suspicious activities at sea based on anomalies in Automatic Identification Systems transmissions.

Automatic Identification Systems (AIS) are a standard feature of ocean-going vessels, designed to allow vessels to notify each other of their position and route, to reduce collisions. Increasingly, the system is being used to monitor vessels remotely, particularly with the advent of satellite receivers. One fundamental problem with AIS transmission is the issue of gaps in transmissions. Gaps occur for three basic reasons: 1) saturation of the system in locations with high vessel density; 2) poor quality transmissions due to equipment on the vessel or receiver; and 3) intentional disabling of AIS transmitters. Resolving which of these mechanisms is responsible for generating gaps in transmissions from a given vessel is a critical task in using AIS to remotely monitor vessels. Moreover, separating saturation and equipment issues from intentional disabling is a key issue, as intentional disabling is a useful risk factor in predicting illicit behaviors such as illegal fishing. We describe a spatial statistical model developed to identify gaps in AIS transmission, which allows calculation of the probability that a given gap is due to intentional disabling. The model we developed successfully identifies high risk gaps in the test case example in the Arafura Sea. Simulations support that the model is sensitive to frequent gaps as short as one hour. Results in this case study area indicate expected high risk vessels were ranked highly for risk of intentional disabling of AIS transmitters. We discuss our findings in the context of improving enforcement opportunities to reduce illicit activities at sea

Loitering with intent—Catching the outlier vessels at sea - Fig 2

<p><b>2a</b>) Prediction surface for time loitering indicator. Scale shows predictions in seconds for time spent in cells. <b>2b</b>) Prediction surface for speed loitering indicator. Scale shows predictions in knots for average speed in cells. <b>2c</b>) Prediction surface for distance loitering indicator. Scale shows predictions in meters for distance travelled in cells.</p

Loitering with intent—Catching the outlier vessels at sea - Fig 3

<p><b>3a</b>) High risk anomalies for time, speed and distance. <b>3b</b>) All high risk anomalies. Anomalies in the trination region bounded by Australian, Indonesia and Papua New Guinean are highlighted red.</p

Loitering with intent—Catching the outlier vessels at sea - Fig 4

<p><b>4a</b>) An example of an anomalous high risk track (in red) identified using the distance indicator, and all other tracks in the 0.5 degree cell (black). Note the vessel track in red moves across latitude rather than longitude, a discernible difference from other vessels transiting in the region. <b>4b</b>) Depicts the time spent in the same 0.5 degree cell, for each track segment shown in 4a, with the anomalous vessel (red) and all other vessels (black) highlighted.</p

High and low risk gaps for two vessels.

<p>High risk gaps are shown in red, low risk gaps in blue. Each gap is indicated by an open circle ‘o’.</p

Geographic study area.

<p>The red box indicates the case study region used in this paper. Light blue dots indicate individual AIS transmissions for the month of September 2014.</p

Model predictions and standard errors for AIS transmissions for the 1^st September 2014.

<p><b>2A</b>) Model predictions, and (<b>2B</b>) standard errors, for the probability of a successful AIS transmission in a 60 minute window on 1^st September 2014. Note that high likelihood of frequent AIS transmission are seen in the dogleg, but low standard erros. In contrast, further West, the standard error for gap transmissions is high.</p