wind engineering

Potential Sites for Off-Shore Wind Power in Australia by

Eleonora Messali and Mark Diesendorf

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WIND ENGINEERING VOLUME 33, N O . 4, 2009

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Potential Sites for Off-Shore Wind Power in Australia Eleonora Messali1 and Mark Diesendorf2, * 1Faculty

of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 26, 20133 Milan, Italy Email: [email protected] 2Institute of Environmental Studies, University of New South Wales, UNSW Sydney NSW 2052, Australia Revised 12 August 2009

ABSTRACT This study identifies potential sites for off-shore wind power in Australia. It uses a systematic framework, a type of multi-criteria analysis with constraints. The method fosters a transparent, participatory process for inputting criteria. The key parameters considered are the annual mean wind speeds, the depth of water, distance from the shore, and locations of existing transmission lines, centres of electricity demand and protected areas. Although ocean depth increases rapidly around most of the long Australian coastline and many regions are almost uninhabited, we find several regions of high potential near populated areas. The best sites are located off the Western Australian coast near the South-West Integrated System (grid). There are also several possible site regions off the coasts of New South Wales, Victoria, Queensland and South Australia.

1. INTRODUCTION Over the past 20 years wind power has been one of the fastest growing electricity generation technologies in the world [1]. Since the potential for on-shore wind energy is limited in densely populated countries, wind farms have started to be sited at sea. Other advantages of off-shore siting are that average wind speeds are higher and more consistent than on land, larger turbines can be installed, and noise and visual impacts are eliminated or reduced. On the other hand, off-shore construction is more complicated, leading to higher investment costs than on land; accessibility to the turbines is more difficult, resulting in higher maintenance costs; and the transmission connection to the grid may be more expensive [2]. Off-shore wind power began in Europe, in the extensive shallow waters of the North Sea. The first installation was in Sweden in 1990. In February 2007, wind power was given high priority when EU member states made a firm commitment to increase the total share of renewable energy in primary energy consumption to 20% by 2020 [3]. The target is to reach 50 GW of off-shore wind energy by 2020. This is the same rate of growth for the next 13 years as in the on-shore sector for the past 13 years [4]. At the end of 2008, Europe had 1471 MW of operating off-shore wind farms in Denmark, United Kingdom, Ireland, Sweden, Germany, Belgium and Finland and the Netherlands. Then off-shore wind represented less than 2 per cent of the European Union’s installed wind [5]. The U.S. east coast, being thousands of miles long, has even more promise than many European countries. According to a study by the US Department of the Interior, the areas off *Corresponding author: [email protected]

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the US coast within a 92.5 km (50 nautical miles) limit represent a potential of 907 GW, which is close to the current peak electricity demand in that country [6]. Five important projects are competing to become the first off-shore wind farm in North America: the Cape Wind project (420 MW), the Bluewater Wind project (600 MW), the LIPA Off-shore wind park (144 MW), a project developed by Wind Energy Systems Technologies LLC off the coast of Texas (150 MW by 2012). In Canada, which also has an very large off-shore potential, the construction of the first phase of the NaiKun project of 320 MW would be followed by four other phases to reach a total of 1750 MW [7]. By 2008, wind power was growing fast also in China, faster than the government had planned and faster in percentage terms than in any other country, having doubled each year since 2005. China’s first off-shore wind farm, with an annual electricity generation of 4.4 GWh, was officially put into operation at the end of 2007 [8]. Until recently the planning of off-shore wind farms has been reported in the literature as mainly involving only a few objectives. For example, a study conducted by Stanford University in California [9] estimated the potential sites by only looking at wind speed data at a height of 80 meters above the sea level and at bathymetry data. Since it is widely recognized that multiple competing objectives are generally pursued besides strictly energy and economic ones, in this work we consider also those objectives reflecting social and environmental concerns. An example that started to define such an interdisciplinary analysis of the potential of offshore wind farms was conducted in 2003 in the western Great Lakes of the USA [10]. In Australia a previous study of offshore wind potential found the best site for the construction of an offshore wind farm is Perth, Western Australia [11]. This study considered only five sites around Australia, excluding from the outset some potentially important areas. In addition the evaluation was conducted using arbitrary scores. The present study uses a systematic transparent repeatable approach.

2. METHOD 2.1. Data Sources The only available free source of offshore wind data is the Renewable Energy Atlas (REA) developed and maintained by the Australian Department of the Environment, Water, Heritage and the Arts [12]. The Australian Mesoscale Wind Atlas, included in the REA, indicates the predicted mesoscale wind speed at a height of 80 metres above ground level and sea surface around all the Australian coasts. The presentation format of the wind speed data consists of a digital map and the main party responsible for the resource is Windlab Digital Map. The map is provided at a resolution of 0.075 degrees and each grid square is intended to be representative of the wind speed across the area. It is possible to operate on the map to find the wind speed values at different distances from the shore. The source of bathymetry data comprised Version 2.0 of the General Bathymetric Chart of the Oceans (GEBCO) One Minute Grid. This was originally released in 2003 and provides bathymetry data on a global grid with a one arc-minute spacing. It is a continuous digital terrain model for ocean and land, with land elevations derived from the Global Land One-km Base Elevation (GLOBE) database. Version 2.0 of the GEBCO One Minute Grid was released in November 2008 and contains version 2.23 of the International Bathymetric Chart of the Arctic Ocean (IBCAO) and improved shallow water bathymetry for some areas. The grid is available for download from the British Oceanographic Data Centre (BODC) in the form of netCDF files, along with free software for displaying and accessing data [13]. Data regarding the presence of built-up areas, parks, reserves, and protected areas on the coast, of powerlines, marine parks, reserves and protected areas, oil and gas platforms and

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ports were obtained using GIS data (vectors and screen based format). They were obtained by courtesy of the MapConnect service developed by Geoscience Australia National Mapping and Information Group. MapConnect uses ArcIMS to access an Enterprise Geodatabase managed by ArcSDE; it is a way to access the national Topographic 250K Geodata Series 3 data free of charge and it allows one to download data according to specific areas of interest. All the data on Australian fisheries were obtained from the Australian Fishery Survey Report 2007 [14] and from maps developed by ABARE [15]. To evaluate the shipping traffic density and migration routes of whales, data sources are maps developed by Australian Maritime Safety Authority, Geoscience Australia and National Oceans Office (2004) [16]. There are no exact data about migratory routes of birds, due to the difficulty of monitoring, so data were obtained on the presence of bird species on the shore at some ‘hot spots’ called Important Bird Areas (IBA) [17] and at the East Asian Australasian Flyway (EAAF) Shorebird Report [18].

2.2. Framework and Method The methodology is a land-environmental compatibility assessment similar to many others used to produce results in problems involving planning, resource allocation, priority setting and selection among alternatives [19]. The procedure is presented in Figure 1. It derives from the ‘Land Suitability Model’ [20], which produces a Compatibility Assessment Map (or Land Compatibility Map) which is continuous when GIS data are available for all the territory that is being analysed. When there are no available continuous data, as in this study, the method only gives information about discrete points or areas located far from one another. From the Land Compatibility Map it is possible to obtain a localization index that measures in a quantitative way whether the land is suitable for a specific purpose, in our case the construction of an off-shore wind farm. The localization index is obtained through the combination of different layers of information consisting of constraints and criteria influencing the final choice of the best sites. The initial sites are identified by a rough analysis phase based on off-shore wind speeds and water depth data. Then the main analysis of the effects of each potential site is conducted through a combination of methodologies used to solve multi-criteria and multi-alternative problems such as Electre methods, Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) implemented in the software Monte Carlo Ranking (MCR) [21]. The method assigns to each potential site a weight factor through a well-defined process and at the end enables us to choose the best ones. Although the method was originally designed to find a single best site, in our case we are looking for a small group of sites with the highest scores and hence rankings. In some phases of the methodology a high level of transparent subjectivity is required in choosing constraints, criteria and weight factors, which can influence the choice of the best alternative(s). The procedure enables us to conduct sensitivity analysis of our choices to variations in the weight factors. Characteristics of the methodology are: participation, integration and rationalization. ·

Participation is relevant, because this process is not limited to providing the stakeholders with information (Informative Participation), nor to asking them for information (Consultation) [22], but also involves them in the choice of potential sites. In this way, a process of social learning is created, in which the stakeholders become aware of the problem, of the alternatives, of the viewpoints of others, and take responsibility [23].

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Off-shore wind farm localization proposal

Analysis phase

Reconnaissance

Designing the potential sites Evaluation phase

Defining criteria & indicators

Defining constraints

Exclusion of potential sites

Deeper analysis Generation of new potential sites

Evaluation of the potential sites (MCR software)

Different methods (MAUT, AHP, Electre)

Change of potential sites

Sensitivity analysis randomization test

List of best potential sites (different rankings)

Choice

Decision Phase

Size of wind farm

EIA

Figure 1: The framework.

·

Integration is intended among the parts that compose the system, among stakeholders and political decision makers, among the stakeholders themselves, between environmental policies and sector-based policies, between technical approaches to solution and decision-making techniques.

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Rationalization implies that the very decision making process must be broken down into phases, establishing the sequence in which they are executed and specifying their aim and the technical means by which they will be achieved.

Details of the method are given in the appendix.

2.3. Coastal Regions of Interest According to wind speed values measured at up to 40 km from the Australian shore, there are four possible interesting regions (wind speed > 6.5 m/s at a height of 80 m) where off-shore

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Table 1: The principal regions of interest State Specific Coastline New South Wales (NSW) from Newcastle to Ulladulla Queensland (Qld) from Gold Coast to Cooktown Victoria (Vic) and South Australia (SA) from Westernport Bay to Whyalla Western Australia (WA) from Bunbury to Geraldton/Carnarvon

Darwin

Northern Territory Queensland 1

Western Australia

South Australia

4

Brisbane New South Wales

Perth

2 Sydney

ACT Adelaide

Canberra

Victoria 3

Melbourne Tasmania Hobart

Figure 2: The principal regions considered.

wind farms can be built and there is sufficient population or industry to take advantage of it. These are shown in Table 1 and Fig. 2. In a preliminary analysis, we also considered two other regions, Tasmania and the Great Australian Bight (SA). Tasmania, being located in the Roaring 40s, has very high wind speeds and significant on-shore potential. It is connected to the mainland by a high-voltage DC transmission line of capacity 469–594 MW [24]. However, we have eliminated it from off-shore generation, because is a small territory and its sea-beds are much steeper than those of other regions. The construction of a large off-shore wind farm would be more difficult and challenging there than in any other area considered in the study. It would also be unnecessary for the foreseeable future, because the region still has large on-shore potential on previously cleared agricultural land. The stretch of Western Australian and South Australian coastline comprising the Great Australian Bight has also been eliminated because, although its geographic location suggests high wind speeds, it is almost uninhabited. Furthermore, there is no transmission line around the Bight bridging the thousands of kilometres between the Western Australian and South Australian transmission grids. If in the long-term future such a link were to be built, this region could possibly provide several gigawatts of wind power from on-shore wind farms.

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The principal grid of Western Australia is the South-West Integrated System which includes Perth and extends north to Geraldton and south to Albany (Region 4). This region has a population of about 2 million. The rest of Western Australia and the Northern Territory each have several mini-grids, isolated from the NEM and from one another. These mini-grids are unsuitable for large-scale grid-connected wind power, whether it be on-shore or off-shore. However, they have potential for small to medium scale on-shore wind power and indeed there are already wind-diesel systems in several isolated townships. Regions 1–3 are the principal sites of Australia’s electricity demand. The east coast of mainland Australia has good wind speed values, especially in the northern and southern parts. We examined not only Regions 1 and 2 but also all the areas located in proximity of each large city. The electricity system is operated as a single interconnected system called the National Electricity Market (NEM) linking Queensland, New South Wales, Victoria, Tasmania and South Australia.

2.4. Constraints Before starting the evaluation of the potential sites, we exclude those that do not respect some fundamental constraints regarding the construction of an off-shore wind farm. It is consequently necessary to define constraints which suit the study case we are dealing with. They represent areas where the construction of a wind farm is prohibited by law, or not advised by commonsense, or too expensive to be sustained. Constraints can be related to energy production and physical limits to the construction. In our study we have applied the following four constraints: 1.

Presence of Marine Parks, Reserves, Protected Areas and ‘No Structures’ sub-zones.

2.

Wind speed (annual average) less than 6.1 m/s at a height of 80 m above sea level.

3.

Water depth greater than or equal to 40 m.

4.

Distance to the closest grid connection point greater than 100 km.

2.5. Sectors and Criteria Involved To evaluate and compare the effects of the construction of an offshore wind farm on the potential sites it is necessary to identify, together with the stakeholders, a set of evaluation criteria that reflect the characteristics of the problem and the values that are at the base of the judgements that the stakeholders express. The criteria do not have to pertain only to the project goal, but to all the positive and negative effects that the stakeholders hope for or fear [25]. In practice, one proceeds by first splitting the evaluation criterion into lower level criteria and, in turn, splitting those into even lower level criteria, until it is possible to associate each one of the criteria at the lowest level (leaf criteria) with an indicator. In this way a hierarchy of criteria is obtained for each evaluation criterion. ·

The indicator chosen for wind power is the annual mean wind speed at 80 m height at the site.

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The criterion called environment is split into two sub-criteria: the first representing the impact on the general marine environment if a site is near a marine park or reserve; the second describing the potential impact on whales and birds, the two most important migratory species in terms of presence and vulnerability potentially affected by the construction of an off-shore wind farm in Australia.

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The indicator for difficulty and cost of construction is the water depth in metres at a specific site.

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As an indicator of visual impact we consider both the distance from the coast in general and the distance to parks, reserves and protected areas, both measured in km.

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The electricity demand criterion is split into two sub-criteria representing the residential and industrial electricity demands respectively. Electricity demand data are not available for all the potential site regions at the level of detail required in the study. Therefore the indicator chosen to be the proxy for domestic electricity demand is the population living in the proximity of the off-shore wind farm site. Predominant industry sectors in the Australian economy are the steel, manufacturing, construction and mining industries. Since it would have been impossible to collect data of electrical demand coming from each industry, it was decided to classify each site according to the ‘type of area’ on the coast. Areas can be categorised into rural, settlement and industrial area looking at their predominant economic sector and as small, medium, moderate and large scale looking at their production.

·

In Australia we found out that in some locations there would be possible interference with three economic sectors: fisheries, shipping traffic, air traffic, and oil and gas extraction. Criteria were introduced for these constraints.

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The economics of location criterion is split into two sub-criteria, one relating to the costs of connection to the electrical grid, and another one relating to the costs of maintenance of the wind farm. The former is given by the distance from a proposed site to the closest point of connection to such grid and the latter is the distance from the site to the closest port.

3. RESULTS The goal of the analysis was not to obtain a value expressed in gigawatts (GW) of the potential offshore wind capacity in Australia, but rather to find possible suitable locations for the first installations of offshore wind farms in Australia. The latter goal had to be chosen because there is insufficient data on bathymetry and steepness of the sites to estimate the full potential. Because the software used to conduct the sensitivity analysis only permits 12 sites to be considered together, we conducted three different studies, each one considering an off-shore region of Australia, and evaluated the best wind power potential in each region. The three studies address: a.

the whole of Australia;

b.

the south-east and the east coasts (Regions 1–3) combined (ie, the region spanned by the National Electricity Market);

c.

the east coast only (Regions 1 & 2).

Thus Studies (b) and (c) enabled us to to gain more detailed information about potential sites in the south-east and east of Australia than from Study (a). In Study (a) we considered the potential sites on the whole Australian coast included in the four regions defined in Table 1. There were 66 sites that met the requirements for wind speed and depth and also were compatible with the constraints. Study (b) derives from the fact that the major interest of the energy companies is located in the east and south-east coasts because these areas are highly populated and the localities located there connected by a unique grid. The region is much longer and more populated than the southern coast and the electricity grid is more extensive. Therefore the evaluation of the best potential site for an off-shore wind farm could be focused on this coast (Study (c)).

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Table 2: Ranking orders for the whole of Australia (Study a) using 5 alternative methods1 Concordance Discordance Weighted Weak Worst Case Index Index Sum Dominance 1 Geraldton 1 Geraldton 2 Woolnorth 2 2 Woolnorth 1 3 Woolnorth 1 3 Whyalla 2 4 Perth 2 4 North Geraldton 1 5 Perth 1 5 Whyalla 1 6Cape Portland 1 6 Woolnorth 2 7Cape Portland 2 7 Rockingham 2 8 Mandurah 1 8 Gladstone 9 Mandurah 2 9 North Geraldton 2 10 Rockingham 2 10 Mandurah 1 11 Joondalup 11 Cape Portland 2 12 Warmambool 12 Port Lincoln

1 Geraldton 2 Woolnorth 1 3 Whyalla 2 4 Melbourne 2 5 Adelaide 6 Melbourne 1 7 Whyalla 1 8 Bargara 2 9 Cape Portland 2 10 Perth 1 11 Woolnorth 2 12 Rockingham 2

1 2 3 4 5 6 7 8 9 10 11 12

Geraldton Woolnorth 1 Whyalla 2 Bargara 2 Perth 2 Gladstone Adelaide Melbourne 2 Whyalla 1 Perth 1 Melbourne 1 Woolnorth 2

1 Geraldton 2 Woolnorth 1 3 Perth 1 4 Whyalla 2 5 Gladstone 6 Perth 2 7 Cape Portland 2 8 Adelaide 9 North Geraldton 1 10 Bargara 1

1 The

five methods are discussed in the appendix. Woolnorth and indeed all potential sites in Tasmania were subsequently eliminated according to the discussion of section 2.3.

Table 3: Characteristics of the best sites located on Australia (Study a) Latitude Longitude Wind Speed Water Depth Distance to (S) (E) (m/s) (m) Shoreline (km) Geraldton 28° 48' 114° 29' 8.8 17 11 North Geraldton 12 28° 38' 114° 34' 8.3 12 3.5 Perth 1 31° 57' 115° 37' 7.8 7 12.5 Woolnorth 1 40° 41' 144° 39' 8.4 8 2.5 Whyalla 2 33° 13' 137° 38' 7.4 10 20 Melbourne 2 38° 1' 144° 51' 7.2 12 11 Bargara 2 24° 50' 152° 40' 7.1 17 20 2 In

case there is more than one potential site close to a place, the name of the place is followed by a number in order to be able to distinguish among the different potential sites.

Results obtained for each of the analyses listed above are now presented.

3.1. Whole Coast – Study (a) Results obtained considering the whole of Australia (Tables 2 and 3)) show that the best site for an off-shore wind farm is Geraldton on the west coast. However, the on-shore population density is low in this region and so, if there is sufficient land available on windy sites near Geraldton, on-shore wind farms may be preferable to off-shore on the basis of costs. Excluding Tasmania for reasons discussed in Section 2.3, the other interesting general locations emerging from this analysis are Whyalla (SA), Melbourne (Vic.) and Perth (WA). Because Perth has a population of about 1.5 million, and little land available for wind farms, this may be an excellent location for off-shore wind farms. Also Melbourne, which is the second largest city of Australia (population 3.5 million) and has a low availability of land, could be a good location. In the case of Melbourne the proposed sites for the installation are located in Port Philip Bay, an area characterised by a high density of shipping traffic. Therefore, attention should be paid to the wind farm’s precise location and layout in order not to interfere with the shipping lanes. There may also be community resistance on the grounds of visual impact. Whyalla, in South Australia, and Gladstone in Queensland are small industrial cities, with limited land suitable for on-shore wind farms. They too are good possible candidates.

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Table 4: Characteristics of the best sites located on the eastern and southern coasts (Study b) Latitude Longitude Wind Speed Water Depth Distance (S) (E) (m/s) (m) to Shoreline (km) Whyalla 1 33° 8' 137° 39' 7.4 12 11 Whyalla 2 33° 13' 137° 38' 7.4 10 20 Melbourne 1 37° 57' 144° 53' 7 7 8.5 Melbourne 2 38° 1' 144° 51' 7.2 12 11 Adelaide 34° 55' 138° 22' 7.2 18 11 Gladstone 23° 50' 151° 21' 6.4 5 10.5 Bargara 1 24° 50' 152° 34' 7 15 10 Bargara 2 24° 50' 152° 40' 7.1 17 20 Port Lincoln 34° 52' 135° 46' 7.7 4 2

Table 5: Characteristics of the best sites located on the Eastern coast Latitude Longitude Wind Speed Water Depth Distance (S) (E) (m/s) (m) to Shoreline (km) Gladstone 23° 50' 151° 21' 6.4 5 10.5 Bargara 1 24° 50' 152° 34' 7 15 10 Bargara 2 24° 50' 152° 40' 7.1 17 20 Wollongong 1 34° 25' 150° 56' 6.6 12 3 Ballina 1 28° 52' 153° 36' 6.7 3 1.5 Emu Park 2 23° 15' 151° 15' 7.2 5 19 Mackay 2 21° 9' 149° 23' 7.1 7 18 All the sites detected as interesting (Table 3) have high values of wind speed and shallow waters, and are close to towns.

3.2. East and South Coasts – Study (b) For all methods except for the Worst Case method (see appendix), the best site in this region is Whyalla (SA), as shown in Table 4. The best of the two detected sites close to Whyalla (11 and 20 km from the coast) is the more distant one due to the higher wind speed value (7.6 m/s versus 7.4 m/s) and to the fact that water depth is slightly lower further from the coast than closer (10 m versus 12 m). Whyalla has high domestic and industrial electrical demand. With a population of about 23,000, it is the third most populous city in South Australia, with iron and steel manufacturing the principal industry. Other favourable sites obtained in this region are the second site located close to Whyalla, Melbourne (Vic), Gladstone (Qld), Bargara (near Bundaberg, Qld), Adelaide (SA) and Port Lincoln (SA). All the sites have medium wind speed values (except Gladstone) and shallow water depth values. Port Lincoln is characterized by a value of distance from the shoreline which is very low (2 km) and therefore attention should be paid to the assessment of the visual impact and possible impact on shipping.

3.3. East Coast – Study (c) Based on wind speeds and water depth, the best potential sites are Bargara (near Bundaberg), Emu Park (near Rockhampton) and Mackay in Queensland (Table 5). Gladstone (Qld), Wollongong (NSW) and Ballina (NSW) present annual average wind speed values lower than 7 m/s and so would be low priority. All the sites in Table 5 are located in shallow waters areas, but in the case of Wollongong and Ballina the distances from shore values are very low and therefore great attention should be paid to the visual

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impact assessment. Ballina in particular is an important tourist site. The sensitivity analysis showed all the results obtained for the different regions have a quite high level of certainty.

4. CONCLUSION Despite the difficulties resulting from the fact that there is not a complete and continuous database of wind speed and bathymetry data, this analysis shows that Australia has some potential for off-shore wind energy. Wind speeds are quite high in several different regions characterised by shallow waters and proximity to the grid. Land availability, distance to the grid and economics would decide whether on-shore or off-shore wind farms are chosen in these areas. Near the Western Australia coast there are a few marine regions characterised by high wind speed meeting all the criteria defined connected to the environment, social, economical aspects and to the electrical demand. Despite very high wind speeds, Tasmania is low priority for offshore wind because of deep waters and the presence of several suitable on-shore sites that have not yet been utilised or fully utilised. There are several areas of interest near the coasts of Victoria, South Australia, New South Wales and Queensland. The most interesting potential sites are located close to the cities of Whyalla (SA), Gladstone (Qld), Rockhampton (Qld), Bundaberg (Qld), Mackay (Qld) and Melbourne (Vic.). The previous study by Jeng [26], considering only some small regions of Australia, was not able to identify these potential sites. One important characteristic of the procedure is that it is possible to change and add new potential sites without difficulty. Since the process is rational and transparent, it is possible to determine why an alternative has not emerged, by looking at the values of index and indicators connected. New alternatives therefore can be determined and deeper analysis can be conducted of the Australian off-shore wind energy potential. Meanwhile, we recommend the development of a demonstration off-shore wind farm close to an Australian city where there is no suitable land for an on-shore wind farm. Perth WA is a suitable candidate.

APPENDIX 1: DETAILS OF METHODS In order to perform the evaluation of the sites, different aspects of methods used to conduct multi-criteria analysis such as MAUT, AHP and Electre are considered [27] The values that have been obtained for the indicators are organized in a matrix, called Matrix of the Effects, whose columns correspond to the alternatives and whose rows to the indicators. In general each of its rows contains a series of values of a particular indicator and consequently the rows are not directly comparable. An indicator in fact measures, in physical units, the effect produced by an alternative on a particular criterion. Before being able to discriminate among the alternatives it’s necessary to fix a single method of judgement on the whole matrix removing the differences between the scales. Utility functions’ role is to connect the values assumed by the indicators to a satisfaction level measurement associated to them. It is therefore necessary to translate each indicator into the ‘value’ assigned by the stakeholders. It’s very difficult to quantify in an absolute way the utility and it is preferred to adopt a non-dimensional preference scale whose range of values goes typically from 0 to 1. After applying a utility function, each indicator is converted into an objective function which has to be maximized. The Evaluation Matrix is then called the Objective Matrix. When the number of objectives is high it is necessary to introduce a synthesis phase: in presence of too much specific information it would be too difficult to take a decision. This phase constitutes a first level of aggregation of the problem. Then another level of aggregation is necessary in order to obtain the global performance of each alternative. There

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are different methods available which allow us to obtain this first aggregation and in our study case we adopted the weighted sum method by direct insertion. By choosing this method it is necessary to specify the weights to use. In this case the new row vector contains, for each column, the weighted sum of the values of the objectives that have been aggregated. That is, if the number of the column of the matrix is n, J is the set of the objectives that need to be aggregated and wj are the weights of these objectives, the i-th element of the aggregated vector xagg is: x iagg = ∑w j x ij

with i = 1,..., n

(1)

j ∈J

After this first level of aggregation, a second level is necessary. A vector of weights is defined that associates a weight to each main criterion obtained from the previous phase. The vector of weights can be found using two different methodologies: a direct insertion, used in this work, or the pair comparison method. Obtained data are used to calculate the weighted sum, and the concordance and discordance matrix. The weighted sum method considers the total performance U(k) of each alternative as a weighted sum of the single performances ui(xi(k)) associated with the individual criteria: m

U (k ) = ∑wiui (x i (k ))

(2)

i =1

Depending on U(k) it is possible to define the final ranking order of the alternatives, which will have at the first rank the potential with the highest score. Concordance and discordance matrix are defined considering the concordance and discordance index. Concordance index crs (which considers all the criteria that don’t oppose the fact that an alternative ar is preferable to as) expresses the extent/degree/level on which we agree on the fact that ar is preferable to as and represents in a certain sense the satisfaction the decision-maker gets choosing a r instead of as [28]. The index is defined as [29]: crs = ∑ wi

where Irs = {i: zir ≥ zis}

(3)

i ∈ I rs

That is, it is the sum of weights wi for which the alternative ar outranks as. The index drs quantifies the extent/degree/level on which an alternative is considered worse than another one and represents the regret the Decision Maker accepts while choosing ar instead of as. The index is defined as: drs =

max wi | z ir − z is | i ∈J rs

max wi | z ir − z is |

where Jrs = {i: zir < zis}

(4)

i

That index is the ratio between the highest performance difference weighted disfavouring the alternative and the highest difference weighted independently whether disfavouring or not. Once discordance and concordance matrices have been obtained and the vector of weights has been defined, the software allows us to obtain five different ranking orders obtained through the following alternative methods:

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Worst Case: This corresponds to a risk aversion logic. The row vector obtained and used to get the ranking order will include, for each column, the minimum value among the ones assumed by the aggregated objectives.

·

Concordance Index and Discordance Index [30]: To evaluate this method is necessary to define a concordance threshold Sc = α (with α < 1) that points out when an outranking can exist based on the values of the concordance index: until it is true that crs < Sc there is no outranking because the conflict area is too big and ar doesn’t prevail convincingly on as. On the contrary if crs ≥ Sc there is an outranking. Discordance threshold Sd = β (with β < 1) works in a similar way but it acts on the index drs. Until it is true that drs > Sd, the difference between the performances of the alternatives is so dramatic that there is a veto against choosing ar instead of as. If we have instead that drs ≤ Sd, the discordance criteria are not enough important to veto that ar is reasonably preferred to as. In conclusion an alternative ar outranks another alternative as if both the conditions crs ≥ Sc , drs ≤ Sd are contemporarily respected. Once the outranking relations are identified it is possible to draw a matrix of pair-comparison between the alternatives in which the boxes where there is no outranking are barred and all the others are empty or a graph whose nodes represent the alternatives and the arcs are oriented in order to reproduce the outranking relations (direction from the preferred alternative to the outranked one).

·

Weighted Sum [31]: by choosing this method it is necessary to specify the weights. In this case the new row vector contains, for each column, the weighted sum of the values of the objectives that have been aggregated. The best alternative is the one which has the highest weighted sum value.

·

Weak Dominance: The weak dominance method proposes a sort of sensitivity analysis on the thresholds making them vary continuously in their variation range.

REFERENCES 1.

Global Wind Energy Council GWEC, http://www.gwec.net

2.

EWEA (European Wind Energy Association) Offshore Wind Energy, fact sheet, 2009, ; EWEA Strategic Energy Review, 2008.

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Chair of the European policy workshop on off-shore wind power development, Berlin declaration, conclusion of the chair, Berlin, 2007. http://www.bmu.de/files/pdfs/ allgemein/application/pdf/eupolicy_declaration.pdf

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EWEA European Wind Energy Association, Delivering Offshore Wind Power in Europe, Brussels, 2007. http://www.ewea.org/fileadmin/ewea_documents/images/ publications/offshore_report/ewea-offshore_report.pdf

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EWEA European Wind Energy Association, EWEA’s response to the European Commission’s Green Paper towards a future maritime policy for the Union: a European vision for the oceans and seas, Brussels, 2007. http://www.ewea.org/fileadmin/ ewea_documents/documents/publications/ EWEA_contribution_to__Maritime_ policy_Green_Paper.pdf

Note: All websites accessed 23 June 2009.

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CEI (China Economy Information Network) website, www1.cei.gov.cn/ce/doc/cenm/ 200803130536.htm; China Daily, China’s first offshore power generator ready, , 2007.

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Hingtgen, J.S., Offshore wind farms in the Western Great Lakes: an interdisciplinary analysis of their potential, Master Thesis, University of Wisconsin-Madison, 2003.

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Jeng, D.S., Potential of Offshore Wind Energy in Australia, University of Sydney, Sydney, 2007.

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Australian Government, Department of the Environment, Water, Heritage and the Arts, Renewable Energy Atlas, (REA), available at the website http://www.environment. gov.au/apps/boobook/mapservlet?app=rea

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Bamford, M., Watkins, D., Bancroft, W., Tischler, G. and Wahl J., Migratory Shorebirds of the East Asian - Australasian Flyway: Population Estimates and Internationally Important Sites, Wetlands International, 2008.

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Baja, S., Chapman, D.M. and Dragovich, D., A conceptual model for assessing agricultural land suitability at a catchment level using a continuous approach in GIS, University of Sydney, The Regional Institute, 2006.

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Pasquini, F., Applicazione di simulazioni Monte Carlo nello sviluppo di un supporto informatico per la scelta tra progetti alternativi (Application of Monte Carlo simulations to develop an information support for the choice among multiple projects), Masters thesis in Environmental Engineering, Politecnico di Milano, 2003.

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Renn, O., Public participation in impact assessment: a social learning perspective, Environmental Impact Assessment Review, 1995.

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Australian Bureau of Agricultural and Resource Economics (ABARE), Energy in Australia 2009, ABARE, Canberra, p. 27.

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Soncini Sessa, R., Weber, E. and Castelletti, A., Integrated and participatory water resources management, Elsevier, Amsterdam, 2007.

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Jeng (2007) cited above.

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Keeney, R.L. and Raiffa H., 1976. Decisions with Multiple Objectives: Preferences and Value-Tradeoffs, John Wiley, New York.

28.

De Leo, G. et al., Fondamenti di Valutazione d’Impatto Ambientale (Environmental Impact Assessment Principles), Politecnico di Milano Università di Parma, 2001.

29.

Colorni, A. and Laniado, E., Dispense del corso di Metodi e Modelli per il Supporto alle Decisioni (Decision Support Methods and Models, course notes) , Politecnico di Milano, 2001.

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31.

Van Delft, H. and Nijkamp, P., Multi-criteria Analysis and Regional Decision-Making, Martinus Nijhoff Social Science Division, Leiden Neederlands, 1977.