استفاده از GIS به منظور بررسی تأثیر مناطق محروم بر هزینه اتصال انرژی امواج به شبکه برق

An increase in the planning and environmental restrictions associated with wind energy has led to a growth in interest towards wave energy. However, as the connection cost of a wave energy development is a driving factor in the development's feasibility, existing wind farm cable-routing techniques used by renewable energy developers may not be satisfactory. A Geographical Information System (GIS) method is presented which optimises the cable route between a wave farm and the electricity network, while taking a range of exclusion zones, such as native vegetation, into account. The optimisation is presented for a South Australian study area, which subsequently showed that exclusion zones reduce the number of suitable locations for wave energy by almost 40%. The method presented also assesses the effect that each exclusion zone applied has upon the number of suitable locations within the study area. The analysis undertaken showed that National Parks and cliffs pose a significant limitation to the potential of a wave energy industry within South Australia. Allowing the transmission route to travel through a National Park, or traverse a cliff, resulted in an increase in the number of locations from which a connection could be made to the electricity grid for less than $10 million of 33% and 50%, respectively. Conservation Parks, Wilderness Areas and native vegetation also have an effect upon the number of suitable locations for wave energy within South Australia. The GIS methods developed may be of assistance to governments in setting appropriate marine renewable energy policy, and also in identifying existing policy which may require amending if the government wishes to pursue and support the development of wave energy.

The implications of anthropogenic climate change and global warming are a serious threat to the world's ecosystems and the prosperity of human civilisations. Climate scientists argue that in order to stabilise the earth's climate and prevent further global warming, the earth requires a 70% cut in present carbon dioxide emissions by 2050 (Flannery, 2005). This urgent need for a reduction in greenhouse gas emissions has forced policy and decision makers to take a more sustainable approach to development. As traditional forms of power generation such as coal and gas emit large quantities of greenhouse gases, governments worldwide are currently implementing policies, which aim to increase the development of renewable energy. In 2001, ‘new renewables,’ which include modern biomass, wind, solar, small-scale hydropower, marine and geothermal energy, comprised 2.3% of the world's primary energy consumption. However, by 2020, Goldemberg (2006) estimates that new renewables will contribute between 6.7% and 12.9% of the world's total energy consumption. Over the past decade, wind power has been the fastest growing renewable energy technology in the world with an average growth rate of 39% per annum (Caglar et al., 2006). However, the growth of wind energy in many countries has been accompanied by an increase in planning and environmental restrictions, predominantly in space-limited countries such as the UK (Baban and Parry, 2001). This has led to the development of offshore wind farms, and greater research and development towards marine renewable energy. Jones and Rowley (2002) report that although offshore wind energy is the fastest growing ocean-based renewable energy, growth in the wave energy industry is expected to reach US$100 million per annum by 2010. The UK Government is currently in the process of introducing marine renewable energy legislation, with the objective of speeding up the deployment of wave and tidal renewable energy technologies. The British Wind Energy Association (BWEA) claims that the worldwide potential extractable wave resource has been estimated to be between 1 and 10 Terawatts (TWs) (BWEA, 2006). Considering worldwide electricity demand is just over 1 TW (IEA, 2004), wave power has significant potential to contribute to global energy demand. Bauen et al. (2003) state that wave energy devices are able to convert between up to 80% of the available resource to useful energy, which is a significant advantage over the 50% conversion rate attained by modern wind turbines (Davies, 2005). Further to this, Agren et al. (2003) specify that the capacity factor, which is the actual amount of power a renewable energy device produces per year divided by the amount of energy that the device could produce per year according to its rated capacity, is critical to the economic feasibility of a renewable energy development. Wave energy devices can generally produce significantly higher capacity factors than offshore or onshore wind energy devices. An additional advantage of wave energy is the ability to predict energy output in advance with more confidence than wind energy (Bauen et al., 2003), which is essential to the successful integration of intermittent renewable energy supply into national electricity grids. Baban and Parry (2001) suggest that one of the biggest issues facing the exploitation of renewable energy is the selection of suitable sites. Geographical Information Systems (GIS) can be of assistance in this task. Multi-criteria decision analysis (MCDA), within the framework of GIS allows multiple competing site selection objectives to be taken into account at once by renewable energy developers. GIS and MCDA techniques are ideally suited to the spatial nature of site selection decision-making problems (Jankowski, 1995). The use of MCDA within GIS analysis has grown significantly in recent times; Malczewski (2006) reports that over 300 GIS-MCDA articles have appeared in refereed journals since 1990. GIS have previously been used in the siting of wind and wave farms in the UK. Baban and Parry (2001) took a range of factors into account in order to evaluate possible wind farm locations in Lancashire, England. Graham et al. (2003) completed a similar study, which evaluated potential wave farm locations off the Scottish coast. Meentemeyer and Rodman (2006) recently completed a study that used GIS to evaluate the site suitability for wind turbines in Northern California, which took a range of physical, environmental and human impact factors into account. In addition to evaluating potential sites for a certain technology, Yue and Wang (2006) have shown that GIS can also be used to evaluate between the suitability of a range of renewable energy technologies, such as wind, solar and biomass, over a specified study area. The use of GIS to assist decision-making in this field is clearly rapidly expanding. Cavallaro and Ciraolo (2005) evaluated four different wind turbine configurations on an Italian Island using non-spatial MCDA techniques. As four locations were evaluated by Cavallaro and Ciraolo (2005) rather than a study area, the use of GIS was not necessary. However, many of the wide range of environmental, economic, social and technical factors, which Cavallaro and Ciraolo (2005) incorporated into the MCDA process, are transferable to the development of spatial site selection tools. One of the most important factors that needs to be taken into consideration within the MCDA process is the connection of the renewable energy farm to the electricity grid. This paper focuses on the development of an electricity cost GIS layer as it is a critical component of a GIS-MCDA site selection tool. The cost involved in transmitting power to the electricity network from an offshore location is much more expensive than from an onshore location, due to the cost of underwater electricity cable infrastructure. Consequently, the significant amount of capital required for a wave energy farm is hindering the development of the wave energy industry (Jones and Rowley, 2002). As the connecting transmission route constitutes a major proportion on the development cost, optimising the cost of the route will be imperative to the feasibility of wave farm developments. Several factors need to be taken into consideration in the planning of a power transmission route between a renewable energy development and the electricity network. The cost obviously needs to be kept to a minimum, which would be achieved by following a direct route between the renewable energy farm and the network. However, Dey and Gupta (2000) discuss that optimal pipeline routing in the oil and gas industry requires the consideration of not only the shortest total distance, but also a range of accessibility and government stipulations. In the case of an electricity cable between a wave farm and the network, taking the shortest path will generally not be possible due to areas such as National Parks, in which development approval for a transmission line would be unlikely to be obtained from government authorities, and accessibility considerations such as cliffs. There are also many other environmental, social and cultural ‘exclusion zones’ which need to be taken into account. A review of the method used to plan the transmission route by an Australian-based wind energy developer revealed that possible exclusion zones are taken into account individually, in order to visually devise a route between the wind farm and the network, which avoids exclusion zones. Whilst this method may be satisfactory in the case of a wind energy development, it is unlikely to be sufficient for a wave energy development, as the transmission route between a wave farm and the network will involve much greater infrastructure costs per metre for the submarine cable than the onshore power lines. The different land and sea transmission infrastructure costs make evaluating a ‘lowest cost path’ around exclusion zones a much more difficult task, which ideally needs to be undertaken by a more complex cost-weighted optimisation method. Genetic algorithms have been effectively used to optimise water distribution network planning (Dandy and Hassanli, 2005), and their use has also extended to many more applications. However, such optimisation techniques deal with a number of possible routes, and a number of possible options for each route. If an analysis can be conducted where there is only one possible option for each alternative route, GIS has the capability to take the spatial nature of the problem into account. Clark and Luettinger (2005) discuss the use of a GIS-based pipeline selection process to select an optimal pipeline route from many alternative routes. The analysis undertaken by Clark and Luettinger (2005) took not only construction cost into account, but also a range of exclusion zones that the pipeline could not travel through. This method would be transferable to the optimisation of a wave farm transmission cable route. Renewable energy MCDA site selection methods were reviewed earlier in the paper. Baban and Parry (2001) included the electricity network as one of the factors in the MCDA method developed for selecting wind farm locations, however the constraints criteria used specified that for a location to be suitable, the electricity network had to be within 10 kms. This method did not take exclusion zones into account, by assuming that the transmission route could follow a direct path between the wind farm and the network. Graham et al. (2003) incorporated the connection cost into a wave farm site selection GIS-MCDA technique. A cost surface map was developed, in which each cell contained the cost per metre of travel through the cell. Once exclusion zone GIS layers have been developed, they could be included in the cost surface map by placing an excessive cost on travel through the cell, which will force the transmission route to traverse around the cell. The lowest cost between each cell in the designated study area and the network could then be evaluated. This method improved upon the proximity criteria used by Baban and Parry (2001), and the current research used a similar method to Graham et al. (2003) to develop a connection cost suitability layer covering the specified study area. In addition to optimising the transmission route, this research has built upon the connection cost technique developed by Graham et al. (2003), by using GIS to evaluate the effect which each exclusion zone layer has upon the connection cost of cells in the study area. This is an important consideration for policy makers. If legislation is introduced which sets a mandatory marine renewable energy target, policy makers need to be sure that there are enough feasible wave farm locations within the legislation's jurisdiction to enable the target to be met. This first requires an estimation of the amount of marine energy that could be developed within the legislation's jurisdiction. Faber Maunsell (2006) conducted a study for the Scottish Government, which predicted that up to 1300 MW of marine energy capacity could be installed within Scottish waters. However, although there may be enough suitable marine energy resource locations to enable this, each location must also be economically feasible to connect to the network. If the transmission route must traverse around, for example, a Marine Protected Area (MPA), the connection cost may render the location unsuitable. Using the cost surface method introduced previously, GIS can be used to define a study area from which a marine energy farm could be connected to the network for less than a pre-determined amount. If the government plans to introduce legislation which sets marine energy targets to make full use of the study area, it is possible using GIS to evaluate which exclusion zones are likely to significantly impede the development of a marine energy within the study area, and the government may choose to amend policy regarding the exclusion zones. For example, if an evaluation is made that 500 MW of marine energy capacity could be installed within a government's jurisdiction, but unless the transmission route traverses through native vegetation this amount is reduced to 100 MW, the government may choose to amend legislation to allow such transmission infrastructure development within areas of native vegetation. A GIS method is presented which optimises the connection of potential locations for wave energy development within South Australia to the electricity grid. This allows an evaluation of the effect that a range of environmental, social and cultural exclusion zones have upon the connection cost and overall area of potential locations. A main focus of this research is to assess, in which exclusion zones a relaxation of government development restrictions would be most beneficial for enabling more wave energy development at a lower cost.

A clear and concise GIS method was presented to assist in the optimisation of a transmission route between a wave farm and the electricity network. The GIS method has considerable advantages over traditional methods such as assessing a range of maps individually and delineating a transmission route by hand, and therefore may be of valuable use to renewable energy developers. The effect which certain exclusion zones had upon the wave energy potential of South Australia was assessed. The baseline cases showed that exclusion zones will have a significant effect upon the development of wave energy in South Australia. Within the analysis area specified, exclusion zones reduced the number of cells which could be connected to the electricity network for less than $30 million by 40%. The most attractive locations, in terms of connection cost, were reduced by over 60% by exclusion zones. This suggests that the rather large impact of the exclusion zones may reduce the potential of a wave energy industry in South Australia. An analysis was undertaken which simulated the effect of the government relaxing development restrictions on each exclusion zone would have upon possible locations for a wave energy farm in South Australia. The analysis showed that restricting the construction of a transmission route through National Parks, and the construction issues involved in the transmission route traversing a large cliff, pose the most significant limitations to the potential of a wave energy industry within South Australia. Enabling construction of a transmission route through National Parks increased the number of cells which could be connected for less than $30 million by 33%, and when considering the most attractive locations in which a connection could be made for less than $10 million, this figure increased to over 40%. Allowing the cable to traverse either a Conservation Park, Wilderness Area or native vegetation increased the number of potential locations for wave energy in South Australia by between 6% and 9%. Quite significantly, if the transmission cable can be constructed to negotiate a 50 m cliff, the number of locations which could be connected for less than $10 million increased by nearly 50%. A wave energy industry is unlikely to develop within South Australia in the short term. This is predominantly due to the Australian Federal Government's continual lack of support for renewable energy, including failing to extend the Mandatory Renewable Energy Target (MRET) beyond 2% (Kent and Mercer, 2006). However, the method presented to assess the effect which certain exclusion zones have upon the wave energy potential of a specified area, may be of use to governments worldwide who are keen to implement policies which aim to increase the development of marine energy. The method has the capacity to evaluate suitable areas for the development of wave energy, which may be of assistance in setting appropriate marine renewable energy policy. The method also enabled an assessment to be made on the effect which different exclusion zones have upon the amount of suitable locations for wave energy, which could assist governments in identifying existing policy that they may wish to amend in order to pursue the growth of wave energy within their jurisdiction. This infers a possible trade off between environmental designations such as biodiversity consideration and greenhouse gas reduction, in which a decision needs to be made by policy makers on which is more important.