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2. Literature reviews


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     Chang et al. [13] review the production and consumption of traditional and renewable energy in China over the past three decades, and present an overview on the research and development of renewable energy in China. The study indicated that the renewable energy in China shows a promising prospect, of which biomass is found to be one of the most promising renewable energy resources with great potential for development. Since 1993, the output of domestic crystalline silicon solar cells soared by 20–30% annually; the total installed capacity of PV systems in China was approximately 22MW at the end of 2002. Seven tidal power stations and one tide flood power station are in operation with a total capacity of 11MW in China. Almost 20% of the primary energy consumed in China is biomass energy.

     Evrendilek and Ertekin [14] assessed the potential of renewable energy sources in Turkey to meet the growing energy demand. Turkey’s limited amount of fossil fuels has a present average ratio of proved reserves of 97.38 quads to production rate of 3.2 quads/yr for about 30 yr. Economically feasible renewable energy potential in Turkey is estimated at a total of 1.69 quads/yr (495.4 TWh/yr) with the potential for 0.67 quads/yr (196.7 TWh/yr) of biomass energy, 0.42 quads/yr (124 TWh/yr) of hydropower, 0.35 quads/yr (102.3 TWh/ yr) of solar energy, 0.17 quads/yr (50 TWh/yr) of wind energy and 0.08 quads/yr (22.4 TWh/yr) of geothermal energy. Pursuit and implementation of sustainability-based energy policy could provide about 90% and 35% of Turkey’s total energy supply and consumption projected in 2010, respectively. Utilisation of renewable energy technologies for electricity generation would necessitate about 23.2 Mha (29.8%) of Turkey’s land resources.

     Krewitt and Nitsch [15] developed a geographical information system (GIS)-based approach to analyse the effect of different nature conservation criteria on the wind energy potential in quantitative terms. The wind energy potential feasibility is demonstrated by quantifying the potential while taking into account detailed site-specific information on nature conservation aspects. Wind energy potential amounts to only 25% of the theoretical potential. Ban on wind energy in areas of medium-to-high visual sensitivity in the Baden-Wu¨ rttemberg case study area reduces the wind potential by a further 20%. Results for two case-study regions in Germany, representing a coastal area with a high wind potential (the state of Lower Saxony) and an inland region with limited wind potential (the state of Baden-Wu¨ rttemberg), suggest that the potential for electricity generation from wind energy even under strict nature conservation constraints in these regions amounts to about 35 TWh/yr, which is considered as a lower estimate of the actual potential.

     Serwan et al. [16] have adopted GIS to locate wind farms in UK. In terms of area, as would be expected, the most suitable areas represent the smallest group, occupying only 3.79% of the total study area while the least suitable sites cover some 73.34% of the area. The suitability map using weighted layers showed a very similar pattern. However, the analysis in terms of areas occupied by each suitability class shows that these have changed slightly in favour of the most suitable sites. The most suitable areas are occupying 8.32% of the total study area while the least suitable sites cover 70.26% of the area.

      Rylatt et al. [17] describe the development of a solar energy planning system, consisting of a methodology and decision support system for planners and energy advisers. The study primarily intends to predict and realise the potential of solar energy on an urban scale, and the system will support decisions in relation to the key solar technologies: solar water heating, PV and passive solar gain. The prototype discussed here relates to the first of these. Based on a methodology for predicting the solar energy potential of domestic housing stock, it is implemented as a relational database application linked to a customised GIS. The methodology takes into account baseline energy consumption and projected energy saving benefits. To support this, the system incorporates a domestic energy model and addresses the major problem of data collection in two ways. Firstly, it provides a comprehensive set of default values derived from a new dwelling classification scheme that builds on previous research. Secondly, novel GIS tools enable key data to be extracted from digital urban maps in different operational modes.

     Bent Sorensen [18] employs GIS to map solar resources on the basis of satellite data (radiation at top of the atmosphere, albedo, downward radiation at surface) and to match it with demand modelling on a habitat basis (population density, energy demand intensity). PV potential use is based on estimates of practical areas for collection use (building roof areas, suitably inclined and oriented surfaces) combined with land use data (important for central receiver fields). Local measurement data have been converted to the GIS grid employed. For the centralised PV system, the estimated potential is taken as 15% of radiation times the fraction of the two area types (1% of all rangeland and 5% of all marginal land (deserts and scrubland)) and a factor of 0.75 in order to account for transmission and storage cycle.

     Nandalal and Sakthivadivel [19] investigated the operational behaviour of Samanalawewa and Udawalawe reservoirs. The model ShellDP was used to study the performance of the Samanalawewa and Udawalawe reservoirs. The model used in the study is based on stochastic dynamic programming (SDP) and simulation techniques. Since, the direct application of SDP for two reservoirs was limited by the dimensionality of the problem, a sequential decomposition method was employed in the model. The results showed an evaporation loss of 9.6106m3, the water released from the reservoir was 533.89106m3, spillage was 3.3106m3 and the power generated was 334.9GWh for Samanalawewa reservoir; for the Udawalawe reservoir, the results showed an evaporation loss of 34.6106m3, the water released from the reservoir was 858.5106m3, spillage was 19.7106m3 and the power generated was 13.6GWh.

     Ramachandra et al. [20] computed the hydropotential of the streams of Bedthi and Aghnashini river basins in Uttara Kannada district of Western Ghats. Potentials at feasible sites are assessed based on stream gauging carried out for a period of 18 months. Computations of discharge on empirical/rational method based on 90 yr of precipitation data and the subsequent power and energy values computed are in conformity with the power calculations based on stream gauging. Their study has explored the possibility of harnessing hydropotential in an ecologically sound way to suit the requirements of the region. Energy that could be harnessed monthly is computed for all ungauged streams in the Bedthi and Aghnashini river catchments based on the empirical or rational method considering the precipitation history of the last 100 yr. The hydroenergy potentials of streams in the Bedthi and Aghnashini river catchments are estimated to be about 720 and 510 million kWh, respectively. The net energy computed for various dam heights indicates that dams of 67m height store enough water to meet the region’s lean season electricity requirements, and the area saved has a bioresource potential of 319 million units, which can cater the thermal energy demand of 312 million units. The cost per unit for various designs of the dam shows a 40.5% reduction in cost for a dam height reduction of 32.75%.


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