By Bruno De Wachter / Published on Thu, 2009-06-18 05:30
Incremental changes can result in substantial cost reductions
The technologies for producing electricity from solar thermal energy can be divided into three main categories:
- Parabolic trough and Fresnel systems
- Central receiver systems, including the solar updraft tower
- Parabolic dish systems, usually combined with a Stirling heat engine
The first commercial CSP plant, which was built in California in the 1980s, used the parabolic trough concept. It has a total capacity of 354 MW. For many years, this was the only large scale CSP plant in the world. Elsewhere, only small demonstration plants were built, as the high investment cost hampered further deployment.
In 2006, a new commercial 1 MW parabolic trough CSP plant was built in Tucson, Arizona. Since then, the development of CSP as a commercial electricity generating technology has taken off. Many CSP projects are currently being built, the majority of which are in Spain and the USA. It is very likely that because of this market boom, investment costs for CSP will go down. The question is how much and how quickly.
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By Bruno De Wachter / Published on Tue, 2009-06-16 05:30
Diversification complicates price predictions
In regards to PV energy, we will focus on grid connected systems only, since they represent the large majority of the market. The cost of a grid connected PV system is composed of the PV module cost and the 'BOS' cost (Balance of System). The BOS consists of the structures for mounting the PV modules and of the power-conditioning equipment that converts the DC power of the modules into the AC grid power.
Prediction not straightforward
Three difficulties arise when trying to predict the future cost development of PV energy starting from existing experience curves.
- The cost decrease over the past four decades was not at all linear. It alternated periods of sharp decline with periods in which it stayed more or less constant. As a result, experience cost curves that do not represent large time spans can result in a distorted perspective.
- Various PV technologies exist and are difficult to represent with a single experience curve. New types of PV systems may break through in the near future that completely change the average cost of PV modules.
- Even if the future cost of individual PV modules can be predicted, this does not mean the cost of electricity generated by those PV systems can be easily determined. Factors such as geographical location, local support mechanisms, and the size of systems will have a major influence on the average PV electricity cost.
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By Bruno De Wachter / Published on Wed, 2009-06-10 05:30
Design improvements provide the main potential - material costs the main barrier
When predicting the learning curve of wind energy, a distinction should be made between on-shore and off-shore wind. While the former started to develop in the mid 1970s, the latter only took off around the year 2000 and is consequently still lacking extensive historical data. As the figures of the NEEDS study show, today’s off-shore wind and on-shore wind electricity prices are of the same order of magnitude.
Cost of system drops faster than cost of turbine
Historical cost development curves of on-shore wind show large differences that depend mainly on the timeframe, the system boundaries, and the geographical area. As a general rule one can say that the experience ratio is higher for the complete system than for the turbine alone. This is confirmed by the bottom-up study of NEEDS, which shows that the relative share of the turbine cost in the complete wind energy cost increased in the past decades.
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By Bruno De Wachter / Published on Tue, 2009-06-09 08:12
Future cost development of renewable energy
Predicting the learning curves
How will the cost of the various renewable energy systems evolve in the future? That is a question a great many people are concerned about. To make the transition to a sustainable energy economy, the development and deployment of renewable energy systems will be indispensable. While all of these technologies presently have a higher cost than traditional energy systems, it is generally believed that they will become cheaper once they have gone through their learning curve.
Predicting this cost development curve was the goal of the NEEDS project (New Energy Externalities Development for Sustainability). The accuracy of decision support tools depends on the reliability of such predictions. It provides investors and policy makers alike with knowledge as to what degree investing in a particular renewable technology is likely to be worthwhile.
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