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The role of concentrating solar power and photovoltaics for climate protectioN

Robert Pietzcker, Potsdam Institute for Climate Impact Research, +49/331/288-2404,

Susanne Manger, Leipzig University, +49/341/9733518,

Gunnar Luderer, Potsdam Institute for Climate Impact Research, +49/331/288-2671,

Thomas Bruckner, Leipzig University, +49/341/9733516,

Nico Bauer, Potsdam Institute for Climate Impact Research, +49/331/288-2540,

Overview

In this paper we discuss the role of solar electricity as well as the relative importance of concentrating solar power (CSP) and photovoltaics (PV) for reaching cost-optimal energy-related greenhouse gas abatement under the constraint of an upper limit on global temperature increase.

The two solar technologies compete in two distinct ways:

(i)Both technologies use the same resource; they are directly powered by the sun. However, this rivalry is reduced by the fact that CSP plants require direct sunlight and large continuous areas, while PV systems can also capture indirect sunlight and can be used on very fragmented areas or on existing buildings.

(ii)Both technologies are deemed “learning technologies” (Neji 2008): Although today their investment costs are much higher than the costs of traditional energy technologies, it is expected that they will become cost-competitive once major investments into capacity increase have taken place.

We use the hybrid energy-economy-climate model ReMIND (Leimbach et al., 2009) to develop an understanding of which parameters determine the use of one or the other solar technology. Our research seeks to either identify strategically important technologies whose promotion could then be implemented through technology policies, or, if the results turn out to be strongly sensitive to small parameter variations, determine which parameters need further research to facilitate future policy-making.

Methods

We first perform a literature review and present recent data on costs, learning curves and regional development potential for concentrating solar power and photovoltaic electricity generation. After consolidating the data into one set of parameters with uncertainty ranges, we implement the two technologies in our state of the art numerical hybrid optimization model ReMIND.

ReMIND consists of three directly coupled modules: an energy model, a climate model, and a macro-economic general equilibrium growth model. ReMIND calculates the optimal amount and timing of investments in different energy technologies by optimizing intertemporal utility under the constraint of a maximum global mean temperature increase of 2°C until 2150. The model includes a wide range of energy technologies, and explicitly models technology development through endogenous learning-by-doing via learning curves.

The model distinguishes between the two solar technologies through differences in their parameterization, such as investment costs, load factor, total potential (amount of land usable for installation), costs for operation and maintenance, learning rates and floor costs.

As our model performs full temporal accounting for learning effects and the total investment required to reach them, a truly optimal outcome is achieved without the need to exogenously set technology preferences. This results in a “first best world” vision of a mitigation pathway that can be seen as an upper benchmark for policy design.

Results

Preliminary results show that solar power generation technologies supply a significant share of electricity in the optimal abatement scenario, as depicted in Figure 1. We calculate option values for a given technology by running different climate stabilization scenarios in which this technology is excluded. These option values serve as indicator for the strategic relevance of individual technologies to achieve the climate protection target. Our preliminary results suggest that excluding solar electricity increases the total mitigation costs from 0.6% to 1% of total discounted GDP.

Figure 1: Total annual electricity production as well as percentage shares of different energy technologies

Sensitivity studies demonstrate how capacity, investment costs and learning parameters influence technology deployment. We are especially interested whether one solar technology is developed at the cost of the other, and if yes, whether this competition occurs in the near future or during the second half of the century.

Preliminary results show a dominance of PV in the first fifty to eighty years, with CSP coming in in later periods. The total share of CSP is low at default values but rises continually as learning rates of PV or investment costs of CSP are decreased. This result can probably be explained by a) the significant learning investments (in the order of several ten to hundred billion US-dollars) which are required for both technologies to reach costs that are competitive compared to other energy technologies, and by b) PV being further on the learning curve, with some of the necessary learning investments already spent.

Further scenario runs to better understand the parameter ranges in which one technology dominates the other are currently under way.

Conclusions

If a stringent climate target of 2°C is to be met, solar electricity will play a major role in the transformation of the energy system. For a better understanding of the relative importance of concentrating solar power and photovoltaics, we used the hybrid energy-economy-climate model ReMIND to calculate cost-optimal energy-related greenhouse gas abatement trajectories.

We focused our research on the competition between the two technologies and found that technology deployment is sensitive to parameter variations. Depending on implemented learning rates and floor costs, one technology may completely dominate the other one or vice versa. Thus, further research into learning effects in both technologies is needed to better understand how investment costs change with capacity increases and time. Also, future implementation of intermittency issues into our model will improve the validity of our results.

References

Leimbach, M., Bauer, N., Baumstark, L., Edenhofer, O. (2009).„Mitigation costs in a globalized world: climate policy analysis with REMIND-R”, accepted for publication in Environmental Modeling and Assessment.

Neij, L.(2008). “Cost development of future technologies for power generation–a study based on experience curves and complementary bottom-up assessments”. Energy Policy 36 (6), 2200–2211.