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[en] Variable renewable energy sources (VRE) for electricity generation, such as wind and solar power, are subject to inherent output fluctuations. This variability has significant impacts on power system and electricity markets if VRE are deployed at large scale. While on global average, wind and solar power currently supply only a minor share of electricity, they are expected to play a much larger role in the future - such that variability will become a major issue (which it already is in some regions). This thesis contributes to the literature that assesses these impacts the ''system and market integration'' literature. This thesis aims at answering the question: What is the impact of wind and solar power variability on the economics of these technologies? It will be laid out that the impact can be expressed in (at least) three ways: as reduction of value, as increase of cost, or as decrease of optimal deployment. Translating between these perspectives is not trivial, as evidenced by the confusion around the concept of ''integration costs''. Hence, more specifically: How does variability impact the marginal economic value of these power sources, their optimal deployment, and their integration costs? This is the question that this thesis addresses. This study comprises six papers, of which two develop a valuation framework that accounts for the specific characteristics of the good electricity, and the specific properties of wind and solar power versus ''dispatchable'' power plants. Three articles then assess quantitative questions and estimate marginal value, optimal deployment, and integration costs. These estimates stem from a newly developed numerical power market model, EMMA, market data, and quantitative literature reviews. The final paper addresses market design. In short, the principal findings of this thesis are as follows. Electricity is a peculiar economic good, being at the same time perfectly homogenous and heterogeneous along three dimensions - time, space, and lead-time. Electricity's heterogeneity is rooted in its physics, notably the fact it cannot be stored. (Only) because of heterogeneity, the economics of wind and solar power are affected by their variability. The impact of variability, expressed in terms of marginal value, can be quite significant: for example, at 30% wind market share, electricity from wind power is worth 30-50% less than electricity from a constant source, as this study estimates. This value drop stems mainly from the fact that the capital embodied in thermal plants is utilized less in power systems with high VRE shares. Any welfare analysis of VRE needs to take electricity's heterogeneity into account. The impact of variability on VRE cannot only be expressed in terms of marginal value, but also in terms of costs, or in terms of optimal deployment. The mentioned value drop corresponds to an increase of costs by 30-50%, or a reduction of the optimal share by two thirds. These findings lead to seven policy conclusions: 1. Wind power will play a significant role (compared to today). 2. Wind power will play a limited role (compared to some political ambitions). 3. There are many effective options to integrate wind power into power systems, including transmission investments, flexibilizing thermal generators, and advancing wind turbine design. Electricity storage, in contrast, plays a limited role (however, it can play a larger role for integrating solar). 4. For these integration measures to materialize, it is important to get both prices and policies right. Prices need to reflect marginal costs, entry barriers should be tiered down, and policy must not shield agents from incentives. 5. VRE capacity should be brought to the system at a moderate pace. 6. VRE do not go well together with nuclear power or carbon capture and storage - these technologies are too capital intensive. 7. Large-scale VRE deployment is not only an efficiency issue, but has also distributional consequences. Re-distribution can be large and might an important policy driver.
[en] This paper provides a comprehensive discussion of the market value of variable renewable energy (VRE). The inherent variability of wind speeds and solar radiation affects the price that VRE generators receive on the market (market value). During windy and sunny times the additional electricity supply reduces the prices. Because the drop is larger with more installed capacity, the market value of VRE falls with higher penetration rate. This study aims to develop a better understanding on how the market value with penetration, and how policies and prices affect the market value. Quantitative evidence is derived from a review of published studies, regression analysis of market data, and the calibrated model of the European electricity market EMMA. We find the value of wind power to fall from 110% of the average power price to 50–80% as wind penetration increases from zero to 30% of total electricity consumption. For solar power, similarly low value levels are reached already at 15% penetration. Hence, competitive large-scale renewable deployment will be more difficult to accomplish than as many anticipate. - Graphical abstract: Wind value factor estimates from a literature review (a), the numerical model EMMA (b), and German historical market data (c). The value factor (wind revenue over base price) decreases with higher penetration rates. Highlights: ► The variability of solar and wind power affects their market value. ► The market value of variable renewables falls with higher penetration rates. ► We quantify the reduction with market data, numerical modeling, and a lit review. ► At 30% penetration, wind power is worth only 50–80% of a constant power source
[en] Low-carbon electricity generation, i.e. renewable energy, nuclear power and carbon capture and storage, is more capital intensive than electricity generation through carbon emitting fossil fuel power stations. High capital costs, expressed as high weighted average cost of capital (WACC), thus tend to encourage the use of fossil fuels. To achieve the same degree of decarbonization, countries with high capital costs therefore need to impose a higher price on carbon emissions than countries with low capital costs. This is particularly relevant for developing and emerging economies, where capital costs tend to be higher than in rich countries. In this paper we quantitatively evaluate how high capital costs impact the transformation of the energy system under climate policy, applying a numerical techno-economic model of the power system. We find that high capital costs can significantly reduce the effectiveness of carbon prices: if carbon emissions are priced at USD 50 per ton and the WACC is 3%, the cost-optimal electricity mix comprises 40% renewable energy. At the same carbon price and a WACC of 15%, the cost-optimal mix comprises almost no renewable energy. At 15% WACC, there is no significant emission mitigation with carbon pricing up to USD 50 per ton, but at 3% WACC and the same carbon price, emissions are reduced by almost half. These results have implications for climate policy; carbon pricing might need to be combined with policies to reduce capital costs of low-carbon options in order to decarbonize power systems. (letter)
[en] Energy and climate policies are usually seen as measures to internalize externalities. However, as a side effect, the introduction of these policies redistributes wealth between consumers and producers, and within these groups. While redistribution is seldom the focus of the academic literature in energy economics, it plays a central role in public debates and policy decisions. This paper compares the distributional effects of two major electricity policies: support schemes for renewable energy sources, and CO2 pricing. We find that the redistribution effects of both policies are large, and they work in opposed directions. While renewables support transfers wealth from producers to consumers, carbon pricing does the opposite. More specifically, we show that moderate amounts of wind subsidies can increase consumer surplus, even if consumers bear the subsidy costs. CO2 pricing, in contrast, increases aggregated producer surplus, even without free allocation of emission allowances; however, not all types of producers benefit. These findings are derived from an analytical model of electricity markets, and a calibrated numerical model of Northwestern Europe. Our findings imply that if policy makers want to avoid large redistribution they might prefer a mix of policies, even if CO2 pricing alone is the first-best climate policy in terms of allocative efficiency. -- Graphical abstract: Display Omitted -- Highlights: •CO2 pricing and renewables support have strikingly different impacts on rents. •Carbon pricing increases producer surplus and decreases consumer surplus. •Renewable support schemes (portfolio standards, feed-in tariffs) do the opposite. •We model these impacts theoretically and quantify them for Europe. •Redistribution of wealth is found to be significant in size
[en] Energy policy often builds on insights gained from quantitative energy models and their underlying data. As climate change mitigation and economic concerns drive a sustained transformation of the energy sector, transparent and well-founded analyses are more important than ever. We assert that models and their associated data must be openly available to facilitate higher quality science, greater productivity through less duplicated effort, and a more effective science-policy boundary. There are also valid reasons why data and code are not open: ethical and security concerns, unwanted exposure, additional workload, and institutional or personal inertia. Overall, energy policy research ostensibly lags behind other fields in promoting more open and reproducible science. We take stock of the status quo and propose actionable steps forward for the energy research community to ensure that it can better engage with decision-makers and continues to deliver robust policy advice in a transparent and reproducible way. - Highlights: • Energy models and data are an important basis for energy policy. • Opening energy models and data benefits actors inside and outside of academia. • Reasons include higher quality science, greater productivity and recognition. • Private barriers must be overcome, but the private and public gains outweigh them. • We provide advice on how and why the community could coordinate a shift to openness.