TY - JOUR A1 - Hirth, Lion T1 - The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment JF - The Energy Journal N2 - This paper estimates the welfare-optimal market share of wind and solar power, explicitly taking into account their output variability. We present a theoretical valuation framework that consistently accounts for the impact of fluctuations over time, forecast errors, and the location of generators in the power grid on the marginal value of electricity from renewables. Then the optimal share of wind and solar power in Northwestern Europe's generation mix is estimated from a calibrated numerical model. We find the optimal long-term wind share to be 20%, three times more than today; however, we also find significant parameter uncertainty. Variability significantly impacts results: if winds were constant, the optimal share would be 60%. In addition, the effect of technological change, price shocks, and policies on the optimal share is assessed. We present and explain several surprising findings, including a negative impact of CO2 prices on optimal wind deployment. KW - Wind power KW - Solar power KW - Variable renewables KW - Cost-benefit analysis KW - Numerical optimization KW - Competitiveness Y1 - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22832 U6 - https://doi.org/10.5547/01956574.36.1.6 SN - 1944-9089 VL - 36 IS - 1 SP - 127 EP - 162 ER - TY - JOUR A1 - Hirth, Lion T1 - The market value of variable renewables: The effect of solar wind power variability on their relative price JF - Energy Economics N2 - 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. KW - Variable renewables KW - Wind and Solar power KW - Market integration of renewables KW - Electricity markets KW - Intermittency KW - Cost-benefit analysis Y1 - 2013 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22900 U6 - https://doi.org/10.1016/j.eneco.2013.02.004 SN - 0140-9883 N1 - This is a post-peer-review, pre-copyedit version of an article published in Energy Economics. The final authenticated version is available online at the DOI 10.1016/j.eneco.2013.02.004. VL - 38 SP - 218 EP - 236 PB - Elsevier B.V. ER - TY - JOUR A1 - Hirth, Lion A1 - Müller, Simon T1 - System-friendly wind power: How advanced wind turbine design can increase the economic value of electricity generated through wind power JF - Energy Economics N2 - Previous studies find that the economic value of electricity (USD/MWh) generated by wind power drops with increasing market share. Different measures can help mitigate the value drop, including electricity storage, flexible conventional plants, expansion of transmission, and demand response. This study assesses another option: a change in design of wind power plants. “Advanced” wind turbines that are higher and have a larger rotor compared to rated capacity (lower specific rating) generate electricity more constantly than “classical” turbines. Recent years have witnessed a significant shift towards such advanced technology. Our model-based analysis for Northwestern Europe shows that such design can substantially increase the spot market value of generated electricity. At a 30% penetration rate, the value of 1 MWh of electricity generated from a fleet of advanced turbines is estimated to be 15% higher than the value of 1 MWh from classical turbines. The additional value is large, whether compared to wind generation costs, to the value drop, or to the effect of alternative measures such as electricity storage. Extensive sensitivity tests indicate that this finding is remarkably robust. The increase in bulk power value is not the only advantage of advanced turbines: additional benefits might accrue from reduced costs for power grids and balancing services. To fully realize this potential, power markets and support policies need to be appropriately designed and signal scarcity investors. KW - Wind power KW - Variable renewables KW - Market value KW - Power market modeling Y1 - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22819 U6 - https://doi.org/10.1016/j.eneco.2016.02.016 SN - 0140-9883 N1 - This is a post-peer-review, pre-copyedit version of an article published in Energy Economics. The final authenticated version is available online at: DOI 10.1016/j.eneco.2016.02.016 VL - 56 SP - 51 EP - 63 PB - Elsevier B.V. ER - TY - JOUR A1 - Hirth, Lion A1 - Ueckerdt, Falko A1 - Edenhofer, Ottmar T1 - Why Wind is not Coal: On the Economics of Electricity Generation JF - The Energy Journal N2 - Electricity is a paradoxical economic good: it is highly homogeneous and heterogeneous at the same time. Electricity prices vary dramatically between moments in time, between location, and according to lead-time between contract and delivery. This three-dimensional heterogeneity has implication for the economic assessment of power generation technologies: different technologies, such as coal-fired plants and wind turbines, produce electricity that has, on average, a different economic value. Several tools that are used to evaluate generators in practice ignore these value differences, including "levelized electricity costs", "grid parity", and simple macroeconomic models. This paper provides a rigorous and general discussion of heterogeneity and its implications for the economic assessment of electricity generating technologies. It shows that these tools are biased, specifically, they tend to favor wind and solar power over dispatchable generators where these renewable generators have a high market share. A literature review shows that, at a wind market share of 30-40%, the value of a megawatt-hour of electricity from a wind turbine can be 20-50% lower than the value of one megawatt-hour as demanded by consumers. We introduce "System LCOE" as one way of comparing generation technologies economically. KW - Power generation KW - Electricity sector KW - Integrated assessment modeling KW - Wind power KW - Solar power KW - Variable renewables KW - Integration costs KW - Welfare economics KW - Power economics KW - Levelized electricity cost KW - LCOE KW - Grid parity Y1 - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22828 U6 - https://doi.org/10.5547/01956574.37.3.lhir SN - 1944-9089 VL - 37 IS - 3 SP - 1 EP - 27 ER - TY - JOUR A1 - Hirth, Lion A1 - Ziegenhagen, Inka T1 - Balancing power and variable renewables: Three links JF - Renewable and Sustainable Energy Reviews N2 - Balancing power is used to quickly restore the supply-demand balance in power systems. The need for this tends to be increased by the use of variable renewable energy sources (VRE) such as wind and solar power. This paper reviews three channels through which VRE and balancing systems interact: the impact of VRE forecast errors on balancing reserve requirements; the supply of balancing services by VRE generators; and the incentives to improve forecasting provided by imbalance charges. The paper reviews the literature, provides stylized facts from German market data, and suggests policy options. Surprisingly, while German wind and solar capacity has tripled since 2008, balancing reserves have been reduced by 15%, and costs by 50%. KW - Balancing power KW - Control power KW - Regulating power KW - Variable renewables KW - Wind power KW - Solar power KW - Market design Y1 - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22856 U6 - https://doi.org/10.1016/j.rser.2015.04.180 SN - 1364-0321 N1 - This is a post-peer-review, pre-copyedit version of an article published in Renewable and Sustainable Energy Reviews. The final authenticated version is available online at: DOI 10.1016/j.rser.2015.04.180 VL - 50 SP - 1035 EP - 1051 PB - Elsevier Ltd ER - TY - JOUR A1 - Hirth, Lion A1 - Ueckerdt, Falko A1 - Edenhofer, Ottmar T1 - Integration Costs Revisited – An economic framework for wind and solar variability JF - Renewable Energy N2 - The integration of wind and solar generators into power systems causes “integration costs” – for grids, balancing services, more flexible operation of thermal plants, and reduced utilization of the capital stock embodied in infrastructure, among other things. This paper proposes a framework to analyze and quantify these costs. We propose a definition of integration costs based on the marginal economic value of electricity, or market value – as such a definition can be more easily used in economic cost-benefit assessment than previous approaches. We suggest decomposing integration costs intro three components, according to the principal characteristics of wind and solar power: temporal variability, uncertainty, and location-constraints. Quantitative estimates of these components are extracted from a review of 100 + published studies. At high penetration rates, say a wind market share of 30–40%, integration costs are found to be 25–35 €/MWh, i.e. up to 50% of generation costs. While these estimates are system-specific and subject to significant uncertainty, integration costs are certainly too large to be ignored in high-penetration assessments (but might be ignored at low penetration). The largest single factor is reduced utilization of capital embodied in thermal plants, a cost component that has not been accounted for in most previous integration studies. KW - Wind power KW - Solar power KW - Integration cost KW - Variable renewables Y1 - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22878 U6 - https://doi.org/10.1016/j.renene.2014.08.065 SN - 0960-1481 VL - 74 SP - 925 EP - 939 PB - Elsevier Ltd ER - TY - JOUR A1 - Edenhofer, Ottmar A1 - Hirth, Lion A1 - Knopf, Brigitte A1 - Pahle, Michael A1 - Schlömer, Steffen A1 - Schmid, Eva A1 - Ueckerdt, Falko T1 - On the Economics of Renewable Energy Sources JF - Energy Economics N2 - With the global expansion of renewable energy (RE) technologies, the provision of optimal RE policy packages becomes an important task. We review pivotal aspects regarding the economics of renewables that are relevant to the design of an optimal RE policy, many of which are to date unresolved. We do so from three interrelated perspectives that a meaningful public policy framework for inquiry must take into account. First, we explore different social objectives justifying the deployment of RE technologies, including potential co-benefits of RE deployment, and review modelbased estimates of the economic potential of RE technologies, i.e. their socially optimal deployment level. Second, we address pivotal market failures that arise in the course of implementing the economic potential of RE sources in decentralized markets. Third, we discuss multiple policy instruments curing these market failures. Our framework reveals the requirements for an assessment of the relevant options for real-world decision makers in the field of RE policies. This review makes it clear that there are remaining white areas on the knowledge map concerning consistent and socially optimal RE policies. KW - Energy KW - Mitigation KW - Integrated assessment modeling KW - Variable renewables KW - Electricity market design KW - Renewable policy Y1 - 2013 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b1570-opus4-22965 U6 - https://doi.org/10.1016/j.eneco.2013.09.015 SN - 0140-9883 N1 - This is a post-peer-review, pre-copyedit version of an article published in Energy Economics. The final authenticated version is available online at: DOI 10.1016/j.eneco.2013.09.015 VL - 40 IS - S1 SP - S12 EP - S23 ER -