Lion Hirth

The Market Value of Variable Renewables

The income that wind and solar power receive on the market is affected by the variability of their output. At times of high availability of the primary energy source, they supply electricity at zero marginal costs, shift the supply curve (merit-order curve) to the right and thereby reduce the equilibrium price of electricity during that hour. The size of this merit-order effect depends on the amount of installed renewable capacity, the slope of the merit-order curve, and the intertemporal flexibility of the electricity system. Thus the price of wind power falls with higher penetration rates, even if the average electricity price remains constant. This work quantifies the effect of variability on the market value of renewables using a calibrated model of the European electricity market. The relative price of German wind power (value factor) is estimated to fall from 110% of the average electricity price to 50% as generation increases from zero to 30% of total consumption. For solar power, the drop is even sharper. Hence competitiveness for large-scale renewables deployment will be more difficult to accomplish than often believed.

The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects Their Welfare-Optimal Deployment

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 output variability over time, forecast errors, and the location of generators in the power grid, and evaluate the impact of these three factors on the marginal value of electricity from renewables. Then we estimate the optimal share of wind and solar power in Northwestern Europe from a calibrated numerical power market model. The optimal long-term share of wind power of total electricity consumption is estimated to be 20% at cost levels of 50 €/MWh, about three times the current market share of wind; but this estimate is subject to 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.

The Optimal Share of Variable Renewables

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 Europes 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. JEL - C61, C63, Q42, Q48, D41

Control Power and Variable Renewables: A Glimpse at German Data

Control power (regulating power, balancing power) is used to quickly restore the supply-demand balance in power systems. Variable renewable energy sources (VRE) such as wind and solar power are often thought to increase the reserve requirement significantly. This paper provides a comprehensive overview of balancing systems in Europe, discusses the role of VRE, and presents empirical market data from Germany. Despite German VRE capacity doubled during the last five years and has surpassed 70% of peak load, contracted control power decreased by 20%, and procurement cost fell by 50%. Today, control power adds only 0.4% to household electricity prices. Nevertheless, we identify several sources of inefficiency in control power markets and imbalance settlement systems and propose a number of policy changes to stimulate the participation of VRE in control provision and to improve the incentives to forecast accurately.

The role of capital costs in decarbonizing the electricity sector

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.

Carpe Diem: A Novel Approach to Select Representative Days for Long-Term Power System Models with High Shares of Renewable Energy Sources

In order to explore scenarios on the future of power systems, a variety of numerical models have been developed. As the share of variable renewable energy sources, particularly wind and solar, is projected to significantly increase, accounting for their temporal and spatial variability becomes ever more important in developing sound long-term scenarios. Computational restrictions prevent many long-term power system models being developed with an hourly resolution; instead they use time slices that aggregate periods with similar load and renewable electricity generation levels. There is to date no reproducible and validated method to derive and select time slices for power system models with multiple fluctuating time series. In this paper, we present a novel and effective method that is easily applied to input data for all kinds of power system models. We utilize this procedure in the long-term power system model LIMES-EU and show that a small number of representative days developed in this way are sufficient to reflect the characteristic fluctuations of the input data. Alongside a validation of the method, we discuss the conditions under which seasonal differentiation, and the use of representative weeks instead of days, is necessary.

Why Wind is Not Coal: On the Economics of Electricity

The economics of electricity is shaped by its physics. A well know example is the non-storability of electricity that causes its price to fluctuate widely. More generally, physical constraints cause electricity to be a heterogeneous good along three dimensions - time, space, and lead-time. Consequently, different generation technologies, such as coal and wind power, produce different economic goods that have a different marginal economic value. Welfare maximization or competitiveness analyses that ignore heterogeneity deliver biased estimates. This paper provides an analytical welfare-economic framework that accounts for heterogeneity for unbiased assessments of power generators. The framework offers a rigorous interpretation of commonly used cost indicators such as ‘levelized electricity costs’ and ‘grid parity’. Heterogeneity is relevant for all generators, but especially for variable renewables such as wind and solar power. We propose a definition of ‘variability’, derive the opportunity costs of variability, and link that concept to the ‘integration cost’ literature. A literature review shows that variability can reduce the value of wind power by 20-50%. Thus it is crucial that economic analysis accounts for the physics of electricity.

Integration Costs and the Value of Wind Power

The integration of wind and solar generators into power systems cause “integration costs” for grids, balancing services, reserve capacity, reduced utilization of the capital stock, and more flexible operation of thermal plants. This paper proposes a market-based valuation framework to analyze and estimate these inte-gration costs. The framework exhaustively accounts for all costs that occur at the level of the power system. It is based on three inherent properties of wind and solar power: variability, uncertainty, and location specificity. Each property has a corresponding cost that can be estimated from numerical models or from observed market data. Surveying the literature indicates that at high penetration rates, say a wind market share of 30%, integra-tion costs can be in the same order of magnitude as wind generation costs. Many previous studies do not fully account for integration costs, underestimating the social costs of variable renewables.

Control power and variable renewables

Control power (regulating power, balancing power) is used to quickly restore the supply-demand balance in power systems. Variable renewable energy sources (VRE) such as wind and solar power are often thought to increase the reserve requirement significantly. This paper provides a comprehensive overview of balancing systems in Europe, discusses the role of VRE, and presents empirical market data from Germany. Despite German VRE capacity doubled during the last five years and has surpassed 70% of peak load, contracted control power decreased by 20%, and procurement cost fell by 50%. Today, control power adds only 0.4% to household electricity prices. Nevertheless, we identify several sources of inefficiency in control power markets and imbalance settlement systems and propose a number of policy changes to stimulate the participation of VRE in control provision and to improve the incentives to forecast accurately.

Balancing Power and Variable Renewables: A Glimpse at German Data

Balancing power (regulating power, control power) is used to quickly restore the supply-demand balance in power systems. Variable renewable energy sources (VRE) such as wind and solar power, being stochastic in nature, ceteris paribus increase the need for short-term balancing. Their impact on reserve requirements is heavily discussed in academic and policy circles and often thought to be large. The paper contrasts a literature survey and model results with descriptive statistics of empirical market data from Germany, providing surprising insights: all models predict VRE to increase balancing reserve requirements - however, despite German VRE capacity doubled during the last five years, balancing reserves decreased by 20%, and procurement cost fell by 50%. Other factors, such as increased TSO cooperation and the recession, must have overcompensated for the growth of renewables. To the extent this specific German experience can be generalized, we interpret this as an indication that balancing power is not necessarily a major barrier to VRE integration at moderate penetration rates. Next to reserve requirements, the paper discusses two additional links between renewables and balancing systems: the supply of balancing power by renewables; and the role of the imbalance price as incentive for forecast improvements. Reviewing these three links, the paper also provides a comprehensive overview of balancing systems.