arXiv subjects

Wolf-Peter Schill

Publications and source records attributed to Wolf-Peter Schill.

At least 19 recordsLinked to original sources

arXiv2026

Long-duration electricity storage needs for coping with Dunkelflaute events in Europe

Coping with prolonged periods of low availability of wind and solar power, also referred to as variable renewable energy droughts or "Dunkelflaute", emerges as a key challenge for realizing decarbonized energy systems based on renewable energy. Here we investigate the role of long-duration electricity storage and geographical balancing through transmission in dealing with such events in Europe, combining a time series analysis of renewable availability with power sector modeling of 35 historical weather years. We find that extreme droughts define long-duration storage operation and investment. Assuming policy-relevant interconnection, the least-cost system in our model capable of coping with the most extreme event requires 351 terawatt hours long-duration storage capacity, corresponding to 7% of yearly European electricity demand. While nuclear power can partially reduce storage needs, the storage-mitigating effect of fossil backup plants in combination with carbon removal is limited. Policymakers and system planners should prepare for a rapid expansion of long-duration storage to safeguard the renewable energy transition in Europe.

physics.soc-ph↗

arXiv2026

Multi-threshold time series analysis enables characterization of variable renewable energy droughts in Europe

Variable renewable energy droughts, so called Dunkelflaute events, emerge as a challenge for climate-neutral energy systems based on variable renewables. Here we characterize European drought events for on- and offshore wind power, solar photovoltaics, and renewable technology portfolios, using 38 historic weather years and an advanced identification method. Their characteristics heavily depend on the chosen drought threshold, questioning the usefulness of single-threshold analyses. Applying a multi-threshold framework, we quantify how the complementarity of wind and solar power temporally and spatially alleviates drought frequency, return periods, duration, and severity within (portfolio effect) and across countries (balancing effect). We identify the most extreme droughts, which drive major discharging periods of long-duration storage in a fully renewable European energy system, based on a policy-relevant decarbonization scenario. Such events comprise sequences of shorter droughts of varying severity. The most extreme event occurred in winter 1996/97 and lasted 55 days in an idealized, perfectly interconnected setting. The average renewable availability during this period was still 47% of its long-run mean. System planners must consider such events when planning for storage and other flexibility technologies. Methodologically, we conclude that using arbitrary single calendar years is not suitable for modeling weather-resilient energy scenarios.

eess.SY↗

arXiv2025

A moderate share of V2G outperforms large-scale smart charging of electric vehicles and benefits other consumers

While battery electric vehicles (BEVs) play a key role for decarbonizing the transport sector, their impact on the power sector heavily depends on their charging strategies. Here we systematically analyze various combinations between inflexible, smart and bidirectional (or vehicle-to-grid, V2G) charging of 15 million electric cars in Germany. Using a capacity expansion model, we find that even a moderate share of bidirectional charging below 30% leads to lower system costs than a fully smartly charging BEV fleet. At a V2G share of 50%, costs are even lower than in a system without any BEVs. This means that the flexibility effect of half of the BEV fleet charging bidirectionally outweighs the demand effect of the whole BEV fleet. We show how costs savings are driven by the ability of V2G to serve demand, especially during hours with high residual load. We also explore the distributional effects of respective electricity price changes. While V2G car owners internalize a substantial share of overall cost savings, the benefits increasingly spill over to other electricity consumers as the share of bidirectional charging grows. We conclude that policymakers should focus on enabling a moderate fleet share of V2G rather than on enabling every car to charge smartly.

econ.GN↗

arXiv2025

An advanced reliability reserve incentivizes flexibility investments while safeguarding the electricity market

To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases the need for power sector flexibility, for which there are promising yet often under-utilized options on the demand side. In this paper, we analyze how a centralized capacity market and an advanced reliability reserve with a moderately high activation price affect investments in demand-side flexibility technologies. We do so for a German case study of 2030, using an open-source capacity expansion model and incorporating detailed demand-side flexibility potentials across industry, process heat, and district heating. We show that a centralized capacity market effectively caps peak prices in the wholesale electricity market and thus reduces incentives for investments in demand-side flexibility options. The advanced reliability reserve induces substantially higher flexibility investments while leading to similar overall electricity supply costs and ensuring a similar level of security of supply. The advanced reliability reserve could thus create a learning environment for flexibility technologies to support the transition to climate neutral energy systems. Additionally, an advanced reliability reserve could be introduced faster and is more flexible than a centralized capacity market. While concrete design parameters are yet to be specified, we argue that policymakers should consider the reliability reserve concept in upcoming decision on capacity mechanisms in Germany and beyond.

econ.GN↗

arXiv2025

A mix of long-duration hydrogen and thermal storage enables large-scale electrified heating in a renewable European energy system

Hydrogen-based long-duration electricity storage (LDES) is a key component of renewable energy systems to deal with seasonality and prolonged periods of low wind and solar energy availability. In this paper, we investigate how electrified heating with heat pumps impacts LDES requirements in a fully renewable European energy system, and which role thermal storage can play. Using a large weather dataset of 78 weather years, we find that electrified heating significantly increases LDES needs, as optimal average energy capacities more than quadruple across all weather years compared to a scenario without electrified heating. We attribute 75% of this increase to a leverage effect, as additional electric load amplifies storage needs during times of low renewable availability. The remaining 25% are the result of a compound effect, where exceptional cold spells coincide with periods of renewable scarcity. Furthermore, heat pumps increase the variance in optimal storage capacities between weather years substantially because of demand-side weather variability. Long-duration thermal storage attached to district heating networks can reduce LDES needs by on average 36%. To support and safeguard wide-spread heating electrification, policymakers should expedite the creation of adequate regulatory frameworks for both long-duration storage types to de-risk investments in light of high weather variability.

physics.soc-ph↗

arXiv2025

Effects of system-blind prosumers in energy models

Prosumer households that generate and store electricity from rooftop PV installations play an increasing role in electricity markets around the world. As retail tariffs usually do not convey time-varying wholesale price signals to households and the rollout of smart meters is low in many countries, prosumers do not necessarily self-consume and feed-in solar electricity in a system-friendly way. The effects of such system-blind behaviours are typically neglected in energy system models, which rarely account for prosumers. In this paper, we embed a calibrated self-generation constraint into a linear capacity expansion model to approximate the incentives of prosumers to minimise their electricity bills. We apply our method to a German case study for 2030 featuring sector coupling with battery electric vehicles. We show that parametrising the self-generation constraint such that the prosumer electricity bill is as low as possible approximates prosumer decisions well for a broad range of tariff schemes. Based on this, we quantify distortions that might arise in energy models that do not account for prosumers. For our case study, we find that the optimal battery storage capacity increases by up to 200% if prosumer constraints are included. The main driver is the imperfect substitutability between home and utility-scale batteries. We conclude that energy system models could benefit from implementing this straightforward method.

econ.GN↗

arXiv2025

Solar prosumage under different pricing regimes: Interactions with the transmission grid

Solar prosumers, residential electricity consumers equipped with photovoltaic (PV) systems and battery storage, are transforming electricity markets. Their interactions with the transmission grid under varying tariff designs are not yet fully understood. We explore the influence of different pricing regimes on prosumer investment and dispatch decisions and their subsequent impact on the transmission grid. Using an integrated modeling approach that combines two open-source dispatch, investment and grid models, we simulate prosumage behavior in Germany's electricity market under real-time pricing or time-invariant pricing, as well as under zonal or nodal pricing. Our findings show that zonal pricing favors prosumer investments, while time-invariant pricing rather hinders it. In comparison, regional solar availability emerges as a larger driver for rooftop PV investments. The impact of prosumer strategies on grid congestion remains limited within the scope of our model-setup, in which home batteries cannot be used for energy arbitrage.

econ.GN↗

arXiv2024

Power sector benefits of flexible heat pumps

Heat pumps play a major role in decreasing fossil fuel use in heating. They increase electricity demand, but could also foster the system integration of variable renewable energy sources. We analyze three scenarios for expanding decentralized heat pumps in Germany by 2030, focusing on the role of buffer heat storage. Using an open-source power sector model, we assess costs, capacity investments, and emissions effects. We find that investments in solar photovoltaics can cost-effectively accompany the roll-out of heat pumps in case wind power expansion potentials are limited. Results further show that short-duration heat storage substantially reduces the need for firm capacity and battery storage. Larger heat storage sizes do not substantially change the results. Increasing the number of heat pumps from 1.7 to 10 million units could annually save around a quarter of Germany's overall natural gas consumption and around half of households' building-related CO2 emissions.

econ.GN↗

arXiv2024

Impacts of electric carsharing on a power sector with variable renewables

Electrifying the car fleet is a major strategy for mitigating emissions in the transport sector. As electrification cannot solve all negative externalities associated with cars, reducing the size of the car fleet would be beneficial. Electric carsharing could allow to reconcile current car usage habits with a smaller fleet, but this may reduce the potential of electric cars to align their grid interactions with variable renewable electricity generation. We investigate how electric carsharing may impact the power sector, combining three methods: sequence clustering of car travel diaries, generation of synthetic electric vehicle time series, and power sector modelling. We show that switching to electric carsharing only moderately increases power sector costs, less than 110 euros per substituted car in our main setting. This effect is largest with bidirectional charging. We conclude that the power sector interactions of shared electric car fleets could still be aligned with variable renewable electricity generation.

econ.GN↗

arXiv2024

Measuring the Dunkelflaute: How (not) to analyze variable renewable energy shortage

As variable renewable energy sources increasingly gain importance in global energy systems, there is a growing interest in understanding periods of variable renewable energy shortage ("Dunkelflauten"). Defining, quantifying, and comparing such shortage events across different renewable generation technologies and locations presents a surprisingly intricate challenge. Various methodological approaches exist in different bodies of literature, which have been applied to single technologies in specific locations or technology portfolios across multiple regions. We provide an overview of various methods for quantifying variable renewable energy shortage, focusing either on supply from variable renewables or its mismatch with electricity demand. We explain and critically discuss the merits and challenges of different approaches for defining and identifying shortage events and propose further methodological improvements for more accurate shortage determination. Additionally, we elaborate on comparability requirements for multi-technological and multi-regional energy shortage analysis. In doing so, we aim to contribute to unifying disparate methodologies, harmonizing terminologies, and providing guidance for future research.

econ.GN↗

arXiv2024

Power sector effects of alternative options for de-fossilizing heavy-duty vehicles -- go electric, and charge smartly

Various options are discussed to de-fossilize heavy-duty vehicles (HDV), including battery-electric vehicles (BEV), electric road systems (ERS), and indirect electrification via hydrogen fuel cells or e-fuels. We investigate their power sector implications in future scenarios of Germany with high renewable energy shares, using an open-source capacity expansion model and route-based truck traffic data. Power sector costs are lowest for flexibly charged BEV that also carry out vehicle-to-grid operations, and highest for e-fuels. If BEV and ERS-BEV are not optimally charged, power sector costs increase, but are still substantially lower than in scenarios with hydrogen or e-fuels. This is because indirect electrification is less energy efficient, which outweighs potential flexibility benefits. BEV and ERS-BEV favor solar photovoltaic energy, while hydrogen and e-fuels favor wind power and increase fossil electricity generation. Results remain qualitatively robust in sensitivity analyses.

econ.GN↗

arXiv2023

Power sector effects of green hydrogen production in Germany

The use of green hydrogen can support the decarbonization of sectors which are difficult to electrify, such as industry or heavy transport. Yet, the wider power sector effects of providing green hydrogen are not well understood so far. We use an open-source electricity sector model to investigate potential power sector interactions of three alternative supply chains for green hydrogen in Germany in the year 2030. We distinguish between model settings in which Germany is modeled as an electric island versus embedded in an interconnected system with its neighboring countries, as well as settings with and without technology-specific capacity bounds on wind energy. The findings suggest that large-scale hydrogen storage can provide valuable flexibility to the power system in settings with high renewable energy shares. These benefits are more pronounced in the absence of flexibility from geographical balancing. We further find that the effects of green hydrogen production on the optimal generation portfolio strongly depend on the model assumptions regarding capacity expansion potentials. We also identify a potential distributional effect of green hydrogen production at the expense of other electricity consumers, of which policy makers should be aware.

physics.soc-ph↗

arXiv2023

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

To reduce greenhouse gas emissions, many countries plan to massively expand wind power and solar photovoltaic capacities. These variable renewable energy sources require additional flexibility in the power sector. Both geographical balancing enabled by interconnection and electricity storage can provide such flexibility. In a 100% renewable energy scenario of twelve central European countries, we investigate how geographical balancing between countries reduces the need for electricity storage. Our principal contribution is to separate and quantify the different factors at play. Applying a capacity expansion model and a factorization method, we disentangle the effect of interconnection on optimal storage capacities through distinct factors: differences in countries' solar PV and wind power availability patterns, load profiles, as well as hydropower and bioenergy capacity portfolios. Results show that interconnection reduces storage needs by around 30% in contrast to a scenario without interconnection. Differences in wind power profiles between countries explain around 80% of that effect.

econ.GN↗

arXiv2022

Bidirectional coupling of a long-term integrated assessment model REMIND v3.0.0 with an hourly power sector model DIETER v1.0.2

Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the spatio-temporal detail required to properly analyze the key role of variable renewable electricity (VRE) for decarbonizing the power sector and end-use electrification. In contrast, power sector models (PSMs) incorporate high spatio-temporal resolutions, but tend to have narrower scopes and shorter time horizons. To overcome these limitations, we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a IAM and a PSM. This framework uses the market values of power generation as well as the capture prices of demand in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REMIND and the PSM DIETER, and confirm the theoretical prediction of almost-full convergence both in terms of decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is <5% for a simple configuration (no storage, no flexible demand), and 6-7% for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. Since our approach is based on fundamental economic principles, it is applicable also to other IAM-PSM pairs.

econ.GN↗

arXiv2021

Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling

To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as 'unintended storage cycling', which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.

econ.GN↗

arXiv2021

Comparison of power sector models by analyzing the impact of modeling features on optimal capacity expansion

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. In a model comparison, we examine the optimal expansion of such technologies with six capacity expansion power system models. The technologies under investigation include base- and peak-load power plants, electricity storage, and transmission. We define four highly simplified and harmonized use cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all use cases. Fixed energy-to-power-ratios of storage can entirely change model optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and end storage levels can impact the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure significantly deviate from linear optimization models, as limited foresight and flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

eess.SY↗

arXiv2021

Optimal supply chains and power sector benefits of green hydrogen

Green hydrogen can help to decarbonize parts of the transportation sector, but its power sector interactions are not well understood. It may contribute to integrating variable renewable energy sources if production is sufficiently flexible in time. Using an open-source co-optimization model of the power sector and four options for supplying hydrogen at German filling stations, we find a trade-off between energy efficiency and temporal flexibility: for lower shares of renewables and hydrogen, more energy-efficient and less flexible small-scale on-site electrolysis is optimal. For higher shares of renewables and/or hydrogen, more flexible but less energy-efficient large-scale hydrogen supply chains gain importance as they allow disentangling hydrogen production from demand via storage. Liquid hydrogen emerges as particularly beneficial, followed by liquid organic hydrogen carriers and gaseous hydrogen. Large-scale hydrogen supply chains can deliver substantial power sector benefits, mainly through reduced renewable surplus generation. Energy modelers and system planners should consider the distinct flexibility characteristics of hydrogen supply chains in more detail when assessing the role of green hydrogen in future energy transition scenarios.

physics.soc-ph↗

arXiv2021

DIETERpy: a Python framework for The Dispatch and Investment Evaluation Tool with Endogenous Renewables

DIETER is an open-source power sector model designed to analyze future settings with very high shares of variable renewable energy sources. It minimizes overall system costs, including fixed and variable costs of various generation, flexibility and sector coupling options. Here we introduce DIETERpy that builds on the existing model version, written in the General Algebraic Modeling System (GAMS), and enhances it with a Python framework. This combines the flexibility of Python regarding pre- and post-processing of data with a straightforward algebraic formulation in GAMS and the use of efficient solvers. DIETERpy also offers a browser-based graphical user interface. The new framework is designed to be easily accessible as it enables users to run the model, alter its configuration, and define numerous scenarios without a deeper knowledge of GAMS. Code, data, and manuals are available in public repositories under permissive licenses for transparency and reproducibility.

cs.CY↗