arXiv subjects

Martin Kittel

Publications and source records attributed to Martin Kittel.

9 recordsLinked to original sources

arXiv2024

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↗

arXiv2024

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↗

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↗

arXiv2023

Overcoming the disconnect between energy system and climate modeling

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making.

physics.app-ph↗

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↗

arXiv2022

Reducing energy system model distortions from unintended storage cycling through variable costs

Energy model distortions may unknowingly appear and represent unrealistic and non-physical effects that can mislead optimal model decisions. A prominent misleading effect is unintended storage cycling that was observed in previous literature by simultaneous charging and discharging of the same storage in 12 of 18 energy models. Especially for popular net-zero energy model scenarios, unintended storage cycling can cause high distortions and, hence, urges for its removal. Methods to remove such misleading effects exist, but are not computational efficient and sometimes not effective such as MILP formulations. Other techniques are successful, but only if renewable energy target constraints exist. This paper explores how to eradicate unintended storage cycling for models without renewable target constraints by correctly setting variable costs of relevant system components. We find through 124 simulations that determining appropriate levels of variable costs depends on the solver accuracy used for the optimization. If set too loose, the solver prevents the removal of unintended storage cycling. We further find that reliable data for variable costs in energy modelling needs to be improved and provide a list of recommended model inputs as well as a minimum variable cost threshold that can significantly reduce the magnitude and likeliness of unintended storage cycling. Finally, our results suggest that variable cost additives may remove other known unintended energy cycling effects, such as unintended line cycling or sector cycling.

physics.soc-ph↗

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

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

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↗