arXiv subjects

Leonard Göke

Publications and source records attributed to Leonard Göke.

17 recordsLinked to original sources

arXiv2026

Double interior-point regularization for large-scale capacity expansion

Capacity expansion is a key tool for planning future energy systems. However, weather-dependent generation and long-duration storage result in problem sizes that exceed the computational limits of conventional interior-point solvers, making it impossible to plan renewable systems that are cost-efficient and reliable across a wide range of weather conditions. To tackle such large problems, this paper introduces the double interior-point regularization (DIP-set) for Benders Decomposition, combining the advantages of traversing the interior of the solution space while remaining close to a reference solution. We benchmark the method on a power-sector problem and an energy-system problem, varying problem size and the level of foresight during operations. Results demonstrate that DIP-set outperforms competing regularizations in all test cases. The speed-up increases with size, reaching 30-50% for the largest problems, which are the most critical for planning renewable systems and are too large for state-of-the-art methods. The key benefit of DIP-set is its ability to mitigate the sharp decrease in convergence as BD approaches the optimal solution.

eess.SY↗

arXiv2026

Surrogate-based prioritization of sub-problems for Benders decomposition in energy planning

Benders decomposition solves optimization problems by separating the first-stage master problem from one or more second-stage sub-problems. While the standard Benders decomposition solves all sub-problems in each iteration, solving only selected sub-problems still guarantees convergence and can reduce solution time, but raises the question of how to select. In this work, we introduce surrogate-based prioritization of sub-problems. The method leverages surrogates to estimate the sub-problems' objectives, assess the current error of the cutting-plane estimator, and then prioritize the sub-problem with the largest error. We implement surrogate-based prioritization within sequential and asynchronous Benders decomposition. Both these algorithms also leverage the surrogate to trigger convergence checks and implement regularization. Benchmarks for an energy planning problem with a few large sub-problems show that the applied prioritization strategy works. The reduction in solution time correlates with the surrogate's accuracy. In our case, geometric interpolation-based surrogates are more accurate than machine learning methods. As a result, prioritization consistently and significantly outperforms the standard algorithm in sequential Benders decomposition. The speed-up increases with the number of scenarios, reaching 33\% with four scenarios and 55% with ten scenarios. In the case of asynchronous parallelization, the impact on performance is less clear, and the average speed-up from prioritization is 19%.

eess.SY↗

arXiv2025

Can They Compete? Cost Competitiveness of Non-Light-Water Reactors for Heat and Power Supply in a Decarbonized European Energy System

Recent pledges to triple global nuclear capacity by 2050 suggest a "nuclear renaissance," bolstered by unconventional reactor concepts such as sodium-cooled fast reactors, high-temperature reactors, and molten salt reactors. These technologies claim to address the challenges of today's high-capacity light-water reactors, i.e., cost overruns, delays, and social acceptance, while also offering additional non-electrical applications. However, this analysis reveals that none of these concepts currently meet the prerequisites of affordability, competitiveness, or commercial availability. Our cost analysis reveals optimistic FOAK cost assumptions of 5,623 to 9,511 USD per kW, and NOAK cost projections as low as 1,476 USD per kW. At FOAK cost, the applied energy system model for Europe in 2040 includes no nuclear power capacity, and thus indicates that significant cost reductions would be required for these technologies to contribute to energy system decarbonization. In lower-cost scenarios, reactors capable of producing high and medium temperature heat compete with other technologies and dominate the system once costs fall below 5,000 USD per kW. Electricity shares reach current levels of approx. 20% once costs are reduced to 3,000 USD per kW or less We conclude that, for reactor capacities to increase significantly, a focus on certain technology lines and streamlined regulation in necessary. Further remaining technological challenges, e.g., new waste streams, must be resolved.

econ.GN↗

arXiv2025

Climate change impacts on net load under technological uncertainty in European power systems

Renewable energy sources play a major role in future net-zero energy systems. However, achieving energy system resilience remains challenging, since renewables depend on weather fluctuations, and future energy systems are subject to major design uncertainty. Existing literature mostly treats these types of uncertainty separately. Therefore, the assessment of uncertainties surrounding climate change and energy system design, and particularly their interactions, is insufficiently understood. To close this gap, we evaluate net load to assess energy system stress without relying on perfect foresight, while maintaining temporal and spatial correlations of the climate system. Net load is calculated from hourly historical and future climate model data translated to energy variables. To scope the extent of plausible energy systems, we consider eight different design scenarios inspired by the European Ten-Year Network Development Plan (TYNDP) and different levels of transmission expansion. We find that climate change impacts on net load are highly sensitive to the energy system design, implying that energy systems can be designed so that they are either hindered or helped by climate change. Furthermore, within a system scenario, climate change can change the frequency and seasonality of high net load events and their technological and meteorological composition. Wind-dominated systems with currently electrified heating levels, for instance, feature a 30% increase of high net load events under climate change, mostly in summer and fall, while fully electrified net zero systems are impacted by high net load events in winter and spring, which decrease by 50% with climate change. Our work thus calls for a wider perspective on energy-climate stress that captures the non-linear interactions of climate change and system design uncertainty, thereby overcoming the current focus on cold Dunkelflauten.

physics.soc-ph↗

arXiv2025

The Liquid Buffer: Multi-Year Storage for Defossilization and Energy Security under Climate Uncertainty

The climate-driven uncertainty of renewable generation and electricity demand challenges energy security in net-zero energy systems. By introducing a scalable stochastic model that implicitly accounts for 51'840 climate years, this paper identifies multi-year storage of liquid hydrocarbons as a key option for managing climate uncertainty and ensuring energy security. In Europe, multi-year storage reduces system costs by 4.1%, fossil imports by 86%, and curtailment by 60%. The benefit of multi-year storage is that a renewable surplus in one year is not curtailed but converted to synthetic oil, with hydrogen as an intermediate product, and stored to balance a future deficit. We find that the required energy capacity for liquid hydrocarbons is 525 TWh, a quarter of the European Union's current oil and gas reserves, complemented by 116 TWh for hydrogen storage. Security of supply remains high and unserved energy only amounts to 0.0035 per thousand, well below the common target of 0.02 per thousand.

eess.SY↗

arXiv2025

Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition

As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field.

math.OC↗

arXiv2025

Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events

Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.

cs.CY↗

arXiv2024

Flexible nuclear power and fluctuating renewables? -- A techno-economic analysis for decarbonized energy systems

Many governments are considering constructing new nuclear power plants to support the decarbonization of the energy system. On the one hand, dispatchable nuclear plants can complement fluctuating generation from wind and PV. On the other hand, escalating construction costs and times raise economic concerns. In this paper, we extensively review construction costs and times. On this basis, we apply a detailed multi-vector energy model to analyze the cost-efficient share of nuclear power in fully decarbonized energy systems, i.e., energy systems that do not utilize any fossil fuels. Our analysis finds that even if, reversing the historical trend, overnight construction costs of nuclear half to 4,000 US-$2018 per kW and construction times remain below ten years, the cost-efficient share of nuclear power in European electricity generation is only around 10%. The analysis still omits the social costs of nuclear power, such as the risk of accidents or waste management. Nuclear plants must operate inflexibly and at capacity factors close to 90% to recover their investment costs, implying that operational flexibility-even if technically possible-is not economically viable. As a result, grid infrastructure, flexible demand in multi-energy systems, and storage are more efficient options for integrating fluctuating wind and photovoltaic generation.

eess.SY↗

arXiv2023

Stabilized Benders decomposition for energy planning under climate uncertainty

This paper applies Benders decomposition to two-stage stochastic problems for energy planning under climate uncertainty, a key problem for the design of renewable energy systems. To improve performance, we adapt various refinements for Benders decomposition to the problem's characteristics -- a simple continuous master-problem, and few but large sub-problems. The primary focus is stabilization, specifically comparing established bundle methods to a quadratic trust-region approach for continuous problems. An extensive computational comparison shows that all stabilization methods can significantly reduce computation time. However, the quadratic trust-region and the non-quadratic box-step method are the most robust and straightforward to implement. When parallelized, the introduced algorithm outperforms the vanilla version of Benders decomposition by a factor of 100. In contrast to off-the-shelf solvers, computation time remains constant when the number of scenarios increases. In conclusion, the algorithm enables robust planning of renewable energy systems with a large number of climatic years. Beyond climate uncertainty, it can make an extensive range of other analyses in energy planning computationally tractable, for instance, endogenous learning and modeling to generate alternatives.

math.OC↗

arXiv2023

How flexible electrification can integrate fluctuating renewables

To phase-out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak-load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak-load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid.

eess.SY↗

arXiv2022

A collective blueprint, not a crystal ball: How expectations and participation shape long-term energy scenarios

The development of energy systems is not a technocratic process but equally shaped by societal and cultural forces. Key instruments in this process are model-based scenarios describing a future energy system. Applying the concept of fictional expectations from social economics, we show how energy scenarios are tools to channel political, economic, and academic efforts into a common direction. To impact decision-making, scenarios do not have to be accurate -- but credible and evoke coherent expectations in diverse stakeholders. To gain credibility, authors of scenarios engage with stakeholders and appeal to the authority of institutions or quantitative methods. From these insights on energy scenarios, we draw consequences for developing and applying planning models, the quantitative tool energy scenarios build on. Planning models should be open and accessible to facilitate stakeholder participation, avoid needlessly complex methods to minimize expert bias and aim for a large scope to be policy relevant. Rather than trying to simulate social preferences and convictions within engineering models, scenario development should pursue broad and active participation of all stakeholders, including citizens.

econ.TH↗

arXiv2022

Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context: A model-based analysis of generation, network, and storage investments

In this paper, we explore centralized and more decentral approaches to succeed the energiewende in Germany, in the European context. We use the AnyMOD framework to model a future renewable-based European energy system, based on a techno-economic optimization, i.e. cost minimization with given demand, including both investment and the subsequent dispatch of capacity. The model includes 29 regions for European countries, and 38 NUTS-2 regions in Germany. First the entire energy system on the European level is optimized. Based on these results, the electricity system for the German regions is optimized to achieve great regional detail to analyse spatial effects. The model allows a comparison between a stylized central scenario with high amounts of wind offshore deployed, and a decentral scenario using mainly the existing grid, and thus relying more on local capacities. The results reveal that the cost for the second optimization of these two scenarios are about the same: The central scenario is characterized by network expansion in order to transport the electricity from the wind offshore sites, whereas the decentral scenario leads to more photovoltaic and battery deployment closer to the areas with a high demand for energy. A scenarios with higher energy efficiency and lower demand projections lead to a significant reduction of investment requirements, and to different localizations thereof.

econ.GN↗

arXiv2021

The Potential of Sufficiency Measures to Achieve a Fully Renewable Energy System -- A case study for Germany

The paper provides energy system-wide estimates of the effects sufficiency measures in different sectors can have on energy supply and system costs. In distinction to energy efficiency, we define sufficiency as behavioral changes to reduce useful energy without significantly reducing utility, for example by adjusting thermostats. By reducing demand, sufficiency measures are a potentially decisive but seldomly considered factor to support the transformation towards a decarbonized energy system. Therefore, this paper addresses the following question: What is the potential of sufficiency measures and what is their impacts on the supply side of a 100% renewable energy system? For this purpose, an extensive literature review is conducted to obtain estimates for the effects of different sufficiency measures on final energy demand in Germany. Afterwards, the impact of these measures on the supply side and system costs is quantified using a bottom-up planning model of a renewable energy system. Results indicate that final energy could be reduced by up to 20.5% and as a result cost reduction between 11.3% to 25.6% are conceivable. The greatest potential for sufficiency measures was identified in the heating sector.

econ.GN↗

arXiv2021

Adequacy of time-series reduction for renewable energy systems

To reduce computational complexity, macro-energy system models commonly implement reduced time-series data. For renewable energy systems dependent on seasonal storage and characterized by intermittent renewables, like wind and solar, adequacy of time-series reduction is in question. Using a capacity expansion model, we evaluate different methods for creating and implementing reduced time-series regarding loss of load and system costs. Results show that adequacy greatly depends on the length of the reduced time-series and how it is implemented into the model. Implementation as a chronological sequence with re-scaled time-steps prevents loss of load best but imposes a positive bias on seasonal storage resulting in an overestimation of system costs. Compared to chronological sequences, grouped periods require more time so solve for the same number of time-steps, because the approach requires additional variables and constraints. Overall, results suggest further efforts to improve time-series reduction and other methods for reducing computational complexity.

econ.GN↗

arXiv2021

A graph-based formulation for modeling macro-energy systems

Averting the impending harms of climate change requires to replace fossil fuels with renewables as a primary source of energy. Non-electric renewable potential being limited, this implies extending the use of electricity generated from wind and solar beyond the power sector, either by direct electrification or synthetic fuels. Modeling the transformation towards such an energy system is challenging, because it imposes to consider fluctuations of wind and solar and the manifold ways the demand side could adjust to these fluctuations. This paper introduces a graph-based method to formulate energy system models to address these challenges. By organizing sets in rooted trees, two features to facilitate modeling high shares of renewables and sector integration are enabled. First, the method allows the level of temporal and spatial detail to be varied by energy carrier. This enables modeling with a high level of detail and a large scope, while keeping models computationally tractable. Second, the degree to which energy carriers are substitutable when converted, stored, transported, or consumed can be modeled to achieve a detailed but flexible representation of sector integration. An application of the formulation demonstrates that the variation of temporal detail achieves an average reduction in computation time of 70\%.

physics.soc-ph↗

arXiv2021

Accounting for spatiality of renewables and storage in transmission planning

The current governance process to plan the German energy system omits two options to substitute grid expansion: First, placing renewables closer to demand instead of where site conditions are best. Second, utilizing storage instead of additional transmission infrastructure to prevent grid congestion. In the paper, we apply a comprehensive capacity expansion model based on the AnyMOD modelling framework to compare the status quo to alternative planning approaches for a fully renewable energy system. To represent spatiality and fluctuations of renewables, the German electricity sector is modelled with great spatio-temporal detail of 32 NUTS2 regions and hourly time-steps. In addition to the German electricity sector, analysis also accounts for exchange of energy with the rest of Europe and demand for electricity and electricity-based fuels, like hydrogen or synthetic gases, from the industry, transport, and heat sector. The results reveal that a first-best solution can be well approximated if the current planning approach also considered storage for congestion management. Placing renewables different has no significant effect in our case, because the available potential must be exploited almost entirely leaving little room for optimization. Furthermore, a sensitivity on the first-best scenario prohibiting additional transmission lines entirely suggests that grid expansion can be substituted at tolerable costs.

physics.soc-ph↗

arXiv2021

AnyMOD.jl: A Julia package for creating energy system models

AnyMOD.jl is a Julia framework for creating large-scale energy system models with multiple periods of capacity expansion. It applies a novel graphbased approach that was developed to address the challenges in modeling high levels of intermittent generation and sectoral integration. Created models are formulated as linear optimization problems using JuMP.jl as a backend. To enable modelers to work more efficiently, the framework provides additional features that help to visualize results, streamline the read-in of input data, and rescale optimization problems to increase solver performance.

physics.soc-ph↗