David Yang Shu

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Shu

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David Yang

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0000-0003-4096-3883

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Publications 1 - 10 of 23

SecMOD: An Open-Source Modular Framework Combining Multi-Sector System Optimization and Life-Cycle Assessment
Reinert, Christiane; Schellhas, Lars; Mannhardt, Jacob; et al. (2022)
Frontiers in Energy Research
Optimization models can support decision-makers in the synthesis and operation of multi-sector energy systems. To identify the optimal design and operation of a low-carbon system, we need to consider high temporal and spatial variability in the electricity supply, sector coupling, and environmental impacts over the whole life cycle. Incorporating such aspects in optimization models is demanding. To avoid redundant research efforts and enhance transparency, the developed models and used data sets should be shared openly. In this work, we present the SecMOD framework for multi-sector energy system optimization incorporating life-cycle assessment (LCA). The framework allows optimizing multiple sectors jointly, ranging from industrial production and their linked energy supply systems to sector-coupled national energy systems. The framework incorporates LCA to account for environmental impacts. We hence provide the first open-source framework to consistently include a holistic life-cycle perspective in multi-sector optimization by a full integration of LCA. We apply the framework to a case-study of the German sector-coupled energy system. Starting with few base technologies, we demonstrate the modular capabilities of SecMOD by the stepwise addition of technologies, sectors and existing infrastructure. Our modular open-source framework SecMOD aims to accelerate research for sustainable energy systems by combining multi-sector energy system optimization and life-cycle assessment.
Bilevel optimization of energy system transition pathways considering competition in markets
Shu, David Yang; Reinert, Christiane; Mannhardt, Jacob; et al. (2024)
The energy system transition towards net-zero greenhouse gas emissions involves multiple decision makers. While greenhouse gas mitigation targets must be jointly achieved, the decision makers are primarily interested in minimizing their individual cost of energy supply. The competing interest of decision makers are commonly neglected in optimization models of the energy system transition. To overcome this shortcoming, we model the energy system transition as a multi-leader-single-follower game: In our model, individual decision makers develop investment strategies in shared electricity and carbon markets. We formulate the game as a bilevel optimization problem that reflects the multi-level nature of the decision-making process. We find an equilibrium solution to the multi-leader-single-follower game by applying the Gauss-Seidel method. Our case study of the European electricity system shows that the bilevel optimization problem results in a transition pathway with higher capacity expansion compared to a centralized approach. Further, the average market clearing price and the spread of locational market clearing prices are lower. As a result, overall costs are reduced when considering trading and carbon allowance costs on top of the investment and operating costs. Hence, considering competition and market behavior is vital in modeling the energy system transition.
Resilience of carbon capture, transport, and storage infrastructure: Trade-offs between cost and emissions
Burger, Johannes; Shu, David Yang; Bardow, André; et al. (2024)
Megatonne-scale direct air capture and storage via calcium-looping: A prospective life cycle assessment?
Bolongaro, Vittoria; Shu, David Yang; McQueen, Noah; et al. (2025)
Flexibility from industrial demand-side management in net-zero sector-coupled national energy systems
Bardow, André; Mayer, Patricia; Heer, Mario; et al. (2024)
Frontiers in Energy Research
National energy systems require flexibility to accommodate increasing amounts of variable renewable energy. This flexibility can be provided by demand-side management (DSM) from industry. However, the flexibility potential depends on the characteristics of each industrial process. The enormous diversity of industrial processes makes it challenging to evaluate the total flexibility provision from industry to sector-coupled energy systems. In this work, we quantify the maximum cost reductions due to industrial DSM in the net-zero sector-coupled Swiss energy system, and the relationship between cost reductions and various industrial process characteristics. We analyze the flexibility of industrial processes using a generic, process-agnostic model. Our results show that industrial DSM can reduce total energy system costs by up to 4.4%, corresponding to 20% of industry-related energy costs. The value of flexibility from industrial DSM depends not only on the process characteristics but also on the system’s flexibility alternatives, particularly for flexibility over seasonal time horizons. As one specific option for industrial DSM, we find that thermal energy storage (TES) technologies available today could realize between 28% and 61% of the maximum cost reductions from industrial DSM, making TES a promising DSM solution and showing that industrial DSM is an accessible and cost-effective flexibility option.
The role of carbon capture & storage to achieve net-zero energy systems: Trade-offs between economics and the environment
Shu, David Yang; Deutz, Sarah; Hartmann, Jan; et al. (2023)
Rigorous synthesis of energy systems by decomposition via time-series aggregation
Bahl, Björn; Lützow, Julian; Shu, David Yang; et al. (2018)
Computers & Chemical Engineering
The synthesis of complex energy systems usually involves large time series such that a direct optimization is computationally prohibitive. In this paper, we propose a decomposition method for synthesis problems using time-series aggregation. To initialize the method, the time series is aggregated to one time step. A lower bound is obtained by relaxing the energy balances and underestimating the energy demands leading to a relaxed synthesis problem, which is efficiently solvable. An upper bound is obtained by restricting the original problem with the full time series to an operation problem with a fixed structure obtained from the lower bound solution. If the bounds do not satisfy the specified optimality gap, the resolution of the time-series aggregation is iteratively increased. The decomposition method is applied to two real-world synthesis problems. The results show the fast convergence of the decomposition method outperforming commercial state-of-the-art optimization software. © 2018 Elsevier Ltd
Insights from Life-Cycle Assessment of the Carbon Capture and Storage Supply Chain from the DMX™ Demonstration in Dunkirk (3D) Project
Shu, David Yang; Bewi Komesse, Helen; Beauchet, Sandra; et al. (2022)
SSRN Electronic Journal ~ Proceedings of the 16th Greenhouse Gas Control Technologies Conference (GHGT-16)
The formation of carbon dioxide (CO2) in industrial processes such as cement or steel production is hard to avoid. To prevent the release to the environment, CO2 can be separated from industrial point sources to be permanently stored in geological storage. However, the required carbon capture and storage (CCS) supply chain entails substantial material and energy demands over the life cycle. To quantify the effectiveness of CCS supply chains in reducing greenhouse gas (GHG) emissions, life-cycle assessment (LCA) considers the environmental impacts over the full life cycle. Furthermore, environmental impact categories beyond climate change can be analyzed to predict potential environmental hot spots in the CCS supply chain. Due to a lack of primary data, LCA studies rely on literature data, proxies, and simulations to predict the environmental impacts of CCS supply chains. However, recent full-scale CCS projects offer the opportunity to increase the accuracy and confidence in the results of LCAs using real-world data from engineering studies. In this paper, we conduct an LCA of a megaton-scale CCS supply chain designed within the Horizon 2020 project DMX™ Demonstration in Dunkirk (3D). The LCA is based on engineering studies for a CCS supply chain for a steel production plant in Northern France. We evaluate the environmental performance of the supply chain for the local energy supply at the steel plant. A life-cycle CCS efficiency of 93 % can be achieved for storing 1 Mt of CO2 annually, which corresponds to a reduction of the GHG emissions of the steel plant by 6.0 %. In addition, environmental impacts in categories other than climate change increase by less than 1.8 % in most impact categories except for ionizing radiation, where an increase of 18.4 % is observed due to the high share of nuclear power in the French electricity grid. Transportation based on ships emerges as the main contributor to several impact categories due to continued reliance on natural gas as fuel. Even in a worst-case scenario, assuming an all-fossil energy supply, a life-cycle CCS efficiency of 64 % can be achieved. Hence, the proposed CCS supply chain can effectively reduce the GHG emissions of steel production already today. Our study underlines the importance of the energy supply in climate change and other impact categories and points towards transportation as a potential future environmental hot spot. To improve the tradeoff between climate change mitigation and environmental impacts shifting to other categories, the development of CCS projects needs to ensure a low-impact energy supply for all steps of the CCS supply chain, including transportation.
Design and Operation of Sustainable Net-Zero Energy Systems
Shu, David Yang (2024)
Limiting human-induced climate change requires steep greenhouse gas (GHG) emission reductions to net-zero across all sectors. Carbon capture, removal, and storage technologies contribute to this target by reducing hard-to-abate emissions in industry and offsetting residual and historical emissions. A lack of real-world data limits the accuracy of environmental assessments of carbon capture, removal, and storage technologies. Here, we conduct a life-cycle assessment of a carbon capture, transport, and storage supply chain based on data from a demonstration project. Our analysis reveals that the integration of the capture plant with a steel plant, along with favorable transport options, enables high capture efficiencies across a wide range of assumptions, and can thus contribute to the decarbonization of the steel industry. Results from technology-specific assessments can be integrated into energy system models to evaluate technology performance in systems. However, this integration, along with long-term constraints, leads to high computation times in scheduling. To address this limitation, we propose a hybrid time-series method. In a case study of an industrial energy system with a direct air capture plant, our approach reduces computation time by three orders of magnitude while achieving near-optimal solutions. Beyond scheduling, carbon capture, removal, and storage technologies affect energy system design. Therefore, we explore the environmental and economic impacts of deploying these technologies withing a national energy system transition. Our analysis finds that reducing climate change impacts using carbon capture, removal, and storage technologies lowers impacts in over half of the considered impact categories, highlighting the value of these technologies beyond climate change mitigation. Achieving net-zero GHG emissions involves coordinating decision-makers. We extend traditional centralized energy system models to account for competing interests between decision-makers. A comparison of decentralized and centralized model results shows that, while both models require carbon capture, removal, and storage technologies to achieve net-zero GHG emissions, the resulting energy system designs differ substantially. In summary, this thesis assesses the environmental impacts of carbon capture, removal, and storage technologies through detailed technology-specific assessments and integrating those assessments into energy system models. We thus reveal system effects beyond the impact of individual technologies and further highlight the need for model extensions to reflect decentralized decision-making.
Optimal operation of energy systems with long-term constraints by time-series aggregation in receding horizon optimization
Shu, David Yang; Baumgärtner, Nils; Dahmen, Manuel; et al. (2019)
Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2019)
Operational optimization of energy systems needs to consider both forecast uncertainty and long-term constraints. Long-term constraints are often introduced by regulations such as network charge reduction policies. In principle, uncertainty can be handled via receding horizon optimization (RHO), which frequently optimizes the operation schedule based on updated forecast data. As fast reoptimization is required in real-time control applications, RHO typically considers the near-term future only and neglects long-term constraints. To explicitly account for long-term constraints in RHO, we propose a hybrid time-series approach that extends the high-resolution detailed block for near-term operation by an aggregated block for the more distant future. The aggregated block is constructed using the k-medoids algorithm. The new approach is applied to a case study and shown to adequately consider long-term constraints with a short computation time. In particular, we reduce operational expenditures by 5.4 % compared to a benchmark RHO that relies on a typical near-term prediction horizon of 48 h. The computation time for a single reoptimization is reduced by four orders of magnitude compared to the problem with the full-resolution time-series. Thus, the hybrid time-series approach enables the efficient operational optimization of energy systems with long-term constraints. © ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. All rights reserved.

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