Jacob Mannhardt


Last Name

Mannhardt

First Name

Jacob

ORCID

0000-0002-6487-6998

Organisational unit

09452 - Sansavini, Giovanni / Sansavini, Giovanni

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2022 - 202510
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Publications 1 - 10 of 10
  • Bilevel optimization of energy system transition pathways considering competition in markets
    Item type: Conference Paper
    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.
  • Climate-resilient energy systems planning via system-informed identification of stressful events
    Item type: Journal Article
    De Marco, Francesco; Mannhardt, Jacob; Oneto, Alfredo Ernesto; et al. (2025)
    Advances in Applied Energy
    As the energy mix increasingly relies on weather-dependent renewable sources, energy systems become more vulnerable to climate variability and extremes. However, current planning approaches struggle to incorporate climate uncertainty in the design phase while maintaining computational tractability. We address this challenge by developing a framework that combines system-informed scenario reduction and stochastic optimization to design climate-resilient energy systems. Our method reduces data complexity by identifying representative climate scenarios that capture stress events through system response. Remarkably, five distinct patterns of multi-day energy shortages emerge across Europe, each characterized by different combinations of renewable resource availability and demand profiles. Stochastic optimization then incorporates these representative climate scenarios with their associated probabilities to design energy systems that are resilient across the full spectrum of climate variability. Results show that climate-resilient designs consistently outperform conventional single-climate designs, achieving lower costs (on average 14.8 bn EUR) for equivalent resilience levels. We identify two trade-off regions with different marginal costs of resilience: a low-resilience and a high-resilience region where marginal costs increase fivefold. Despite higher costs, trade-offs between the cost of resilience investments against energy not supplied justify pursuing the high levels of resilience. Combinations of onshore wind and hydrogen storage emerge as effective mitigation against multi-day events of energy shortage. This framework provides energy planners and policymakers with quantifiable insights into resilience investment strategies and technology selection for future climate-aware energy planning.
  • Accurately modeling long-term storage with minimum representative hours in large-scale renewable energy systems
    Item type: Working Paper
    Mannhardt, Jacob; Kunz, Lukas; Sansavini, Giovanni (2025)
    arXiv
    Energy system optimization often relies on time series aggregation to ensure computational tractability. Aggregation generally loses the chronology of time steps, which renders the storage level representation challenging. Typically, this challenge is addressed by using representative days (RD) to utilize intra-day chronology, even though representative hours (RH) can describe the input time series more accurately at fewer representative time steps than RD. However, until now, the use of RH storage representation methods has been limited by either high computational complexity, poor accuracy in clustering and storage representation, or restricted applicability. Here, we present a novel storage representation method based on RH that combines the high accuracy of RH time series aggregation with the high computational efficiency of methods based on RD. Through benchmarking the four most established storage representation methods on a model of a net-zero European energy system, we find that the proposed method can reduce the solving time by over 95% for the same objective value compared to the most established RD and RH methods. The proposed method exhibits particular strengths at strong aggregations of around 100 to 500 representative hours per year, making the method especially applicable to large-scale and sector-coupled transition pathway models. The developed method for accurately modeling both short-term and long-term storage, along with the presented findings, is of practical relevance to energy system modelers who seek computational tractability in large-scale applications while avoiding the misallocation of storage and conversion capacities.
  • Assessing and Enabling the Feasibility of the European Energy Transition Under Myopic and Constrained Technology Deployment
    Item type: Other Conference Item
    Mannhardt, Jacob; Gabrielli, Paolo; Sansavini, Giovanni (2023)
  • Obstacle or opportunity? The impact of a natural gas phase-out on the decarbonization of the European energy system
    Item type: Other Conference Item
    Mannhardt, Jacob; Gabrielli, Paolo; Sansavini, Giovanni (2022)
  • Collaborative and selfish mitigation strategies to tackle energy scarcity: The case of the European gas crisis
    Item type: Journal Article
    Mannhardt, Jacob; Gabrielli, Paolo; Sansavini, Giovanni (2023)
    iScience
    Following the disruption of Russian natural gas flows to Europe, we investigate the impact of collaborative and selfish behavior of European countries to tackle energy scarcity and supply electricity, heat, and industrial gas to end users. We study how the operation of the European energy system will need to adapt to the disruption and identify optimal strategies to overcome the unavailability of Russian gas. Those strategies include diversifying gas imports, shifting energy generation to non-gas-based technologies, and reducing energy demands. Findings suggest that: (1) selfish behavior of Central European countries exacerbates the energy scarcity for many Southeastern European countries; (2) proactive collaborative energy savings, together with a mild winter, can fully relieve the stress of the gas shortage; (3) diversification of gas imports leads to bottlenecks in the gas network, especially in Southeastern Europe; and (4) electricity generation is mostly shifted to coal-based power plants, causing higher carbon emissions.
  • Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system
    Item type: Journal Article
    Mannhardt, Jacob; Gabrielli, Paolo; Sansavini, Giovanni (2024)
    iScience
    Short-term planning of myopic decision-makers jeopardizes the long-term energy transition, especially since constraints in deploying clean energy technologies further inhibit their rapid scale-up. Here, we show that the European energy transition followed myopic decision-making in the past and investigate how policy-based tools can secure the energy transition against myopic planning. Short-term decision-making in the European energy transition may fail to comply with climate goals and lead to substantial over-capacities. Carbon prices can only effectively internalize long-term climate goals if they account for constrained technology deployment, increasing to around 400 EUR/tCO2 in 2050. Idealized carbon prices, conversely, fail to incentivize the decarbonization of those sectors that stand at the beginning of their transition, such as renewable heating or carbon sequestration. Our exploration of myopic decision-making contributes to the understanding of the inhibiting barriers and bridges the gap between short-term decision-making and the long-term energy transition.
  • Bet on horses, not unicorns – The impact of technology optimism on the decarbonization of the European energy system
    Item type: Other Conference Item
    Mannhardt, Jacob; Sansavini, Giovanni (2024)
    EURO 2024 Conference Handbook & Abstracts: 33rd European Conference on Operational Reseach (EURO XXXIII)
  • Overcoming the central planner approach – Bilevel optimization of the European energy transition
    Item type: Journal Article
    Shu, David Yang; Reinert, Christiane; Mannhardt, Jacob; et al. (2024)
    iScience
    The energy transition is a multinational challenge to mitigate climate change, with a joint reduction target for greenhouse gas emissions. Simultaneously, each country is interested in minimizing its own energy supply cost. Still, most energy system models neglect national interests when identifying cost-optimal transition pathways. We design the European energy system transition until 2050, considering competition between countries in a shared electricity and carbon market using bilevel optimization. We find that national objectives substantially impact the transition pathway: Compared to the model solved using the common centralized optimization, the overall installed capacity increases by just 3% when including national interests. However, the distribution of the installed capacity changes dramatically by more than 40% in most countries. Our results underline the risk of miscalculating the need for national capacity expansion when neglecting stakeholder representation in energy system models and demonstrate the need for cooperation for an efficient energy transition.
  • ZEN-garden: Optimizing energy transition pathways with user-oriented data handling
    Item type: Journal Article
    Mannhardt, Jacob; Ganter, Alissa; Burger, Johannes; et al. (2025)
    SoftwareX
    Welcome to the ZEN-garden: ZEN-garden is an open-source optimization software to model multi-year energy system transition pathways. To support research focused on the transition of sector-coupled energy systems toward net-zero emissions, ZEN-garden is built upon two principles: Optimizing highly complex sector-coupled energy transition pathways and supporting user-friendly data handling through small, flexible, and robust input datasets. ZEN-garden separates the codebase from the input data to allow for very diverse case studies. Lightweight and intuitive input datasets and unit consistency checks reduce user errors and facilitate using ZEN-garden for both novice and experienced energy system modelers.
Publications 1 - 10 of 10