Profitability of power-to-heat-to-power storages in scenarios with high shares of renewable energy

Intermittent electricity generation from variable renewable energies will lead to an increased demand for flexibility options in the future. Power-to-heat-to-power storage technologies present high potentials for large-scale application. However, investments in such technologies are still hampered by technical and economic challenges. To address the latter the possible revenues in electricity markets need to be analyzed. For this, we simulate the German electricity market in ambitious defossilization scenarios. We use different operational strategies for the storage (minimizing system costs versus maximizing storage profits) that show a wide range of storage profitability. The operator benefits from its attributed market power (i.e. assuming perfect foresight in a rolling horizon window) to generate positive net profits. Further research may focus on market situations with increased market competition

Carbon-neutral power system enabled e-kerosene production in Brazil in 2050

Rich in renewable resources, extensive acreage, and bioenergy expertise, Brazil, however, has no established strategies for sustainable aviation fuels, particularly e-kerosene. We extend the lens from the often-studied economic feasibility of individual e-kerosene supply chains to a system-wide perspective. Employing energy system analyses, we examine the integration of e-kerosene production into Brazil’s national energy supplies. We introduce PyPSA-Brazil, an open-source energy system optimisation model grounded in public data. This model integrates e-kerosene production and offers granular spatial resolution, enabling federal-level informed decisions on infrastructure locations and enhancing transparency in Brazilian energy supply scenarios. Our findings indicate that incorporating e-kerosene production can bolster system efficiency as Brazil targets a carbon-neutral electricity supply by 2050. The share of e-kerosene in meeting kerosene demand fluctuates between 2.7 and 51.1%, with production costs varying from 113.3 to 227.3 €/MWh. These costs are influenced by factors such as biokerosene costs, carbon pricing, and export aspirations. Our findings are relevant for Brazilian policymakers championing aviation sustainability and offer a framework for other countries envisioning carbon-neutral e-kerosene production and export

High Performance Computing vs. Heuristic: A performance benchmark for optimization problems with linear power flows

We address the crucial aspect of unmanageable computing times of large-scale Energy System Optimization Models. Such models provide insights into future energy supply systems while keeping an overall perspective. However, the degree of phenomena or processes to be modeled is ever-increasing. With PIPS-IPM++ a novel solver is presented which is designed for linear optimization problems with linking variables (i.e. investment decisions) and linking constraints (i.e. power flow constraints). Compared to existing approaches for computing time reduction it makes use of High Performance Computing. Here, we present a benchmark study that compares the performance of PIPS-IPM++ with a usual speed-up heuristic (Temporal Zooming). Our results show that speed-ups between factor 10 and 15 are achievable with PIPS-IPM++ and Temporal Zooming, respectively. Despite PIPS-IPM++ has a great potential to parallelize solving, the tested version of the solver is especially useful for models without investment decisions (i.e. optimal power flow problems)

Network reduction methods for integrated energy systems using power grids and gas pipelines

Against the background of the need for a rapid decarbonization towards a climate-neutral energy system, green hydrogen and methane are taking up an increasingly important role. Two of the driving factors are the need for seasonal energy storage solutions and the emission reduction of applications where direct electrification is difficult to achieve. To better understand challenges and solutions in the transformation of infrastructures, energy system optimization models are a useful tool. In these models, an adequate representation of the corresponding infrastructure across different spatial resolutions is a prerequisite for deriving recommendations on future network expansion. To this end, we compare different grid reduction methods for a joint network of power grid and gas pipelines. In this context, clustering methods such as k-medoids and graph partitioning methods are considered. One of the main challenges during the reduction is maintaining spatial information about key infrastructure such as import terminals, cavern storage, and optimal locations for large-scale electrolysis and gas turbines. To analyse the impact of different reduction methods on the results of the overall energy system model, in this presentation the comparison is performed for two scenarios focusing on a high share of imported energy carriers and distributed and domestic generation, respectively. Thus, a well-suited grid reduction method is identified for a wide range of future energy systems

Green energy carriers and energy sovereignty in a climate neutral European energy system

Meeting the goals of the Paris Agreement poses significant challenges to provide renewable energy for the power, heating, transport, and industrial sector. Both green hydrogen and methane are considered key energy carriers for reaching these climate targets. However, future needs for an effective infrastructure deployment are highly uncertain, particularly concerning the timely and substantial expansion of renewable electricity generation in Europe. To better understand the trade-offs between domestic production and large-scale energy imports and the corresponding infrastructures needs, we use the energy system optimisation model REMix. We consider different strategic European story lines and constraints on expansion of pipelines and power grids. The results indicate that European energy sovereignty is feasible but comes at a 2.8% higher cost compared to stronger cooperation with resource-rich areas such as the British Isles or the Maghreb region. In contrast, preventing any network expansion lead to an increase of up to 15.2%. Especially limited network expansion in conjunction with energy sovereignty makes controversial technologies such as nuclear energy necessary. With regard to the extensive adaptations of energy infrastructures required to achieve the emission reduction goal, the timely and substantial expansion of electricity generation from renewable sources in particular is to be regarded as crucial

Pushing computational boundaries: Solving integrated investment planning problems for large-scale energy systems with PIPS-IPM+

Energy policies for setting the course of future energy supply often rely on models of energy systems with increasing interdependencies. On the mathematical side this translates into linking variables and constraints in the structure of optimization problems. Challenges concerning limited computing resources are often tackled from the applied side since generic parallel solvers are not available. This means that modelers today aim to simplify real-world models when implementing new features, despite of lots of effort spent for improving them before. This prevents accurately modeling of all system components. We tackle this challenge by combining both domain knowledge from the application side and the solver side and demonstrate our solution for a real-world model which is practically not solvable with existing methods. Therefore, we parameterize instances of the energy system optimization model REMix having more than 700 Mio. non-zeros. For the first time, these model instances incorporate both the optimization of a full hourly operational time horizon and path-dependent long-term investment planning for the German power system. These instances are annotated in a way, that the corresponding linear problems (LPs) decompose into blocks of similar size. To solve the annotated LPs, the new interior-point solver PIPS-IPM++ is applied. It treats large numbers of linking variables and constraints using a hierarchical algorithm and enables efficient scaling on parallel hardware. In this sense, we expand the boundaries of what is computationally possible when solving LPs in energy systems analysis. Accordingly, using the best possible real-world models becomes practicable, which enables the calibration of simplified models in a domain where validation is difficult

Carbon-neutral power system enabled e-kerosene production in Brazil in 2050

Rich in renewable resources, extensive acreage, and bioenergy expertise, Brazil, however, has no established strategies for sustainable aviation fuels, particularly e‑kerosene. We extend the lens from the often‑studied economic feasibility of individual e‑kerosene supply chains to a system‑wide perspective. Employing energy system analyses, we examine the integration of e‑kerosene production into Brazil’s national energy supplies. We introduce PyPSA‑Brazil, an open‑source energy system optimisation model grounded in public data. This model integrates e‑kerosene production and offers granular spatial resolution, enabling federal‑level informed decisions on infrastructure locations and enhancing transparency in Brazilian energy supply scenarios. Our findings indicate that incorporating e‑kerosene production can bolster system efficiency as Brazil targets a carbon‑neutral electricity supply by 2050. The share of e‑kerosene in meeting kerosene demand fluctuates between 2.7 and 51.1%, with production costs varying from 113.3 to 227.3 €/MWh. These costs are influenced by factors such as biokerosene costs, carbon pricing, and export aspirations. Our findings are relevant for Brazilian policymakers championing aviation sustainability and offer a framework for other countries envisioning carbon‑neutral e‑kerosene production and export

Evaluation of uncertainties in linear energy system optimization models using HPC and neural networks

Within the interdisciplinary BMWK-funded project UNSEEN, experts from High Performance Computing, mathematical optimization and energy systems analysis combine strengths to evaluate uncertainties in modeling and planning future energy systems with the aid of High Performance Computing (HPC) and neural networks. Energy System Models (ESM) are central instruments for realizing the energy transition. These models try to optimize complex energy systems in order to ensure security of supply while minimizing costs for power production and transmission. In order to derive reliable and robust policy advice for decision makers, hundreds or even thousands of ESM problems need to be solved in order to address uncertainties in a given model and dataset.Mixed-integer linear programs (MIPs), a direct extension of Linear programs (LPs), can be used to formulate and compute more concrete and realistic energy systems. Since the availability of fast LP solvers is a major prerequisite for optimizing MIPs, the development of an open-source scalable distributed-memory LP solver, called PIPS-IPM++, was started in a preceding project and can already outperform state-of-the-art solvers. A second prerequisite for efficient MIP solving is the availability of MIP heuristics. For this purpose, we develop a generic MIP framework including reinforcement learning methods. Moreover, we aim to implement an efficient automated HPC workflow for generating, solving, and postprocessing numerous ESM problems with a special structure in order to develop new tools for better predictions about the future of our energy system. This novel approach couples multiple existing and new software packages to achieve the project goals