• Energy Research
• 2025-2025close
• 7. Clean energy close
• DE close
• EU close
• NL close
• AU close

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur. It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation. This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets. While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below: corine/* CORINE Land Cover (CLC) database Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/ Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata natura/* Natura 2000 natural protection areas Source: https://www.eea.europa.eu/data-and-maps/data/natura-10 Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata gebco/GEBCO_2014_2D.nc GEBCO bathymetric dataset Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/ Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf je-e-21.03.02.xls Population and GDP data for Swiss Cantons Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html Terms of Use: https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html nama_10r_3popgdp.tsv.gz Population by NUTS3 region Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en Terms of Use: https://ec.europa.eu/eurostat/about/policies/copyright GDP_per_capita_PPP_1990_2015_v2.nc Gross Domestic Product per capita (PPP) from years 1999 to 2015 Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015" Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4 ppp_2019_1km_Aggregated.tif The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution. Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647 Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777 License: Creative Commons Attribution 4.0 International Licens data/bundle/era5-HDD-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics. data/bundle/era5-runoff-per-country.csv - Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics. shipdensity_global.zip Global Shipping Traffic Density Creative Commons Attribution 4.0 https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density seawater_temperature.nc Global Ocean Physics Reanalysis Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be accurately quantified and integrated into energy management system with existing heuristic battery degradation models. This paper proposed a hierarchical deep learning based battery degradation quantification (HDL-BDQ) model to quantify the battery degradation given scheduled BESS daily operations. Particularly, two sequential and cohesive deep neural networks are proposed to accurately estimate the degree of degradation using inputs of battery operational profiles and it can significantly outperform existing fixed or linear rate based degradation models as well as single-stage deep neural models. Training results show the high accuracy of the proposed system. Moreover, a learning and optimization decoupled algorithm is implemented to strategically take advantage of the proposed HDL-BDQ model in optimization-based look-ahead scheduling (LAS) problems. Case studies demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a microgrid testbed. 12 pages

Energy consumption in solving computational problems has been gaining growing attention as one of the key performance measures for computers. Quantum computation offers advantages over classical computation in terms of various computational resources; however, proving its energy-consumption advantage has been challenging due to the lack of a theoretical foundation linking the physical concept of energy with the computer-scientific notion of complexity for quantum computation. To bridge this gap, we introduce a general framework for studying the energy consumption of quantum and classical computation, based on a computational model conventionally used for studying query complexity in computational complexity theory. Within this framework, we derive an upper bound for the achievable energy consumption of quantum computation, accounting for imperfections in implementation appearing in practice. As part of this analysis, we construct a protocol for Landauer erasure with finite precision in a finite number of steps, which constitutes a contribution of independent interest. Additionally, we develop techniques for proving a nonzero lower bound of energy consumption of classical computation, based on the energy-conservation law and Landauer’s principle. Using these general bounds, we rigorously prove that quantum computation achieves an exponential energy-consumption advantage over classical computation for solving a paradigmatic computational problem—Simon’s problem. Furthermore, we propose explicit criteria for experimentally demonstrating this energy-consumption advantage of quantum computation, analogous to the experimental demonstrations of quantum computational supremacy. These results establish a foundational framework and techniques to explore the energy consumption of computation, opening an alternative way to study the advantages of quantum computation. Published by the American Physical Society 2025

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored the use of reinforcement learning (RL) as a promising alternative. This paper introduces TreeC, a machine learning method that utilizes the covariance matrix adaptation evolution strategy metaheuristic algorithm to generate an interpretable EMS modeled as a decision tree. Unlike RBC and MPC approaches, TreeC learns the decision strategy of the EMS based on historical data, adapting the control model to the controlled energy grid. The decision strategy is represented as a decision tree, providing interpretability compared to RL methods that often rely on black-box models like neural networks. TreeC is evaluated against MPC with perfect forecast and RL EMSs in two case studies taken from literature: an electric grid case and a household heating case. In the electric grid case, TreeC achieves an average energy loss and constraint violation score of 19.2, which is close to MPC and RL EMSs that achieve scores of 14.4 and 16.2 respectively. All three methods control the electric grid well especially when compared to the random EMS, which obtains an average score of 12 875. In the household heating case, TreeC performs similarly to MPC on the adjusted and averaged electricity cost and total discomfort (0.033 EUR/m$^2$ and 0.42 Kh for TreeC compared to 0.037 EUR/m$^2$ and 2.91 kH for MPC), while outperforming RL (0.266 EUR/m$^2$ and 24.41 Kh). Accepted version Knowledge based system

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between "normal operation", in which automatic generation control (AGC) is sufficient to guarantee system security, and "adverse operation", in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC. 10 pages