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Abstract Future “net-zero” electricity systems in which all or most generation is renewable may require very high volumes of storage in order to manage the associated variability in the generation-demand balance. The physical and economic characteristics of storage technologies are such that a mixture of technologies is likely to be required. This poses nontrivial problems in storage dimensioning and in real-time management. We develop the mathematics of optimal scheduling for system adequacy, and show that, to a good approximation, the problem to be solved at each successive point in time reduces to a linear programme with a particularly simple solution. We argue that approximately optimal scheduling may be achieved without the need for a running forecast of the future generation-demand balance. We consider an extended application to GB storage needs, where savings of tens of billions of pounds may be achieved, relative to the use of a single technology, and explain why similar savings may be expected elsewhere.

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

With the advent of employing bio-fuels along with the diesel in compression ignition engines the study of performance and emission characteristics have occupied the prominence, owing to diversified multi responses. As the limited information is available about the application of Taguchi based GTMA process to maximize the overall performance and emission characteristics of diesel engine, in the present work the investigation was carried out to maximize the overall utility by employing the Taguchi based GTMA process. By following the user preference rating, weights for the response characteristics namely brake thermal efficiency, brake specific fuel consumption, carbon monoxide and oxides of nitrogen were calculated using graph theory and matrix approach (GTMA). The parameter hydrogen induction played a major role to an extent of 78.62% while Injection opening pressure playing a minor role with a contribution of 7.06%. The optimal parameters condition was at mid-level of the governing parameters namely IOP, CR and volume of hydrogen inducted. The predicted results were within 95% of confidence interval of the optimal values. Therefore, the hydrogen inductance into the cylinder not only improving the performance but also minimizing the emission characteristics.

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

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.

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8 code with Google's TensorFlow 2.0 library. The effort was based to a large extent on the process of appropriate automatic transformation of data generated by PARCS simulator, which was later used in the process of the ANN development. Various methods that allow obtaining better accuracy of the ANN predicted results were studied, such as trying different ANN architectures to find the optimal number of neurons in the hidden layers of the network. Results were later compared with the architectures proposed in the literature. For the selected best architecture predictions were made for different core parameters and their dependence on core loading patterns. In this study, a special focus was put on the prediction of the fuel cycle length for a given core loading pattern, as it can be considered one of the targets for plant economic operation. For instance, the length of a single fuel cycle depending on the initial core loading pattern was predicted with very good accuracy (>99%). This work contributes to the exploration of the usefulness of neural networks in solving nuclear reactor design problems. Thanks to the application of ANN, designers can avoid using an excessive amount of core simulator runs and more rapidly explore the space of possible solutions before performing more detailed design considerations.

While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy

In power systems with high penetration of power electronics, grid-forming control is proposed to replace traditional Grid-Following Converter (GFL) in order to improve the overall system strength and resist small-signal instability in weak grids by directly forming the terminal voltage. However, sufficient headroom of both active and reactive power must be made available for Grid-Forming Converter (GFM) to operate, potentially leading to sub-optimal operation in steady states. This presents a new research problem to optimally allocate between GFM and GFL to balance the ability of GFMs to improve the grid strength and the potential economic loss resulting from reserved headroom. An optimization framework under software-defined grids is proposed, for the first time, to dynamically determine the optimal allocation of GFMs and GFLs in power systems at each time step of system scheduling according to system conditions, which ensures both system stability and minimum operational cost. To achieve this, the system scheduling model is expanded to simultaneously consider the constraints related to active and reactive power reserves for GFMs, as well as the system level stability. Case studies conducted on the modified IEEE 30-bus system demonstrate significant economic benefits in that the optimal proportion of GFMs in the power system can be dynamically determined while ensuring power reserve and grid stability constraints.