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Assessment of CO_(2)storage prospect based on physical properties of Rio Bonito Formation rock units

Energy Geoscience 2024年第1期5卷 321-330页

作者： Richardson M.Abraham-A Stephanie San Martín Cañas Isabella F.S.Miranda Colombo C.G.Tassinari Institute of Energy and Environment(iee) University of São Paulo(USP)Brazil Research Centre for Greenhouse Gas Innovation(RCGI) University of Sãao Paulo(USP)Brazil

The physical properties of the rock units associated with the Rio Bonito Formation are presented in this study with the focus on modelling reservoir quality based on petrophysics-derived parameters to evaluate CO_(2)storage *** involves the modelling of the reservoir depths,thicknesses,flow zone indicators(FZI),and effective permeability(Keff)and presenting the CO_(2)storage efficiency factors peculiar to the rock units of the study *** results presented by this study for the stated objectives are not quite common in the *** values range from 200 mD to higher than 2000 mD,and FZI values are generally above 1.0 mm and up to 13.0 mm within the portions covered by the drilled *** sandstone units recorded are up to 20 m thick in some *** Keff and FZI models indicate the sandstone reservoirs as permeable units to support the injection and circulation of CO_(2)within the potential reservoir units of the Rio Bonio Formation across São Paulo *** from some points in the southeastern part of the study location,where the Rio Bonito are delineated at depths less than 800 m(minimum CO_(2)storage depth based on best practices),other portions are deeper,ranging from 950 m to 3500 ***-bedded layers will affect the integrity of the rocks as CO_(2)storage tanks or reservoir seals/traps/overburden within the *** bed thicknesses are up to 20 m in some ***,hybrid CO_(2)reservoir units are feasible,especially in portions where thin siltstone layers are sandwiched between sandstone units to provide considerable thicknesses based on CO_(2)storage *** current study shows that useable areas considering reservoir thickness,depth,and other physical qualities will significantly control the CO_(2)storage efficiency of the study *** studies featuring a detailed geophysical exploration of the site to confirm the availability and saturations of preexisting fluid(hydrocarbon and water)are encouraged

关键词： Rio Bonito Effective permeability Fluid transmissibility CO_(2)storage prospect Reservoir quality Overburden integrity Flow zones

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Natural Gas Power Plants in the System Security Role—The Situation and Solutions in Brazil

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Energy and Power Engineering 2024年第4期16卷 151-178页

作者： Ciro Galvão Hirdan Katarina de Medeiros Costa Eduardo Guedes Pereira Edmilson Moutinho dos Santos Dorel Soares Ramos Polytechnic School (EP) University of S&#227o Paulo S&#227o Paulo Brazil Institute of Energy and Environment (iee) University of S&#227o Paulo S&#227o Paulo Brazil

This study aims to apply a methodology to analyze the regulatory obstacles for the development of thermal power plants in a country. The study uses Brazil as an application case, although mostly of the findings can be useful to other countries. It also discusses other policies and actions by the government that may contribute to the growth of natural gas-fired power plants. Using deductive and comparative research methods, the first part of this method focuses on the stage of thermoelectric power generation in Brazil. The subsequent sections present the role of the integration of gas and electricity sectors, the main obstacles to the expansion of natural-gas power generation, how the country has deal with the expansion of natural-gas power generation, as well as how other obstacles has been faced. In raising the research question, when comparing the research results of selected countries, it should be noticed the impact that the experience and lessons elsewhere have had on policies formulation on Brazil. Our conclusions pointed out that adequate policies for the natural gas-electricity Brazilian market integration make advisable the unification of electricity and natural gas agencies. We recommended developing a mathematical model to support decision-making on natural gas and electricity integration.

关键词： Component Formatting Style Styling Insert

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Vertical power flow forecast with LSTMs using regular training updatestrategies

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能源与人工智能(英文) 2022年第2期8卷 92-99页

作者： Katharina Brauns Christoph Scholz Alexander Schultz AndréBaier Dominik Jost Fraunhofer iee Königstor 5934119 KasselGermany

The strong growth of renewable energy sources as well as the increasing amount of volatile energy consumptionis leading to major challenges in the electrical grid. In order to ensure safety and reliability in the electricitygrid, a high quality of power flow forecasts for the next few hours are needed. In this paper we investigateforecasts of the vertical power flow at transformer between the medium and high voltage grid. Forecastingthe vertical power flow is challenging due to constantly changing characteristics of the power flow at thetransformer. We present a novel approach to deal with these challenges. For the multi step time series forecastsa Long-Short Term Memory (LSTM) is used. In our presented approach an update process where the model isretrained regularly is investigated and compared to baseline models. The model is retrained as soon as asufficient amount of new measurements are available. We show that this retraining mostly captures changesin the characteristic of the transformer that the model has not yet seen in the past and therefore cannot bepredicted by the model without an update process. To give more weight to recent data, we examined differentstrategies in terms of the number of epochs and the learning rate. We show that our new approach significantlyoutperforms the investigated baseline models. On average, we achieved an improvement of about 8% with theregular update process compared to the approach without update process.

关键词： Vertical power flow Forecasting LSTM

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A Reinforcement Learning approach for the continuous electricity market ofGermany: Trading from the perspective of a wind park operator

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能源与人工智能(英文) 2022年第2期8卷 67-78页

作者： Malte Lehna Björn Hoppmann Christoph Scholz RenéHeinrich Fraunhofer Institute for Energy Economics and Energy System Technology(iee) Königstor 5934119 KasselGermany

With the rising extension of renewable energies, the intraday electricity markets have recorded a growingpopularity amongst traders as well as electric utilities to cope with the induced volatility of the energysupply. Through their short trading horizon and continuous nature, the intraday markets offer the abilityto adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producersof renewable energies utilize the intraday market to lower their forecast risk, by modifying their providedcapacities based on current forecasts. However, the market dynamics are complex due to the fact that thepower grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligenttrading strategies are required that are capable to operate in the intraday market. In this work, we proposea novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possiblesolution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ theProximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introducedthat enables the trading of the continuous intraday price in a resolution of one minute steps. We test ourframework in a case study from the perspective of a wind park operator. We include next to general tradeinformation both price and wind forecasts. On a test scenario of German intraday trading results from 2018,we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of theDRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increasethe performance in future works.

关键词： Deep Reinforcement Learning German intraday electricity trading Deep neural networks Markov Decision Process Proximal Policy Optimization Electricity price forecast

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Physical properties of sandstone reservoirs: Implication for fluid mobility

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Energy Geoscience 2022年第4期3卷 349-359页

作者： Richardson M.Abraham-A Fabio Taioli Anthony I.Nzekwu Institute of Energy and Environment University of Sao Paulo(IEE-USP)Sao PaloBrazil Department of Applied Geophysics Federal University of TechnologyAkure(FUTA)Ondo StateNigeria

Core samples representing depths of hydrocarbon-bearing zones are not readily accessible for reservoir *** the other hand,wireline logs with incorporated seismic data,which can be archived over a more extended period while retaining their original forms,are typically more available for research ***,the study relies on wireline logs with seismic data to predict the reservoirs'fluid mobility by evaluating the hydraulic(flow)units,reservoir depths,fluid saturations,and geothermal *** also indicates the associated water cut(C_(w))within Ritchie oil and gas field,Niger Delta considering a three-phase(oil-gas-water-bearing)reservoir(R_(A))and an oil-saturated reservoir(R_(B))delineated across three wells(R_(W1),R_(W2) and R_(W3)).Research activities combining the presented factors to achieve the stated objectives are not quite common within the study *** shows lower,average and upper limits of the flow unit factors and irreducible water saturation(S_(wirr))within the *** study shows the relationship between hydraulic units/fluid saturations and fluid mobility/associated C_(w) within the sandstone *** maximises porosity(Ф)for the theoretical flow units'prediction during qualitative and quantitative estimation based on the adopted ***,the study reveals that water saturation(S_(w))and hydrocarbon/water ratios substantially control C_(w),and other contributing factors include thermal gradients and S_(wirr).The flow unit factors are also significant and will encourage fluid *** evaluated reservoirs(R_(A) and R_(B))are below 10400 ft(3170 m)across wells R_(W1),R_(W2) and R_(W3) within the Agbada Formation of a geothermal gradient up to 2.7℃/100 m;therefore,they have good thermal conditions to enhance hydrocarbon mobility and increase S ***,the reservoir should feature significant hydrocarbon extraction via primary *** average water cut(C_(w-avg).)(12.3%)estimated for reser

关键词： Water cut Thermal gradient Fluid saturations Hydrocarbon mobility Sandstone reservoir Reservoir quality Agbada Formation Niger Delta

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Power flow forecasts at transmission grid nodes using Graph Neural Networks

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Energy and AI 2023年第4期14卷 189-200页

作者： Dominik Beinert Clara Holzhuter Josephine M.Thomas Stephan Vogt Fraunhofer Institute for Energy Economics and Energy System Technology iee Joseph-Beuys-Straße 8Kassel34117Germany Intelligent Embedded Systems Lab University of KasselWilhelmshöher Allee 73Kassel34121Germany

The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating,and weather-dependent power *** ensure grid stability,grid operators rely on power forecasts which are crucial for grid calculations and *** this paper,a Multi-Task Learning approach is combined with a Graph Neural Network(GNN)to predict vertical power flows at transformers connecting high and extra-high voltage *** proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning *** use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be *** the same time,dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each *** effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators,comprising large portions of the operated German transmission *** results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding *** is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks.A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.

关键词： Power flow forecasting Graph Neural Network Graph Convolutional Network Embedding Multi-Task Learning

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Carbon isotopic composition of modern soil and paleosol as a response to vegetation change on the Chinese Loess Plateau

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Science China Earth Sciences 2005年第1期48卷 93-99页

作者： LIU Weiguo NING Youfeng AN Zhisheng WU Zhenghai LU Huayu CAO Yunning State Key Laboratory of Loess and Quaternary Geology IEE Chinese Academy of Sciences Xi'an 710075 China State Key Laboratory of Loess and Quaternary Geology IEE Chinese Academy of Sciences Xi'an 710075 China State Key Laboratory of Loess and Quaternary Geology IEE Chinese Academy of Sciences Xi'an 710075 China Northwest Sci-Tech University of Agriculture and Forest Yangling 710000 China State Key Laboratory of Loess and Quaternary Geology IEE Chinese Academy of Sciences Xi'an 710075 China State Key Laboratory of Loess and Quaternary Geology IEE Chinese Academy of Sciences Xi'an 710075 China

The relationship between the carbon isotopic composition of paleosols and paleovegetation on the Loess Plateau is still unclear. One of the main reasons is that we are short of knowledge about the characteristics of the carbon isotopic composition of modern soil in this area. A preliminary investigation of the carbon isotopic compositions of the modern soil and the loess/paleosol sequence on the Loess Plateau shows that the carbon isotopic composition of modern soil is consistent with the distribution of modern plants on the Loess Plateau, where the ecosystem is dominated by a mixture of C4 and C3 plants. Comparing theδ13C values of modern soil and loess-paleosol sequences from the Xunyi profile, we conclude that C3 plants dominated the landscape during loess sediment stages, while C4 plants expanded during paleosol stages.

关键词： modern soil, paleosol, carbon isotope, paleovegetation, Loess Plateau.

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Lightning Overvoltages on Rural Distribution Lines

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高电压技术 2008年第12期34卷 2564-2569页

作者： A. Piantini D. M. Duarte F. Romero CENDAT-iee/USP University of Sao Paulo Av. Prof. Luciano Gualberto 1289 Sao Paulo Brazil

This paper presents information concerning the characteristics of lightning overvoltages on overhead power distribution *** on the protection measures against transients caused by both direct and indirect strokes is a... 详细信息

关键词：农村配电线路雷电过电压保护间接雷击

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Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

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Energy and AI 2023年第4期14卷 283-293页

作者： Malte Lehna Jan Viebahn Antoine Marot Sven Tomforde Christoph Scholz Fraunhofer Institute for Energy Economics and Energy System Technology(iee) Germany Kassel University:Intelligent Embedded Systems Germany TenneT TSO BV Netherlands AI Lab Reseau de Transport d’Electricite(RTE)France Kiel University:Intelligent Systems Germany

The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy *** a consequence,active grid management is reaching its limits with conventional *** the context of the Learning to Run a Power Network(L2RPN)challenge,it has been shown that Reinforcement Learning(RL)is an efficient and reliable approach with considerable potential for automatic grid *** this article,we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent,both for the RL and the rule-based *** main improvement is a N-1 strategy,where we consider topology actions that keep the grid stable,even if one line is ***,we also propose a topology reversion to the original grid,which proved to be *** improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%.In direct comparison between rule-based and RL agent we find similar ***,the RL agent has a clear computational *** also analyse the behaviour in an exemplary case in more detail to provide additional ***,we observe that through the N-1 strategy,the actions of both the rule-based and the RL agent become more diversified.

关键词： Deep reinforcement learning Electricity grids Learning to run a power network Topology control Proximal policy optimisation

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Enabling Virtual Met Masts for wind energy applications through machinelearning-methods

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能源与人工智能(英文) 2023年第1期11卷 26-42页

作者： Sandra Schwegmann Janosch Faulhaber Sebastian Pfaffel Zhongjie Yu Martin Dörenkämper Kristian Kersting Julia Gottschall Fraunhofer IWES Küpkersweg 7026129 OldenburgGermany Fraunhofer iee Königstor 5934119 KasselGermany Technical University of Darmstadt Karolinenplatz 564289 DarmstadtGermany Hessian Center for AI(hessian.AI) Mornewegstraße 3064293 DarmstadtGermany

As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services.

关键词： Virtual Met Mast(VMM) Wind power Machine learning Reanalysis data Site assessment Weather Research and Forecasting Model(WRF) Measure-Correlate-Predict(MCP)

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