Consideration Of Uncertainties Research Articles

Benchmark dose modeling for epidemiological dose-response assessment using case-control studies.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Feasibility of coaxial deep borehole heat exchangers in southern California

This paper investigates the feasibility of coaxial deep borehole heat exchanger (CDBHE) applications to the University of California San Diego (UCSD) campus. By collecting different geophysical source data for various formations and well logs around the UCSD campus, a multilayered thermophysical model for the ground on the site is established. Water circulation within a closed coaxial loop system considers the geothermal energy extraction under uncertainty consideration of the unknown deeper layers heat flow gradient as coupled with the variation of pipe insulation properties, flow rates, outer pipe diameter, grout, and depths between 1 and 4 km. A finite-element framework models the Navier–Stokes fluid flow and heat transfer in the CDBHE system, validated with a field test on CDBHE from the literature. Results show that a 4-km CDBHE could produce a thermal power of 600 kW under the optimum geological conditions at the UCSD site: the water flow rate of 2.78 L/s and a ground thermal gradient of 60 ℃/km. Thermal power shares from different layers indicate that deeper formation layers contribute more to the thermal power than the shallower layers because increasing the CDBHE length from 1 to 4 km can lead to a maximum of 900% increase in thermal power and a 50% expansion in thermal plume for a CDBHE with an insulated inner pipe between the upper and lower bound heat flow bounds. An inner pipe with an insulated depth of 2 km produces only 1–6% less power than a fully insulated inner pipe for the 4-km CDBHE, and thus, a partially insulated vacuum-insulated tube (VIT)-plastic inner pipe is suggested as the best practice. Furthermore, the CDBHE thermal power increases by 5% when the grout thermal conductivity increases from 1 to 3.65 W/(K∙m), close to the formation thermal conductivity, and then maintains almost the same, and the 4-km CDBHE with flow rates of 2.78–6.94 L/s at the UCSD site can directly supply a low-temperature heating radiator system for room heating. This study suggests practical ranges for geothermal energy extraction for southern California. A CDBHE with a well-insulated inner pipe of 0.05 W/(m∙K), the thermal power of lower and upper-bound heat flow cases can vary by 60% from the mean. Finally, water as the working fluid is more efficient than CO2, doubling CDBHE's thermal power. The effects of the investigated factors provide guidelines for future geothermal resource exploitation in southern California.

Uncertainty-Aware Time Series Anomaly Detection

Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise. In this paper, we propose LSTMAE-UQ (Long Short-Term Memory Autoencoder with Aleatoric and Epistemic Uncertainty Quantification), a novel approach that incorporates both aleatoric (data noise) and epistemic (model uncertainty) uncertainties for more robust anomaly detection. The model combines the strengths of LSTM networks for capturing complex time series relationships and autoencoders for unsupervised anomaly detection and quantifies uncertainties based on the Bayesian posterior approximation method Monte Carlo (MC) Dropout, enabling a deeper understanding of noise recognition. Our experimental results across different real-world datasets show that consideration of uncertainty effectively increases the robustness to noise and point outliers, making predictions more reliable for longer periodic sequential data.

Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system

Aim. Development of method for prediction by energy-saturated extended technical objects magnetic silencing based on magnetostatics geometric inverse problems solution and magnetic field spatial spheroidal harmonics calculated in prolate spheroidal coordinate system taking into account of technical objects magnetic characteristics uncertainties. Methodology. Spatial prolate spheroidal harmonics of extended technical objects magnetic field model calculated as magnetostatics geometric inverse problems solution in the form of nonlinear minimax optimization problem based on near field measurements for prediction far extended technical objects magnetic field magnitude. Nonlinear objective function calculated as the weighted sum of squared residuals between the measured and predicted magnetic field COMSOL Multiphysics software package used. Nonlinear minimax optimization problems solutions calculated based on particle swarm nonlinear optimization algorithms. Results. Results of prediction extended technical objects far magnetic field magnitude based on extended technical objects spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system using near field measurements with consideration of extended technical objects magnetic characteristics uncertainty. Originality. The method for prediction by extended technical objects magnetic cleanliness based on spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system with consideration of magnetic characteristics uncertainty is developed. Practical value. The important practical problem of prediction extended technical objects magnetic silencing based on the spatial prolate spheroidal harmonics of the magnetic field model in the prolate spheroidal coordinate system with consideration of extended technical objects magnetic characteristics uncertainty solved. References 48, figures 2.

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Feasibility of Photonuclear Transmutation of Long-Lived Fission Products Extracted from Used Nuclear Fuel

To achieve the goal of net-zero carbon emission in energy production, nuclear power capacity and waste generation are expected to expand significantly in the next few decades. In the condition of continuous fuel recycling, long-lived fission products (LLFPs) are dominant contributors to the disposal impacts of nuclear waste. In this study, six LLFPs, including 99Tc, 129I, 135Cs, 126Sn, 93Zr, and 79Se, were identified as the primary contributors to more than 99% of long-term radiotoxicity of disposed nuclear waste across a wide range of fuel cycle scenarios. To reduce the amounts of LLFPs sent to geological repositories, the nuclear transmutation of LLFPs is being pursued. Specifically, this work systematically assessed the feasibility of transmuting LLFPs via photonuclear reactions. Photon transmutation is physically viable for the identified primary LLFPs except for 99Tc. For the five transmutable LLFPs, the achievable photon transmutation performance without isotopic separation was evaluated based on scoping calculations and consideration of nuclear data uncertainties. Using an extremely intense laser Compton photon source of 1019 γ /s, the effective transmutation half-life can be reduced to a few years. However, the absolute transmutation rates of LLFPs remain below 1 kg/yr. The energy required to power the photon source for transmuting all LLFPs produced in a nuclear reactor exceeds the net energy output of the reactor. Several potential strategies for improving photon transmutation performance were analyzed. None can substantially enhance the performance to make it practical for industrial applications.

Efficient Approximation of Varying Fiber Orientation States in Injection Molded Parts Under Consideration of Multiple Manufacturing Uncertainties

AbstractThe production of high-quality fiber reinforced polymer parts is an important aspect in several industrial areas. However, due to unavoidable uncertainties in material and manufacturing processes, the part quality scatters. One important aspect here is the fiber orientation, being crucial for the thermo-mechanical properties of the part and being influenced by the uncertain material state and process conditions. Process simulations are an important tool for predicting the fiber orientation, but state-of-the-art simulations are normally deterministic and represent only one specific case. Performing enough deterministic simulations to model manufacturing uncertainties requires high numerical effort. Therefore, this work presents methods to quickly and efficiently approximate the fiber orientation under varying material and process parameters, requiring only a few simulations as input. Different schemes for approximation are evaluated and compared with each other and with 3D process simulations.

Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.

The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined. A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

Gravimetric and SIM-Headspace GC-MS for Residual Organic Solvents Detection in Halal and Wholesomeness Food Analysis

The demand for halal foods and beverages is increasing globally. While most halal analysis focuses on porcine, this study focuses on assessing residual organic solvents to ensure their halal compliance and wholesomeness, following several Malaysian standards and guidelines. A significant challenge in this study was the volatility of the residual solvents during the preparation of standards and quality control. To address this issue, a gravimetric technique was employed and effectively minimized the difference between theoretical (1,000 ppm) and actual (710 – 892 ppm) concentrations of the residual organic standard stock solution, except for acetone (588 ppm). The aim of this study was to establish a validated, reliable, and accurate method using SIM-headspace GC-MS to identify and quantify residual organic solvents for halal and wholesomeness analysis. Confirmation of each residual organic solvent was achieved by comparing the obtained spectra with the NIST 11 spectral database, containing 70,832 compounds, with similarity ranging from 80.9% to 96.6%, except for acetonitrile at 52.2%. The validation parameters were carried out according to ISO 17025:2017, the Center for Drug Evaluation and Research, and the European Guidelines. The parameters included recovery ranging from 95.65% to 95.68%, precision from 10.08% to 19.65% RSD, linearity between 0.996 to 0.999, limit of detection from 0.01 to 0.08 ppm, and limit of quantification from 0.02 to 0.24 ppm. Uncertainty considerations were limited to recovery, precision, and linearity, as other uncertainties were negligible based on the bottom-up approach using in-house validation data. This combination of gravimetric and SIM-headspace GC-MS techniques has provided valuable insights for discussions and collaborations among halal authorities worldwide to establish a consensus analytical methodology for halal and wholesomeness assessment.

A probabilistic approach for the levelized cost of energy of floating offshore wind farms

This paper aims to analyze the levelized cost of energy (LCOE) of floating offshore wind farms from a probabilistic point of view. Understanding and addressing the uncertainty associated with the main parameters that influence the wind energy generated by a wind farm during its lifetime is crucial for the economic evaluation of offshore wind energy in the broader energy landscape. The methodology for probabilistic assessment of LCOE is introduced, and the uncertainty in input parameters are discussed. In a base case study, an assumed Floating Offshore Wind Farm (FOWT) consisting of 250 5-MW wind turbines is considered. The use of bias and randomness in key random variables is discussed and studied in detail. Results indicate that LCOE estimates of 15 EURc/kWh for offshore wind turbines are achievable with reasonable confidence, while estimates of 5 EURc/kWh require careful consideration of uncertainty in the wind farm’s parameters. The feasibility analysis showed that techno-economic parameters are more influenced by wind characteristics and efficient use of wind turbines than by the cost of the wind farm. This paper provides general guidance on how to carry out early-stage techno-economic analysis of FOWFs.

Psychosis Prognosis Predictor: A continuous and uncertainty-aware prediction of treatment outcome in first-episode psychosis.

Machine learning models have shown promising potential in individual-level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision-making. We devised a recurrent neural network architecture incorporating long short-term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points. To account for model uncertainty, we employed a novel fuzzy logic approach to integrate the level of uncertainty into individual predictions. We predicted antipsychotic treatment outcomes in 446 first-episode psychosis patients in the OPTiMiSE study, for six different clinical scenarios. The treatment outcome measures assessed at both week 4 and week 10 encompassed symptomatic remission, clinical global remission, and functional remission. Using only baseline predictors to predict different outcomes at week 4, leave-one-site-out validation AUC ranged from 0.62 to 0.66; performance improved when clinical data from week 1 was added (AUC = 0.66-0.71). For outcome at week 10, using only baseline variables, the models achieved AUC = 0.56-0.64; using data from more time points (weeks 1, 4, and 6) improved the performance to AUC = 0.72-0.74. After incorporating prediction uncertainties and stratifying the model decisions based on model confidence, we could achieve accuracies above 0.8 for ~50% of patients in five out of the six clinical scenarios. We constructed prediction models utilizing a recurrent neural network architecture tailored to clinical scenarios derived from a time series dataset. One crucial aspect we incorporated was the consideration of uncertainty in individual predictions, which enhances the reliability of decision-making based on the model's output. We provided evidence showcasing the significance of leveraging time series data for achieving more accurate treatment outcome prediction in the field of psychiatry.

Integrating heterogenous robustness levels to the multi-objective target-oriented robust optimization of a microalgal biorefinery

Integrated biorefineries offer an efficient way to produce biofuels by enhancing profit through the generation of marketable by-products and by reducing environmental impact by reutilizing waste streams originating from different processes within the system. Employing a variety of conversion technologies, integrated biorefineries yield a wide range of products and byproducts, encompassing biofuels, biochemicals, and bioenergy, all derived from biomass feedstocks. Several mathematical methods have been developed in order to optimize the design of biorefineries with consideration for uncertainty, including the multi-objective target-oriented robust optimization (MOTORO) approach that enables target-seeking behavior while maximizing the tolerable deviation of parameters in the system. However, the phenomenon of heterogenous robustness levels that tradeoff risks with each other has not been investigated in both MOTORO and in wider robust optimization literature. The present study addresses this gap by applying multiple, potentially non-equal degrees of robustness to each customer's demand, allowing stakeholders to place unequal risks on each customer. As a result, limited resources may be more efficiently utilized in line with existing knowledge or preference regarding uncertainties. The proposed improvement was applied to an algal biorefinery system with two customers, where robustness successfully exhibited tradeoffs with each other while retaining inverse relationships with system underperformance and variation as evaluated from a Monte Carlo simulation.

Investigation of the time-dependent bearing capacities of concrete structures in different environments considering climate change

The escalating trend of emissions of greenhouse gases, including CO2, prompts significant modifications in climate variables, such as temperature and relative humidity (RH). For concrete civil infrastructures, the carbonation and chlorination corrosion processes are sensitive to changes in climate variables. The focus of this study is to evaluate the time-sensitive deterioration of concrete components in different corrosion environments with consideration of large uncertainties of climate change. Future projections for atmospheric CO2, temperature, and RH until the end of the twenty first century are examined. By implementing an aging corrosion model that incorporates climate change, the study observes that under the SSP5-8.5 emission scenario, the average carbonation depth for the RC structures in the general atmospheric environment increases by 58.7%. Furthermore, the chlorination corrosion rate in marine environments nearly doubles that under aging conditions alone. By 2100, corrosion-affected RC beams in the SSP5-8.5 scenario will lose 9.5 and 29.2% of their flexural capacity, respectively. Moreover, under carbonation and chlorination environments, the beams will face an additional loss of 20.7 and 27.6% in shear capacity, respectively. In performing the above calculations, we have considered the uncertainty prediction of climate change and the uncertainty analysis of the influence of model parameters and material variability.

Resilient Neuroadaptive Distributed Fixed-Time Attitude Coordination Control for Multiple Spacecraft.

This work studies the attitude coordination tracking problem for multiple spacecraft with consideration of unintended faults (communication link faults and actuator faults), inertial uncertainties, and external disturbances under a directed communication graph. A resilient neuroadaptive distributed fixed-time control scheme is investigated to solve this challenging problem. First, an improved adaptive distributed observer is established for followers to estimate the states of the leader when considering communication link faults. The proposed observer improves the resilience against communication link faults. Subsequently, to further cope with the problem of actuator faults, inertial uncertainties, and external disturbances, based on the proposed observer and the technique of adding a power integrator, a neuroadaptive distributed fixed-time attitude coordination controller is developed. Unlike the existing controllers, the proposed one requires less information when dealing with faults and lumped uncertainties, and has a lower-computational cost. Moreover, the fixed-time stability of the closed-loop system is ensured under the designed resilient neuroadaptive distributed control scheme. Finally, comparative simulations are carried out to manifest the effectiveness of the investigated coordination control method.

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Explicit consideration of geomaterial uncertainties in the load resistance factor design of piles driven into rock-based intermediate geomaterials

The study presents a method for estimating the end bearing of piles driven into rock-based intermediate geomaterials (IGMs). Existing design methods, rooted in soil mechanics, often fall short when applied to IGMs. Moreover, the geomaterial (geological and ground properties) uncertainties are neglected and the piles are designed based on subsurface information from distant boreholes. To address this gap, the study introduces an approach that explicitly considers geomaterial uncertainties by combining empirical models and geostatistical simulation to predict end bearing. Using data from 87 piles driven into various rock-based IGMs with dynamic load testing as the construction control technique, this study introduces the Proposed Design Method (PDM) for the prediction of end bearing. PDM is compared to the Current Design Method (CDM), which is based on traditional soil-mechanics methods. Predictions from these methods are subsequently evaluated against the measured end bearings. The results demonstrate that while the PDM yields comparable predictions to the CDM, it offers a more robust consideration of geomaterial uncertainties. The PDM leads to an approximate 13.60% to 19.35% increase in uncertainties compared to CDM across various IGMs. These findings advance the understanding of pile behavior in the presence of geomaterial uncertainties and inherent variability.

Fabrication tolerant multi-layer integrated photonic topology optimization

Optimal multi-layer device design requires consideration of fabrication uncertainties associated with inter-layer alignment and conformal layering. We present layer-restricted topology optimization (TO), which we believe to be a novel technique which mitigates the effects of unwanted conformal layering for multi-layer structures and enables TO in multi-etch material platforms. We explore several approaches to achieve this result compatible with density-based TO projection techniques and geometric constraints. Then, we present a robust TO formulation to design devices resilient to inter-layer misalignment. The novel constraint and robust formulation are demonstrated in 2D grating couplers and a 3D polarization rotator.

How wind forecast updates are balanced in coupled European intraday markets

AbstractThe market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future.