92 research outputs found
Characterizing and Modeling Wind Power Forecast Errors from Operational Systems for Use in Wind Integration Planning Studies
Analysis of Wind Ramping Product Formulations in a Ramp-constrained Power Grid
Flexible ramping products are designed to compensate the variability and uncertainty of load and intermittent generation. Since their market implementation by the California Independent System Operator (CAISO) and Midcontinent System Operator (MISO), flexible ramping products have garnered much attention. However, it is still unclear how to best formulate wind power plants’ participation in the ramping requirement. This paper investigates different wind ramping product formulations and increasing wind power penetration in the context of a security-constrained unit commitment (SCUC) model. We demonstrate that the ramping model that captures both the intra- and inter-temporal output ramp capability of individual wind power plants reflects the true ramp contribution of the wind fleet. With increasing wind penetration, wind generation curtailments can support the grid’s ramping needs. In addition, we found that increased wind penetration has the potential of lowering ramping and production costs. Numerical case studies performed on the TAMU 2000-bus synthetic network support the findings
Inherent Synchronization in Electric Power Systems with High Levels of Inverter-based Generation
The synchronized operation of power generators is the foundation of electric
power system stability and key to the prevention of undesired power outages and
blackouts. Here, we derive the condition that guarantees synchronization in
electric power systems with high levels of inverter-based generation when
subjected to small perturbations, and perform a parametric sensitivity to
understand synchronization with varied types of generators. Contrary to the
popular belief that achieving a stable synchronized state is tied chiefly to
system inertia, our results instead highlight the critical role of generator
damping in achieving this pivotal state. Additionally, we report the
feasibility of operating interconnected electric grids with a 100% power
contribution from renewable generation technologies with assured system
synchronization. The findings of this paper can set the basis for the
development of advanced control architectures and grid optimization methods and
has the potential to further pave the path towards the decarbonization of the
electric power sector
STint: Self-supervised Temporal Interpolation for Geospatial Data
Supervised and unsupervised techniques have demonstrated the potential for
temporal interpolation of video data. Nevertheless, most prevailing temporal
interpolation techniques hinge on optical flow, which encodes the motion of
pixels between video frames. On the other hand, geospatial data exhibits lower
temporal resolution while encompassing a spectrum of movements and deformations
that challenge several assumptions inherent to optical flow. In this work, we
propose an unsupervised temporal interpolation technique, which does not rely
on ground truth data or require any motion information like optical flow, thus
offering a promising alternative for better generalization across geospatial
domains. Specifically, we introduce a self-supervised technique of dual cycle
consistency. Our proposed technique incorporates multiple cycle consistency
losses, which result from interpolating two frames between consecutive input
frames through a series of stages. This dual cycle consistent constraint causes
the model to produce intermediate frames in a self-supervised manner. To the
best of our knowledge, this is the first attempt at unsupervised temporal
interpolation without the explicit use of optical flow. Our experimental
evaluations across diverse geospatial datasets show that STint significantly
outperforms existing state-of-the-art methods for unsupervised temporal
interpolation
Including operational aspects in the planning of power systems with large amounts of variable generation:A review of modeling approaches
Autonomous Energy Grids
Current frameworks to monitor, control, and optimize large-scale energy systems are becoming increasingly inadequate because of significantly high penetration levels of variable generation and distributed energy resources being integrated into electric power systems; the deluge of data from pervasive metering of energy grids; and a variety of new market mechanisms, including multilevel ancillary services. This paper outlines the concept of autonomous energy grids (AEGs). These systems are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, are extremely secure and resilient (self-healing), and can self-optimize in real time to ensure economic and reliable performance while systematically integrating energy in all forms. AEGs rely on cellular building blocks that can self-optimize when isolated from a larger grid and participate in optimal operation when interconnected to a larger grid. This paper describes the key concepts and research necessary in the broad domains of optimization theory, control theory, big data analytics, and complex system theory and modeling to realize the AEG vision
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Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems
In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Even in ‘ideal’ laboratory environments, cells can experience a high degree of heterogeneity in their environments. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of Multiscale Multiobjective Systems Analysis (MiMoSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MiMoSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MiMoSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures.
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Integrated Energy Planning with a High Share of Variable Renewable Energy Sources for a Caribbean Island
Although it can be complex to integrate variable renewable energy sources such as wind power and photovoltaics into an energy system, the potential benefits are large, as it can help reduce fuel imports, balance the trade, and mitigate the negative impacts in terms of climate change. In order to try to integrate a very large share of variable renewable energy sources into the energy system, an integrated energy planning approach was used, including ice storage in the cooling sector, a smart charging option in the transport sector, and an excess capacity of reverse osmosis technology that was utilised in order to provide flexibility to the energy system. A unit commitment and economic dispatch tool (PLEXOS) was used, and the model was run with both 5 min and 1 h time resolutions. The case study was carried out for a typical Caribbean island nation, based on data derived from measured data from Aruba. The results showed that 78.1% of the final electricity demand in 2020 was met by variable renewable energy sources, having 1.0% of curtailed energy in the energy system. The total economic cost of the modelled energy system was similar to the current energy system, dominated by the fossil fuel imports. The results are relevant for many populated islands and island nations
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