ELEVATE Publications

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https://hdl.handle.net/20.500.14394/54517

Now showing 1 - 5 of 13

Weatherization and Energy Security: a Review of Recent Events in ERCOT
(2022-01-01) Zakeri, Golbon; HMaria Hernandez, Maria; Lackner, Matthew; Manwell, James
Purpose of Review This review addresses the question of energy security. With the transition of energy generation fleet to cleaner, more sustainable electricity production, energy security is a topic of increasing importance. Recent Findings Recent events in Texas brought the concept of energy security to the fore. In this review, we examine the makeup of electricity generation and the causes of the February 2021 blackout of Texas. We will investigate the cost/benefit of winterization in Texas and ask why this was not undertaken subsequent to a similar event in 2011. Summary We investigate the case of Texas blackout of February 2021 and estimate the cost of prevention of this undesirable outcome. We suggest that market mechanisms need to be in place to incentivize electricity producers to ensure energy security going forward.
VPeak: Exploiting Volunteer Energy Resources for Flexible Peak Shaving
(2021-01-01) Bovornkeeratiroj, Phuthipong; Wamburu, John; Irwin, David; Shenoy, Prashant
Traditionally, utility companies have employed demand response for large loads or deployed centralized energy storage to alleviate the effects of peak demand on the grid. The advent of Internet of Things (IoT) and the proliferation of networked energy devices have opened up new opportunities for coordinated control of smaller residential loads at large scales to achieve similar benefits. In this paper, we present VPeak, an approach that uses residential loads volunteered by their owners for coordinated control by a utility for grid optimizations. Since the use of volunteer resources comes with hard limits on how frequently they can be used by a remote utility, we present machine learning techniques for carefully selecting which days to operate these loads based on expected peak demand. VPeak uses a distributed and heterogeneous pool of volunteer loads to implement flexible peak shaving that can either selectively target hotspots within the distribution network or perform grid-wide peak shaving. Our results show that VPeak is able to shave up to 26% of the total demand when selectively shaving peaks at local hotspots and up to 46.7% of the demand for grid-wide peak shaving.
The Sustainability Of Decarbonizing The Grid: A Multi-Model Decision Analysis Applied To Mexico
(2022-01-01) Mercado Fernandez, Rodrigo; Baker, Erin
Mexico recognizes its vulnerability to the effects of climate change, including sea level rise, increasing average temperatures, more frequent extreme weather events and changes to the hydrological cycle. Because of these concerns Mexico has a vested interest in developing sustainable strategies for mitigating climate change as it develops its electricity grid. In this study, we use a set of sustainability criteria to evaluate a number of model-derived pathways for the electricity grid aimed at meeting Mexico's climate goals. We use a multi-step approach, combining pathways from multiple large scale global models with a detailed electricity model to leverage geographic information into our multi-criteria sustainability analysis. We summarize the overall ranking of each expansion plan with the use of the weighted sum method. We find that the expansion plans with more than 20% of energy coming from carbon capture and storage (CCS) technologies tend to be less sustainable. While CCS technologies have low GHG emissions, they have high air pollution and water-use and require the development of extensive pipeline networks. In particular, these CCS characteristics pose concerns from an environmental justice perspective as high air pollution and water-use can significantly effect local communities: the plan with the most CCS has an extra 14 kg/GWh of weighted air pollution emissions and 199,000 liters/GWh of weighted water use compared to the plan with the most renewables. This analysis provides novel insights on tradeoffs that decisions makers must consider when looking at different sustainable development options to reach long term climate goals.
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning
(2022-01-01) Bansal, Akansha Singh; Bansal, Trapit; Irwin, David
ABSTRACT Solar energy is now the cheapest form of electricity in history. Unfortunately, signi.cantly increasing the electric grid’s fraction of solar energy remains challenging due to its variability, which makes balancing electricity’s supply and demand more di.cult. While thermal generators’ ramp rate—the maximum rate at which they can change their energy generation—is .nite, solar energy’s ramp rate is essentially in.nite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warnings to adjust thermal generator output in response to variations in solar generation to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Speci.cally, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal spectral data collected by the recently launched GOES-R series of satellites. Our model estimates a location’s near-term future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-speci.c solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-speci.c characteristics. We evaluate our approach for di.erent coverage areas and forecast horizons across 25 solar sites and show that it yields errors close to that of a model using ground-truth observations.
PeakTK: An Open Source Toolkit for Peak Forecasting in Energy Systems
(2021-01-01) Bovornkeeratiroj, Phuthipong; Wamburu, John; Irwin, David; Shenoy, Prashant
As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.

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