In this paper, we developed an Electromagnetic Transient (EMT) model tailored for large cryptocurrency mining loads to understand the cross-interaction of these loads with the electric grid.
Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level.
The electrification of heavy-duty vehicles (HDEVs) is a rapidly emerging avenue for decarbonization of energy and transportation sectors.
Hosting capacity analysis (HCA) examines the amount of DERs that can be safely integrated into the grid and is a challenging task in full generality because there are many possible integration of DERs in foresight.
Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources.
One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand.
The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e. g. difficulty in guaranteeing intra- and inter-operator repeatability.
One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize its region of attraction as the stability region.
We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas.
The February 2021 Texas winter power outage has led to hundreds of deaths and billions of dollars in economic losses, largely due to the generation failure and record-breaking electric demand.
This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification.
This article presents a use-inspired perspective of the opportunities and challenges in a massively digitized power grid.
In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability.
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation.
It also conducts a preliminary study on using energy storage and load rationing to mitigate rotating blackout's adverse impact on the grid.
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids.
In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i. e., a set of linear equations.
Furthermore, the column-and-constraint generation algorithm is used to solve the two-stage robust planning problem and tighten theoretical guarantees.
Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.
In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration.
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.
Computers and Society
This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs).
To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector.
This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes.