From Data to Knowledge to Action: Enabling the Smart Grid
- URL: http://arxiv.org/abs/2008.00055v1
- Date: Fri, 31 Jul 2020 19:43:48 GMT
- Title: From Data to Knowledge to Action: Enabling the Smart Grid
- Authors: Randal E. Bryant, Randy H. Katz, Chase Hensel, and Erwin P.
Gianchandani
- Abstract summary: "The Grid" is a relic based in many respects on century-old technology.
Many people are pinning their hopes on the "smart grid"
Initial plans for the smart grid suggest it will make extensive use of existing information technology.
- Score: 0.11726720776908521
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Our nation's infrastructure for generating, transmitting, and distributing
electricity - "The Grid" - is a relic based in many respects on century-old
technology. It consists of expensive, centralized generation via large plants,
and a massive transmission and distribution system. It strives to deliver
high-quality power to all subscribers simultaneously - no matter what their
demand - and must therefore be sized to the peak aggregate demand at each
distribution point. Ultimately, the system demands end-to-end synchronization,
and it lacks a mechanism for storing ("buffering") energy, thus complicating
sharing among grids or independent operation during an "upstream" outage.
Recent blackouts demonstrate the existing grid's problems - failures are rare
but spectacular. Moreover, the structure cannot accommodate the highly variable
nature of renewable energy sources such as solar and wind. Many people are
pinning their hopes on the "smart grid" - i.e., a more distributed, adaptive,
and market-based infrastructure for the generation, distribution, and
consumption of electrical energy. This new approach is designed to yield
greater efficiency and resilience, while reducing environmental impact,
compared to the existing electricity distribution system. Initial plans for the
smart grid suggest it will make extensive use of existing information
technology. In particular, recent advances in data analytics - i.e., data
mining, machine learning, etc. - have the potential to greatly enhance the
smart grid and, ultimately, amplify its impact, by helping us make sense of an
increasing wealth of data about how we use energy and the kinds of demands that
we are placing upon the current energy grid. Here we describe what the
electricity grid could look like in 10 years, and specifically how Federal
investment in data analytics approaches are critical to realizing this vision.
Related papers
- A Perspective on Foundation Models for the Electric Power Grid [53.02072064670517]
Foundation models (FMs) currently dominate news headlines.
We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities.
We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
arXiv Detail & Related papers (2024-07-12T17:09:47Z) - Non-Intrusive Electric Load Monitoring Approach Based on Current Feature
Visualization for Smart Energy Management [51.89904044860731]
We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management.
We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
arXiv Detail & Related papers (2023-08-08T04:52:19Z) - Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - Evaluating Distribution System Reliability with Hyperstructures Graph
Convolutional Nets [74.51865676466056]
We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning.
Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
arXiv Detail & Related papers (2022-11-14T01:29:09Z) - Machine learning applications for electricity market agent-based models:
A systematic literature review [68.8204255655161]
Agent-based simulations are used to better understand the dynamics of the electricity market.
Agent-based models provide the opportunity to integrate machine learning and artificial intelligence.
We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
arXiv Detail & Related papers (2022-06-05T14:52:26Z) - Knowledge- and Data-driven Services for Energy Systems using Graph
Neural Networks [0.9809636731336702]
We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs)
The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality.
Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
arXiv Detail & Related papers (2021-03-12T13:00:01Z) - Virtual Microgrid Management via Software-defined Energy Network for
Electricity Sharing [10.13696311830345]
This article proposes an approach to build a virtual microgrid operated as a software-defined energy network (SDEN)
The proposed cyber-physical system presumes that electrical energy is shared among its members and that the energy sharing is enabled in the cyber domain by handshakes inspired by resource allocation methods utilized in computer networks, wireless communications, and peer-to-peer Internet applications (e.g., BitTorrent)
This article concludes that the proposed solution generally complies with the existing regulations but has highly disruptive potential to organize a dominantly electrified energy system in the mid- to long-term.
arXiv Detail & Related papers (2021-02-01T06:09:40Z) - GridTracer: Automatic Mapping of Power Grids using Deep Learning and
Overhead Imagery [9.955168581633663]
We propose to automatically map the grid in overhead remotely sensed imagery using deep learning.
We develop and publicly-release a large dataset ($263km2$) of overhead imagery with ground truth for the power grid.
arXiv Detail & Related papers (2021-01-16T07:23:42Z) - Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers.
We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid.
To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
arXiv Detail & Related papers (2020-03-02T20:32:10Z) - A Review of Blockchain-based Smart Grid: Applications,Opportunities, and
Future Directions [0.0]
This paper provides a review of blockchain architecture, concepts, and applications in smart grids.
Various potential opportunities for blockchain technology with smart grids are also discussed.
arXiv Detail & Related papers (2020-01-31T07:00:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.