Motivates the need for improvements in information representation required by complex systems such as the space environment and presents the details of an April 2022 Workshop to explore the connection between Space Weather, Power Grid Resilience, and Open Knowledge Networks.
This is an essay in the 'Space Data Knowledge Commons' series, with the goal of the knowledge commons to cultivate a community of practice and the tools for interconnecting and making more accessible space data and knowledge. The piece captures the well-developed space data use case to connect solar phenomena, space weather, and impacts to the electric power grid, thereby defining key dimensions that a space data knowledge graph would need to capture. It motivates the need for improvements in information representation required by complex systems such as the space environment and presents the details of an April 2022 Workshop to explore the connection between Space Weather, Power Grid Resilience, and Open Knowledge Networks.
At 2 a.m. on February 15, the Electric Reliability Council of Texas (ERCOT) declared an Energy Alert Level 3 and utilities began rotating outages due to high consumer demand. The heightened alert was the result of ERCOT officials nervously watching the frequency of the electric power grid drop outside of the narrow 60 hertz band, a number affected by innumerable moving pieces and dynamics from the weather to the operation of the power grid to the user demand on the system.
Reference: Unsplash (https://unsplash.com/photos/U6tYeEhEVTk)
The events of February 2021 reawakened the world to the precarity of the power grid, a massively complex and integrated system whose resilience in the face of the variability of the natural and human world is anything but guaranteed. The way we see and attempt to control the grid is like trying to know everything about a room we aren't standing in when all we have is a temperature reading from a thermometer within it.
The grid is at the whim of the myriad forces of the natural world and the vicissitudes of human behavior, a truly complex system. Now, as our Sun's cycle ramps back up in activity, we are again being reawakened to a threat to the power grid that has the potential to push the system beyond its tipping point.
In March 1989, the Hydro-Québec power grid collapsed leaving 6 million people without electricity for approximately nine hours [North American Electric Reliability Corporation (NERC), 1990]. The day before, a solar flare and accompanying release of plasma and magnetic fields sent a mountain of energy propelling toward Earth at a million miles an hour. The complex interactions of the solar cloud of plasma with the near Earth space and terrestrial environment – “space weather” – pushed the electric power grid to a tipping point that could not be understood within any single one of those systems [Boteler, 2019]. In addition to power grid blackouts, space weather has the potential to degrade and destroy satellites, disrupt aviation, enhance corrosion of oil/gas pipelines, and knock out Global Navigation and Satellite Systems (including GPS) and various communication systems.
Example of damage space weather can cause to power grid transformers. Photos courtesy of PSE&G.
Space Weather disturbances are yet another inextricable factor in creating a resilient power grid. A project within the National Science Foundation (NSF) Convergence Accelerator called The Convergence Hub for the Exploration of Space Science (CHESS) has been working to understand this factor, especially in the context of convolved influences such as terrestrial weather, social behavior, and policy.
Now, while we are immersed in an ongoing global pandemic and climate crises, the CHESS project is convening the cross-disciplinary community that covers the entire Sun-to-Power Grid system to conduct a 'simulation game' to identify the research & development and operational gaps and to propose solutions for those gaps.
The electric power grid is perhaps the most critical system affected by space weather, yet ironically may be the least well-specified. During periods of enhanced space weather activity, a series of physical processes beginning with the launch of a coronal mass ejection (CME) or a high speed stream (HSS) from the Sun gives rise to intense electric currents reaching millions of Amperes surrounding the Earth, which then become electric currents on the ground flowing through electrical transmission lines. This phenomenon, known as Geomagnetically Induced Currents (GICs), can disrupt the operation of high-voltage power grid transformers via overheating and generation of harmonics, potentially leading to failures. The March 1989 geomagnetic storm mentioned is an example, but we know that more severe solar events have occurred (see 1859 Carrington Event) and will happen again.
Space weather is a global and imminent threat, falling in the same likelihood of occurrence in the next five years as “pandemic influenza” on the United Kingdom Risk Registry [UK Cabinet Office, 2017], but we are less prepared for a space weather disaster than a pandemic. Progress is precluded by an artificial separation of the relevant disciplines.
Power utility companies measure and keep vast amounts of data on transformer malfunctions, ground currents, and various other effects of the large-scale currents, but do not have access to, nor a comprehensive understanding of, the physics of the space environment. The space scientists that study such large ionospheric and magnetospheric currents generally do not know nor have any access to power system information about GICs and transformer malfunctions. Conversely, power utility companies may not know what events are related to space weather, and which are not. This disconnection is the main problem our proposal seeks to solve.
A geomagnetic disturbance (GMD) that in isolation may be relatively benign, may have severe effects when compounded by other factors, such as a snowstorm, power system load, or regular/ongoing equipment repairs and maintenance. This is exacerbated by the fact that we do not have a unified national power grid, but rather a consortium of regional utility providers, which have markedly different sizes, capabilities, levels of preparedness, and design structure. As a result, there is disagreement about the vulnerability of transformers and the grid to GMDs [NERC, 2011].
To reimagine grid resilience, data from diverse fields must be open and broadly usable and the traditionally disparate communities must be connected.
The Sun-to-Power Grid information flow as discovered and designed by the CHESS project. Design courtesy of Cristina Gonzalez (https://www.behance.net/exercisingcement)
Complex systems require deeper consideration of the relationships between its interacting parts. For the power grid this might mean understanding the activity of the Sun and space environment, the changes in the daily weather patterns, the behavior of the population in a given area dictating demands on their local grid, and the downstream effects from changes to one part of the grid on the rest of the interconnected network.
You might think of this complexity like a beehive. The flourishing of the hive depends on the actions of all of the bees within it, gathering pollen, relaying information about food sources, transforming the pollen into honey and shelter as it returns. The beehive is a magnificent information processing system in which all of the interacting parts are connected to make the functioning of the entire system possible.
The lesson is that better understanding of a complex system lies in the relationships between its parts, in it's graph. A graph is a collection of parts and, importantly, the relationships that connect them. One of the most recognizable examples of a graph is a social graph (or network), which represents the structure people joined by some definition of their connection.
Sample social network visualization [Cambridge Intelligence, 2021]
Networks [graphs] are also at the heart of some of the most revolutionary technologies of the 21st century, empowering everything from Google to Facebook, CISCO, and Twitter. At the end, networks permeate science, technology, business and nature to a much higher degree than it may be evident upon a casual inspection. Consequently, we will never understand complex systems unless we develop a deep understanding of the networks behind them. -Albert-László Barabasi [Barabasi, 2015]
The expressive capacity of the graph, or network, structure has led to transformational understanding of many complex systems.
A more resilient electric power grid requires the graph structure to connect information about it. The technology that will provide that capability is the knowledge graph, a semantic technology that does two things:
Bring data from all parts of the system together in a scalable and structured resource that ensures the data are calibrated, quality controlled, time synchronized and geolocated to enable convergent analysis.
Create a data ecosystem that provides a natural machine-human interface to these diverse data and enables a robust and efficient approach to posing and answering questions.
KGs explicitly structure information using a data model/schema/ontology that defines entities (objects, events, situations or abstract concepts) and their relationships. It is a collection of interlinked descriptions of entities – objects, events or concepts. An example is DBpedia [Lehmann et al., 2015], a collection of 400 million facts describing 3.7 million things, obtained from Wikipedia--all of which are themselves interlinked, as is the page structure on the Wikipedia website.
Typically, a KG is a representation of a single domain's knowledge. Their power is in the graph-based approach to the information representation to which new relationships can be defined that link different domain's knowledge. Linked KGs becomes a Knowledge Network (KN). Since 2019, the Convergence Hub for the Exploration of Space Science (CHESS) project has been building an Open KN (OKN) for Space Weather, beginning with the space weather impact on the electric grid as a powerful use case for reimagining space weather information as a network.
Linking data in a machine- and human-readable manner via KGs overcomes artificial disciplinary boundaries. Via the CHESS OKN solar physicists can interact with power grid operators. Power grid operators from one state can interact with those from another, or even a different country. Thus, the graph representation is a vital component of creating more diverse, cohesive, cross-disciplinary communities (convergence and Enhancing the Effectiveness of Team Science), improving scientific creativity and discovery; in short, treating complex systems holistically.
Design courtesy of Cristina Gonzalez (https://www.behance.net/exercisingcement)
How can the connected information be used to bring resilience to the power grid? How can information be presented to power grid operators in a meaningful and actionable manner?
These are questions that animate the next step in the CHESS project and the Sun-to-Power Grid community: a three-day workshop that will bring together participants from each interconnected part of the community to identify the research & development and operational gaps and to create solutions for those gaps. The event will be structured around a space weather-to-power grid ‘simulation game,’ a low-risk, cost-effective environment to unite researchers, decision-makers, and operators to assess the preparedness for threats and hazards posed by space weather on the electric power grid.
The CHESS OKN is the illustrative example of how we will build a space data knowledge commons [McGranaghan et al., 2021]. First, we will identify the powerful space data use cases, answer questions such as: What topics are relevant in this application of space data? How do they connect with each other? What communities are present for each topic? What people and data materials are being produced in those communities?
The knowledge commons will emerge as a synthesis of these use cases, emerging the most important dimensions, cultivating the participatory community, and self-organizing into a sustainable, diverse, and inclusive system.
Boteler, D. H. (2019). A 21st century view of the March 1989 magnetic storm. Space Weather, 17, 1427– 1441. https://doi.org/10.1029/2019SW002278
Lehmann, Jens et al. ‘DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia’. 1 Jan. 2015 : 167 – 195. DOI: 10.3233/SW-140134.
McGranaghan, R., Klein, S. J., Cameron, A., Young, E., Schonfeld, S., Higginson, A., … Thompson, B. (2021). The need for a Space Data Knowledge Commons. Structuring Collective Knowledge. Retrieved from https://knowledgestructure.pubpub.org/pub/space-knowledge-commons.
NERC, “The 1989 System Disturbances” NERC Disturbance Analysis Working Group Report, “March 13, 1989 Geomagnetic Disturbance.” , pp. 8-9, 36-60.
NERC, “2011 North American Electric Reliability Corporation (NERC) Workshop on Geomagnetic Disturbances (GMDs).” , accessed via: http://www.nerc.com/files/GMD_Draft_Proceedingst_Nov_10_2011_v3.pdf on May 30, 2019.