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ARTIFICIAL INTELLIGENCE
peak loads, crucial inputs to power system
planning, using learning attributes
like multi-linear regression or artificial
neural network models.
AI applications to mitigate longstanding
business risks are also thriving. Paramount
among these is cybersecurity.
By analyzing patterns and trends, AI algorithms
can predict potential failures,
cyber threats, and demand fluctuations,
enabling
proactive risk
mitigation.
AI
offers the opportunity to be both an offensive
and defensive force-multiplier for
cybersecurity applications, noted EPRI's
Renshaw. In tandem, AI shows promise
for wildfire risk evaluation and detection.
" These include analyzing satellite
data to identify areas of higher risk as
well as conducting real-time analysis of
observation station camera data to look
for smoke and trigger faster responses
to deal with fires at earlier stages when
they are easier to contain, " he said.
In addition, AI tools appear to be contributing
to emissions reduction. Competitive
generator Vistra, for example, in
2020 piloted a McKinsey QuantumBlack
multilayered neural-network model to
learn about the effects of complex nonlinear
relationships (such as temperature
and humidity) using two years' worth of
data gleaned from Vistra's coal-fired Martin
Lake Power Plant in Texas. It then deployed
a heat rate optimizer (HRO) that
enabled Martin Lake to run 2% more efficiently
in just three months, saving the
plant $4.5 million and abating 340,000
tons of carbon. Vistra has since rolled the
HRO out to at least another 67 powergeneration
units across 26 plants, for an
average 1% improvement in efficiency.
Finally, AI applications are also evolving
for emissions monitoring. Beyond
plant-based Predictive Emission Monitoring
System (PEMS) solutions, such
as pioneered by ABB in the late 2000s,
at least one entity-Climate TRACE, a
coalition originally funded in part by a
Google.org grant-is monitoring power
plant emissions from space using satellites.
In 2023, it reported the world's
thermal power plants emitted 98.36
billion tonnes of carbon dioxide equivalent
emissions-or 21.82% of total
global emissions.
Steep Barriers for an AI Expansion
While lauded for its accountability, Climate
TRACE has also triggered some
apprehension in the power sector that
satellite monitoring unveils sensitive
emission data, illustrating two key barriers
associated with AI: data privacy
8
and data security. On the flip side, data
sharing and accessibility are also growing
concerns, given the hurdles many
organizations encounter in obtaining
high-quality data due to proprietary constraints,
data
silos,
or apprehensions
about disclosing sensitive information.
Another barrier is trusting the data itself.
" Data is at the heart of all AI technologies.
Without sufficient quantity,
quality, and cleanliness of data, AI systems
can potentially produce erroneous
results, " Renshaw noted. That concern
is especially growing prevalent as generative
AI gains traction in the power industry.
While a statistical model makes
predictions based on rationale, a generative
AI system can " hallucinate, " meaning
it
returns
inaccurate
responses,
because
they lack constraints that limit
possible outcomes.
" Obviously, if you're in an engineering
environment, if you're trying to solve
operational issues, those aren't good, "
William Hendricks, vice president of
sales for Cognite's Americas business,
noted in June during POWER's 2023
Connected Plant Conference. To tackle
that concern, Cognite last year launched
an " intuitive, composable, visual workspace "
dubbed " Industrial Canvas, " a
collaborative environment that leverages
contextualized data and generative AI.
Yet another emerging glaring issue
concerns bias. " The datasets that are
used to train AI models may suffer from
bias, which is then transferred into the AI
model. This can cause existing biases in
data to be perpetuated or reinforced in
the AI models used. As such, AI practitioners
need to be aware of such potential
biases and attempt to remove or eliminate
their effects, such as using alternative
datasets and cleaning the data, "
Renshaw explained. Threats posed by
bias are emerging specifically with the
rise of AI in electricity market operations,
given potential market abuse and distortions
through automated or algorithmic
trading. However, approaches that use
AI to detect anomalies in power markets
also appear to be evolving.
Along with bias, another barrier experts
have highlighted concerns the massive
task of data contextualization-the
connection of data, using efficient data
mining and data alignment, from various
point solutions and systems within the
organization to get a deeper data " story. "
Renshaw, meanwhile, highlighted " the
lack of explainability " as another key
weakness in AI models. " A model can
provide an answer, but few can provide
www.powermag.com
an explanation or context clues of why
it provided the response that it did, " he
said. " AI explainability is a rapidly developing
field and will continue to improve. "
On the business side of AI, costs are
also a growing concern. " Cloud infrastructure
costs, if you decide to go to
the cloud, it's a huge expense that I don't
think anybody saw coming, and now it's a
big portion of our budgets, " ABB's Jones
noted. While securing the right skill to
sustain AI solutions poses another challenge,
Jones also highlighted a larger
need for change management. Change
management involves systematically
guiding individuals, teams, and organizations
through the transition from existing
practices to new processes and technologies.
Its key goals are to overcome
cultural resistance and foster buy-in. " If
you don't put the change management
in place, something new like this will fall
flat, " Jones said.
Looming Regulations
While the industry grapples with how to
best leverage AI amid its bittersweet potential,
the government has stepped up
in notable ways to offer guidance, with
an aim to foster the responsible use of
AI. In October 2023, President Joe Biden
issued Executive Order (EO) 14110,
which establishes new standards for AI
safety and security, protects privacy, and
promotes innovation and competition.
In the three months following the order,
federal agencies have used the Defense
Production Act to compel AI developers
to provide vital information to the Department
of Commerce. The Commerce
Department, at the end of January, also
proposed a rule that would require U.S.
cloud providers to alert the government
when foreign clients train the most powerful
models, which could be used for
harmful activity.
Elsewhere around the world, the European
Union in June 2023 enacted the
world's first comprehensive AI law, a riskbased
approach that requires providers
of high-risk AI systems in critical industries,
like power, to complete an assessment
to ensure systems conform with
mandatory requirements for trustworthiness.
Some European experts suggest
the law will impose steep compliance
costs on the energy sector, though they
acknowledge more detailed data is needed
on AI system costs, which could provide
a clearer picture of the law's impact
on profitability. ■
-Sonal Patel is a POWER
senior associate editor.
POWER | February 2024
https://www.Google.org http://www.powermag.com
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