POWER and RE+ Tech Supp September 2022 - 42

RENEWING THE PROMISE OF CLEAN ENERGY
NET ZERO
The POWER Interview: The Importance of
AI and Machine Learning
A
rtificial intelligence (AI) and
machine learning (ML) are becoming
synonymous with the
operation of power generation facilities.
The increased digitization of power
plants, from equipment to software,
involves both thermal generation and
renewable energy installations.
Both AI and ML will be key elements
for the design of future energy systems,
supporting the growth of smart
grids and improving the efficiency of
power generation, along with the interaction
among electricity customers
and utilities.
The technology group Wärtsilä is a
global leader in using data to improve
operations in the power generation
sector. The company helps generators
make better asset management
decisions, which supports predictive
maintenance. The company uses AI,
along with advanced diagnostics, and
its deep equipment expertise greatly
to enhance the safety, reliability, and
efficiency of power equipment and
systems.
Luke Witmer, general manager,
Data Science, Energy Storage & Optimization
at Wärtsilä, talked with POWER
about the importance of AI and ML
to the future of power generation and
electricity markets.
POWER: How can artificial intelligence
(AI) be used in power trading,
and with regard to forecasts
and other issues?
Witmer: Artificial intelligence is a very
wide field. Even a simple if/else statement
is technically AI (a computer
making a decision). Forecasts for price
and power are generated by AI (some
algorithm with some historic data set),
and represent the expected trajectory
or probability distribution of that value.
Power trading is also a wide field.
There are many different markets
that span different time periods and
different electricity (power) services
that power plants provide. It's more
than just buying low and selling high,
though that is a large piece of it. Forecasts
are generally not very good at
predicting exactly when electricity
price spikes will happen. There is always
a tradeoff between saving some
power capacity for the biggest price
spikes versus allocating more of your
power for marginal prices. In the end,
as a power trader,
it is important to
remember that the historical data is
not a picture of the future, but rather a
statistical distribution that can be leveraged
to inform the most probable
outcome of the unknown future. AI is
more capable at leveraging statistics
than people will ever be.
POWER: Machine learning and AI in
power generation rely on digitalization.
As the use of data becomes
more important, what steps need to
be taken to support AI and machine
learning while still accounting for
cybersecurity?
Witmer: A lot of steps. Sorry for the
lame duck answer here. Regular whitehat
penetration testing by ethical hackers
is probably the best first step. The
second step should be to diligently
and quickly address each critical issue
that is discovered through that process.
This can be done by partnering
with technology providers who have
the right solution (cyber security practices,
certifications, and technology) to
enable the data flow that is required.
POWER: How can the power generation
industry benefit from machine
learning?
Witmer: The benefit is higher utilization
of the existing infrastructure.
There is a lot of under-utilized intra42
Supplement to POWER & RE+ 2022
structure in the power generation industry.
This can be accomplished with
greater intelligence on the edges of
the network (out at each substation
and at each independent generation
facility) coupled with greater intelligence
at the points of central dispatch.
POWER: Can machines used in
power generation learn from their
experiences; would an example be
that a machine could perform more
efficiently over time based on past
experience?
Witmer: Yes and no. It depends what
you mean by machines. A machine
itself is simply pieces of metal. An
analogy would be that your air conditioner
at home can't learn anything,
but your smart thermostat can. Your
air conditioner needs to just operate
as efficiently as possible when it's told
to operate, constrained by physics.
Power generation equipment is the
same. The controls however, whether
at some point of aggregation,
or transmission intersection, or at a
central dispatch center, can certainly
apply machine learning to operate
differently as time goes on, adapting
in real time to changing trends and
conditions in the electricity grids and
markets of the world.
POWER: What are some of the uses
of artificial intelligence in the power
industry?
Witmer: As mentioned in
the response
to question 1, I think it appropriate
to point you at some definitions
and descriptions of AI. I find wikipedia
to be the best organized and moderated
by experts.
In the end, it's a question of intelligent
control. There are many uses of
AI in the power industry. To start listing
some of them is insufficient, but,
POWER and RE+ Tech Supp September 2022

Table of Contents for the Digital Edition of POWER and RE+ Tech Supp September 2022
