DAY2_4.1_Jesper Vauvert_Digitalisation in EE_EN.pdf

J. Vauvert
JUST ENERGY TRANSITION, DIGITAL TRANSFORMATION AND GENDER EQUALITY
Day 2 “”Digitalise to Electrify”
Digitalisation in Energy Efficiency for
Industries
October 25th 2022

25th October 2022
Overview of presentation
I. Key drivers
II. Summary of study approach
III. Desk review
IV. Organizing inputs and applications
V. Key areas for digitalization and EE
VI. Key enabling technologies
VII. Specific technology potentials
VIII. Some case examples
IX. Challenges and risks
X. Policy and legal issues
XI. Select references
PUBLIC TALK - JUST ENERGY TRANSITION, DIGITAL
TRANSFORMATION AND GENDER EQUALITY
Page 1

25th October 2022 Page 2
I. Key drivers
• Worldwide data flows have
increased exponentially over the
last 12 years, and are expected to
continue that trend
• World wide the number of
connected devices [from IV] is
also increasing almost
exponentially
• It is therefore no surprise that the
opportunities (for EE and other
things) arising from this increased
interconnectedness has grown
immensely and will continue to
do so in the forseeable future

I. Key drivers
• Investment in EE
startups, largely tapping
into opportunities and
business models based
on new digital
technologies and
analytics is growing fast
[from IV]

I. Key drivers
• The nature of digitalization
enables many new
interconnected services,
and strategies for
improving EE, but also ups
the ante as regards
technical and
organizational
preparedness [from IV]

II. Summary of study approach
1. Desk review
2. Expert interviews/w. questionnaire
3. Analysis & narration of key findings

III. Desk review
Information, by type and source, in desk review:
1. Online material
2. Documents and reports
3. Research publications
***************
1. IEA
2. WB/ESMAP
3. UNIDO
4. EU
5. ASEAN
6. ADB and AfDB
7. GIZ
8. Danish Energy Agency
9. Research institutions such as KTH, Fraunhofer Institute etc.

V. Key areas for digitalization and EE
Some key areas of where digitalization has made enormous advances
possible
1. Computing – high speed, advanced computing technology, incl. cloud computing is necessary to process vast amounts of
data
2. Data collection & transmission – smart meters, interconnected sensors, and high speed, high volume communications
equipment is necessary to collect and transmit and share the large amounts of data generated at high speed
3. Data analysis/modelling – is necessary to interpret, and generate actionable outputs from the large amounts of data to
optimize operations
4. Design – using the above large amounts of operational data, and analysis/optimization based on that to improve design of
machinery, facilities etc.
5. Process optimization and control – using real (or nearly real-) time models such a digital twins based on AI, machine
learning (ML) etc. you can automate controls through connected actuators etc. that adjust processes based on the digital
models that run in parallel to the actual plant/machinery (also known as model predictive control).
It is also possible for example to let the algorithms and automated controls attune your operations to e.g. time of use
electricity tariffs, avoid penalties (e.g. for exceeding maximum demand) etc.
6. Maintenance – through constant real time monitoring, AI and ML, you can more accurately predict when and where
maintenance is required, or react faster should something fail, than if this is based exclusively on human interactions
7. Logistics – many logistics and supply chain operations can be optimized using IOT connected mobile sensors, transmitters
etc. Coupled with AI and ML you can for example optimize routing, inventories, orders, distribution and so on.
8. M&V – data and analytics can quickly and accurately estimate various impacts of interventions, and quantify savings

VI. Key enabling technologies
Key enabling technologies
• IoT/IIoT/high speed/high volume wireless data transmission, and SCADA/B(E)MS
• Big data analytics; AI/ML/Algorithms
• Digital twin/Model Predictive Control
• Additive manufacturing
• Automation/AI controlled robots
Expert feedback
• Many of the interviewed experts emphasize that digitalization is no silver bullet and that “basics” need to
be attended to as the first priority. In line with this “basics first” approach, they suggest that the greatest
immediate potential for use of the new digital/smart technologies lies in e.g. simple IOT connected on/off
controls
• Many furthermore point to the immense potential of AI/Machine learning for Energy Management,
diagnostics, control and M&V, but emphasize the critical importance of multiple concurrent, high
resolution, real time (or near) datasets and processing power/smart algorithms (AI) to make that avalanche
of information actionable
• One of the flip sides of the rapid digitalization is the increased vulnerability to cyber threats which have
become commonplace with increasingly menacing impacts. Cybersecurity therefore has to be addressed
seriously as part of the digitalization process

VII. Specific technology potentials

VIII. Some case examples
• The application of digital twins and artificial intelligence will enhance energy and motor
management and maintenance and systems efficiency strategies. First assessments
indicate a significant contribution i.e. an improvement of energy efficiency of 20–25% and
an increase of operational efficiency of 25%
• A digital twin for grinding wheel as a product integrated and web-based knowledge
sharing platform has been developed. It integrates the data collected in each phase of
the grinding wheel from the manufacturing to the conditioning phase. The quantifiable
benefits from the digital twin for productivity and efficiency is an increase in energy and
resource efficiency by 14.4%
• In most buildings chillers consume substantial energy. Mounting sensors on chillers to
measure operational performance, resulting data can be used to develop a digital twin of
the chiller plant utilizing both design and operational data. An integrated next-generation
artificial intelligence platform can then be used to apply technologies like machine
learning, virtual reality and augmented reality, using the digital twin data to improve
efficiency by around 25%
Source: Personal communication from R. Babb, Industrial EE expert

IX. Challenges and risks
General challenges
– Availability and cost of required data infrastructure (e.g. IoT connected
devices; data transmission/storage/processing infrastructure)
– Connectivity and data processing constraints/costs (e.g. communication
protocols/interoperability; bandwith/capacity; data centres/server capacity,
availability and access to external datasets)
– The energy use by the ICT technologies and connected devices themselves
– Human capacity constraints (e.g. to develop, maintain, interpret and act on
e.g. ML and AI generated outputs)
– Human behavior (e.g. reluctance to adopt new technologies)
Cyber threats
– Data theft/privacy concerns
– Severance of communication lines/data flows
– Unauthorized/hostile takeover/control of
systems/communication/machinery/Ransomware

X. Policy and legal issues
Policy/legal issues
– Data ownership/sharing arrangements
– Communications protocols/standards
– Regulatory models outdated (e.g. tariff structures/billing, smart demand
response/demand flexibility etc.)
– Privacy and security
– Economic disruption (e.g. change of required skill sets/transition to new
jobs/skills will require up/re-skilling of workforce)

XI. Select references
I. “Energy Analytics for Development”, ESMAP knowledge series
027/17, World Bank/ESMAP, July 2017
II. “Digitalization and Energy”, IEA, 2017
III. “Energy Efficiency in ASEAN: Trends and Financing schemes”,
ADBI Working paper series no. 1196, ADB Institute, October
2020
IV. “Energy Efficiency 2021”, IEA, 2021
V. “Accelerating clean energy through industry 4.0 –
manufacturing the next revolution”, UNIDO, 2017

XII. Contact Details
Jesper Vauvert
Energy Efficiency Adviser, Municipal Energy
Management Systems
South African – German Energy Program (SAGEN IV)
GIZ, Hatfield Gardens, Block C, 1st floor 333
Grosvenor Street Hatfield 0028 Pretoria, South
Africa
jesper.vauvert@gfa-group.de

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