This is the last in a series of 'Show and Tell' webinars from the Ofgem Strategic Innovation Fund Discovery phase, covering projects focused on increasing flexibility sources in the energy system and hydrogen deployment and integrations. The energy system is made up of a complex range of activity across networks, markets, supply, and demand. A range of organisations play crucial roles in managing various parts of this system. Working across traditional boundaries can create opportunities for better integration of services to consumers, who typically experience the system as a whole. Innovative whole system solutions are required to optimise the system, reducing costs whilst enhancing the experience of consumers. You will hear from SIF projects looking to increase sources of energy system flexibility and integrate hydrogen effectively into the system. The Strategic Innovation Fund (SIF) is an Ofgem programme managed in partnership with Innovate UK, part of UKRI. The SIF aims to fund network innovation that will contribute to achieving Net Zero rapidly and at lowest cost to consumers, and help transform the UK into the ‘Silicon Valley’ of energy, making it the best place for high-potential businesses to grow and scale in the energy market. For more information on the SIF visit: www.ofgem.gov.uk/sif Or sign-up for our newsletter here: https://ukri.innovateuk.org/ofgem-sif-subscription-sign-up

1. Strategic Innovation Fund
Project ‘Show and Tell’ webinar
Whole Systems Integration;
Flexibility & Hydrogen Integration
23 May 2022

2. Welcome
Matt Hastings, Deputy Director, Innovate UK

4. Introduction: Whole System
Integration challenge
Manu Ravishankar, Innovation Lead, Innovate UK

5. Whole Systems Integration Challenge
Aim: To consider and develop whole system approaches across energy supply, demand and
networks for better integration and optimisation of the energy system
Themes include:
Increasing
flexibility sources
in energy system
Hydrogen
deployment and
integration
New technology
development for
RE integration
Circular economy
for resource
efficiency
Image courtesy: Energy Systems Catapult, Systems thinking in the energy system

6. Agenda – Whole Systems Integration, Part 2
1. Fast Flex SPEN
2. INCENTIVE - Innovative Control and Energy
Storage for Ancillary Services in Offshore Wind
SSEN
Q&A on projects 1 & 2
3. Crowdflex: Discovery NGESO
4. CEV: Critical factors for the adoption of smart
homes for energy efficiency and implications for
consumers and providers
NGN
Q&A on projects 3 & 4
14:35am – 10 minute break
5. HyNTS Compression NGGT
6. Green Hydrogen Injection into the NTS NGGT
7. Nuclear Net-Zero Opportunities (N-NZO) NGGT
Q&A on projects 5 6 & 7
15:55am – end of session

7. Fast Flex
Goran Strbac & Luis Badesa, Imperial College London
Douglas Wilson, GE Digital

8. Benefits of addressing regional frequency stability
through demand-side flexibility
FastFlex – Discovery Phase
Scottish Power Energy Networks, Imperial College London, GE Digital

9. Discovery Phase:
Cost-benefit analysis for demand-side flexibility
to support regional stability in Scotland
• Quantify the value of demand-side resources to support regional stability
9
Control approach vs. Hardware approach
Use advanced monitoring
and control to access
demand-side flexibility
Invest in built-for-purpose assets:
• Grid-scale batteries
• Synchronous condensers

10. Regional frequency stability challenge
in the future Scottish system
10
Future largest infeed loss in Scotland: 1.4GW, from HVDC
Ancillary services are needed across all regions of the network
• Because inter-area oscillations appear when inertia is not evenly distributed
in the grid (e.g. high wind capacity in Scotland but most of the electric demand
located in England).
England
Scotland
(high wind)
(high load)
• Ignoring inter-area oscillations
could be dangerous: higher
RoCoF and lower frequency
nadirs than the Centre Of
Inertia could lead to
unexpected blackouts.

11. Modelling software: ACES model
Ancillary-services Constrained
Energy Scheduling model
11
• Ancillary services dynamics (with timescale of milliseconds) are mapped into
an economic optimisation (with timescale of hours)
Frequency dynamics: Unit Commitment (stochastic):

12. Demand-side response within ACES
• The ACES model has been enhanced during the Discovery Phase to include
a regional Demand-side response (DSR) model
• This model follows a pre-defined demand profile, and allows the DSR assets
to contribute to frequency response via demand alleviation.
• Current demand profiles consider electric vehicles (EVs) and heat pumps
(HPs)
– Demand considered is in line with NGESO FES 2021 scenarios, for 2030
projections.
– 10% of this demand is considered to be in Scotland.
12

13. Main results
from the ACES modelling
• The value of the FastFlex approach in Scotland is estimated at ~£440m,
due to lower capital investment required when compared to grid-scale assets
for supporting stability (i.e. synchronous condensers and battery storage).
– This is for a base case that allows zero-carbon operation of Scotland.
• It was estimated that 1GW of demand-side response from EVs and HPs in
Scotland would replace 10GVAs of synchronous condensers and 500MW of
battery storage.
• Additional benefit of flexible loads vs. built-for-purpose assets is the option to
modulate the volumes of ancillary services procured, depending on the
stability challenge in the grid.
– During periods when the largest infeed loss is smaller, a lower volume of
stability services from DSR could be procured by the system operator,
while hardware does not give this option.
13

14. Future steps
• While EVs and HPs have been shown to bring significant benefits for
reducing the need for alternative ancillary services, the value of DSR obtained
in the simulation results never saturates, meaning that the current
projections for EV and HP adoption by 2030 are not sufficient to fully
guarantee stability in Scotland from the demand side.
• This is due to the significant challenge of securing against a loss of
1.4GW in Scotland.
• This implies an opportunity for enhanced flexibility options such as
vehicle-to-grid (V2G) chargers to provide additional system support by using
the same demand-side assets. As well as broadening the pool of FasfFlex
resources to include a diverse portfolio of flexible loads.
• Future work should focus on analysing the benefits of additional demand-
side flexibility options, as well as analyse the necessary market design. 14

15. Confidential. Not to be copied, distributed, or reproduced without prior approval.
Fast Flex
Applying flexibility for frequency services
with network stability
May 25, 2022

16. Confidential. Not to be copied, distributed, or reproduced without prior approval.
F l e x i b i l i t y
Locational Fast Balancing Process
Wide area
synchrophasor
view of regional
response to
disturbance
Fast dispatch of
available resources
to match event
trigger
Trigger in <0.5s Resource Selection & Trigger
Resource Provision (market)
Response
Validation &
Settlements
Detect & extract size
of disturbance &
enable / inhibit
locations
Power
Time
Grid-forming windfarm (VSM) with energy
limiter control provides <1s very response for
in-area disturbance (local control)
Dispatchable DER setpoint change provides
longer-term response, but slow action.
EV charging control provides medium-speed
sustained response. May be combination of
multiple devices.
BESS control provides fast-acting response
and gap-filling. Small energy storage
means lower cost device, or storage can
be used for other energy needs.
Compiled response uses available resources to
provide a well-defined fast-responseservice.
Demand response sheddable load provides
fast, sustained response, but only to be
used for severe and infrequent events.
16

17. Confidential. Not to be copied, distributed, or reproduced without prior approval.
F a s t B a l a n c i n g S e r v i c e s
Need for a new Flexibility Service for Stability
Centrally Dispatched Balancing Control
Inertial response
Load Frequency Control (Secondary Control)
Energy Market & Constraint Management
Fast Frequency Response
Primary Frequency Control
1s
Time from disturbance
Frequency-Proportional Regulation (Local control)
10s 30s
2s
Power in proportion to frequency deviation with small delay
Mainly supplied by batteries
Natural machine inertia or controlled response.
Slows frequency slide.
Power in proportion to frequency deviation with large delay
Mainly supplied by conventional generators
Service for dispatchable
power balancing resource
for a region. Generation
or demand may supply.
Long-term balancing
supply and demand
Regional
sensitivity.
Keep within
network limits
Locational Fast Balancing Response
Fast acting, sustained response with regional
sensitivity. Diverse flexible resources.
NEW APPROACH Wide Area Location-Sensitive Control
Increasing
participation
of
flexible
demand,
DER
&
storage,
but
too
slow
for
grid
stability
30min
Conventional
response
becoming
scarce,
expensive
&
too
slow
FOCUS
OF
PROGRAM
17
Frequency-proportional
control, WITHOUT
regional sensitivity

18. Confidential. Not to be copied, distributed, or reproduced without prior approval.
F a s t F l e x N e x t S t e p s
Overview of Future System and Expected Participants
Distributed
Control
Comms
Resources
Higher latency resources
Low latency resources
Real Time Control Method
Market Management
Real-time grid stability
requirements
Measurement infrastructure
Whole system market
(ESO)
Service Arbitrator
(DSO)
Area inertia measurement
Area boundary strength identifier
Area largest loss calculator
Requirement identifier & predictor
D-network capability validation
Low latency, high throughput PDC
Real-time visualisation
Reporting
Real-time analytics
Value of predictable
response in area
Capacity & Deployment
LFC Integration
Stacked services
Resource incentives
Real-time Availability
Availability Predictor
Stacked services
Settlements & Reporting
Fast event trigger
Location sensitivity
Response volume
Resource Assembler
Local control estimator
Resource dispatcher
Validation & course-correct
Graceful degradation
Data sources
PMU, RTU, meter
Field interface
control unit /
failover
Field interface
control unit /
failover
Local control
Field interface
control unit
F & ROCOF
STAKEHOLDERS
• T&D INFRASTRUCTURE
OWNERS
• SYSTEM OPERATOR &
DSOs
• PLATFORM/SYSTEM
DEVELOPER
• ACADEMIC
• RESOURCE PROVIDERS –
DEMAND SIDE, BESS, DER

20. Confidential. Not to be copied, distributed, or reproduced without prior approval.
T H E I N E R T I A C H A L L E N G E
Effect of Sparse Centres of Inertia – GB
ROCOF hits loss-of-mains limits in north & south
Frequency change takes time to propagate
→ Angles diverge → Stability risk
Average system RoCoF within GB 0.125Hz/s limit, but threshold exceeded in both the north & south GB (not Midlands). Risk
of regional DER tripping, or in extreme case, loss of angle stability in network.
-0.125Hz/s
ROCOF hits loss-of-mains limits in north & south
ROCOF
(Hz/s)
TEAL
SELL
1 sec
20

21. Confidential. Not to be copied, distributed, or reproduced without prior approval.
Experience & Learning relevant to Fast Flex
1998 1st real-time
oscillation monitoring
2014-17 VISOR GB-wide
WAMS system
2015-19 EFCC Location-sensitive
Fast Frequency Control (WAMPAC)
using distributed resources.
2016-19 FITNESS WAMPAC-
enabled Digital Substation
2019-22 S. Australia Islanding Protection
WAMS Scale-up
US & world
2005 Phasor-
based WAMS
TARGET
• Stable, secure,
efficient grid
• Unconstrained high
renewable mix
• Full use of flexibility
and D-grid services
2016-19 EU MIGRATE
Low inertia / high power
electronic grid monitoring
Locational Fast Balancing
WAMPAC (Iceland)
2019- NGESO Effective Area
Inertia Monitoring System
2017- Iceland WAMPAC locat-
ional fast balance & island control
2019-22 D-RESTART Blackstart
service from distribution resources
2021-23 SYNERGY Stacked services
for Distribution Restoration AND
Locational Fast Balancing
SSE Stability control
in high PE penetration
Power Potential Services from
D-grid delivered for Whole System
2021-25 UKPN Constellation Distributed
intelligence for resilience (WAMPAC & AI)
SPEN-GE GE-UK GE-INTERNATIONAL
SIF Fast Flex
Expanding role of
flexibility in low
inertia grid

22. Confidential. Not to be copied, distributed, or reproduced without prior approval.
F a s t F l e x N e x t S t e p s
Roadmap for Flexibility in
Locational Fast Frequency
• Communications infrastructure, architecture, latency and security: developing the framework
o Transmission system level
o Distribution and controlled agent level
• Resilience design & analysis, graceful degradation
• Measurement standards and guidance
• Commercial & market mechanism: 2-level approach
o Bulk regional requirement
o Incentivise and reward participants
o Validation, evaluation and settlements for response providers
• Demonstrate blending of multiple resources to form a single predictable service
• Process to define requirements for blocks of FlastFlex response per region
• Availability of variable resources: predict regional resource reliably available
• Design & trial of the control and management system for a business-as-usual implementation
• Industry engagement & consultation

23. Confidential. Not to be copied, distributed, or reproduced without prior approval.
F a s t F l e x N e x t S t e p s
Partnership
Company Position Roles
SPEN Transmission & Distribution Lead partner Co-ordination of the overall programme
Implementation of the trials on SPEN region
Transmission monitoring infrastructure
Distribution-connected resource monitoring & control
Relationships with distribution service providers
Imperial College London Partner Studies and analysis supporting the technical method
Consulting input on the service market arrangements
Steering and strategy guidance
GE Digital Partner Development of the control and monitoring algorithms & systems
Hardware-in-the-Loop test environment
Live system implementation
Steering and strategy guidance
National Grid ESO Partner Regional requirements for frequency service
Oversight of system-level technical and commercial process (input from ICL & other partners)
Arrangements / authorisation for trials at system level
Industry engagement & consultations
Transmission Network Owners NGET, SSE Partner or Stakeholder Monitoring infrastructure & communications
Flexibility providers Partners and/or
Stakeholders
A diverse portfolio of participants included for live demonstration of the approach. Inputs from resources
required for both technical and commercial/market arrangements.
• Electric Vehicle charging station provider(s)
• Windfarm operator(s) providing controlled response and synthetic inertia
• BESS operator (s)
• Generator operator(s)
• Demand side aggregator

24. INCENTIVE - Innovative
Control and Energy Storage
for Ancillary Services in
Offshore Wind
Simon Stromberg, SSEN
Robert Keast & David Plunkett, Carbon Trust

25. INCENTIVE
Show and tell
Public

26. Motivation
26
Mission: bring a suite of technology solutions to
commercialisation that will allow offshore wind farms
to provide inertia stability to the onshore networks.
This in turn will enable an accelerated rollout of
offshore wind whilst maintaining system stability,
ensuring energy security, and keeping system costs
down.
To achieves this requires technical, market, regulatory
and commercial innovation*.
TODAY FUTURE
*https://www.carbontrust.com/resources/energy-storage-for-offshore-wind-with-innovative-converter-control
Public

27. 27
Discovery Phase scope
WP1 - CBA
WP2 – regulatory review
WP3 – technology review
Qualitatively select
shortlist from all
possible
technology options
Assess ownership possibilities for shortlisted technology
options
Conduct CBA on shortlisted technology options
Determine testing requirements for the robust and fair
assessment of novel technologies
Conclusions
Alpha Phase
scoping
Public

28. 28
Technology shortlisting
Longlist
All technologies capable of providing inertia
BESS
w/ grid
forming
STATCOM
w/
supercaps +
grid forming
Sync. Con.
Flow
battery
w/ grid
forming
Hydrogen +
hydrogen
turbine
HVDC
terminal
w/ grid
forming
Wind
turbine
w/ grid
forming
CCGT
Pumped
hydro
Demand
side
options
Public

29. 29
Technology shortlisting
Shortlist
Technologies for study in Discovery Phase
BESS
w/ grid
forming
STATCOM
w/
supercaps +
grid forming
Sync. Con.
HVDC
terminal
w/ grid
forming
Wind
turbine
w/ grid
forming
Public

30. 30
Outcome of CBA
Public

31. 31
Outcome of ownership analysis
BESS
w/ grid
forming
STATCOM
w/
supercaps +
grid forming
Sync. Con.
HVDC
terminal
w/ grid
forming
Wind
turbine
w/ grid
forming
INCENTIVE
technology
Regulated
network
company
ownership
OWF owner
ownership
Significant
regulatory
barriers
Some
operational and
regulatory
complexity
Low operational
and regulatory
complexity
Low operational
and regulatory
complexity
Ownership not
feasible
Some
operational and
regulatory
complexity
Some
operational and
regulatory
complexity, but
less than BESS
Low operational
and regulatory
complexity
Ownership not
feasible
Low operational
and regulatory
complexity
Public

32. 32
Outcome of testing requirements
Generic
simulation
Use generic
models of
INCENTIVE
technologies
and OWFs.
Site-specific
simulation
Use generic
models of
INCENTIVE
technologies
with models of
specific OWFs.
Control
hardware in
the loop
Use control
hardware
provided by
INCENTIVE
technology
suppliers
Commercial
confidence
Increasing understanding of performance and system effects of INCENTIVE technologies
Full
hardware
Could be scale
test or field trial
of one
INCENTIVE
technology
supplier
Technology-
specific
simulation
Use specific
models
provided by
INCENTIVE
technology
suppliers
Public

33. Alpha Scope
33
August September October November December January February
Initial Beta scoping activities
• Explorative activities with technology suppliers and OWF developers to
assess need for and feasibility of technology demonstration
Commercial assessment
• Building on CBA and ownership models in Discovery
• Further analysis and combination of business case and ownership
Technology testing
• Commencing work on testing programme developed in Discovery
Combining outcomes
and Beta scoping
• Bringing together all the
findings of Alpha to narrow
down technology options of
interest and agree scope for
Beta
Public

34. Any questions?
34
Public

35. simon.stromberg@sse.com
robert.keast@carbontrust.com
david.plunkett@carbontrust.com
Contact details
Public

36. Q&A – Whole Systems Integration challenge
1. Fast Flex
2. INCENTIVE - Innovative Control and Energy Storage for Ancillary Services in
Offshore Wind

37. Crowdflex: Discovery
Nina Klein, National Grid ESO
Freddie Barnes, Element Energy
Kieron Stopforth, Octopus Energy

38. CrowdFlex
Discovery Show & Tell

39. Problem to address
Challenge
• More renewable generation which is non-dispatchable
• More electric vehicles and heatpumps which increase demand
• So flexibility must shift from supply-side to demand-side
• A smart, flexible and reliable energy system is needed
Opportunity
• Domestic consumers offer a nascent, but large flexibility resource
• Currently largely untapped, due to limited understanding and existing
market design
• Crowdflex explores novel stochastic flexibility services, reflecting the
statistical and distributed assets
• Could enable lower cost and lower carbon system operation and reduce
capacity and network investment costs
CrowdFlex aims to establish domestic flexibility as a reliable energy and
grid management service

40. CrowdFlex – Project Overview
Objectives
1. to understand and align ESO/DNO requirements for domestic flexibility
services and consider interaction with the statistical nature of flexibility
2. to identify the technology capability and consumer behaviour parameters
to explore in a real-world trial
3. to understand how the statistical nature of flexibility can be developed into
reliable modelling of domestic demand and flexibility
Discovery: feasibility study (complete)
Alpha: design of trial/model (pending funding)
Beta: delivery and testing of trial/model (pending funding)
Core Technologies
• Domestic assets & automation: EVs, heatpumps, white goods
• Smart metering
• Consumer segmentation analysis
• Statistical modelling methods

41. Approaching the problem
Developing outcomes
• Conducted ~17 interviews to capture user needs for ESO/DSOs
procuring flexibility:
• SO challenges, respective current services and appetite for
domestic flexibility
• Key features to be investigated in a trial
• Undertook quantitative consumer segmentation work to identify high
flexibility potential characteristics and researched customer
engagement needs
• Conducted ~5 interviews with ESO to understand user needs for
aggregators modelling flexibility:
• reviewed approaches for stochastic forecasting of generation and
demand
Additional activities
• Engagement with relevant projects: EQUINOX, BiTraDER, DRS Trial,
SIF Flexible Heat, and BEIS Heatpump Ready
• Dissemination/feedback meetings with key organisations: BEIS,
Ofgem, and Citizens Advice
Partners
Additional engagement

42. Understanding ESO/DSO requirements
Current system challenges are addressed
through various energy markets & flexibility
services
• Discovery confirmed there is strong appetite
within ESO/DSO for domestic flexibility to
play an active role
• Identified the markets and services suitable
for domestic flexibility
• Only most rapid of response services
thought to be beyond technical capabilities
of domestic assets
• Balancing via energy markets can be
declared close to time of delivery, location
independent (aligned with PAS-Routine)
• System critical & operational services
must deliver response when called upon
(aligned with PAS-Response) and require
declaration well ahead of time
BM – Balancing Mechanism, DC – Dynamic Containment, DM – Dynamic Moderation, DR – Dynamic Regulation,
FR – Fast Response, STOR – Short Term Operating Reserve, NOA – Networks Options Assessment.
The procurement timescales and response times of various energy markets and
services available to flexible assets

43. Key dimensions for a Trial
For the two flexibility categories, Discovery lays out parameters required for a
large-scale trial of domestic flexibility
1. PAS-Routine type flexibility – e.g. energy markets:
• Timewise vector of baseline demand
• Timewise vector of projected flexible capacity, for each time interval,
through a year
2a. PAS-Response type flexibility – system operational events e.g. Response,
___Reserve:
• Firm response may vary throughout the year, therefore, response should be
tested multiple times under a variety of conditions (season, weather,
time of day, concurrent PAS-Routine incentives)
• A rapid response required, likely procured via an automated response
2b. PAS-Response type flexibility – system stress events e.g. Capacity Market and
___NOA agreements:
• Tests during system stress events (e.g. cold weather for demand-led
peaks, summertime for supply-led stress) to ensure reliable response
• Services may be called via automated response or manually, similar to
the “Big Turn up/Down” experiments from CrowdFlex: NIA

44. Statistical modelling of domestic demand
There is value in a data-intensive understanding, forecasting and
modelling for domestic demand and flexibility
• Discovery identified a high-level approach for modelling domestic
demand and flexibility
1. Underlying demand (stochastic)
2. Overlay flexibility potential (deterministic limits)
3. Expected flexibility outturn (stochastic)
• Considered use cases for modelling forecasts:
a) Improved demand-side visibility
• Reduce energy imbalance & operational reserve requirements
• Better utilise existing capacity & network infrastructure,
delaying reinforcement
b) Forecasting availability for flexibility services
• Reduce operational costs (incl. constraints, reserve and
energy balancing)
• Reduce capacity & network reinforcement investment
• Domestic energy modelling can form part of the Virtual Energy System
ecosystem by integrating with the Common Framework (currently
under development)
Demand forecast:
Low accuracy
Demand forecast:
High accuracy
Demand Supply
higher
lower
expected
demand
required
supply
Operational
Reserve
Requirement
likelihood 50% 0%
volume
0
MW
90
MW

45. Stochastic delivery of flexibility services
• Currently flexibility services
procure a declared firm
capacity, i.e. deterministic
• However, domestic flexibility
is inherently stochastic
• Its capacity is best described
by statistical, rather than
deterministic methods
• Procuring flexibility statistically
via a PDF, would eliminate
the need to derate capacity
• Reducing over
procurement
• Providing system
savings for all
stakeholders
0
MW
90
MW
flex
volume
higher
lower
Expected flex
Flex capacity must be
derated to ensure high
confidence in delivery –
this underutilises and
undervalues demand
assets.
Supply capacity
Flexibility: Deterministic Flexibility: Stochastic
Declaring the entire PDF distribution enables the
ESO to realistically view flex potential and offset
any lower delivery confidence with visibility of
other possible system changes. This enables more
efficient management of resources – leveraging
value and reducing system costs.
This means the ESO
must procure more
supply side flexibility
capacity – increasing
system operation
costs.
VRES forecast
Thermal plant
loss
DSR Flex
Compound
probability
(most
efficient)
Demand flex
Derating
Expected flex
capacity and
Probability
Density
Function
declared
Additional
capacity
required on
supply side
Declared flex
capacity

46. Discovery learnings and Alpha plans
1. Statistical Approaches to Services
• Identify current/future “system needs” and associated parameters
• Take a spectrum approach to flexibility services
• Investigate deterministic approaches for near-term utilisation of domestic assets
• In parallel, develop pathways to introduce stochastically procured services
• Develop approaches for stacking multiple flexibility services
2. Trial Design
• A future trial must identify priority domestic flexibility services to test and:
• Determine timewise vector of: baseline demand, flex potential, flex out-turn (24-7-365)
• Determine asset availability/capacity for system operation & stress events
• Identify efficient financial & information remedies to incentivise Routine and
Response type services
3. Model Specification
• Identify data needs & modelling approach for statistical demand and dynamic
flexibility forecasting techniques
• Align with Common Framework (where possible) for VirtualES integration
Stakeholder Engagement
• Engage with Ofgem/BEIS to understand potential regulatory/policy barriers
• Engage with industry players to gather feedback and disseminate learnings

47. Value and potential benefits
CrowdFlex adds value beyond existing projects to date, by focusing on:
• understanding and evolving ESO/DSO needs, not just developing asset technical
capability
• testing delivery reliability and statistical significance, targeting large numbers of
participants & events
• statistical modelling for the VirtualES ecosystem, in combination with a real-world trial
Learning from CrowdFlex has the potential to:
• Lower customer bills
• through system wide savings and revenues from services
• Reduce costs of system balancing and network reinforcement
• through access to and confidence in domestic assets
• Enable greater market participation on the demand side
• through novel statistical approaches to flexibility services
• Increase use of renewable generation and lower carbon emissions
• through the demand-side supporting the energy transition

48. Q&A
CrowdFlex

49. CEV: Critical factors for the
adoption of smart homes for
energy efficiency and
implications for consumers
and providers
Laura Robson & Keith Owen, NGN
Diane Gregory-Smith, Newcastle University
Danielle Butler & Jessica Cook, National Energy Action

50. Collaboration between:
• Northern Gas Networks (NGN)
• Newcastle University
• National Energy Action (NEA)
• Northern Powergrid
10027307 – CEV Critical factors for the adoption
of smart homes for energy efficiency:
Implications for consumers and providers

51. Need to be decarbonised
each week for the next 25
years to reach net-zero by
2050
20,000 homes
The problem
Small behavioural changes at
household level are a part of this,
making consumers a vital part of the
decarbonisation journey
Huge potential benefits for consumers
derived from greater energy efficiency

52. Project Overview
01
02
03
Academic review
covering the smart-home literature from the
academic side
Industry review
synthesising research and project reports on
smart homes and consumer perspectives
Joint review
combining the academic and industry reviews
04
05 Webinar
to present the findings
Good practice framework
on smart home adoption
06 Online resource
that will make all reviews, reports, the
Framework and webinars available
Six key outputs:

53. Methodology
Plan the review
• Define research questions
• Define search criteria
• Define exclusion criteria
Conduct the
review by
analysing papers
• Academic review
• Industry review
Synthesise the
reviews into a
Framework
• Identify areas of
interest
Report emerging
themes and
recommendations

54. Approach
• Electronic database
Scopus (15,878
documents following
initial keywords
search)
• Call for evidence and
stakeholder
consultation
• Multiple outputs and
resources; a broader
search strategy
Evidence Review
• The keywords
selection revolved
around the term
“smart home”
• Examples of specific
agreed
keywords: smart
technology; adoption
(enablers/barriers);
water and/or energy;
heating; energy
conservation;
vulnerability
Inclusion Criteria
Agreed across teams as
follows:
*Published/produced
in English
*Published/produced
since 2012
*Focused primarily but
not exclusively on the UK
context
* Peer-reviewed for
academic and industry &
policy specialist resources
Results
More than 3,000 filtered
and reviewed sources;
followed by in-depth
analysis of 139:
*69 sources industry and
policy-oriented reports
*70 academic sources
Academic & Industry/Policy-Based Literature
Evidence Review

55. Smart home
systems
Key Adoption Factors
Cost and
benefits
Environmental
User
Support networks
and communities
Policy, industry
and regulation

56. Output: Framework

57. Creating a tool to enable adoption and identifying where
action is required

58. • Giving customers adequate information on both financial and non-financial
benefits of smart technologies
• Appropriate controls and safeguards to allow tailored use dependent on
individual needs
• Building smart home systems with flexibility and interoperability
• Following the principle of inclusion by design
• Engagement strategies for users experiencing digital exclusion and/or
technology anxiety
• Training frontline workers and installers to give tailored, accessible and
appropriate advice
• Ensuring that data security and privacy statements adhere to data protection
regulation(s)
• Mitigating the risks of miscommunication or mis-selling in a growing market
Recommendations for a fair and inclusive smart transition

59. What comes next?
Looking to the future
Alpha
• Testing our framework with stakeholders and users
• Developing a 'tool' to make the framework more useable
• Identifying potential mitigating actions for barriers
• Co-designing possible solution concepts
Beta
• Further development of our solution
concepts, through meaningful inclusion, to
prototype technology-based or service
solutions to the adoption of smart home
systems
Other Projects
• Apply the framework to future, and pre-
existing, projects in this area, including the
NGN Customer Energy Village related
projects (NIA funded)
• Identify opportunities to work with a range
of stakeholders to help progress their goals

60. Thank you
Any questions?

61. Contact details
Newcastle Uni
Savvas Papagiannidis - savvas.papagiannidis@newcastle.ac.uk
Diana Gregory-Smith - diana.gregory-smith@newcastle.ac.uk
National Energy Action
Jessica Cook - jess.cook@nea.org.uk
Danielle Butler -danielle.butler@nea.org.uk
Northern Gas Networks
Laura Robson – lrobson@northerngas.co.uk
Keith Owen - kowen@northerngas.co.uk

62. Q&A – Whole Systems Integration challenge
1. Crowdflex: Discovery
2. CEV: Critical factors for the adoption of smart homes for energy efficiency and
implications for consumers and providers

63. 10 minute break
See you soon…..

64. WELCOME BACK!
Manu Ravishankar, Innovation Lead, Innovate UK

65. HyNTS Compression
Steve Johnstone,
Joerg Keil, Keith Armstrong & Andrew Cooknell, Siemens Energy

66. HyNTS Compression
Discovery: Show & Tell Webinar
23rd May 2022

67. 67
National Grid
HyNTS Compression
SIF Discovery Show & Tell Webinar – 23rd May 2022
• Compression system is required to move gas where required, depending on demand.
• More power is required to compress hydrogen
• New compression systems cost £40 - £50m and there are approximately 70 compresser units on the NTS.
• The opportunity to repurpose compression assets could significantly reduce the cost of the energy
transition.

68. 68
National Grid
HyNTS Compression
Work Package High Level Feasibility
WP1: Project Management General project management, meetings & stakeholder engagement
WP2: Business Case & Requirements
Development
Develop functional requirements of project, demonstration location
optioneering, initial development of test plan, initial CBA
WP3: Gas Turbines & Alternative Drive
Systems
Feasibility of hydrogen as a fuel gas, power requirements for
compression of hydrogen, impact of blends on gas turbine, review tests
undertaken on gas turbines and alternative drive systems
WP4: Compressor Equipment Feasibility of repurposing chosen compressor, impact of blends, review
tests undertaken on compressors and alternative compressor systems
WP5: Site Infrastructure & Equipment Feasibility of compression demonstration at FutureGrid, review ancillary
equipment required
WP6: Build, Commission & Test Initial development of testing requirements
SIF Discovery Show & Tell Webinar – 23rd May 2022

69. 69
National Grid
HyNTS Compression – User Needs
• Review of modelling undertaken​
- Based on Project Union​
- System Transformation 2050 Scenario​
- Significant reduction in gas demand, due to electrification
• Engaged with OFGEM, BEIS, HSE, GT & compressor
OEM’s​​
- Encouraged discussion requirements to ensure outputs are
beneficial to the energy system
- Development of understanding of technology which is
currently available and in development​
• User needs mean there may be a requirement for
variable hydrogen blends to be compressed
SIF Discovery Show & Tell Webinar – 23rd May 2022

70. 70
National Grid
HyNTS Compression – Project Overview
An assessment was undertaken on two compressors:
• The compressors were assessed with hydrogen blends of 20%
and 50% and for 100% hydrogen
Dresser Rand DeLaval
Performance data sheets Available Available
Performance maps Available Available
Thermodynamic design data Available Available
Digital design data files Available Available
Material certificates In clarification Available – impeller certificate required
Pressure Difference Achieved (bar)
- Constant vol. flow
- Variable discharge pressure
CH4: 10.8
20% H2: 8.0
50% H2: 5.3
100% H2: 1.0
CH4: 16.7
20% H2: 12.3
50% H2: 7.3
100% H2: 1.3
Most suitable for
re-engineering
SIF Discovery Show & Tell Webinar – 23rd May 2022

71. 71
National Grid
HyNTS Compression – Project Overview
• The original compressor designs are not
suitable for use with 100% hydrogen
• A new initial design was created for 100%
hydrogen
- Design consists of 4 impellers and a gearbox
- Would achieve the same pressure ratio
as natural gas.
- assessed for potential to handle variable
blends of hydrogen
• A repurposed gas turbine could provide the
power required to compress hydrogen and
hydrogen blends
SIF Discovery Show & Tell Webinar – 23rd May 2022

72. 72
National Grid
HyNTS Compression – Project Overview
• A suitable location for the compressor has been
determined at FutureGrid.
• The gas compressor would have a restricted
runtime of between 13 and 20 minutes.
• Large volume of H2 is required for demonstration
- 1600kg/hr of H2 required at peak load
- 1300kg/hr of H2 required on recycle mode
• To increase the gas compressor runtime to the limit
of the gas supply a gas cooler will be required.
• The available power supply will be adequate to
start and run the gas compressor
SIF Discovery Show & Tell Webinar – 23rd May 2022

73. 73
National Grid
HyNTS Compression - Benefits
• Potential to repurpose gas turbines for use with
hydrogen eliminates CO2 emissions from compressor
stations
• The use of compression will provide resilience to a
hydrogen network
• There are approximately 70 compressor units on the
NTS, which would cost £40m - £50m to replace
• Repurposing compression assets, could
significantly reduce of the cost of the energy
transition to the consumer
SIF Discovery Show & Tell Webinar – 23rd May 2022

74. 74
National Grid
HyNTS Compression – Alpha Phase
• Undertake modelling activities to develop compression
requirements
- Develop future hydrogen demand scenarios considering hydrogen
production and interactions between gas and electricity networks.
• Assess suitability of ancillary equipment for use with
hydrogen
• Develop conceptual designs
- Modifications to repurposed gas turbine and compressor
- New compressor for 100% hydrogen
- Hydrogen cab
- Demonstration site
• Refine transportation feasibility assessment
SIF Discovery Show & Tell Webinar – 23rd May 2022

75. Green Hydrogen Injection
into the NTS
Mohammad Hassaan & Alison Cartwright, CNG Services
Michael Azih, EE
William Mezzullo, Centrica

76. Green H2 into NTS Technical Regime
Discovery Phase – Slides for Show and Tell
76

77. Electricity Supply Water Supply Electrolysis
Hydrogen
Storage
Hydrogen
Compression
Hydrogen
Metering
Hydrogen
Blending
Outline of the Main Components of the Technical Regime
As part of the technical regime to inject hydrogen into the NTS, there are 7 key
different stages as shown in the graphic below.
• Water and electricity is needed to supply the electrolyser
• The electrolyser generates hydrogen
• The hydrogen is then compressed and metered
• Hydrogen is then injected into the NTS and blended into the natural gas stream

78. Discovery Phase Conclusions
Feasibility
It is feasible and straightforward to inject H2 into the NTS, building on biomethane as
at Somerset Farm. The plant required in an electrolyser and compressor with a buffer
between them and H2 energy measurement. The extent of downstream gas quality
measurement after blending is an area to review to reduce costs and simplify the
process. The following are the main conclusions from the Discovery Phase
GSMR and HSE Consent
• The 100% H2 injection pipeline would be owned by NGG, and no specific GSMR
changes are required as it blends into the NTS
• Post blending H2 limit is 0.1% which needs flow of 200,000 scmh of natural gas
which is easily achieved on the 4 NTS Feeders in Scotland
Electrolyser
• There are advantages from an electrolyser delivering 99.999% hydrogen as this may
remove the need for separate H2 composition measurement
• Medium term vision is for 70 bar electrolysis with no need for compression
• Rainwater as the source of water is best option if feasible
Compressor
• Oil free reciprocating and diaphragm compressors are recommended due to their
commercial availability and technical features with zero oil contamination.
H2 Flow Metering
• Ultrasonic flow metering is recommended as it is low cost and accurate,
• The cheapest and accurate solution for a desired non invasive measurement.
Site Selection
• For a pilot project, e.g. 1 MW electrolysers, 200 scmh of H2, the key is the GSMR
limit for H2
• Main NTS feeders will allow blending without any gas going to customers that is
outside the 0.1% limit
• Sites close to wind farms that can be expanded could be best option to
demonstrate the injection and the GoO scheme

79. 79
Key Insights
LCOH (p/kWh) and load factor vs. electrolyser capacity, for a system with
a 20 MW wind farm, 5 MW solar system, and grid export connection
• We have modelled 10 electrolyser scenario configurations.
– Of all scenarios, the 5 MW electrolyser coupled with a 20
MW wind farm, 5 MW solar farm, and grid export connection
outputs the lowest LCOH at 13.46 p/kWh.
– Results indicate an ideal 4:1 ratio of [wind farm
MW]:[electrolyser MW] for non-battery scenarios.
• Electrolysers ranging from 1 MW to 35 MW are able to produce
between 0.16 ktH2/yr to 6.23 ktH2/yr.
• The current GSMR H2 requirement limits the capacity of green
H2 electrolysis to 10 MW (at 100% load factor).
• Carbon savings vary between 1 – 44 ktCO2/yr, dependent on
electrolyser capacity and load factor.
• A grid connected 35 MW electrolyser has the potential to
reduce curtailed energy by 90 GWh/yr.
• Of all cost categories, the electricity fuel cost is always the most
significant cost component.
• While the additional cost of injection into the NTS is relatively
low compared to the production costs, the compression &
distribution costs for a transport use-case significantly increases
LCOH.
• A green H2 CfD subsidy at 12.7 p/kWh strike price can
potentially produce a profitable project with an IRR of 17.0%.
4.6 4.6 4.8 5.3 5.7
6.7
9.6
12.5
7.5 7.5 7.5 7.5 7.4
7.5
7.6
7.6
3.3
1.3 0.9
0.9
0.9
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
16
18
20
22
0.2
Electrolyser Capacity (MW)
LCOH
(p/kWh)
3
0.8
15.0
1 35
0.2
0.7
5
13.7
8
0.7 0.2
15
10
0.6 0.2
0.3
0.3
13.7 14.0
25
0.5
Load
Factor
(%)
16.2
13.5
18.3
21.5
Load Factor
NTS Injection
Miscellaneous
Battery Storage
Electricity
Electrolyser

80. 80
For the NTS injection case, a range of economic performance is seen across the different
modelled scenarios and electrolyser capacities.
• Of all scenarios, the 5 MW electrolyser coupled with
a the 20 MW wind farm, 5 MW solar farm, and grid
export connection (i.e. W+S+GE with 5 MW
electrolyser) produces to lowest outputted LCOH at
13.46 p/kWh.
• For a modelled 20 MW wind farm, an electrolyser
capacity of ~5 MW (for cases without a battery) or
~10 MW (for cases with a battery) produce the
lowest LCOH, indicating an ideal 4:1 ratio or 2:1 ratio
of [wind farm MW]:[electrolyser MW] respectively.
• Generally observed, the addition of a battery to the
system is less financially favourable.
• For scenarios which have the potential for grid
import, LCOH changes are a less sensitive to
variation in electrolyser capacity (with fixed RE
capacity) as a load factor of 1 can always be achieved
by leveraging electricity input from the grid.
LCOH
(p/kWh)
Electrolyser Capacity (MW)
1 3 5 8 10 15 25 35
W+GE 16.67 14.28 14.03 14.20 14.58 15.75 19.53 23.03
W+GIE 16.63 14.30 14.05 14.14 14.33 14.74 15.38 15.60
W+S+GE 16.20 13.69 13.46 13.64 13.99 15.01 18.35 21.49
W+S+GIE 16.40 13.95 13.75 13.89 14.12 14.57 15.27 15.52
GI 21.73 18.69 17.95 17.47 17.32 17.01 16.75 16.58
W+GE+B 34.04 20.34 18.11 17.24 17.33 18.57 22.56 26.06
W+GIE+B 33.91 19.35 16.97 15.98 15.83 15.89 16.12 16.12
W+S+GE+B 33.91 19.18 17.09 16.38 16.45 17.50 21.07 24.21
W+S+GIE+B 34.10 18.92 16.59 15.71 15.57 15.68 16.01 16.05
GI+B 38.98 23.77 20.76 19.00 17.60 17.20 16.87 16.66
LCOH (p/kWh) for varying electrolyser capacities (MW) and scenarios

81. 81
Optimised LCOH: Cumulative effect of each best case scenario results in a profitable outcome
with an 17.0% IRR using a H2 strike price of 12.7p/kWh.
13.46
9.81
1.48
0.84
1.01 0.32
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Electrolyser
Efficiency
LCOH
(p/kWh)
Base Case Electrolyser
Capex
Electricity Cost Capital
Structure
Optimised
Case
-27%
Overall potential for LCOH cost reduction for W+S+GE configuration with 5 MW Electrolyser
• There is potential to reduce the expected
LCOH by 27% reduction relative to the base
case if the best case hypothesis holds true.
• Within the bounds of the sensitivity
parameters used, the largest proportion of
potential cost reduction is contributed by
electrolyser capex (~40%).
• This is not necessarily aligned to future
potential for cost reduction, since electricity
dominates the cost contribution to LCOH (
~53%).
• With a CfD strike price of 12.7p/kWh, this
results in a 17.0% project IRR and NPV20 of
£9.3m
• With a less favourable strike price of
10.2p/kWh (£4/kgH2), this particular
connection configuration is still profitable
with an IRR of 3.1% and NPV20 of £0.23m*.
*In this case, the weighted discount rate is 2.4%. N.B. this case is contingent on a relatively low cost of capital.

82. 82
Centrica’s Contribution
Role in the project:
• Provided an assessment of customer demand for hydrogen trends,
barriers and opportunities.
• Explained the mechanisms required for tracking and trading the
hydrogen from production to the end use customer.
• Provided an overview of the verification process for Hydrogen
Certificates and associated GHG emissions, drawing comparisons from
the biomethane certification process.
• Drew on Centrica's experience of operating electrolysers using excess
power from renewable electricity and demonstrated how electrolysers
can take advantage of varying electricity prices if managed through
dynamic bidding platforms.
Next Steps:
• Establish rules for certification, guarantees of origin (GoO) and
calculating low carbon hydrogen standards requirement.
• Link financial support to GHG pathways.
• Demonstrate how excess or curtailed electricity can be used to
generate hydrogen through virtual sleaving.

83. Alpha Phase Summary
Workstreams for Alpha Phase
• Workstream 1 – Specification Limit for H2 into NTS in a 2023 pilot
• Workstream 2 – FEED for Electrolyser, Buffer, Compression, Injection, Blending Monitoring
• Workstream 3 – Pilot Site Identification and Site Specific FEED Layout Drawings
• Workstream 4 – Commercial Regime for H2 Injection
• Workstream 5 – Financial Support for H2 injection
• Workstream 6 – Strategic Investment in H2 production in SSEN area north of Dundee
Participating Parties
• NGG
• CNG Services
• Element Energy
• Centrica
• SSEN
• Supplemented with specific expertise including from REA., Gas Unie and UK consultants
Key Outcomes from Alpha Phase
Application for BETA Phase including:
- Resolve Discovery Phase outstanding technical issues
- FEED Completed with site specific layout drawings for favoured
pilot site
- Suppliers identified for Major plant items
- Base Case Commercial Regime for Pilot and Commercial Flows
- Engagement with BEIS and potential H2 producers on project
economics and viability
- Identify Strategic Green H2 investment opportunities north of
Dundee

84. Nuclear Net-Zero
Opportunities (N-NZO)
Sukhbinder Singh, Frazer-Nash Consultancy

85. COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Nuclear Net Zero Opportunities
SIF Discovery Phase – Show and Tell
23rd May 2022

86. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Project Objectives
86
 Define a set of end-user scenarios for low carbon hydrogen demand.
 Determine how future nuclear-hydrogen siting options, under current regulatory frameworks,
can service this demand using the NTS.
 Consider new credible siting options for nuclear-hydrogen production and determine the
additional benefits these may provide to transporting hydrogen to the end-users.
 Consider the barriers to siting nuclear with hydrogen cogeneration, including policy,
regulatory, technical, economic, societal and wider energy systems issues.

87. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Forecasted Hydrogen Demand
87
Low Demand High Demand
2035 2050 2035 2050

88. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Stakeholder Engagement - Barriers
88

89. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Network Impact Assessment
89
• For each demand scenario the model solves, for all but two pipes
• The ANT location doesn’t impact the networks solvability.

90. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Future Siting Scenarios
90
Vectorisation Rasterisation

91. © Frazer-Nash Consultancy Ltd. All rights reserved.
COMMERCIAL IN CONFIDENCE
COMMERCIAL IN CONFIDENCE
Conclusions
 Energy intensive industry is suggested to be the first sector to adopt 100% hydrogen. According to
the multi criteria analysis conducted as part of this study, Teesside, Humberside and Merseyside
industrial clusters each have potential ANT sites in close proximity, in the form of current
licenced sites, proposed future sites or both.
 Network impact analysis concluded that there are no significant siting implications to
connecting an ANT to the NTS. Also, hydrogen demand modelling indicated that the current
network has the capability to deal with each of the future demand scenarios, however
additional feeders may be required to optimise network configuration and upgrades could be
necessary for assets, such as compressor stations.
 Assessment of potential policy and regulatory barriers indicated there are no barriers that
completely prohibit nuclear and hydrogen cogeneration sites. It is recommended that
consultation and collaboration between the relevant regulatory authorities takes place as early as
possible to ensure future amendments are aligned to enable deployment of these technologies.
91

92. Q&A – Whole Systems Integration challenge
5. HyNTS Compression
6. Green Hydrogen Injection into the NTS
7. Nuclear Net-Zero Opportunities (N-NZO)

93. Now Open for Ideas - Ofgem’s Strategic Innovation Fund
A £450m fund for large scale electricity and gas energy network innovation
Each challenge area has key themes which must be addressed. The projects
against these can be technical, social, commercial and/or market innovations.
Supporting a just energy transition
Preparing for a net zero power
system
Improving energy system resilience
and robustness
Accelerating decarbonisation of
major demands
Inclusivity, accessibility, and cost of
living crisis
A fully decarbonised power system by
2035
Energy security and energy system
durability
Decarbonisation of heat, transport,
and buildings
Round 2 Challenges
Supporting
Launch Events – Wednesday 25 May 11:00 – 12:30 and 13:30 – 15:00
Check out the link in the chat

94. Thank you
Manu Ravishankar, Innovation Lead, Innovate UK