This document summarizes research on integrating energy efficiency and renewable energy. It discusses how energy efficiency improves the integration of renewable energy by reducing resource requirements, improving matching of renewable energy to demand, enabling optimization of renewable energy use, and allowing sectoral integration. It also discusses using power-to-heat conversion to increase energy system flexibility and the ability to integrate variable renewable electricity. Market adoption of new energy technologies follows an S-curve diffusion process, and financial support is important near grid parity to help new technologies gain market share.

1. Integration of energy efficiency
and renewable energy
- multiple benefits!
Prof. Peter D. Lund
Aalto University, School of Science
Espoo-Otaniemi, Finland
peter.lund@aalto.fi
23 March 2017

2. Outline of the lecture
Research results based on following journal papers:
[send an email to peter.lund@aalto.fi if you want a copy of the papers]
1. Salpakari, P. Lund. Optimal and rule-based control strategies for energy flexibility in
buildings with PV. Applied Energy 161 (2016) 425-43.
2. J.Salpakari, J. Mikkola, P.D. Lund. Improved integration of large-scale variable renewable
power in cities through optimal control of DSM and power-to-heat measures. Energy
Conversion and Management 126 (2016)649-661.
3. Peter Lund. Market penetration rates of new energy technologies. Energy Policy 34
(2006), 3317-3326.
4. Peter D. Lund. Energy policy planning near grid parity using a price-driven technology
penetration model. Technological Forecasting & Social Change 90 (2015) 389-399.
Peter Lund 2017
Main contents:
①Enabling large scale local renewable power use through
better integration and flexibility
②Market take-up and adoption of these new technologies

3. Forthcoming energy transition
Peter Lund 2017
http://www.shell.com/global/future-
energy/scenarios/new-lens-scenarios.html
• Fossil fuels : Today >80%; 22% by 2100 of energy
• Electricity important : future power supply > power demand ?
IEA scenario (2015)

4. Impacts from much variable
renewable electricity (VRE)
Peter Lund 2017
No VRE
Much VRE

5. Renewables & energy efficiency
Relevance of EE to large-scale RE schemes:
① Reduces resource requirement (‘less needed’)
② Improves matching of RE (‘more flexibility’)
③ Enables optimizing of RE (‘ better use’)
④ Sectorial integration possible (‘close to end-use’)
Link between energy efficiency (EE) and renewable
energy/electricity (RE) increases towards the end-use:
‘supranational’  national urban (cities) homeowner (building)
Peter Lund 2017

6. Flexibility approaches for the
energy system transformation
Peter Lund 2017

7. Cost & magnitude of flexibility
Peter Lund 2017
Ref: Energy Innovation, 2015

8. Power-to-X (P2X) conversion
for increased flexibility
Peter Lund 2017
Oil
Coal
Peat
Natural gas
Biomass
Waste-to-
energy
Nuclear
Hydro
Wind
Solar power
Heat pumps
Imports
Electricity
Heat
Fuel
Electricity
pool
Heat pool
Fuel pool
House-
holds
Services
Transport
Industry
PRIMARY
ENERGY
NEW
FINAL
ENERGY
OLD FINAL
ENERGY
(BALANCE
NODES)
ADVANCED
CONVERSION
CONVENTIONAL
CONVERSION
DEMAND
Unconverted
CHP
District
heating
CHP
Industrial
Separate
heat
production
Separate
electricity
production
Residential
heat
production
Oil
refineries
P2H
elecheat
P2C
eleccold
P2G
elecgas
P2L
gasbiofuel
Biofuel
biomassbio
-fuel
Electricity
and heat
storage
1 2
ENERGY
SERVICES

9. Curtailment for increased system
flexibility with RE power
Peter Lund 2017
• Curtailment = Cutting off
• Energy lost with curtailment =
capacity factor (RE≈8-40%) ×
curtailment level × curtailment time
• Utilization= curtailed RE for P2X,
EV & battery charging, etc.
• Advantage = Sizing of RE >> Self-
use limit of power (as RE surplus can
be curtailed)
• Active curtailment= RE power
could provide very fast power ramp-
up and ramp-down

10. • Power-to-thermal: surplus or curtailed
RE converted to thermal energy; P2H=heat
• Heat (resistance, heat pump) or cooling
(refrigeration cycle)
 η (resistance) ≈100%
 COP (heat pump) ≈300%
Power-to-thermal strategy
• Coupled to thermal
demand/storage
• The building could also
function as ’thermal’
storage

11. Rationale of P2H (Power-to-Heat)
EU-27 household
final energy
67%
15%
14%
4%
Space heating
Appl&Light
Hot water
Cooking
1. Thermal energy (heat, cooling) dominate final
energy use in built environment
• >50% of final energy use in cities
• Up to 80% of EU-27 household final energy
2. With a high RE share of power, marginal value of
electricity < marginal value of heat
3. Thermal storage is proven and cheap (~$5-
25/kWh), from short-term small size to long-term
large-size (max. unit ~1-2GWh, 100-200 MW),
from centralized to decentralized schemes
4. P2H decreases price volatility at high RE shares
and allows to integrate more RE
Peter Lund 2017

12. Case 1. Large wind scheme with
P2H for urban use: Case Helsinki
Peter Lund 2017
Power and heat demand &
plants in Helsinki
1GWel ;1.3GWth,CHP ; 2.0GWth,peak
(coal, gas); district heating
Big wind scheme:

13. Results: cross-sectoral integration
2. Wind with P2H ≤ Power + Heat demand 1349 MW wind power
= 60% of electricity per year (2% of heat)Ref: Lund, P. : Large-scale urban renewable electricity schemes - integration and interfacing aspects. Energy Conversion and Management, 2012
1. Wind ≤ Power demand  max. 486 MW wind power
= 20-25% % of yearly electricity in Helsinki
Peter Lund 2017
1349 MW
486 MW
1
2
(’worst day’ Feb 6)
Wind
power
Heat
Elec
(1 year)

14. Case 2: Improved flexibility
through DSM + P2H + Storage
Peter Lund 2017
• Helsinki case as above, but in
addition:
 Solar + Wind
 DSM+Storage
• Control strategies for flexibility:
a) load and supply matching b)
link to electricity market +
investments

15. Fig Electricity load in Helsinki with shiftable components (DSM). The
duration that a load can shift its consumption is indicated in the legend
Improved flexibility with large-scale variable renewable power in cities through optimal demand side management and
power-to-heat conversion
Energy Conversion and Management, Volume 126, 2016, 649–661
http://dx.doi.org/10.1016/j.enconman.2016.08.041
Shiftable loads 35-350
MW, or 7-50% of hourly
power demand
Power profile + shiftable loads
Peter Lund 2017
Electricity demand

16. http://dx.doiorg/10.1016/j.enconman.2016.08.041
Surplus VRE
production
Peter Lund 2017
• DSM enables more
VRE (wind and PV)
• P2H very effective
(cross-sectorial
integration to
increase flexibility)
Improved
integration
due to DSM
Baseline (topmost surface)
No surplus (VRE<Load)
Improved
integration
due to P2H

17. http://dx.doi.org/10.1016/j.enconman.2016.08.041
Cash-flow when linked to spot
electricity market
Peter Lund 2017
Baseline
1
2 3
4

18. Case 3: Flexibility in 1 building +
PV
Peter Lund 2017
• Same as above, but 1 house+PV
• Optimization: minimize variable
costs & maximize self-use of PV
• 13–25% savings in electricity bill with cost-optimal control (1h-based price)
• 8–88% decrease in electricity fed into the grid

19. Peter Lund 2017
Market take-up of new
technologies (diffusion)
”Diffusion is a process in which the innovation is distributed to
members of the social system through different channels having
different threshold for adoption of new innovations” (Rogers)
• Key elements of diffusion of innovations
– Innovation; Communication channel; Time; Social system; Adopters
• The diffusion process explains the penetration of a new
technology to the market over time
time
Innovation Communication
channels
Social
system

20. Peter Lund 2017
Decision making process linked to
technology diffusion or adoption
Knowledge
Communication channels
Diffusion of
innovation
Social system
Previous experience
Needs/problems
Norms of the society
Degree of innovation
Personal
character:
Social status
Personality
Communication
behaviour
Innovation
characteristics:
Compatibility with values
Advantages?
Prev. experiences,needs?
How complex to use?
How easy to try? Visibility?
Accept or
Reject
Persuasion Decision Realization Verification

21. Peter Lund 2017
Diffusion Model
)0(
)0(1
0
f
f
a
−
=
)(
0
0
1
1
)( tt
ea
tf −⋅−
⋅+
= β
Classical diffusion equation also so-called
epidemic model
(f=market share, β=adoption rate, t=time)
Bass model: influence of internal (q) and
external (p) information
Other models: Gompertz, Mansfield,
Nonsymmetric models
Diffusion equation/model describes the adoption of the new
technology over time, e.g. change of the market share

22. Peter Lund 2017
Analyzing the diffusion equation
Importance of parameter β [%/year]
• β = adoption rate or penetration rate of the new technology
• Steepness of the penetration depends on the β; affected by attributes of innovation
(e.g. price), type of decision (e.g. collective or authority), communication channel
(e.g. media), social system (e.g. norms), promotion efforts
)(
0
0
1
1
)( tt
ea
tf −⋅−
⋅+
= βS-curve
)0(
)0(1
0
f
f
a
−
=

23. Results from diffusion models
Peter Lund 2017
Effectiveness of policy measures in transforming the
energy system. Energy Policy 35 (2007) 627-639
Market penetration rates of new energy
technologies. Energy Policy 34 (2006), 3317-3326

24. Peter Lund 2017
Modelling diffusion of innovations and
decision making with two choices
• Binary choice model (BCM) describes the choice between technologies A and B
(e.g. substitution of old with new)
• Probability to purchase the new product A is P (logit probability); C=price=direct
cost + other costs (e.g. behavioral factors, inconvenience costs) σ=arb. parameter;
• BCM could be extended to other factors as well; replace C with a vector of factors
0 %
20 %
40 %
60 %
80 %
100 %
-50 -30 -10 10 30 50
Probabilityofpreferences
Price difference (€/MWh)
Technology A
Technology B
B cheaper than A A cheaper than B

25. Importance of financial support to
market penetration and shares
Peter Lund 2017
1. Price affects market penetration
(both potential and decisions)
2. Above grid parity (old cheaper than
new), fast penetration requires
“over-subsidization”
3. At grid-parity (new≈old), support
necessary to gain a market share
4. Clearly below grid-parity (new
cheaper than old) no support
5. Dynamic energy policy support
important, i.e. adjusting the support
levels over time as new technology
gets cheaper
Energy policy planning near grid parity using a price-
driven technology penetration model. Technological
Forecasting & Social Change 90(2015)389-399.
0,0 %
0,5 %
1,0 %
1,5 %
2,0 %
0,01 % 0,10 % 1,00 % 10,00 %
Growthinmakertshare(dfi/yr)
Market share (fi)
GLO Nuclear 1966-2011
GLO PV 1978-2012
GLO Wind 1982-2012
GER Wind 1990-2012
GER PV 1990-2012
USA NG 1994-2011
USA NG 5-year avg
Price ratio: 0.1
Price ratio: 0.2
Price ratio: 0.3
Price ratio: 0.5

26. Extras
Peter Lund 2017

27. Example: Curtailing solar power
in Finland
0%
20%
40%
60%
80%
100%
0% 50% 100%
Reductioninsolaryield
Power cut-off limit (% of nominal PV power
installed)
Peter Lund 2017

28. Helsinki Wind+CHP+DH base case
(simple integration, P2H=elec.boiler)
Peter Lund 2017
25%
55%
62% 10%0%
1%
Wind power share:
Technical simulation 1-hour steps over 1 yr; no overflow of power from the city
Self-use limit
power heat
yearly demand
Present

29. Effects on remaining power system
from VRE
Peter Lund 2017
A

30. Optimal and rule-based control strategies for
energy flexibility in buildings with PV (2)
Peter Lund 2017
• 8–88% decrease in electricity fed into the grid
• 13–25% savings in electricity bill with cost-optimal control (1h-based price)

31. Other findings with
improved flexibility
Peter Lund 2017
1. Reference: RE power limited to self-use of electricity
2. Power-to-Thermal (P2T) strategy: RE is oversized, surplus converted
into thermal energy (heat or/and cold)
RE of yearly electricity demand
1.RE coupled to power
system and limited to
self-use of power
2. RE coupled to both
power and thermal system
through P2H
Shanghai (China)
Solar PV
25% 54-58%
(+116-132%)
Helsinki (Finland)
Wind Power
25% 55-62%
(+120-148%)
Delhi (India)
Solar PV
22% 50-52%
(+127-136%)
Similar results for Conception (Chile) and Dhahran (Saudi-Arabia)

32. Social behaviour and decisions
• Consumer interface and decisions (e.g. Daniel Kahneman)
Peter Lund 2017

33. Peter Lund 2017
Analyzing the diffusion equation
Importance of parameter β [%/year]
• β = adoption rate or penetration rate of the new technology
• Steepness of the penetration depends on the β; affected by attributes of innovation
(e.g. price), type of decision (e.g. collective or authority), communication channel
(e.g. media), social system (e.g. norms), promotion efforts
What else can we see from
the diffusion curve?
• Denote α= βt0+ln (a0) 
• Inflexion point, maximum at t= α/β (f=50%)
• Relative market growth (%/yr)
Year-to-year growth starts to decline
when market share grows !
)(
0
0
1
1
)( tt
ea
tf −⋅−
⋅+
= β
S-curve

34. How does ‘price’ influence market
penetration of energy technologies ?
Peter Lund 2017
• Binary choice model
• Growth mainly self-financed
• Influence of support
E.g. at low market share, Y2Y growth
of a new technology:
dVi /Vi ≈ [a1 +a2] × Cold/Cnew
Vi =volume or capacity
C =cost of technology
a1= yearly growth of power demand
a2= replacement rate of old capacity
Model verification