Managing Smart in Smart Grid:

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21 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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Authors
:

Anne Rønning,
Guro Nereng
,
Camilla Skjerve
-
Nielssen,

Andreas Brekke
,

Bernt Bremdal

R
e
port

no
.:

OR.
25
.
12

ISBN:

978
-
82
-
7520
-
676
-
1

ISBN:

82
-
7520
-
676
-
6


Figure: Tieto

Managing
Smart in Smart Grid:

Macro
perspectives




Energy

and power

use and ways to reduce





Managing Smart in Smart Grid:


Energy and power

use and ways to reduce





© Østfoldforskning








Managing Smart in Smart Grid:


Energy and power

use and ways to reduce


© Østfoldforskning



Report no.:

OR.
25.12

ISBN no.:

978
-
82
-
7520
-
676
-
1

Report type:



ISBN no.:

82
-
7520
-
676
-
6

Commissioned report



ISSN no.:

0803
-
6659


Report title:

Managing Smart in Smart Grid:

WP 1A: Macro analysis
-


Energy
and power use

and ways to reduce


Author(s):

Anne
Rønning, Guro Nereng
,
Camilla Skjerve
-
Nielssen,

Andreas Brekke, Bernt
Bremdal

Project number:

1373

Project title:

Manage Smart in Smart Grid

Commissioned by:

Company contact:

Tieto AS

Lars Ødegaard

Keywords:

Confidentiality:

Number of pages:



Smart
Grid Concepts



Power
peak
reduction



Value

oriented

energy use



Energy Efficiency

Open

63

Approved:

Date:
16.04.2013





Project Manager





Research Manager












Managing Smart in Smart Grid:


Energy and power

use and ways to reduce


© Østfoldforskning



Table of contents


S
ummary

................................
................................
................................
................................
.

1

Preface

................................
................................
................................
................................
....

3

1

Introduction

................................
................................
................................
..............................

4

1.1

An introduction to how Smart Grid concepts may be utilised in the consumer end

...................

5

1.1.1

The

deployment of Advanced Metering Infrastructure in Norway

................................
..

6

1.1.2

Demand Response: The e
nd consumer responds to a market need

...........................

6

1.1.3

The Prosumer


an entity consuming and producing energy

................................
........

7

2

Energy and power usage

................................
................................
................................
.........

9

2.1

Global energy use

................................
................................
................................
...................

9

2.2

Electricity use in Norway: A large heating proportion

................................
.............................

12

2.3

Specific energy use distribution
in offices

................................
................................
..............

13

2.4

Specific energy use distribution in households

................................
................................
.......

15

2
.4.1

Household appliances: More efficient but rising in number

................................
.........

19

2.4.2

Households: Variations in electricity use among different users

................................
.

20

2.5

Power use: Variations in time

................................
................................
................................

21

2.5.1

Power use in households

................................
................................
...........................

21

2.5.2

New consumption: Power use for charging electric vehicles

................................
.......

23

2.6

Distributed energy ge
neration

................................
................................
................................

25

2.6.1

Environmental characteristics of the distributed energy generation technologies

.......

26

2.7

Batteries and distributed storage technologies

................................
................................
.......

26

2.8

Neighbourhoods and the possibilities of bottom
-
up smart grid markets

................................
..

27

2.8.1

Industrial ecol
ogy: One company’s waste is another’s resource

................................
.

28

2.8.2

Local thermal grids between buildings

................................
................................
........

29

2.8.3

Plus customers


delivering locally produced electricity to the grid

.............................

31

2.8.4

Net nearly zero, zero and plus energy buildings

................................
.........................

31

2.8.5

Existing buildings and energy production possibilities

................................
................

32

2
.8.6

Developing neighbourhoods goes hand in hand with Smart Grid concepts

................

33

3

Reducing energy use and regulating power peaks

................................
................................

34

3.1

Energy efficiency

................................
................................
................................
...................

34

3.1.1

Environmental benefits from decreased energy use

................................
...................

34

3.1.2

Energy efficiency and savings from Demand Response

................................
.............

35

3.2

M
easures to
regulate power peaks and environmental consequences

................................
.

36

3.2.1

The possibility of
avoiding

new grid construction

................................
........................

36

3.2.2

Balancing deficit and surplus intermittent renewable electricity

................................
..

37

3.2.3

Demand
-
Response: Potenti
als on peak clipping and energy savings

........................

38

3.3

Public instruments

................................
................................
................................
.................

41

3.3.1

Relevant political instruments

................................
................................
.....................

41

3.3.2

Barriers

................................
................................
................................
......................

45

4

Towards Energy Effectiveness

................................
................................
..............................

47

4.1

An Energy effectiveness approach to buildings

................................
................................
......

47

4.2

Functionality, effectiveness and zone approach

................................
................................
.....

50

4.2.1

Can space comprise a potential demand
-
response “device”?

................................
....

50

4.2.2

A simple layout design approach

................................
................................
................

53


Managing Smart in Smart Grid:


Energy and power

use and ways to reduce





© Østfoldforskning

4.3

E
nergy effectiveness in Green ICT

................................
................................
........................

53

4.3.1

Power Usage Effectiveness and Energy Reuse Factor

................................
...............

54

4.4

Towards a set of efficiency parameters for prosumers in a Smart Grid

................................
.

54

5

Concluding remarks

................................
................................
................................
...............

57

6

References

................................
................................
................................
............................

58

Annex 1

Some definitions and translations

................................
................................
..........................

64





Managing Smart in Smart Grid:


Energy and
power

use and ways to reduce


© Østfoldforskning


1

S
ummary

This report is a deliverable from Work Package 1A (WP1A) in the Manage Smart in Smart Grid
(MSiSG)
project organized under the R
ENERGI

program and funded by the Norwegian Research
Council.

The
MSiSG
project is a common initiative between the Norwegian Centre of Expertise Smart
Energy Markets (NCE SEM), Ostfold Research, Institute for Energy Technology

(IFE)
,
Tieto,
moreCom, Ostfold University College and Statsbygg.


Smart Grid research projects are em
erging all over the world. Norway with its
hydro power dominated
electricity production and comparably low electricity prices have different framework conditions from
other countries for utilising Smart Grid technology. It has been said that Norway needs t
o pursue its
own dialect for the Smart Grid. This report could be said to explore what can be the environmental

motivations for the Norwegian Smart Grid dialect.


The backdrop for
the

report is the
global
drive

for reduced climate gas emissions. As shown
in the
report, how energy is provided and used is worldwide

one of the major contributors to climate gas
emissions.
Smart Grid technology and concepts may contribute to

reduce
energy
use reduction and
control power peaks that
buildings

contribute

to
,
i.e.
the

household and services sector
, which
constitutes the sectors that MSiSG project has focused upon.


After briefly introducing the new possibilities that Smart Grid concepts may provide in end
consumption, such as Demand
-
Response and “prosumption”, the r
eport describes energy

and power
usage as it is today.
First the global energy system is described, then we elaborate on the
peculiarities of Norwegian energy use and finally describe what types of appliances and purposes for
which electricity is used in N
orwegian offices and households. This will enable the reader bo
th to see
Norwegian energy use
in the global context, as well as getting an idea about what energy or electricity
budge
t

posts that contributes to both energy and power use. New types of energy

use, distributed
energy production and power storage are described as well as energy use seen from an
are
a/neighbourhood perspective.


The section named “Reducing energy use and regulating power peaks” sets out from what we call the
“traditional energy ef
ficiency” angle. The link between energy efficiency and climate change mitigation
is explained, again in a Norwegian context. Then we investigate how Smart Grid concepts could
contribute to environmental benefits. Here we also briefly explore alternative w
ays the Norwegian
energy system could be utilised to curb climate changes, so that the advocates of Smart Grid
implementation could bear this in mind in their considerations. Some projects that have investigated
the potential for power peak clippings and e
nergy savings due to energy use flexibility give
the reader
an idea of these aspects.


A
n overview of existing political instruments

directed towards the building sector

is presented. These
instruments

do, or could, influence
energy reduction or conversion,
the rollout of Smart Grid
tec
hnology an
d distributed renewable energy.
B
arriers
against the implementation of measures
are
also
discussed
.


Finally, in the last section we introduce a functional oriented approach to express
and
measur
e

energy use. Indicators for energy use should incorporate energy
effectiveness

rather than energy

Managing Smart in Smart Grid:


Energy and power

use and ways to reduce



2


© Østfoldforskning

efficiency
, and mirror the function of the building and satisfy a set of requirements by the user.

From
this the zone concept is introduced. The zo
ne concept focuses on how to optimise the use of different
areas
in a building
at any given time and to define the main functions
use of each zone
and its related
energy
.





Managing Smart in Smart Grid:


Energy and power use and

ways to reduce


© Østfoldforskning


3

Preface


This report is a deliverable from Work Package 1A (WP1A) in the Manage Smart in Smart Grid
(MSiSG)
project organized under the R
ENERGI

program and funded by the Norwegian Research
Council. The overall aim of the MSiSG project is
to
establish
new understand
ing, technology, models
and services for effective energy ma
n
agement for private and public end users based on Advanced
Metering Infrastructure.


The main objectives of the project are:



Develop
increased understanding, including models and methods for how
new market and
regulation
mechani
sms
can be
influenced by the market actors.



Use, and if needed develop new metrics

for measuring and tracking energy effectiveness
related

to individual
behavior
and energy
use
.



As a contribution to the simulation and
scenario centre in NCE Smart Energy Markets establish a
simulator for the study of micro and macro effects.



Develop new models for trade between market place and the end user market.



Develop a minimum of two new services and two new product types for end u
sers.



Educate one
PhD candidate.


The project is a common initiative between the Norwegian Centre of Expertise Smart Energy Markets
(NCE SEM), Ostfold Research, Institute for Energy Technology

(IFE)
,
Tieto
, moreCom, Ostfold
University College and Statsbygg.


The project
had
five principal

work packages

at the outset
:


WP1A
:

Macro analysis: Energy efficiency, effectiveness
and management in buildings; le
d by
Ostfold Research

WP1B
:

Microanalysis;
le
d by Institute for Energy Technology

WP2:

Modelling

WP3:

Develop new s
ervices into the Micro Grid; le
d by moreCom/Tieto

WP4:

PhD program

WP5

Project management and reporting


This document
is
a delivery from
WG1a
and
provides information

regarding energy
and power use
,

management in buildings within the context of Smart Grid concepts
and environmental impacts.



Managing Smart in Smart Grid:


Energy and power

use and ways to reduce



4


© Østfoldforskning

1

Introduction

“Smart Grids are electricity networks that can intelligently integrate the behaviour and actions of all
users connected to it
-

generators, consume
rs and those that do both


in order to efficiently deliver
sustainable, economic and secure electricity supplies.”

The European
Technology Platform for

SmartGrids

(2011)


Energy underlies every activity and quite simply enables people to live their lives. It is essential for
growing, producing and preparing food, washing clothes, washing bodies, providing stable indoor
temperatures, moving bodies, manufacturing buildings an
d all the things that are in them, and a range
of other necessary or pleasant functions. Thus, it is no wonder that access to energy is a most
important issue for individuals, nations and the global population as a whole.
Energy use is not evenly
distribut
ed across nations, across applications or related to the time of the day.


E
nergy
supply
comes
from
many different
energy sources
.
In the early days of
man
kind all energy
was supplied through food
.

The
introduction of fire gave

wider

possibilit
ies

to

cook

and preserve food
and also to
craft materials and heat homes. Still, the use of energy did not boom until the industrial
revolution and the development of turbines and engines.

In today’s society, large debates on energy
sources are focused especially aro
und two themes: environmental impacts and energy
supply
security.
Fossil energy sources lead to large emissions of greenhouse gases but also other energy
sources have environmental impacts associated.
Many nations import fuels and are looking for ways
to r
educe their dependency on fossil fuel sources.


Within this picture, so called Smart Grids may remedy some of the inequalities in distribution of
energy and make individual houses and buildings, as well as larger regions, better acquainted to
secure energy

supplies.
More specifically, as can be seen in the initial citation, Smart Grids deal with
the distribution of electricity, which is energy of high quality. The
Smart Grid

is a wide concept that
entails the application of integrated information and commun
ication technology between the
applications and actors of the electricity grid, from the power producers to the end consumers.
Traditionally, the electricity grid has supported only the supply

of electricity from typical energy
producers, i.e. various type
s of power plants, to end users, e.g. households and industry. The energy
and
the
information
flow one direction only.
In the Smart Grid this is no longer valid.

E
merging
technologies within the Smart Grid concept may enable new ways of controlling energy use, curbing
peak loads and facilitating both the introduction of distributed, local energy production and large scale
intermittent renewable energy production.


Smart Grid research projects are emerging all over the world. Norway with its hydro power dominated
electricity production
,

comparably low electricity prices
, dispersed settlements and large
electricity

use
per capita
ha
s

different framework conditions fro
m other countries for utilising Smart Grid technology.
It has been said that Norway needs to pursue its own dialect for the Smart Grid

(e.g
.

Sintef
,

2012)
.
Th
e

work
report
ed here

is an

explor
ation of
the environmental

motivations for the Norwegian Smart
Gr
id dialect.


This report is part of the project
Manage Smart in Smart Grid
, which

aims at developing new services
and/or products
in the intersection between the end consumers of electricity and
Energy Service
Companies or what we have coined Smart Energy
Service Provider (SESP)

(Aas
,

2012).
A goal is to

Managing Smart in Smart Grid:


Energy and power use and

ways to reduce


© Østfoldforskning


5

pursue the innovation of services

that will be de
sirable to all actors in the value chain
, and that will
contribute to
reliability of power generation and distribution as well as
more efficient use of
energy
resources.
As the name of the project hints at; the focus is finding new ways of
managing

energy and
power based on the opportunities that the smart grid may offer. New business models,

contract types

or
trading
concepts that enable flexible use of
electricity may be the outcome of the project.


In the project we have investigated the potential that Smart Grid technologies offer in terms of energy
management with specific focus on what has been defined as
consumer flexibility
. Consequently our
scop
e extends beyond the traditional view on energy management whereby energy efficiency is
translated into a reduction of kWh per square meter, which is a purely a technical parameter. It is the
classic reference used to assess and benchmark the energy charac
teristics of buildings. Rønning et
al. (2007) and Bremdal et al (2011) advocate that the indicator is insufficient since aspects like the
function of the building in relation to the users’ needs, the buildings adaptability etc. have to be taken
into consid
eration. Sustainable buildings require an approach and indicators for measurement
beyond the traditional energy efficient focus
. The approach should reflect

a goal to s
pend energy
effectively when it is needed, where it is needed.


In other words emphasi
s is
put
on what brings value to the user of the building and in what way
energy management supports the purpose of the building and the value creating tasks that the users
perform, hence our term “
value oriented energy use”.



Having in mind the above des
cription of the Smart Grid, the project’s area of research and innovation
is hence close to the end consumers, rather than dealing with for example power g
rid operations and
maintenance.


Most of the current research focuses on the

technicalities of the
co
nsumer side, with essential focus
on demand
-
response. Research is also being concentrated on an emerging market that includes
varying supply due to use of intermittent resources.
The
MSiSG

project’
s combination of these is a
slightly different

approach.



1.1

An introduction to how Smart Grid concepts may be utilised in
the consumer end

Before exploring the “whys” and “hows” on the macro level of energy use, energy efficiency and
power peaks

and its potential environmental impacts

in context of utilising Smart
Energy concepts
, it
will be useful to have a certain insight in the possible functionality that SmartGrid concepts can offer
to the end consumer of electricity.
The aim of this section is to provide a
brief

understanding of the
key functionality

and elemen
ts

from the end user perspective. More elaborate investigation on how
this functionality can be utilised in the energy market is described in
far more detail in
the “sister
project” IMPROSUME (Shandurkova et al.
,

2012) as well as in the
other work packages

in the
MSiSG

project

(
Shandurkova
,
2010,
Ottesen, 2011
,
Høynes, and Berntzen
,

2011
,
Aas
,
2012
,

Roos
,

2012
).


Managing Smart in Smart Grid:


Energy and power

use and ways to reduce



6


© Østfoldforskning

1.1.1

The

deployment of Advanced Metering Infrastructure in Norway

Advanced meters in households

One
important
building block in a Smart Grid system is the Advanced Metering Infrastructure (AMI).
During the course of the Manage Smart In Smart Grid project, a
new regulation has been adopted in
Norway:
Within January 1
st

201
9
, advanced meters

and
energy c
ontrol syst
ems

shall be instal
led in all
households in Norway
.

AMI has therefore played an important role over the course of the project.

The
regulation prescribes electricity meters that measures energy consumption each hour and that may
be adjusted to meter every 1
5 minutes. The readings will automatically be sent once a day to the grid
operator and if allowed by the end consumer; to a third party such as an Energy Service Company
(ESCO)

providing energy efficiency services

or a Smart Energy Service Provider (SESP)

that
offer
services related to energy tariff negotiation, energy budgets etc.
(Aas
,

2012,
Shandurkova
et al.
,

2012)
.
According to the regulation
,

the meters must
be equipped with a display,
demonstrating the
energy consumption in
terms of
kWh and in energy

cost,

as well as a switch allowing for remote
disconnection of the consumer from the grid

(
LOVDATA
,

2011).

These types of meters may open up for new
functionalities

that again may influence energy
consumption or power use

(Bellona
,

2011):

1.

Household custo
mers will pay for their actual consumption, not according to an average
consumption curve which has been the case until now

2.

This again may enable various pricing schemes and

other

incentives that may influence when
the consumer
s

uses energy
. There are vari
ous
possible
tariff schemes
such as

dynamic
tariffs
/real time tariffs
, critical peak pricing or time of use
-
rates
.

a.

Either by pre agreed automation of parts of the energy consumption


Remote Load
Control

b.

Or by inducing behavioural change due to price
information

3.

New ways of presenting “real time” energy consumption information may, isolated or together
with the above principles, also cause change in energy consumption

Hourly metering in the service sector


End consumers in the public and private serv
ice sector that consumes more than 100

000 kWh/year
do already have the obligation of hourly metering. It is however reasonable to believe that the extent
of

highly
advanced meters in this sector
vary significantly
.


1.1.2

Demand Response
:

The end consumer
responds to a market need

The functionality labelled number 2 in the list above is a key element in a concept called Demand
Response: Systems that are
designed to encourage consumers to change their electricity usage in
response to changes in the price of
the electricity or other incentives. In other words; it is a response
in the end user segment to a demand in the gri
d; hence Demand Response. Thus, a Demand
R
esponse program is any program which communicates with the customer
s

and enables them to

Managing Smart in Smart Grid:


Energy and power use and

ways to reduce


© Østfoldforskning


7

either
re
duce energy consumption
or shift
it

to another time.

Another term for this concept is user
flexibility or end
-
user flexibility


The US Department of Energy divides Demand Response into three basic types, as documented in
Shandurkova (2010):


1.

Peak clipping


demand is
decreased

at critical hours, typically a few hours per year, when
prices/costs are high due to contingences such as generator outages, failures of transmission
lines or excessive demand conditions

2.

Load shifting


consumers
shift

demand from hi
gh
-
priced periods to lower
-
priced, typically on
a daily basis

3.

Valley filling



demand is increased at hours where price is low (for example in the night hours
when wind turbines are still producing a large amount of electricity
)




Figure
1
-
1

Schematic figure of load shifting and valley filling (Kupzog et al. 2011)



1.1.3

The Prosumer


an entity consuming and producing energy

When
a building or a cluster of buildings

in addition to the Demand Response functionality produce
energy that may be delivered to the grid, the building or neighbourhood is
called

a ”prosumer”, since
the entity both consumes and produces energy. Due to the relatively small amounts of energy that

may be

produced on or in the vicinity of buildings, this is
often labelled
micro
-
generation
1


-
generation).




1

Micro generation often seem to be linked to renewable energy, but it may also not be based on
fossil fuel
; for
example Micro Co
-
Heat and Power plants fuelled on natural gas.


Managing Smart in Smart Grid:


Energy and power

use

and ways to reduce



8


© Østfoldforskning

When a building is referred to as a prosumer, one is not only indicating that there is energy generation
onsite, but also referring to
the role
t
he building
(s)

and/or its owner is playing in the energy market.
As
stated in Shandurkova et al (2012:p 10),”a prosumer is more than just a consumer that starts
producing energy. A prosumer must be engaged in the energy market, if only weakly”.


A prosume
r building
could
also be a so called
zero sum

or positive energy building;
if
the surplus
energy delivery makes the annual energy balance of the building end up at zero or positive.
When
using these terms one is
usually simply
referring to the building an
d

its energy balance, not its market
role.



In principle, an entity could be a prosumer regardless of whether it is heat or electricity that is
delivered to a district heating grid or electricity grid respectively. When not specified in the report, it is
r
eferred to
the production for the electricity grid.


If there exist
s

a possibility to store el
ectricity either combined with micro
generation or not, there is a
possibility of storing energy when prices and demand is low, and feed this to the grid when the

prices
are high. This could be

by the means of for example

the battery of an electrical vehicle or other
types

of storage devices.





Managing Smart in Smart Grid:


Energy and power use and

ways to reduce


© Østfoldforskning


9

2

Energy
and power usage

This chapter presents statistics on energy use. In order to say something about the potential of S
mart
Grid technologies, knowledge is needed on how much energy is used, from which sources, where the
energy is used, for what services and at what times of the day.
Smart Grids are especially related to
electricity but electricity as a high quality energy

source can substitute other energy sources and thus
there is a need to understand the coupling between energy for different applications.
The chapter
starts with presenting the use of various energy sources at a global scale. Thereafter, the scope is
narr
owed to energy use in Norway. More specifically, electricity use for various functions in different
building types in Norway is presented, also showing variations in time.


2.1

Global energy use

Cullen and Allwood (2010) site Claude Summers to make energy use
understandable. In 1971,
Summers wrote: “A modern industrial society can be viewed as a complex machine for degrading
high
-
quality energy into waste heat while extracting the energy needed for creating an enormous
c
atalogue of goods and services”.
In the y
ear Summers wrote his article, the total final consumption of
energy sources was approximately 4
674

Mtoe (million tons of oil equivalents) which

equates
54.4

PWh (Peta Watt hour = 1*10
15

Wh) (IEA
,

2011). Since the 40 years from the article was written, the
use of energy sources has almost doubled to around 8
353

Mtoe or 9
7.1

PWh as shown in
Figure
2
-
1

from IEA
(2011).



Figure
2
-
1

Global energy use from IEA (2011)


The figure also includes a categorization of fuels (i.e. energy sources)

which is used. Scrutinizing
these numbers gives
Figure
2
-
2

which shows the relative proportion of each energy source used.




Managing Smart in Smart Grid:


Energy and power

use and ways to reduce



10


© Østfoldforskning


Figure
2
-
2

Relative proportion of energy sources in the world.

The figure shows that the relative final consumption of fossil fuels has decreased for the last forty
years, mainly due to an increase in electricity consumption. We know, however, that generation of
electricity is to a large extent based on fossil sources

so the relative decrease might be misleading.


So far, the statistics presented have shown the total energy use and the sources for its supply.
Understanding how energy use can be reduced requires that the information is coupled with
information about th
e applications energy is used for. Cullen and Allwood (2010) have produced a
Sankey diagram based on the flows from primary energy sources to end uses as
presented in

Figure
2
-
3
.




Figure
2
-
3

Tracing energy flows through society (from Cullen and Allwood 2010).


Managing Smart in Smart Gri
d:


Energy and power use and

ways to reduce


© Østfoldforskning


11


The figure gives a very condensed presentation of
how ene
rgy flows through society and the relatively
small amount of end functions it is used for. A short glimpse on these functions reveal
s

that about half
of them
are

related to human needs in
buildings, including food

production
. Looking at the energy
sources, one can see that Cullen and Allwood (2010) use a different categorisation than IEA (2011)
and the amount of electricity use seems much larger in the former than the latter (43% versus 17%).
This is probably due to how final

consumption is defined in IEA statistics where only end use of
electricity is accounted for. Smart Grid technologies may affect both the amount of energy used and
which sources that can be employed and keeping track of both these dimensions is thus import
ant.


Another important dimension is coupled to place.
Energy use is not evenly distributed across regions
in the world
. In 2009, the OECD
countries were

accountable for 42.8% of total energy use but
were
only accountable for 18.1% of the world population
and 30.1% of
the
production of energy sources
(IEA
,

2011).
Even the use of various energy sources varies between different regions, even on a
smaller scale. Norway ranks 13
th

on the infamous list of energy consumption per capita
in the World
(
EIA
,

2011
)
,

b
ut ranks second in the
OECD (and probably in the World)

when only considering
electricity consumption
.

In the REMODECE project, Norwegians


electricity consumption and habits

in
households

were

compared
to 11 other European countries
2

(REMODECE
,

2009). Th
e average
monthly electricity consumption per household for the different countries is presented in
Figure
2
-
4

below.



Figure
2
-
4
:
Monthly el
ectricity consumption
per household
in 12 European countries (REMODECE D9,
2008)


Here we can see that Norway’
s electricity consumption exceeds the other countries’ consumption
by
far
.
The reason is that electricity traditionally has been used for heating purposes in Norway. The
reason for this again has been the abundance of relatively cheap hydro power, historic
ally and very
generally speaking.

The issue of electricity use in Norway will be further explored in the next section.


Through this overview of global energy use, we have got a rough introduction to the amounts of
energy used globally and in different reg
ions, the energy sources that form the basis for energy use
and some crude categories on the end uses of energy. Finally, we learnt that the
people in Norway



2

Portugal, Belgium, Denmark, Greece, Bulgaria, Italy, Romania, France, Czech Re
public, Germany and Hungary


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are

large consumers of electricity. To understand this better, we turn to the specific uses of ele
ctricity
in Norway.


2.2

Electricity

use in Norway
: A large heating proportion

According to Statistics Norway,
a
round 50 per cent of the end
use

of energy
in Norway
is electricity
,
excluding the off
-
shore
sector (SSB
,

2010). Energy use in households comprises of 20% of the
onshore energy use and the service sector approximately 15% (SSB
,

2010
and
Arnstad et al 2010).

As shown

in
Figure
2
-
5

, electricity
stands for a substantial part of the energy used in households and
services (including construction

in these figures
).
Figure
2
-
6

demonstrates that the share and relative
share of energy consumption in sectors have been quite stable over the years since 1990, and that
the
detailed
2007
distribution
to the left
could be assumed to be typical. In
the right
figure, the service
sector b
elongs to the “other industries” group.




Figure
2
-
5
: Energy consumption by sector
and energy source 2007.
Petajoule

(SSB
2010)


Figure
2
-
6
: Energy consump
tion by sector (1990
-
2010)
.
TWh
(SSB 2010)


The extensive use of electricity in
households and in services

is quite unique for Norway

and related
to access to electricity as an energy source
.
Norway’s geography coupled with good incentives lead
to massive

construction of hydro power facilities in the 19
th

and the 20
th

century. Infrastructure
development has thus been tuned towards electricity consumption and electricity has

historical been
the
predominant choice for
space
heating
, tap water heating

and coo
king.

After introducing new
building regulations in 2007 and
establishing
the governmental agency Enova in 2001, with a
mandate to facil
itate conversion of heating systems, the share of waterborne heating and air
-
to
-
air
heat pumps has risen.

Air
-
to
-
air hea
t pumps generally cut the electricity use to 1/3 compared to
electrical heating panels.

However
,
electricity
still

remains
the dominant heating
source in

the overall
picture
.



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Hydro

pow
er accounts for about 98
-
99 per
cent of the total electricity produc
tion

in Norway
(SSB
,

2010)
.
There are several characteristics of hydro power that are relevant when considering Smart
Grid implementation

in Norway, and when comparing to implementation in other countries.
Hydro
power
: 1)

is to a large degree

schedulable (and in that sense predictable) due to the predominant type
of power plants with large dams

(as compared to river based hydro electricity)
; 2) gives
the possibility
of quick and cost efficient regulation of the power
;

and
3)

is
a
renewable ene
rgy

source
. Hydro power
is however unpredictable in the sense that weather conditions from year to year will influence the
a
vailable stored power (Statnett
,

2011).


2.3

Specific e
nergy use
distribution
in
offices

When
implementing Demand
-
Response
concepts, such as automatic decrease of electricity use for a
small period, more detailed knowledge on what appliances and functions that actually contribute to
energy and electricity use
over the year
is needed. This must
then
be supplemented with an anal
ysis
on what kind of appliances and functions may be reduced, moved to another time, or switched off
entirely

combined with information on their momentary power usage
. In this section
yearly specific
energy use distribution for offices is presented, and in

the next section the same is shown for
households.


The energy authorities in Sweden have in the project STIL2 measured the energy use in 123 offices
in 2005. The buildings are between 200 and 30

000 square meters in size (Statens energimyndighet,
2007).

The specific energy use for these buildings is presented in

Table
2
-
1
. Here it is important to be
aware that Swedish buildings are not heated with electricity to the same ex
tent as Norwegian
buildings. 90
% of the buildings in this study are heated with district

heating.
Electricity for heating and
heat pumps only amounts to 6% of the average energy distribution because of this.
The “share”
column

in the

table
is therefore
supplemented with a “share when heating is subtracted” in order to
make all the other energ
y specific use
relevant in a Norwegian context. If heating is subtracted from
the total amount, the yearly energy use is 102 kWh/
m
2

(Statens energimyndighet, 2007).



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Table
2
-
1
: Specific electricity use per app
liance, average values and distribution (Statens
energimyndighet, 2007
, modified with an extra column
).

Electricity distribution

[kWh/
m
2
/year]

Share

Share

when heating is
subtracted

Lighting
:

23,0

21,2 %

22,5
%

Computer room/servers
:

10,7

9,9 %

10,5
%

PC
units
:

15,4

14,2 %

15,1
%

Other appliances
:

8,0

7,4 %

7,8%

Printers

1,1

1,1 %


Copy machines

1,6

1,5 %


Compressed air

0,4

0,4 %


Kitchen

2,4

2,2 %


Cantina

0,7

0,6 %


Laundry equipment

0,2

0,2 %


Engine heater

1,5

1,4 %


Total operational
electricity
:

57,0

52,7 %

56%

Fans
:

17,9

16,5 %

17,5%

Heating and heat pumps
:


6,5

6,0 %

Subtracted

Pumps
:

5,5

5,1 %

5,4%


Condensation cooler
:

0,8

0,7 %

0,8%

Lift
:

0,7

0,6 %

0,7%

Circulation fans
:

2,6

2,4 %

2,5%

Air conditioning units
:


10,6

9,8 %

10,4%

Total
“property

related”
electricity

44,5

41,1 %

37,4%

Other

6,8

6,2 %

6,7%

Total

108,2

100,0 %

100% (102

kWh/m
2
)


The table shows that for operational electricity, it is lighting, followed by PCs and servers, which use
most electricity in an office. Fans and air conditioning are the highest consumers
of “what the project
has named “property related”

electricity, if el
ectricity for heating is not considered. The energy
distribution is also presented graphically in
Figure
2
-
7
. Here, the energy for heating and heat pumps is
removed to make it more relevant for Norwegian conditions.



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15


Figure
2
-
7
: The specific energy consumption’s distribution for different appliances


2.4

Specific e
n
ergy use
distribution
in households


REMODECE (Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe) is an
EU project completed in 2008 measuring energy consumption in households in 12 countries, including
Norway. Measurements were
performed on a varying number of appliances in 100 households per
country. As well as monitoring the households’ appliances, the consumers’ behaviour and comfort
levels were also measured, i.e. both quantitative and qualitative data
were

collected (Sæle et

al.,
2010).


S
pace heating (including heat pumps and other appliances) consumes between 10

000 and 12

000
kWh annually depending on the outdoor temperature, which is about 60 % of the total electricity
consumption in Norwegian households (Sæle

et al., 2010). The appliances share of the households’
total electricity consumption can be viewed in
Figure
2
-
8
. Here we see that water heating and lighting
have large shares, followed by fridge/freezers, washers, electronics, cooking and computers.

23 %
10 %
15 %
8 %
18 %
9 %
10 %
7 %
Lighting
Computer room/servers
PC units
Other appliances
Fans
Other residential electricity
Air conditioning units
Other

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Figure
2
-
8
:

Percent shares of electrical end
-
uses in Norwegian households 2006
-
2007 (Sæle et al.,
2010)



Table
2
-
2

presents the measured and average annual energy dem
and per appliance in Norwegian
households. Stratification and
share of
ownership
are

used to correct for biases in the measurement
sample strata

and then result in average yearly consumption per household.


The results show that, after water heating and lighting, freezers are consuming most energy per
appliance in Norwegian households, followed by refrigerators with and without freezer, Plasma TVs
and ovens. Clothes dryers, washing machines and dishwashers ar
e also quite energy consuming
appliances. We can also see that entertainment appliances such as PCs and TVs also consume a
considerable amount of electricity.


When the data is corrected for ownership per household, presented in the far right column, we s
ee
that freezers are still the most energy consuming part of Norwegian households’ consumption after
lighting and water heating, although only 73 % have such a unit. Ovens and washing machines are
also large
contributors
, but this is more due to their high

ownership rates of 96 %, while, again, fridges
with freezers take a large part even though the ownership is only 66 %. Only 47 % of households
have clothes dryers, which makes this less energy consuming for the households in total.

Space heating + misc.
Water heater
Lighting
Cooling
Cooking
Washing
PC with accessories
Electronics
60
%
15
%
6
%
5
%
2
%
2
%
3
%
3
%

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17

Table
2
-
2
: Results from REMODECE in Norwegian households. Annual electric energy demand in
households corrected for strata and appliance ownership (Sæle et al., 2010)
, heating extacted

Appliance

(Unit)

Measured yearly
consumption

(
kWh/appliance)

Ownership

(Percent)

Average yearly
consumption

(kWh/household)

Water heater

2987

85 %

2539

Lighting

1000

100 %

1000

Refrigerator w/o freezer

307

52 %

160

Refrigerator w freezer

374

66 %

247

Freezer

631

73 %

461

Washing machine

209

96 %

201

Clothes dryer

267

47 %

125

Dishwasher

206

88 %

181

Desktop PC

220

70 %

154

Laptop PC

87

72 %

63

Router for internet

51

67 %

34

Wireless access point

74

25 %

19

Printer

26

61 %

16

TV CRT

172

70 %

120

TV LCD

223

50 %

112

TV Plasma

325

50 %

163

DVD recorder/player

21

75 %

16

HI
-
FI

103

100 %

103

Satellite/cable/air set top box

84

39 %

33

Electric cooker/oven

280

96 %

269

Microwave oven

30

10 %

3

Water kettle

24

50 %

12

Sum of measured

7701

-

6031



As demonstrated in an earlier section, Norway uses by far the most electricity in the REMODECE
countries due to the tradition of utilising electricity for heating.
If we, as an exercise, remove the
electricity used for space and water heating (75 %)
in

Nor
way, Norway

would end as the country with
the
3
rd

highest consumption,
even
when the other countries still have a certain amount of electricity
used for space and water heating included. This indicates that the
, by comparison,

low electricity
prices makes
Norwegians consume more
electricity
also for other uses, such as lighting and
appliances
, compared to other countries
.
Table
2
-
3

shows Norway’s consumption for the different
appliances compared to the other countries and to the average of all 12 countries.



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Table
2
-
3
: Ann
ual electric energy in appliances, kWh/year per appliance (REMODECE D10)



Here we see that Norwegians use more than average on freezers, washing machines, televisions
(CRT and LCD) and Hi
-
Fi. As expected, we use a considerable amount more on lamps (lighting).
What has previously not been comm
on in Norway is to have air conditioning (cooling) in homes, but in
this study this type of appliance have a very high value. That is because heat pumps are included in
this category.
A

little less than average
is used
on refrigerators (with and without fr
eezer), clothes
drying, dishwashing, desktop PCs, Plasma TVs and water kettles. For the remaining appliances
,

Norwegians

have an approximately average consumption
.
Although our electricity use per
fridge/freezer is lower than average, 60 % of Norwegian households have more than one unit, which
contributes to the large total amount of electricity use.
Norwegians

also tend to have large
fridge/freezers. 43 % have freez
ers with more than 250 l volume, which explains the high electricity
use on these types of units.





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2.4.1

Household appliances: More efficient but rising in number

Most electrical appliances in the home have become much more energy efficient since
the
1980
s
,
pa
rticularly white goods as freezers, dishwashers and tumble driers

as shown

in
Figure
2
-
9
.


Figure
2
-
9
: Energy efficie
ncy the last 25 years (Gram
-
Hanssen, SBi presentation

2010
)


The figure shows that many appliances have cut their energy use with almost 50% during 25 years.
Judging from this, one would expect households to use ever decreasing amounts of energy.
Figure
2
-
10

presents, however, that there has been an increase in the number of appliances each household
owns.



Figure
2
-
10
: Number of appliances in Danish households (Gram
-
Hanssen, SBi presentation
, 2010
)


When these two graphs are combined we understand that t
he energy use has risen,
since

the total
number of appliances in a household has increased. Particularly the n
umber of televisions,
refrigerators, dishwashers and microwave ovens has increased considerably (Gram
-
Hanssen, SBi
presentation
, 2010
).

The growing number of appliances has thus outweighed the energy efficiency
achieved per appliance.





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2.4.2

Households:
Variat
ions in electricity use among different users

In a study from 2005, Gram
-
Hanssen found that people in different age groups use energy for
different causes. People more than 60 years old use more electricity on fridges and freeze
r
s than
other age groups,

because they often have old products and because their fridges are over
dimensioned as their children have moved out of the house. They also use a larger share of their total
electricity use on lighting, but less on dishwashing, cleaning and drying (Gram
-
Hansen et al., 2005).


Single people also typically use more of their total electricity use on fridges, freezers and lighting than
the rest of the population. Families with children typically use less electricity per person than other
people, as children
use less than adults. Teenagers however, use 20
-
30 % more electricity than
adults. Energy use for dishwashing, cleaning and drying is larger for families than other groups. The
category named “remaining”, which includes cooking, is also larger than for oth
er groups, which
probably is due to many gadgets in the household (Gram
-
Hansen et al., 2005).


The same study further categorizes homeowners into three groups;

1.

The conscious energy savers. Those who mean it, and does it.

2.

Those who don’t do it, even
though they would like to.

3.

Those who don’t think they can


or should


do something.


Other variations are present in a household’s electricity use in type / size of dwellings and the number
of persons living there. Households living in villas typically u
se twice as much electricity than people
living in apartments. Some of this can be explained with that villas have certain uses that apartments
do not have, e.g. outdoor lighting. Further, there are typically fewer people in an apartment household
and they

usually have a smaller income. Number of persons per household and income are the two
most significant reasons behind the amount of electricity consumed in a household. Still, when the
number of persons in a household increases, the electricity use
per pe
rson

decreases (Gram
-
Hansen
et al., 2005).


More information on energy use related to
behavior, behavior change, motivation and stimuli can be
found in the report ”Manage Smart in Smart Grid


Work package 1B” (Falmyr and Simensen
,

2010)


From t
he last
se
ction
s

related to households
we have seen what
applications that lead

to

use of
electricity, how much different applications contribute and we have got additional information about
how electricity use varies across building types and
user
groups.

A very im
portant additional issue is
connected to when electricity is used. As there are seasonal and daily variations and the power grid
has a set capacity there may be good reasons to distribute power use differently. The next section will
look into why time vari
ations is important and give examples on how electricity use is distributed
across the day and across applications.





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2.5

Power use: Variations in time

Simply put, the dimensioning factor for the power grid capacity is the power consumption at the time
when

the maximum load occurs (Statnett
,

2011). Black o
uts will happen if maximum load capacity is
reached.
Maintaining the power peak lower than the maximum capacity is therefore a question of
energy supply security
, and further; may possibly avoid the constru
ction of more grid infrastructure.
These issues are relevant both on the national level (central grid), regional level
(regional grid)

and
local level (local grid)
.

T
he transmission has on a couple of occasions been close to the maximum
load in parts of th
e grid (Statnett
,

2010).
The most recent record took place in January of 2013 when
24 180 MW was consumed

(
Tu
2013
-
2).
The previous record peaks were set in January 2010:
23 994
MW in the Norwegian system and 69 639 MW in the Nordic system
(Statnett
,

201
1
)
.


2.5.1

Power use in households

Power use profiles where
measured or simulated
power use throughout a day
is visualised
can aid in
understanding Smart Grid technologies’ potential in reducing
or moving power

use.


The figures
below are from a REMODECE pre
-
study by SINTEF.
Figure
2
-
11

shows the
power

use

in
villas during a 24 hour period for a typical summer day and winter day. It also diff
erentiates between
weekdays and weekends.



Figure
2
-
11
: Total power consumption for four typical days in villas for weekends/weekdays in typical
load periods (winter/summer) (SINTEF, 2006)


As anticipated,
the

energy
consumption and the power peaks are lower
on summer days than winter
days. The difference between weekdays and weekends in the summer is not noticeable, but the
power peaks are
postponed to later in the day in the weekends. In
the wintertime the same pattern
occurs, although

the

maximum power
is a little
higher

in the weekends than on weekdays.

Figure
2
-
12


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shows the hourly distributed power
consumption in a typical household for water heaters only, again
differentiated between weekdays and weekends.



Figure
2
-
12
: Profiles for power consumption in water heaters for weekends and weekdays (SINTE
F,
2006)


Her
e

we can see a similar pattern as for the overall
power

consumption; the use has shifted to later in
the day on weekends. The peak on weekends is at noon rather than at 8.30 AM, and is 100 watts
more than on weekdays.


Figure
2
-
13

presents the
s
imulated power distribution between different purposes
and appliances
in
the Norwegian household sector
as a whole
on

the
day with the highest energy consumption in
2001
.
This was 05.02.2001 and was the coldest day that year
(SINTEF, 2006)
. This demonstrates the
dominating contribution of space heating and tap water heating.





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Figure
2
-
13
:
Simulated

power
distributio
n
between

different
purposes in

the
Norwegian
household
sector o
n

the day with the highest energy consumption in 2001 (SINTEF, 2006)


Most of the appliances; stove, PC/TV/radio, lighting, washer, dryer and dishwasher,
all
follow the
same pattern with peaks

around 9
-
10 AM and 5 AM. The peaks for heating, however, are a little later
in the morning and evening, and the drop is relatively steep from 7 PM until 2
A
M where it flattens out
until the morning. Water heating and fridge/freezer have a peak in the
morning similar to the other
appliances, but do not peak in a similar way in the evening. Instead they rise gradually from about 6
PM and have a small peak at 11 PM.


2.5.2

New consumption: Power use
for

charging electric vehicles

Norway was as of 2012 the leadi
ng electric vehicle (EV) market in the world, and Oslo

is
also
recognized as the 'EV capital of the world', with the highest EV density of any capital city
. This is
thanks to the country’s policies to promote the implementation of these zero emission vehic
les,
according to Avere


the
European Association for Battery, Hybrid and Fuel Cell Electric Vehicles
(
Avere, 2012
)
. In

the near future the numbers are expected to steadily rise, due to the extension of
car incentives until 2018 (ibid) as well as improvem
ents in technology and cost.


The rise in electricity need that will stem from these vehicles as well as with hybrid cars is not
considered a challenge, since it is predicted to be relatively small compared to the total present and
future electricity prod
uction in Norway. A simplified analysis (gronnbil.no 2010) estimates a yearly

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need of 0
.
75 TWh if 200

000 chargeable vehicles are on the road by 2020 and 10
.
5 TWh in 2050,
provided that 90% of private cars are EVs. The Klimakur sector analysis for transpor
t (Klimakur
,

2010)
suggest
ed
130

000 electric and
hybrid vehicles by 2020 with a yearly
need of 0
.
4 TWh electricity. As
a

comparison, current yearly electricity production in Norway is about 125 TWh (ibid), and through the
Green certificates scheme,
26
.
4 T
Wh

new renewable electricity production will be established in
Norway and Sweden within 2020 (OED
,

2012
-
2).


However, the power usage might be a bigger challenge, especially with the rapid chargers now
entering the market. When and where charging will tak
e place will be important. In
Table
2
-
4
, the

EV
charging technologies in Norway and their power usage is listed.


Table
2
-
4
:
EV charging technologies
in Norway and their power usage
.

Type of charging

Power usage

Reference

Normal/s
low charging
, present:

2
.
5 kW

(elbil.no
,

2012)

Normal/slow charging, next
generation

(Mode 3


type 2):

7 kW
-
12 kW

(elbil.no
,

2012)

Semi Rapid
-
charging:

20 kW

(Transnova
,

2012)

Rapid
-
charging:

43 kW
(AC/DC)


50 kW
(CHAdeMO)

(Transnova
,

2012)



Seen from an individual household perspective
, introducing the

next generation slow chargers

will not
cause too many challenges. But
having in mind the Sintef household power simulation in
Figure
2
-
11
,
where the power use on a winter weekday varies between 2
.
3 kW in the

middle of the night and 3,3

kW in the evening, and picturing a local grid with a high density of households with EVs, one
understands that it will matter at what time most charging will take place.


Semi
-
Rapid
and Rapid
Charging will not likely be installed in households to replac
e normal charging
but rather be used for extending the range of
a vehicle (elbil.no 2, 2012),
e.
g.

be offered as a service
along main tho
roughfares
. Different estimates suggest that between 60% and 90% of the charging will
be

done at home

(gronnbil.no
,

201
0)
.


One regional grid analysis regarding the consequences of
electrical
vehicles (and other new energy
using or energy reducing technologies) is that of the greater Oslo area (Lislebøe et al
.,

2012). With a
1% annual growth in EV market share, there will
be around 60

000 cars in the region by 2020. With
normal charging

of 4 hours this will constitute around 15 MW rise in power need; around 0
.
35% of the
historical maximum power usage in Oslo and Akershus: 4

250 MW. Another comparison: Current
flexibility is

850 MW through electrical boilers that may be remotely shut down.


However, the study also states that the future utilisation of rapid chargers comprise a great insecurity
and possible variation.
Figure
2
-
14

shows

different scenarios of rapid charging and their power
consumption for the region in 2020. Average rapid charging varies between 1 and 0
.
25 hours, while
the share of the EV fleet that is charging at a peak lo
ad moment varies between 5 and 20%. The
largest

power usage of 485 MW is substantially
more

than the 15 MW normal charging estimation,
constituting 11
.
5 % of the historical maximum power usage.



Managing Smart in Smart Grid:


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© Østfoldforskning


25


Figure
2
-
14
:
Rapid charging scenarios for Oslo and Akershus. Hours of charging, share of vehicles
charging and power need
(MW)

(
Lislebø et al
,
2012)


Depending on local grid capacity, it is likely that both slow
and
rapid charging will become vital to
control so tha
t electrical vehicles are not charged at moments of peak power
, especially rapid
charging
. This is perhaps one of the most obvious possibilities for utilisation of Smart Grid technology.
Charging could for example be controlled automatically by price signa
ls coupled with guarantees that
a car would be fully charged by a certain time, for example in the morning.


The electrical vehicles also represent a storage potential

for electricity. This is briefly described in
Chapter
2.7
.




2.6

Distributed

energy

generation

For a building or neighbourhood to become a prosumer, energy needs to be produced on site.
This is
often referred to as micro production or distributed energy generation


since it will be scattered
around compared to the traditional large scale utilities that is now the norm.
Shardurkorva et al
.

(
2012
)

is referencing
a definition of
distributed

generation to be
electricity production that is on
-
site or
close to the load centre and typically ranges between 1 and 5000 kW in capacity.


The
MSiSG

report “Distributed Generation and Storage
Technical

Portfolio”

(Orr
,

2011)
introduces
briefly all the
production technologies for electricity that is relevant for small scale production
.
Among
the

topics are
:

C
urrent status in terms of market penetration and future prospects

of the technologies.






Managing Smart in Smart Grid:


Energy and power

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26


© Østfoldforskning


The generation methods presented are

(ibid)
:



Micro
w
in
d turbines



Solar
: Photo V
oltaics


(PV)



Micro CHP


Combined Heat and Power, may be based on fossil fuel (e.g. oil, coal, propane,
natural gas) of bio energy (
solid

biomass, liquid bio fuels or bio gas).



Small hydro
power (Less than 10 MW)



Marine Energy: Ti
dal
energy, wave energy

In addition,
heat
p
umps
are presented,
since
they are
a means of producing

heating and cooling

in an
efficient manner
, with electricity as input
.


Conversion
of excess

heat to electricity

that can be fed t
o the grid

is also a
possibility, given a
technological development.
Al
though the conversion efficiency is
physically
limited (Hagelstein
,

2003)
,

it would still be better than letting
excess heat

escape, provided that the payback period would be
satisfactory to investors.



2.6.1

En
vironmental

characteristics of the distributed energy generation
technologies


Apart from
the
environmental impacts
stemming
from the production of all these technologies,
the
followin
g aspects are worth pin pointing: Firstly
,

the obvious fact that CHP based on fossil fuels is not
a renewable method of electricity and heat production. Introducing micro CHPs would lead to
numerous small fossil emission points,

something we as of today do not have in Norway, and

something that wo
uld be
next to
economically
impossible

to capture and store in the foreseeable
future. In other words


if the intention is to introduce renewable distributed energy generation, fossil

based CHP is not an option in a Norwegian context.


Bio

based CHP
is a
renewable option, since it is regarded as a carbon neutral fuel, and emissions
only stemming from
harvesting,
production and transport of the
fuel will
contribute to net climate gas
emissions
. There are many types of
b
io
fuels,

some of which are not re
garded as
sustainable,
therefore the European Union has developed and steadily updates
sustainability

criteria for biomass
and bio fuels (
ec.europa.eu
,

2010).






2.7

Batteries

and d
istribute
d

s
torage
t
echnologies

In a Smart Grid context, the
possibility

of storing electricity locally is particularly
interesting

because it
would enable distributed energy producers to store energy when demand and price is low, and
either
use oneself or
export it to the grid when demand and prices are high.


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Energy and power use and

ways to reduce


© Østfoldforskning


27

The
MSiSG

repor
t “Distributed Generation and Storage
Technical

Portfolio” (Orr
,

2011) introduces
briefly relevant

storage technologies, their current status in terms of market penetration and future
prospects of the technologies:



Batteries in
Electric Vehicles



Compressed

Air Energy Storage (CAES)




Flywheels




Fuel Cells

When it comes to storage, it is relevant
in a demand
-
response context
to also consider thermal
storage capacities and investigate the possible interplay with the electrical syste
m.
If the thermal
inertia of

the storage capacity is
high
, energy supply
can

be disconnected for small
periods

of time
without affecting the
temperature in the consuming end. Examples of this are



well insulated hot water tanks; as we have seen electrically heated tap water is common

in
Norway



electrical floor heating (
inertia will also

depend on the floor material)



water borne distribution systems



exposed concrete surfaces/ thermal mass



2.8

Neighbourhoods and the possibilities of bottom
-
up smart grid
markets

The
author

and city planner Jane Jacobs is
renowned

for her design principles for creating well
functioning urban spaces

(
Jacobs, 1961
)
. She advocates creating so
-
called mixed use areas, which
means that the buildings in an urban area should have different functions

from each other, so that
humans always will occupy the space for different reasons, in different ways and at different times of
the day. This creates livelier and safer urban spaces

(
ibid
).


Jacobs’ principles can also be
applied for

energy provision for neighbourhoods or ur
ban areas. If the
buildings have mixed usage and purpose;

if they provide

different
functionality and are of different
ages and
hence possess variable
energy needs, they
may

utili
s
e each other’s
surplus
energy
or
e
nergy production

and also take advantage of the varying
need for different kinds of energ
y at
different times of the day. The energy traded could be heat or electricity, and this could form the basis
for mini energy markets that again in reality would be a

bottom
-
up development of the smart grid.


In the following we present the current status on local energy grids and markets in Norway, as well as
peek into the near future of net zero and plus energy buildings. Initially we will describe a concept that
co
uld serve as inspiration for implying a neighbourhood approach when developing or retrofitting
service and household buildings, namely Industrial ecology.



Managing Smart in Smart Grid:


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© Østfoldforskning

2.8.1

Industrial ecology: One company’s waste is another’s resource

Although consciousness about a holisti
c approach to energy systems is relatively new with regards to
buildings for housing and services, the same approach has been applied for quite some time within
industry, then coined Industrial ecology (IE).
IE

is the
holistic and systemic

analysis of
indu
strial
areas
,
their patterns of material flows and technological dynamics
,
with the intent
ion

to create a
sustainable system (Thoresen, 1998). In practice, this means that companies within a certain area
cooperate and utili
s
e

and trade

each other’s bi
-
prod
ucts,
something
which simultaneously has an

positive

impact on the environment and their economy (Industrial Symbiosis Institute, 2008).

The
IE
approach
considers the complete systems, not only
with
regard to energy
use
and production, but
also raw
material consumption, water consumption, emissions to air, water and soil and waste
production, with the aim to create closed loop cycles of these components to minimi
s
e the impact on
the environment (
Thor
esen,
1998
).


The most cited

IE system
example can

be found in
Kalundborg, Denmar
k

(Industrial Symbiosis
Institute, 2008).

An
other

example of Industrial Ecology is Ora Ecopark, located outside Fredrikstad.
Twelve
medium to large size companies
are

partners in the Ora Ecopark. These partners represent
wide
ly different
businesses
, such as the p
roduction of foodstuff, chemicals
), polymers
and paints
, as
well as se
veral waste handling companies
. Except for
two companies
, their common denominator is
that they are located within a radius of 4
-
500 meters (Thores
en, 2000).
Figure
2
-
15

below shows the
flow diagram of Ora Ecopark, the way it was in year 2000.






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© Østfoldforskning


29


Figure
2
-
15
: Map over IE
exchanges

at Ora Ecopark
.

The energy
flow is

the most relevant to
the MSiSG

project and
is therefore the only process of which
we go into detail
. Two of the largest companies


Kronos Titan
and

Denofa


are dependent on large
amounts of thermal energy in the form of steam. For this purpose, each of them has installed oil
heated and electricity heated boilers operating in parallel. Frevar
-

a munic
ipal
waste
company
located close by
-

produces steam from combustion of municipal waste.

Steam from

Frevar

is
distributed to Kronos Titan and Denofa, covering
a substantial part

of the steam consumption in both
companies. Common piping for steam distributi
on joins
all
three companies, so that
also the two
steam consuming companies may serve as each other’s backup system
. In addition, Kronos Titan
returns condensate to Frevar from part of the steam supplied from this company (Thoresen, 2000).


2.8.2

Local thermal

grids

between buildings

One existing example of a mini grid allowing service sector buildings to trade energy is the Vulkan
area in Oslo where the
buildi
ngs
have a shared
thermal

energy central

(Sæther et al
.,

2011)
. This
enables that
energy produced by on
e building can be utilized in another. For example,
the southward
oriented Bellona house
has installed solar hot water collectors which

in spring, summer and early fall
will provide
some of the
hot water for the two hotels on the site
. The hotels will have

a year round
need for hot tap water, while the Bellona house itself has its main need for heating during winter time
since it is predominantly an office building
. The energy central
’s main energy source consist of three
heat pumps that utilises
fourteen
3
00 meter deep wells
for producing heating or cooling and for
Examples of present IE
-
exchanges
:




steam



condensate return



clean hot water



clean cold water



process spill water



electric energy transfers



waste sulphuric acid



iron sulphate waste



polyester waste



wood chips for energy
production



filter waste



bleaching soil



industrial waste



sludge



etc.


Frevar

Denofa
Reichhold
Jotun

Kronos

Kemira
Masse
Gjbr
.sent.

Ødeg
./
Østfold
Gj
.vinn.
Fr.stad
Blikk

Gyproc
Norsk

Leca

Mills
Unger
Glomma
Papp
2
2
2
2
2
9
17
18
4
6
5
3
8
6
6
7
5
11
12
6
13
14
6
13
15
6
16
15
16
19
10
Peterson
20
1
Borg
Havn
6
6
21
Fr.stad
Energiv
.
22
23
1

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