1) The document discusses assessing the value of household demand side management (DSM) in Sweden in the presence of electric vehicles (EVs). 2) It describes a mathematical model that simulates the hourly potential for household DSM and EV load shedding sold on the Swedish balancing market, considering market price and volume data from the past ten years. 3) The results indicate that household DSM holds potential in Sweden due to winter availability and limited competition, though current grid conditions do not provide sufficient value. DSM would require lower costs to be profitable. Synergies were found between DSM and EVs when bid together.

1. Gaspard LEBEL
KTH-ICS – Sweden
gaspard.lebel@grenoble-inp.org
Claes SANDELS
KTH-ICS – Sweden
claes.sandels@ics.kth.se
Lars NORDSTRÖM
KTH-ICS – Sweden
larsn@ics.kth.se
Sandra GRAUERS
Vattenfall – Sweden
sandra.grauers@vattenfall.com
Context
The need of demand side flexibility
Production-Consumption balance in the power
grid must be maintained around-the-clock. While
such Balance Margins were historically provided
by production units like hydro power or gas
turbines, Utilities would now like to introduce
demand flexibility on the future grid, thanks to
Demand Response (DR) solutions.
Such demand side flexibility would become
essential in the coming years, face to huge plans
for developing stochastic renewable energies
[German & Northern Europe cases] and the lack
of flexibility from conventional production units
like nuclear power eigher peak load increasing
cases [French case].
The unknowns for the future
Demand Response profitability stays however
quite unknown, notably due to irregular Balancing
Market rules across Europe.
While the offensive development of the French
company Voltalis in Households electrical
heaters load-shedding (Household DSM)
asthonises most of European energy actors, an
high penetration of Electrical Vechicles (EVs)
could pick up as well a large part of DR’s value at
the cost of other Balancing Margins Aggregators.
In such a context, the project aimed at assessing
the value of Household DSM in presence of Evs
(fig. 1), thanks to a mathematical model focused
on Sweden and a parrallel analysis of the
French, German and Swedish Balancing
Markets.
Calculate
Tin(h+1)
h=h+1
Tout(h+1)
h< nbHourWinter?
END
Yes
No
Calculate
P(h+1)
DSMpot (h+1)
Merit Order
Selection
1°) EVpot (h+1)
2°) DSMpot (h+1)
DSMsold(h+1)
Update
P(h+1)
P(h+1)
EVsold(h+1)
Tin(h+1)
Tin(h)
DSMpot(h+1) EVpot(h+1)
Update
EVload(h+1)
EVload(h+1)
Calculate
EVpot(h+1)
EV load
profile
BMvolume(h+1)
BMprice(h+1)
Calculate
DSMincome(h+1)
EVincome(h+1)
Household
Overheating
Compensation
Select scenario,
household & market
characteristics
START
h=1
Tref(h+1)
DSMincome(h+1)
EVincome(h+1)
Household DSM holds finally a
potential good position among the
low voltage DR solutions in
Sweden, thanks to its winter
availability, a reduced competition
from office & apartment buildings
and an impact of less than ±2%
on dwellers power consumption.
However, the Swedish grid does
not experience enough grid
weaknesses to afford sufficient
value to DR yet, partly thanks to a
large Hydro flexibility and not
enough Windpower prognosis yet.
To make Household DSM
profitable, it would require
operation costs of around
55€/hous./year (fig.3) that is
around 50% less of the current
costs.
Contrary to what was initially
expected, synergies appeared
between Household DSM and
EVs, so that join-bidding of both
solutions on the Balancing Market
leads to increase DR value.
Household Aggregators
for Demand Response in Europe
Is there a feasible business model for aggregators, especially in Sweden?
European energy actors hope to solve in a close future lot of power system issues thanks to Smart
Grid solutions like Demand Response in domestic households. But are these solutions financially
profitable? The project aimed at answering this question, by looking first at the French, German
and Swedish Balancing Market rules and then by simulating numerically the Swedish market.
SMART GRID, DEMAND RESPONSE, DSM, LOAD SHIFTING
AGGREGATORS, ELECTRICAL VEHICLES, DOMESTIC HEATERS
KEY
WORDS
Model
The model (fig. 2) simulates the hourly
Household DSM and EVs load shedding potential
sold, depending on the outside temperature and
the market needs. It has been considered the
price and volume data of the Swedish Balancing
Market for the ten past years, to get a both
technical and profitability analysis of Demand
Response in Sweden (deterministic model).
The model considers bidding on the hourly
Balancing Market and bidding on the annual
Peak Power Reserve Market (supplying of a fix
volume of up margins 24hr a day during four
winter months).
Project awarded by the Veolia Energy Prize Trophées Performances
n° 1487
Merit Order bids selection:
1°) EV
2°) Household DSM
+ Booking of DSM basis for Peak load
market
Heaters’ Power reduction:
P= (1-DSMsold/DSMtot)*Pmax
& EVs load-shifting (up to 3hr)
Income calculation
= (Upprice – Spotprice) * DSMvolume –
Compensationoverheating
DSM potential calculation
depending on Tin, Tref, Tmin and Tmax
Calculation of Tin for the next Hour:
HouseholdDSM,EVs&
BalancingMarketsimulation
Mean
inputprofiles
Up Balancing Needs Heaters Load EVs Load
Fig. 2 : Global flow-chart of the model
Results
0 5 10 15 20 25
0
20
40
60
80
100
120
Upvolumes(inMW)
0 5 10 15 20 25
0
2000
4000
6000
8000
MeanPower/home(inW)
0 5 10 15 20 25
0
20
40
60
80
100
120
MeanPower(inMW)
RTE Gross
RTE Absorbed
TH Asfap
TH Rand
Tref
Tmin
Tmax
Tin
CIRED 2013 poster session – paper n°1487 – session n°6
Fig. 1 : Physical and financial flux for Demand Response activation on the
Balancing Market
0 10 20 30 40 50 60 70 80
0
10
20
30
40
Income(inM€/a)
Net annual income of Hous. DSM depending on the operation cost
Capacity market strategy (0€/MWh)
Effektreserven strategy (3.3€/MWh)
0 10 20 30 40 50 60 70 80
0
200
400
600
800
Capacity(inMW)
Operation cost (in €/hous./a)
Optimized installed Hous. DSM capacity(in MW)
Figure 3: Mean annual income of Hous. DSM sales on both the Capacity
or Peak Reserve markets– Sweden 05-11