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Resilient Demand Management
Algorithms and Their Impact
1
European Utility Week 2015, Vienna, Nov 3
Session: Smart Distribution System Control Over Heterogeneous
Communication Networks– SmartC2Net
SmartC2Net Consortium
Sandford Bessler, FTW
Rationale of this work
Main concept: exchange energy flexibility information
Demand management system architecture
Energy planning algorithms
Experimental results
Concluding remarks
Outline
2
Higher loads in households, which include electric house heating/HVAC,
electric cars, etc. may produce overflow in LV grid.
High PV deployment in households may create excessive generation.
Because of flexible loads, households demand can be remotely controlled
to reduce energy costs, without reducing comfort.
The heavy use of communication requires however resilience mechanisms
against communication failures
Rationale
3
Energy flexibility: minimum and maximum consumption
trajectory
Flexibility models examples
Main Concept: exchange flexibility plans
4
0
2
4
6
8
10
12
14
16
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Energy[kWh]
TimeperiodsEflexmin Eflexmax
EV charging flexible load Electric house heating flexible load
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6 7
Energy[kWh]
Timeperiods
Eflexmaxatt Eflexminatt
Components
Aggregation controller (DMC)
Demand response head-end (OADR VTN)
Many CEMS’s (OADR VENs)
Operation principle
CEMS sends a load profile based on local
objectives and external set points, and
adds flexibility information
The aggregation controller adapts the
individual plans by computing set point
profiles (plans) for each CEMS, only if
- the load limit (given) or
- the generated power limit are exceeded,
CEMS Demand Control
System Architecture
5
Demand Response platform
Home
energy
controller
CEMS
Demand
response
Head-end
asset
setpoint
profile
asset
flexibility,
load profile
Home
energy
controller
CEMS
EV
Charging
Pgen
HVAC
Model
Pload
EV
events ext.
temp.
CEMS setpoints,
PV irradiation,
prices, vg-load
CEMS flexibility,
load profile
Aggregated
energy
function
irradiation
forecast
bg-load
profile
day
ahead
prices
OADR2.0b
Demand Response platform
Home
energy
controller
CEMS
Demand
response
Head-end
asset
setpoint
profile
asset
flexibility,
load profile
Home
energy
controller
CEMS
EV
Charging
Pgen
HVAC
Model
Pload
EV
events ext.
temp.
CEMS setpoints,
PV irradiation,
prices, vg-load
CEMS flexibility,
load profile
Aggregated
energy
function
irradiation
forecast
bg-load
profile
day
ahead
prices
OADR2.0b
Demand Response platform
Home
energy
controller
CEMS
Demand
response
Head-end
asset
setpoint
profile
asset
flexibility,
load profile
Home
energy
controller
CEMS
EV
Charging
Pgen
HVAC
Model
Pload
EV
events ext.
temp.
CEMS setpoints,
PV irradiation,
prices, vg-load
CEMS flexibility,
load profile
Aggregated
energy
function
irradiation
forecast
bg-load
profile
day
ahead
prices
OADR2.0b
Demand Response platform
Home
energy
controller
CEMS
Demand
response
Head-end
asset
setpoint
profile
asset
flexibility,
load profile
Home
energy
controller
CEMS
EV
Charging
Pgen
HVAC
Model
Pload
EV
events ext.
temp.
CEMS setpoints,
PV irradiation,
prices, vg-load
CEMS flexibility,
load profile
Aggregated
energy
function
irradiation
forecast
bg-load
profile
day
ahead
prices
OADR2.0b
Central site
Demand
response
Head-end
asset
setpoint
profile
asset
flexibility,
load profile
EV
Charging
PV
generation
HVAC
Modelnon-flexible
load
EV
availab.
CEMS setpoints,
price updates
CEMS flexibility,
load profile
Aggregation
Controller
irradiation
forecast
non-flex.
load
forecast
day-ahead
pricesoutdoor
temp.
Customer
Energy
Management
controller (CEMS)
internet
OADR 2.0b
Controller interactions
6
15 min
DMC - Demand Management Control
CEMS - Customer Energy
Management System
Investigation of CEMS
on a large scale
Simulations based on
traffic measurements
of the developed
monitoring and control
algorithems
Focus on impact of ICT
performance on Use
Case viability
CEMS – ICT in large scale deployments
7
CEMS Use Case in Simulator – Individual Household
CEMS Use Case in Simulator (4G) – Benchmark Area (Støvring, DK)
Comparison of (non-) exclusive wireline/wireless access networks for DSL /
UMTS based Households in (sub)urban area
UMTS shows significantly higher delays compared to DSL (negligible packet loss)
No negative impact on performance of CEMS (including shared com. networks)
Use of preexisting com. infrastructure is a viable strategy
CEMS – ICT in large scale deployments
8
Given are transformer power limits LVmaxp , LVminp, but no details on the grid
topology
Calculate optimal setpoints for each household i, and time period:
Minimize the MSE to the requested power profile
Smooth the setpoint curve
- If the total planned consumption of the houses is below the limit
Simple solution: setpoints correspond to the required power
If loads can be linearly controlled, proportional setpoints can be used even
in overload situation
- In case of large on/off loads (heating) and overload:
Use flexibility of house i to find the limits Pmini,and Pmax
i
Allocate either the minimum necessary or the maximum power
Algorithms to avoid overloading the grid
9
xiPimin + (1- xi )Pi
max
i
å £ LVmax, xi = {0,1}, iÎ N
Test bed for Demand Management
10
38 houses in a low voltage
(LV) benchmark grid
- 8 electric cars charging in
1-2 intervals per day
- 5 kW electric heating in
winter
- PV generation in every
house
The benchmark grid
11
Performance indices
12
oi =1
n(Pi - LVmax ) / LVmax
i=1
n
å , i |Pi > LVmax
Define as load overload index (oi) the relative occurrence of total power
exceeding nominal transformer power LVmax:
Define the daily energy costs using day-ahead prices. The saving index is
the relative difference between the costs in a fixed-price and day-ahead
price scenario.
-50
-40
-30
-20
-10
0
10
20
30
40
50
1 3 5 7 9 11131517192123252729313335373941434547495153555759616365676971737577798183858789919395
rela vepricestoavg.44.23€/Mwh
si =1- ciPiin
i
å / c Piin
i
å
No control (no DMC)
Baseline DMC: no load limit at the transformer, prices.
Excessive demand: reduce load if demand exceeds the limit
Excessive generation: limit the overall injected power into the LV grid
Interruption – communication failures
Changing the energy price is used for demand control
Summary of tests
13
-20
0
20
40
60
80
100
120
1 4 7 1013161922252831343740434649525558616467707376798285889194
kW
totalload totalsetpoint
Energy costs comparison
14
Scenario Constant price Day-ahead price Day-ahead price
excessive demand
Total energy/day 765kWh 1011kWh 1118 kWh
Total cost 33,75€ 39,70€ 48,25 €
Resulting avg.
price 44,23 €/MWh 39,28 €/MWh 43,15 €/MWh
Savings (si) 0 11% 2,5%
-60
-40
-20
0
20
40
60
80
100
120
1 3 5 7 9111315171921232527293133353739414345474951535557596163656769717375777981838587899193
NoDMCscenario
Totalload
No demand management
control, constant price.
Demand limit set to 70 kW
The reference power is always
below the limit
Demand management
base-line scenario, prices,
no load limit.
-40
-20
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91
load setpoint
-20
0
20
40
60
80
100
120
1 4 7 1013161922252831343740434649525558616467707376798285889194
kW
totalload totalsetpoint
Load performance comparison
15
-60
-40
-20
0
20
40
60
80
100
120
1 3 5 7 9111315171921232527293133353739414345474951535557596163656769717375777981838587899193
NoDMCscenario
Totalload
No demand management
control, constant price.
Demand limit set to 70 kW
The reference power is always
below the limit
Demand management
base-line scenario, prices,
no load limit.
-40
-20
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91
load setpoint
Scenario Baseline no
limitation
Excessive
demand
Excessive
generation
Overload
index (oi) 2,6% 1,36% 0%
Using prices in the objective may create overload !
Overload index is reduced, as setting the limit leads to load shedding
Total power generation is well controlled by the limit
-120
-100
-80
-60
-40
-20
0
20
13 57911131517192123252729313335373941434547495153555759616365676971737577798183858789919395
Datenreihe1
Datenreihe2
Excessive generation (summer)
Limit = -100 kW
prices +10 €/MWh
Impact of temporary energy price increase
16
0
10
20
30
40
50
60
70
80
90
100
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
kW
period
impactofincreasedprice+10between18:00-19:00ontotalload
load:increasedprice baselineload deltaprice
Interruption scenario:
- all CEMS become suddenly disconnected from the DMC.
- Benefit of our approach: Cached plan is followed by the local algorithm
Changes in consumption cannot however be coordinated by the DMC
Resilient operation
17 -60
-40
-20
0
20
40
60
80
525456586062646668707274767880828486889092940 2 4 6 8101214161820222426283032343638404244464850
networkfailurebetweenperiods60-76
totalload setpointdis
connecte
d
Qualitative Evaluation of the Demand Management Scheme
18
Efficient scheduling of flexible loads allows higher individual house
consumption, as well as high penetration of PV installations.
Energy cost optimization shifts the load to the low price energy periods,
and saves in the tested scenario around 11% compared to the fixed price.
Independently of its cause, excessive total demand can be controlled at
the DMC to avoid overloading the grid. The savings become in this case
negligible (measured 2,5%) .
The injection of PV generated power in the grid is efficiently limited
similarly to the excessive demand case.
If the control communication network fails, cached plans allow for resilient
operation of CEMS’s during several hours.
Concluding Remarks
19
The SmartC2Net results clearly show that intelligent distribution
grid operation can be realized in a robust manner over existing
communication infrastructures even despite the presence of
accidental faults and malicious attacks.
20
Walter Schaffer
Head of Electrical Network
Salzburg Netz
Austria
Panel Discussion: Impact and Roadmap for Utilities
21
Aurelio Blanquet
Director
EDP
Portugal
Nuno Silva
Deputy Director
EFACEC Energia
Portugal
… visit our exhibition at booth B.m06!
Demo Schedule
- Tuesday (Nov. 3rd):
14-16h - MV Control
16-18h - Demand management
- Wednesday (Nov. 4th):
9-11h - External Generation Site
11-13h - Demand management
13-15h - MV Control
15-18h - External Generation Site
For further discussions and demos…
22
Thursday (Nov. 5th) 9-11h - Demand management
11-13h - External Generation Site
13-15h - MV Control