IGREENGrid 2 Public Workshop -...
Transcript of IGREENGrid 2 Public Workshop -...
IGREENGrid 2nd Public Workshop
Javier Contreras
University of Castilla – La Mancha
04/12/2014
Madrid, Spain
Outline
Introduction
RES (Renewable Energy Sources)
Forecasting Tools
EES (Electrical Energy Storage) for Insular
Networks
Power Analysis Tools
Scheduling Tools
Planning Tools
Overview and Final Notes
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IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Introduction (1/14)
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IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Sao Miguel - Azores
(EDA)
La Graciosa - Canary Islands (ITC) Pantelleria - Italy (W4E) Crete - Greece (HEDNO)
Great Island of
Braila - Romania
(Electrica)
Introduction (2/14)
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IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Introduction (3/14)
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SiNGULAR Consortium SiNGULAR Consortium
5 Universities 5 Universities 3 Distribution 3 Distribution
System Operators
8 Energy
Utilities/SMEs
8 Energy Companies/
Utilities/SMEs
• UBI (Portugal) (Coordinator)
• AUTH (Greece)
• UCLM (Spain)
• POLITO (Italy)
• UPB (Romania)
• EDA (Portugal)
• HEDNO (Greece)
• Electrica (Romania)
• SmartWatt (Portugal)
• ITC (Spain)
• Concepto Sociológico (Spain)
• W4E (Italy)
• Commune di Pantelleria (Italy)
7 European countries: Portugal, Spain, Italy, Switzerland, Greece, Romania & Cyprus
Different types/sizes of islands will be considered
Introduction (4/14)
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Investigate Investigate
Effects of large-scale
integration of RES and
DSM on the planning
and operation of
insular (non-
interconnected)
electricity grids
Recommend
insular electricity grids
Recommend
Scalable and Replicable Solutions for regulatory, technical and economic challenges of integrating very large shares of RES in insular electricity grids
Develop &
Develop &
Validate
Operation and
planning procedures
and tools in five pilot
sites
Introduction (5/14)
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Pilot Sites Peak
Load [MW]
Voltage
Levels [kV]
Generation Mix
Crete
Greece (HEDNO) 650 150/20/15/.4
Thermal (Steam, ICE, CCGT)
/Wind/PV
Sao Miguel
Azores (EDA) 75 60/30/10/.4
Thermal (ICE)
/Geothermal/Hydro/Wind
Great Island of
Brailla Romania
(Electrica)
15 110/20/.4 Mainland Romania + Wind
Pantelleria
Italy (W4E) 7 10.5/.4 Thermal (ICE)/PV/(Wave)
La Graciosa
Canary Islands (ITC) 1.2 20/.4 Thermal (ICE)/(Wind)/(PV)
Pilot Sites
Introduction (6/14)
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Introduction (7/14)
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Introduction (8/14)
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Inertial Sea Wave Energy Converter -
ISWEC 1:8 scaled model test
http://www.pantelleriaisland.it/
Introduction (9/14)
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Batteries
Control and power
conditioning unit
Loads
Microgrid for La Graciosa: combine PV,
wind and diesel systems to supply the
electrical needs
Introduction (10/14)
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Area 278 km2
Height 1.501 m.
Population 10.890 inhab.
Electricity Plant (Diesel)
nominal power 13,3 MW
Peak demand 7 MW
Wind Farm 11,5 MW
Hydroelectric Substation 11,3 MW
Pumping Station 6 MW
Upper Reservoir 380.000 m3
Lower Reservoir 150.000 m3
New Diesel systems 0
RES penetration 80%
Total energy:
44,87 GWh
Wind Energy: 27 GWh
Hydro: 6,87 GWh
Diesel: 11 GWh
Wind-Pumped-Hydro System
Introduction (11/14)
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Introduction (12/14)
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High High electricity
costs
2 - 5 times the values of
continental grids
High potential for energy efficiency measures
RES competitive to conventional
generation
High level of High level of RES
High variability of hourly
generation mix & costs
Need for RES forecasting tools
High potential for active demand
High variability in consumption
High variability in consumption
Need for demand
forecasting
High potential for active demand
Too small to
market
Too small to have
electricity market
Difficult to institute short-
term offer-based auctions
Dynamic price signal based on
actual generation costs
A/S market (provided by small-scale resources)
Increased
Services
Increased need for
Balancing Services
Demand Response programs
Storage / Hybrid Plants
Electric Vehicles
Why Insular/Singular?
Introduction (13/14)
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Active DSOs
Distribution level RES
Residential level DR
Large-Scale Wind
Network Infrastructure
Electric Vehicles
Energy Efficiency Storage
Smart &
Sustainable Grid
Introduction (14/14)
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Faster exploitation of the rich RES potential of the
islands
Reduction in fuel imports and the associated costs for
electricity production
Reduction in CO2 emissions, Cleaner environment,
Stimulation of tourism in the islands
Enhancement of system security, reliability, and
quality of supply through the provision of fast and low-
cost reserves from flexible loads, EES and PEVs, and
the active participation of small electricity consumers
Strong synergies between energy-oriented academic
and industry partners with complementary skills and
expertise
We are working
towards…
RES Forecasting Tools (1/5)
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Meteorological forecast service Meteorological forecast service
Regional level
forecast
MV substation MV substation
level forecast
Individual RES Individual RES
facilities
Generation Generation
Scheduling
Price signals
Predictive DSM
RES generation RES generation
management
Predictive Predictive
Power flow
Wind farms
PV plants
Small hidro
Consumption
Predictive Predictive
reliability
Assets Forecast
Application
RES Forecasting Tools (2/5)
0
1
2
3
4
5
6
7
8
9
10
0 12 24 36 48 60 72 84 96 108 120 132 144 156 168
Win
d P
ow
er
(MW
)
Time horizon (hours)
Averaged Measured Wind Power (nospillage)
Deterministic wind power forecast
Probabilistic Forecast ( Q10-Q90 )
0.00
0.01
0.02
0.03
0.04
0.05
0.1
9
0.9
3
1.6
7
2.4
2
3.1
6
3.9
1
4.6
5
5.3
9
6.1
4
6.8
8
7.6
3
8.3
7
9.1
1
Fre
qu
en
cy
Bins
KDE
Beta Distribution 0.00
0.01
0.02
0.03
0.04
0.05
0.1
9
0.9
3
1.6
7
2.4
2
3.1
6
3.9
1
4.6
5
5.3
9
6.1
4
6.8
8
7.6
3
8.3
7
9.1
1
Fre
qu
en
cy
Bins
KDE
Beta Distribution
0.00
0.05
0.10
0.15
0.20
0.25
0.1
9
0.9
3
1.6
7
2.4
2
3.1
6
3.9
1
4.6
5
5.3
9
6.1
4
6.8
8
7.6
3
8.3
7
9.1
1
Fre
qu
en
cy
Bins
KDE
Beta Distribution
0.00
0.02
0.04
0.06
0.08
0.10
0.1
9
0.9
3
1.6
7
2.4
2
3.1
6
3.9
1
4.6
5
5.3
9
6.1
4
6.8
8
7.6
3
8.3
7
9.1
1
Fre
qu
en
cy
Bins
KDE
Beta Distribution
KDE - Kernel Density Estimation forecast technique
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AUPEC 2014 – Smart Power for Everyone
RES Forecasting Tools (3/5)
The model provides a point forecast and a probabilistic forecast
(between percentile of 5% and 95%). The forecast is refreshed every
24 hours. NWP data (air density, wind speed, wind direction, etc.) is
used to feed the model.
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RES Forecasting Tools (4/5)
Forecasting Day
Forecasting Hour 0h, 6h, 12h, 18h
If there´s more than one variable of the same type
Upper quantile Beta distribution
Lower quantile Beta Distribution
Refresh Buttom
Image Download
Forecasting Variable
Point Forecast
Uncertainty Forecast
Download Options
Recent past forecast for 24 hours-ahead
Forecast Up to 7 days
Forecast platform
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RES Forecasting Tools (5/5)
Wind forecast (Azores)
and more...
Website
of the platform
Load forecast (Crete)
PV forecast (Crete)
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AUPEC 2014 – Smart Power for Everyone
EES for Insular Networks (1/5)
EES
PUMPED
HYDRO-
POWER
ENERGY
STORAGE
(PHES)
COMPRES
SED AIR
ENERGY
STORAGE
(CAES)
FLYWHEELS
ENERGY
STORAGE
SYSTEM
(FESS)
FLOW
BATTERIES
ENERGY
STORAGE
(FBES)
CHEMICAL STORAGE /
BATTERIES ENERGY STORAGE
(BESS)
Lead-Acid Li-Ion NaS
APPLICABLE
GRID SYSTEM
SIZE
Mostly > 200
MW > 500 MW
100 kW - 200
MW 25 kW - 10 MW < 10 MW < 10 MW > 100 MW
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Different technologies for different system
sizes and application
Different objective functions:
Maximum integration of RES in stand-alone
applications
Reliability of the autonomous system
EES for Insular Networks (2/5)
EES will support
the operation of
an LV network
EES will support
the operation of
an LV network
Power system of La Graciosa Island
Load PV
EES associated to micro-
generation
LV
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EES for Insular Networks (3/5)
Desalination plant in Lanzarote
Po
we
r (k
W)
Power input to desalination Plant
Time
WITH storage
NO storage
EES associated to large wind
farm and desalination plant
EES will provide
continuous power to
the plant
EES will provide
continuous power to
the plant
Power output of wind Farm
Wind Power Production
Wind Power Prediction Time
Po
we
r (k
W)
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EES for Insular Networks (4/5)
Load
Thermal Power
Wind Power
Curtailments for 2011~12% Wind production
Data obtained by HEDNO
for 2011
For each wind farm
calculated (Matlab
model):
• Theoretical wind power
production
• Wind power
curtailments
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EES for Insular Networks (5/5)
Grid Code Requirements
Insular
Networks Rated Voltage
(kV) Voltage range
Rated
Frequency
(Hz)
Frequency range
(Hz) Power factor
Harmonics/
Flicker
Portugal 6, 6.9, 10, 5,
30, 60 - 50 ±2% (95%)
±15% (100%) Not specified EN Standards
Spain 220, 132, 66 105% (0.3s) 49.85-0.15 49.85-0.25 [5min]
47.5-5 [5min] 0.95 inductive
to 0.95
capacitive
EN Standards
Italy 10.5 85-110% 47.5-51.5 0.8 to 1
inductive IEC Standards
Greece 6.6, 15, 20,
150 LV and MV:
±10%
HV: -5% to 8%
50 49-51Hz [95%]
42.5-57.5 [100%] IEC 61000-3-7 IEC Standards
Denmark 10, 15, 20, 30,
33, 50, 60,
132, 150
±10% 49.5-50.2 50.2-52 [15min]
47-47.5 [20sec] 0.975 inductive
to 0.975
capacitive
EN and IEC
Standards
Germany 110, 220, 380 49-50.5 47.5-51.5 0.95 inductive
to 0.95
capacitive
EN and IEC
Standards
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Power Analysis Tools (1/10)
Photovoltaic systems
Loss factor components
Energy from sea waves (based on ISWEC prototype)
ISWEC model
RES Characterization
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Power Analysis Tools (2/10)
Photovoltaic systems
4 6 8 10 12 14 16 18 200
200
400
600
800
1000
1200
140007/18/2012
hours
Irra
dia
nce[W
/m2]
data
1 day before
data
2 days before
data
3 days before
Gpyr
clear sky
4 6 8 10 12 14 16 18 200
200
400
600
800
1000
1200
140007/23/2012
hours
Irra
dia
nce[W
/m2]
data
1 day before
data
2 days before
data
3 days before
Gpyr
variable sky
• Examples of days with their categorization
• 3-hour predicted values interpolated in polynomial
form
4 6 8 10 12 14 16 18 200
200
400
600
800
1000
1200
140007/12/2012
hours
Irra
dia
nce[W
/m2]
data
1 day before
data
2 days before
data
3 days before
Gpyr
“broken” clouds
The “broken clouds” phenomenon appears when sky is mainly clear, but the passage of clouds affects
irradiance evolution; the phenomenon is evident starting from 1-minute measurements; if the 15-min
average of active power is calculated, the presence of broken clouds is smoothed
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Power Analysis Tools (3/10)
Photovoltaic systems
• Comparison between PV power measurements and
simulations
• Cases with variable sky and clear sky
0 10 20 30 40 50 600
100
200
300
400
500
600
700
800
90007/12/2012
quarter of hours
Avera
ge A
C p
ow
er
[kW
]
Pfore
Pmeas
0 10 20 30 40 50 600
100
200
300
400
500
600
700
800
90007/18/2012
quarter of hours
Avera
ge A
C p
ow
er
[kW
]
Pfore
Pmeas
• Bad results? • NO: failure of a portion in the PV arrays (confirmed)
• The model is useful also for diagnosis purposes
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Power Analysis Tools (4/10)
Photovoltaic systems
• Comparison between PV power measurements and
simulations
• Case with variable sky
0 10 20 30 40 50 600
100
200
300
400
500
600
700
800
90007/23/2012
quarter of hours
Avera
ge A
C p
ow
er
[kW
]
Pfore
Pmeas
No failure in the PV arrays
The model is effective
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Power Analysis Tools (5/10)
Electricity from sea waves
ISWEC 1:8 scaled model test
(Insean, Rome)
• Sealed hull
• Gyroscope
• Power generator (PTO)
PTO
hull
gyro
ISWEC (Inertial Sea Wave
Energy Converter)
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Power Analysis Tools (6/10)
Electricity from sea waves
Estimation of the
ISWEC
performance wave
height
Hs(m)
wave period Te (s)
Scatter diagram:
occurrences (h)
Spectrum
Power Matrix
power output (kW)
wave
height
Hs(m)
Wave
Height
Σ Productivi
ty
wave period Te (s)
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Power Analysis Tools (7/10)
Electricity from sea waves
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η(t)
Pi (kW)
t (s)
Pg (kW)
t (s)
• Energy storage
(Batteries)
• Power storage
(Ultra Capacitors)
• Flywheel (possible storage) • Sea waves
• Electricity grid
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Power Analysis Tools (8/10)
Electricity from sea waves
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Energy storage allows to:
• decouple the generated power from the power delivered to the grid
• smooth the power output
• give constant power to the grid in some time intervals
• delay and predict the delivered power level variation
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Power Analysis Tools (9/10)
Probabilistic load-flow results
Test system: voltage profiles at the 16 nodes during the day
Time step for the analysis: 10 min, single Monte Carlo repetition
Voltage
profiles
supply nodes
1, 2 and 3
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Power Analysis Tools (10/10)
Reliability Model with Network
Reconfiguration
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Scheduling Tools (1/8)
Scenario Generation for Dependent Stochastic
Processes
37
space
Historical Data
( ) ( ) ( ) ( )d Dp P s t q Q t
SARIMAModel
B B y B B
Dependent
Sto
chastic P
rocesses
Forecast Model Error Time Series Scenario Generation
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Scheduling Tools (2/8)
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Scenario Generation for Wind production
Example – 2 Wind Farms (Cross-Correlated)
Anemos Aiolikis – Kasteli, 6.3 MW Ydroaioliki – Kasteli, 9.35 MW
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Win
d P
rod
uct
ion
[MW
]
Hours
REAL FORECAST
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
10,00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Win
d P
rod
uct
ion
[MW
]
Hours
REAL FORECAST
Initial Set: 50
Scenarios Reduced Set: 20
Scenarios
Scheduling Tools (3/8)
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Scenario Generation for PV production
Example – 2 PV Plants (Cross-Correlated)
Attiki (0.15 MW) Viotia (1.0 MW)
0,00
0,02
0,04
0,06
0,08
0,10
0,12
0,14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
PV
Pro
du
ctio
n [M
W]
Hours
REAL FORECAST
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0,90
1,00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
PV
Pro
du
ctio
n [M
W]
Hours
REAL FORECAST
Initial Set: 50
Scenarios Reduced Set: 20
Scenarios
Scheduling Tools (4/8)
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Subject to:
Network Constraints
Subject to:
Thermal and RES Units
Operating Constraints
Power Balance and Reserve
Req.
Network Constraints
Minimize [Total Production
Cost]
Forecasts/Scenarios for
(Load, RES Production,
etc.)
Forecasts/Scenarios for
System Parameters
(Load, RES Production,
etc.)
Unit Data Unit Data
(Technical, Economic,
Availabilities, etc.) Network Data
MILP models
Unit Commitment &
Unit Commitment &
Dispatch Schedule
(Energy, Reserves)
Shadow Prices of
Constraints (SMP,
LMP, …)
Shadow Prices of
System
Constraints (SMP,
LMP, …)
Power Flows in
Network Lines
Scheduling Tools (5/8)
Scheduling Models – Improvements in the
Production Mix
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Scheduling Tools (6/8)
Scheduling Models – Improvements in the
Production Mix
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Scheduling Tools (7/8)
Integrated Software / General Features
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Stand-alone application
Connection with MySQL Database
Multi-lingual (English, Greek, …)
Easy-to-use by dispatchers
Interactive Map
Scheduling Tools (8/8)
Scheduling with probabilistic forecast
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Planning Tools (1/10)
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Development of optimization models for generation and network expansion
planning
Minimize the investment and operating cost of candidate RES generator, cost of
system losses and cost of existing power plants
Planning Tools (2/10)
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RES Generation Expansion Model
Planning Tools (3/10)
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RES Generation Expansion Model
Planning Tools (4/10)
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Case Study: La Graciosa (Canary Islands)
Planning Tools (5/10)
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Joint RES and Distribution Expansion Model
Node without demand
Existing substation
Candidate substation
Existing fixed feeder
Existing replaceable feeder
Candidate branch to install new feeder
1
6
4
5
7
17
8
9
10
122
11
18
16
21
151922
25
24
26
14
20
13
23
3
Planning Tools (6/10)
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Joint RES and Distribution Expansion Model:
Solution 1
1
6
4
5
7
17
8
9
10
122
11
18
16
21
151922
25
24
26
14
20
13
23
3
TR1A1
A1
A1
A1A1
R1
P1
W1
W2
W2 P2
W1 P1
P1
Node without demand
Node with demand
Existing substation
Uninstalled substation
Alternative 1 in branch subject to replacement
Alternative 2 in branch subject to replacement
Alternative 1 in prospective branch
Alternative 2 in prospective branch
R1
R2
Alternative 1 in prospective branch
Alternative 2 in prospective branch
Alternative 1 for candidate transformer
Alternative 2 for candidate transformer
Alternative 1 for conventional generator
Alternative 2 for conventional generator
Alternative 1 for wind generator
Alternative 2 for wind generator
C1
C2
W1
W2
A1
A2
TR1
TR2
A1
A2
Planning Tools (7/10)
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Joint RES and Distribution Expansion Model:
Solution 2
Node without demand
Node with demand
Existing substation
Uninstalled substation
Alternative 1 in branch subject to replacement
Alternative 2 in branch subject to replacement
Alternative 1 in prospective branch
Alternative 2 in prospective branch
R1
R2
Alternative 1 in prospective branch
Alternative 2 in prospective branch
Alternative 1 for candidate transformer
Alternative 2 for candidate transformer
Alternative 1 for conventional generator
Alternative 2 for conventional generator
Alternative 1 for wind generator
Alternative 2 for wind generator
C1
C2
W1
W2
A1
A2
TR1
TR2
A1
A2
1
6
4
5
7
17
8
9
10
122
11
18
16
21
151922
25
24
26
14
20
13
23
3
TR1A1
A1
A1
A1
A1
R1
P1
W1
W1
W2 P2
W1 P1
P1
W1
R1
Planning Tools (8/10)
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Joint RES and Distribution Expansion Model:
Solution 3
Node without demand
Node with demand
Existing substation
Uninstalled substation
Alternative 1 in branch subject to replacement
Alternative 2 in branch subject to replacement
Alternative 1 in prospective branch
Alternative 2 in prospective branch
R1
R2
Alternative 1 in prospective branch
Alternative 2 in prospective branch
Alternative 1 for candidate transformer
Alternative 2 for candidate transformer
Alternative 1 for conventional generator
Alternative 2 for conventional generator
Alternative 1 for wind generator
Alternative 2 for wind generator
C1
C2
W1
W2
A1
A2
TR1
TR2
A1
A2
1
6
4
5
7
17
8
9
10
122
11
18
16
21
151922
25
24
26
14
20
13
23
3
TR1
A1A1
A1
A1P1
W1
W1
P2W1 P1
P1
W1
R1
TR1
W2
A1
Planning Tools (9/10)
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IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Costs [k$] Solution 1 Solution 2 Solution 3
Investment 0247.459 0256.974 0280.850
Maintenance 0205.154 0203.972 0204.581
Production 2576.963 2581.107 2585.652
Losses 0000.328 0000.328 0000.302
Unserved energy 0000.000 0000.121 0000.000
Total 3029.904 3042.502 3071.385
Results
Planning Tools (10/10)
54
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Product Service
SoS and QoS MW
Energy MWh
Demand Side Participation
DR DSM
Market delivers price signals
Market V-Q Restrictions
Tariff
Hedging Risk Adversion
Demand
Response)
Demand (including Demand
Response)
Adequacy of
Supply
Adequacy of
Supply
Marginal
Cost (LMP)
Marginal
Cost (LMP)
Generation
planning
Generation
expansion
planning Network
planning
Network
expansion
planning
Through demand-side
programs, customers will be
encouraged to be more
flexible in consumption
Need for integrating demand
response programs into long-
term investment planning
Create an adequate
regulatory framework that
allows network solutions
beyond the “investing in
copper” approach
Consumers’ willingness to adjust demand
profile.
• Own-price elasticity
• Cross-price elasticity
Overview and Final Notes
(1/2)
55
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
Our research is oriented towards the development of
methodologies, mathematical models and software
tools for the efficient, reliable and secure operation of
insular electricity networks under large-scale integration
of various RES technologies (e.g. wind, PV, small hydro
and wave energy).
The aim is to develop integrated sophisticated tools for
RES forecasting, as well as for short-term scheduling
and
long-term planning of insular electricity grids, involving
risk and uncertainty in a smart grid environment, and
also EES, electric vehicles, VPPs and DSM.
Overview and Final Notes
(2/2)
56
IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014
The ultimate goal is the generation of effective
solutions and information so that the integration of
insular and highly variable RES is maximized, while the
associated negative effects from a technical and
economic perspective are minimized, towards the
enhancement of the system security, reliability, and
quality of supply.
To sum up: advanced mathematical optimization models
are being developed, tested and validated in real-world
cases, handling forecasting, operations and planning
of power systems in an integrated, novel and improved
manner.
IGREENGrid 2nd Public Workshop
Javier Contreras
University of Castilla – La Mancha
04/12/2014
Madrid, Spain