Modeling and simulation of the Power Energy System of Uruguay in

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Modeling and simulation of the Power
Energy System of Uruguay in 2015
with high penetration of wind energy
R. CHAER*
E. CORNALINO
E. COPPES
Facultad de Ingeniería & UTE
UTE
UTE
Uruguay
Uruguay
Uruguay
SUMMARY
This paper presents the results of detailed
simulations of the operation of the power
system of Uruguay, for the year 2015
when it reached the target of 1200 MW of
wind power in the system.
This level of win integration will be near
the 60% of de peak of demand.
The results show that this level of
integration is manageable thanks to the
large installed hydro power capacity in
the country.
A picture of 2015
Capacity vs. Peak
•The ceil-goal for 2015 is to reach 1200 MW of
installed wind power capacity.
60%
•The Expected demand for 2015 is about 1500
MW on average, with a peak of 2000 MW.
•Wind penetration then will be of 60% in
capacity and 33% in energy
Energy
33%
•How the system will be operated in 2015 with
this such high share of wind power?
•Are the available resources enough to
compensate the wind power variations?
Uruguay – 2015 (expected generation by source)
Wind Stochastic Model - CEGH
A stochastic model with Gaussian Space
Correlations with Histogram was identified
using hourly series of wind measures obtained
in 7 sites distributed over the country during 2
year .
The model represents the temporal and
geographical correlation between sites
and patterns the wind direction for site.
Also represents the annual and daily
seasonality the resource
Details
This stochastic model was used to perform a simulation of operation of the
system in 2015 using an integration time-step of one hour with the
simulator SimSEE.
All power plants in Uruguay are represented in this simulation, including:
hydroelectric plants with their storage capacity and gas-oil, fuel-oil,
bio-fuel and natural gas fired thermal units.
Two major questions
Is the model used for long-term
planning valid for dealing with high
wind energy integration?
Does the energy system of Uruguay
has enought resources to handle the
time variability of wind?
Modelling for Long Term Planning
The model used for the optimal planning of
investments in generation uses a weekly time
step divided into four time bands. (patamares)
This model is executed hundreds of thousands of times
for determining the optimal investment plan.
Due to weekly integration step the model is not able
to adequately represent the effect of time variations of
the wind farms output over the rest of the generating
units.
Modelling
Long term vs. Short term
Investment Planning - Long term
Time Step: weekly dividen in 4 hourly-bands
(PATAMARES)
Hydros with reservoires: Bonete
Wind Farms Output: stochastic, averaged by hourly-band
Operation Planning - Short term
Time Step: hourly
Hydros with reservoires: Bonete, Palmar and Salto Grande
Wind Farms Output: stochastic, averaged by hourly-band
Wind Energy (Weekly – Hourly)
There is no significant differences since the
weakly model of wind farms production was
calibrated averaging the hourly model.
Hydroelectric (Weekly – Hourly)
Could be in the order of 582 GWh per year of energy
surplus available from the hydro-subsystem in the
actual operation of the system. This surplus is achieved
by the effect of better management of the lakes with
short-term storage capacity.
Exports (Weekly – Hourly)
Could be in the order of 50 GWh per year of energy
surplus available for export in the actual operation of
the system. This surplus is achieved by the effect of
better management of the lakes with short-term storage
capacity.
Exportable surplus = Overcost
• For expansion planning it was assumed that the
system purchases all the energy generated by wind
farms at 65 USD/MWh; and that all the energy
surplus are exported at 10 USD/MWh to
neighboring countries.
• So a 50 GWh/year of exportable surplus
respresents a overcost of 50000 x (65-10) = 2
MUSD/year.
• This represent an increase in the price of wind
energy of 0.75 USD/MWh.
Prices used for the long
term planning.
The estimates of the optimal amount of wind power to be
installed in the system were performed initially priced at 90
USD/MWh and subsequently with 70 and 65 USD/MWh
obtaining the same results in terms of the amount of power to
be installed within the margin of error of the used tools.
Therefore this change in price due to surpluses recorded in
the hourly simulation over the weekly one, does not change
the conclusions regarding the optimal amount of wind power
to be installed in the system.
First conclusion
As shown, the economic impact of the
difference between both runs, changes the price
of wind power less than the price variations that
were considered in the study of sensitivity.
Then, we consider validated the model used for
planning respect to the result of install 1200
MW in the next years.
But in the future to continue adding wind farms
to the system, the weekly time step must be
shorten to represent adecuality the filtering efect
of the small lakes. For filtering the wind energy
variation they are not small.
Does the energy system of
Uruguay has enought
resources to handle the time
variability of wind energy?
What are the variations?
•
•
•
•
•
•
•
1 second
1 minute
1 hour
1 day
1 weak
1 month
1 year
Turbine-Machine intertial filter
Geographical filter inter-farms
Forecasting
Extra load following resources
Traditional operation resources
Salto Grande
(50% UY)
945MW
8 days
Palmar
333MW
22 days
Baygorria
108MW
3 days
Hydroelectric
Plants
1541 MW
Bonete
155MW
140 days
Fast fuel fired units
6 x 50 MW = 300 MW GT Punta del Tigre
8 x 10 MW = 80 MW Engines
2 x 100 MW = 200 MW GT La Tablada
540 MW CC (in bidding proccess)
Interconnections
• 2000 MW With Argentina
• 570 MW with Brasil
• For planning purposes the import is
considered closed.
01/03/2015
01/03/2015 03:00
01/03/2015 06:00
01/03/2015 09:00
01/03/2015 12:00
01/03/2015 15:00
01/03/2015 18:00
01/03/2015 21:00
02/03/2015
02/03/2015 03:00
02/03/2015 06:00
02/03/2015 09:00
02/03/2015 12:00
02/03/2015 15:00
02/03/2015 18:00
02/03/2015 21:00
03/03/2015
03/03/2015 03:00
03/03/2015 06:00
03/03/2015 09:00
03/03/2015 12:00
03/03/2015 15:00
03/03/2015 18:00
03/03/2015 21:00
04/03/2015
04/03/2015 03:00
04/03/2015 06:00
04/03/2015 09:00
04/03/2015 12:00
04/03/2015 15:00
04/03/2015 18:00
04/03/2015 21:00
05/03/2015
05/03/2015 03:00
05/03/2015 06:00
05/03/2015 09:00
05/03/2015 12:00
05/03/2015 15:00
05/03/2015 18:00
05/03/2015 21:00
06/03/2015
06/03/2015 03:00
06/03/2015 06:00
06/03/2015 09:00
06/03/2015 12:00
06/03/2015 15:00
06/03/2015 18:00
06/03/2015 21:00
MW
A week of 2015
Demanda y generación eólica - 1° semana de Marzo - posibles realizaciones
1800
1600
Demanda
EOL_1
EOL_2
EOL_3
1400
1200
1000
800
600
400
200
0
01/03/2015
01/03/2015 03:00
01/03/2015 06:00
01/03/2015 09:00
01/03/2015 12:00
01/03/2015 15:00
01/03/2015 18:00
01/03/2015 21:00
02/03/2015
02/03/2015 03:00
02/03/2015 06:00
02/03/2015 09:00
02/03/2015 12:00
02/03/2015 15:00
02/03/2015 18:00
02/03/2015 21:00
03/03/2015
03/03/2015 03:00
03/03/2015 06:00
03/03/2015 09:00
03/03/2015 12:00
03/03/2015 15:00
03/03/2015 18:00
03/03/2015 21:00
04/03/2015
04/03/2015 03:00
04/03/2015 06:00
04/03/2015 09:00
04/03/2015 12:00
04/03/2015 15:00
04/03/2015 18:00
04/03/2015 21:00
05/03/2015
05/03/2015 03:00
05/03/2015 06:00
05/03/2015 09:00
05/03/2015 12:00
05/03/2015 15:00
05/03/2015 18:00
05/03/2015 21:00
06/03/2015
06/03/2015 03:00
06/03/2015 06:00
06/03/2015 09:00
06/03/2015 12:00
06/03/2015 15:00
06/03/2015 18:00
06/03/2015 21:00
MW
Net Demand
1600
Demanda neta - 1° semana de marzo - posibles realizaciones
Demanda Neta_1
Demanda Neta_2
Demanda Neta_3
1400
1200
1000
800
600
400
200
0
January 2015
hydrology assuming historical low
January 2015
assuming very high hydrology
Hourly Simulations 2015
• considering two scenarios, one with 1200 MW wind power
installed capacity and one without wind power.
• Results were analyzed from the point of view of increasing
variability of power generation, induced by wind
generation. To do this we compared the variations in power
from one hour to the following for energy sources with
possibilities of fast regulation: hydraulics, gas turbines and
engines.
• We do that by comparing the cumulative probability plots
of the time differences of power, P(h) - P(h-1), of the
simulated cases with wind and without wind.
Fast-Thermal units variations
Cumulative probability of hourly changes of thermal power
600
simulation with wind dP_term
400
simulation with wind dP_term
dP térmicas (MW)
200
000
0%
-200
-400
-600
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Hydro power
Cumulative probability of hourly changes of hydro power
800
600
simulation with wind
simulation with wind
400
historic (S. G. Regulating)
historic (S. G. NO Regulating)
dP Hid (MW)
200
000
0%
-200
-400
-600
-800
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Hydro subsystem
Cumulative probability of hourly changes of hydro power
800
600
simulation with wind
simulation with wind
400
historic (S. G. Regulating)
historic (S. G. NO Regulating)
dP Hid (MW)
200
000
0%
-200
-400
-600
-800
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
It notes that the expected time
variations of the hydraulic
power to the system without
wind power are the same level
as the historical variations of
the periods in which Salto
Grande is not carrying out
secondary regulation, while the
expected variations for the
system with 1200 MW of wind
are similar to the historical
variations in when Salto
Grande is carrying secondary
regulation.
Exports
Cumulative probability of hourly changes of exported power
600
simulation with wind dP_expo
400
simulation with wind dP_expo
dP exportación (MW)
200
000
0%
-200
-400
-600
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Exports
As can be seen in 80% of time is not necessary to use
exporting power to regulate the system.
It is remarkable that to meet the requirements in the
remaining 20% of the time Uruguay has the ability to export
to Argentina through the existing interconnection of 2000
MW and to Brazil through the interconnection of 70 MW
already in operation plus 500 MW currently under
construction.
If the interconnections are not able to handle these increases
of 200 MW the solution should be to reduce the power
generated in the wind farms in those situations.
Second Conclusion
• The system should be capable to
handle the variability of the 1200
MW in 2015.
• If the 2570 MW interconnection
with Brazil and Argentina can not
be used to absorb variations of 200
MW in less than 20% of the time,
you should use to reduce the power
generated by wind farms in those
circumstances.
That all folks.
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