probabilistic load flow in microgrid assessment and ... load flow in microgrid assessment and...
TRANSCRIPT
![Page 1: Probabilistic Load Flow in Microgrid Assessment and ... Load Flow in Microgrid Assessment and Planning Studies Electrical Power and Energy Conference 2012 Oluwabukola Oke & David](https://reader033.vdocument.in/reader033/viewer/2022051406/5aa9aa8d7f8b9a86188d1b74/html5/thumbnails/1.jpg)
Probabilistic Load Flow in Microgrid
Assessment and Planning Studies
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Oluwabukola Oke & David. W. P Thomas
Presenter: Oluwabukola Oke
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Introduction
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0
10000
20000
30000
40000
50000
60000
70000
80000
2005/6 2007/8 2009/10 2014/15 2020
To
nn
es
CO
2
University’s CO2 Emission
Target
Target
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Current Scenario and Drivers
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Targets and Strategies
University’s Carbon Management Strategies1.improvements in energy efficiency of buildings, including insulation, heating & lighting
2. more efficient use of existing equipment including switching off when not in use
3. generation of energy from small/medium scale
renewable energy systems4. provision of information and training to staff and students to engage them with the
objectives of the Plan
5. a cultural change in the use of high energy consumption activities within premises and
a strategy to replace with lower energy alternatives
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Wind Farm and Microgrid Location
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University Park Electricity Map
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Research Aim
To determine an appropriate in-feed point for
the wind farm output to fully harness the
benefits of the DG
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Data Analysis
• Load flow is carried out for a
typical high load day (during
winter exams) and a typical
low load day (summer
holiday)
• Major demand often peaks
during active day period
(08.30-18.00)
• Wind data for a 10year period
(2001-2010)using the same
time period as the load.
• Wind speed follows the
Weibull distribution.
• High load day (Day A): 2.2318 and 4.95
for shape and scale parameters
• Low load day (Day B): 1.9636 and 4.51
for shape and scale parameters
• 5% and 20% standard deviation for the
low and high load days respectively
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Typical University’s Electricity Usage Graph
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University Park Electricity Map
1
2
3
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Probabilistic Load Flow as Decision
Tool
• Newton Raphson Method
• Monte Carlo Simulation with 10000 samples
• Main parameters evaluated in making
decision:
• Substation voltage values
• Power flow on the line
• Total system losses
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Voltage Profile for Day A
0.991
0.993
0.995
0.997
0.999
1.001
1.003
0 5 10 15 20
Vo
lta
ge
(P
U)
Substation Number
No-DG
Location 3
Location 2
Location 1
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0.997
0.998
0.999
1
1.001
1.002
1.003
1.004
0 5 10 15 20
Volt
age
(PU
)
Substation Number
No DG
Location 3
Location 2
Location 1
Voltage Profile for Day B
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Typical Power Flow
-6 -4 -2 0 2 4 6 80
0.2
0.4
0.6
0.8
1
Power Flow from1-17
F(P
F)
CDF
No DG
Location 1
Location 2
Location 3
Limit
-10 -8 -6 -4 -2 0 20
0.2
0.4
0.6
0.8
1
Power Flow 1-17F
(PF
)
CDF
No DG
Location 1
Location 2
Location 3
Limit
Day A Day B
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Total System Losses
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
No DG DG @ Location 1 DG @ Location 2 DG @ Location 3
Sys
tem
Lo
sse
s (M
W)
Day A (MW)
Day B (MW)
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Précis
• Probabilistic load flow has been described as a tool for
deciding the optimal point for locating RE generators within a
practical microgrid.
• The effect of seasonal and diurnal variations (in load demands
and wind speeds) were considered in determining possible
risk while incorporating RE generators in an existing system.
• System was assumed balanced.
• The interactive effect of other RE generators (PVs, CHP)
proposed for the microgrid to be considered in future work.
Electrical Power and Energy Conference 2012
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ThankThank You for Your Attention
Special thanks toSpecial thanks to
The University of Nottingham,The University of Nottingham,
Schlumberger Foundation,Schlumberger Foundation,
Midlands Energy Graduate SchoolMidlands Energy Graduate School