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Probabilistic Load Flow in Microgrid
Assessment and Planning Studies
Electrical Power and Energy Conference 2012
Oluwabukola Oke & David. W. P Thomas
Presenter: Oluwabukola Oke
Introduction
Electrical Power and Energy Conference 2012
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
Electrical Power and Energy Conference 2012
Current Scenario and Drivers
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
Electrical Power and Energy Conference 2012
Electrical Power and Energy Conference 2012
Wind Farm and Microgrid Location
University Park Electricity Map
Electrical Power and Energy Conference 2012
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
University Park Electricity Map
1
2
3
Electrical Power and Energy Conference 2012
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
Electrical Power and Energy Conference 2012
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
Electrical Power and Energy Conference 2012
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
Electrical Power and Energy Conference 2012
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
Electrical Power and Energy Conference 2012
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)
Electrical Power and Energy Conference 2012
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
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
