probabilistic load flow in microgrid assessment and ... load flow in microgrid assessment and...

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


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


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



Wind Farm and Microgrid Location

University Park Electricity Map


Research Aim

To determine an appropriate in-feed point for

the wind farm output to fully harness the

benefits of the DG


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


Typical University’s Electricity Usage Graph

University Park Electricity Map

1

2

3


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


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


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


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


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)


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.


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

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