Download Peak Demand Analysis for the Look

EURECA 2013-
Peak Demand Analysis for the Look-ahead Energy Management System:
Peak Demand Analysis for the Look-ahead Energy
Management System: A Case Study at Taylor’s University
Nadarajan1, Aravind CV2*
1
Applied Electromagnetic and Mechanical cluster,
Computer Intelligence Applied Group, Taylor’s University, Malaysia
*[email protected]
1. Introduction
The most common factor influence the energy management
is the active energy consumption (KWh), the reactive energy
consumption (KVARh) and the peak demand (KW).
Conventionally the utility system put their effort on the
reduction of KWh consumption and on addressing the reactive
energy demand to improvise the power factor. However for the
medium voltage and high voltage consumers’ proper KW
demand management implies to reduce the use of contracted
power, adjusting to the new requirement and avoiding the
demand limit penalties [1]. Figure 1 shows the concept in the
power management. As can be seen the power management is
interlinked and the possible energy management between the
KW and KVAr the net power consumption can be reduced. In
order to find the demand requirement to propose new system
architecture to address the demand requirement the peak
demand analysis is to be investigated. Peak demand is the
power consumed over a predetermined period of time, typically
between 8 to 30 minutes. The power is calculated using a
power demand meter, which records the highest KW value in
the period of measurement, over a month’s time. The purpose
of demand control is not to exceed the contracted maximum
demand limit. The common way is to isolate the non-critical
load during peak hours. A number power demand modeling
and analysis, towards optimization of demand curve [2] as well
as forecasting [1] are the subjects of interest in recent years.
However, accuracy and resolution of the model are important
[3]. We have utilized the data on energy management from the
Taylor’s University, Malaysia laced at latitude of 3°07'51.99"N
and longitude of 101°59'11.77"E. The demand analysis is based
on the utilized power between Jan 2011 till April 2013. Our
initial study is to derive the average peak demand requirement
and suggest a KW management system for energy
sustainability. From our study a detailed proposal on the energy
management by suggesting a KW framework is to be presented
towards the end of this research work.
107
Improvising
Utilising
Improvise
KVAR Management
Avoiding
Adjusting
KW Management
Reducing
Abstract— To derive an energy management for sustainable
energy usage the peak demand analysis is highly critical. This
paper presents the investigations on the peak demand analysis for
the existing power system network at Taylor’s University. From
the analysis it is inferred that the average peak demand of
3000kW could be managed with proper kilowatt management.
The analysis pertaining to the computations of power analysis and
a proposed framework to support the analysis is presented.
Further to stabilising the load requirement, equally the economics
of the system is improvised by about 7.33%.
Keywords— peak demand, energy management, economics
Architecture
usage
low power devices
demand
penalty
power factor
KWhr Managed
Figure 1. KWhr Management Strategy
2. Methodology
The methodology involved in this investigations is as shown
in the Figure 2 and the power system architecture is as shown
in Figure 3. The computation procedure for the demand
analysis and the net KW demand computation is as below. Let
the contracted power be (P C ), the maximum demand is (P D )
then the power used in excess (P E ) is computed as
PE = PC - PD
(1)
where P Dm is the actual peak demand value from the
maximum demand meter and K d is the demand factor
P D = P Dm X K d
(2)
The penalty by the supplier to the utility is computed as
P P = (P C - P D ) X K P
(3)
where K P is the penalty factor. Therefore the actual KW value
(P A ) computed is given by
P A = [(P C - P D ) X K P ] + [P Dm X K D ]
(4)
.
Demand Meter
Start
KW
merge
Data
Collection
Unit Consumption
Energy Data Base
extract
Data Analysis
Power Demand Analysis
Origin
Mathematical
Tool
End
Figure 2. Methodology Employed
EURECA 2013-
Peak Demand Analysis for the Look-ahead Energy Management System:
From Subang Jaya Substation
11KV 50Hz Three
phase Feeder 1
11KV 50Hz Three
Phase Feeder 2
Contracted Power (Pc)
100VA,
11KV/110V
Maximum Demand
Meter (PD)
KW
Kf
Pc -PD
P+(Pc -PD)Kf
KWhr
Energy Meter (P)
MSB 1
MSB 2
1600KVA,
11KV/433V
(Pc -PD)Kf
KW
MSB 4
MSB 3
1600KVA,
11KV/433V
2500KVA,
11KV/433V
2500KVA,
11KV/433V
Normally
Open
Lighting Load
Power Load
Lighting Load
Power Load
Figure 3. Power System Architecture for the Peak Demand Computations
3. Results and Discussions
Table 1 shows the percentage share of the pay bill to the
Tenaga nasional. As can be seen the average peak demand (PD)
is about 20.87% and the KWh utility is about 78.77% for the
three year period. If the average peak demand is catered
through a energy management system the power system
network ideally becomes sustainable. Figure 4 shows the unit
consumption and during the second quarter the unit
consumption is predominantly high and at the same time the
peak demand (as in Figure 5) is critically very high. The peak
demand is addressed through the design of a renewable
structure and reconfigures the existing power system
architecture in our further investigations.
Peak Demand Limit
Average Peak Demand
3600
Penalty
3400
3300
3200
3100
3000
2900
2800
Dec
Oct
Nov
Sep
Aug
July
June
Month
2011
2012
Figure 5. Power Demand Analysis
4. Conclusions
The initial investigations on the power demand curve analysis
for the look-ahead energy management system is presented in
this work. It is inferred that about 20.87% of the pay bill
accounted to the peak demand requirement. A sustainable
framework based on this analysis would be further investigated.
5. References
1000000
950000
900000
850000
800000
750000
2010
Month
2011
Dec
Oct
Nov
Sep
Aug
July
June
May
April
Feb
[1]
Mar
700000
May
2010
1050000
Jan
Unit Consumption, PU [KWhr]
1100000
April
Jan
Feb
2700
2600
1150000
Peak Demand
Mar
Maximum Demand, PD [KW]
3500
[2]
2012
[3]
Figure 4. Unit Consumption of Energy
García-Ascanio and C. Maté, “Electric power demand forecasting
using interval time series: A comparison between VAR and
iMLP,” Energy Policy, vol. 38, no. 2, pp. 715-725, Feb. 2010
N. Li, L. Chen, and S. H. Low, “Optimal demand response based
on utility maximization in power networks,” in 2011 IEEE Power
and Energy Society General Meeting, 2011, pp. 1-8
J. Widén and E. Wäckelgård, “A high-resolution stochastic model
of domestic activity patterns and electricity demand,” Applied
Energy, vol. 87, no. 6, pp. 1880-1892, Jun. 2010.
Table 1. Percentage of Pay Bill Ratio at Taylor’s University
Year
2010
2011
2012
KWh
79.38
78.58
78.37
PD
20.64
21.35
20.63
Renewable
0
0.07
0.09
108
Penalties
0.01
0
0