IMPLEMENTATION OF COST –EFFECTIVE MEASUREMENT AND VERIFICATION

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					  IMPLEMENTATION OF COST –EFFECTIVE MEASUREMENT AND
VERIFICATION PROCESS IN DSM LOAD MANAGEMENT PROJECTS IN
                      MINING SECTOR

                                       GS Donev, OD Dintchev
                                     Tshwane University of Technology

 ABSTRACT

 The purpose of this paper is to demonstrate how a                Was the Project successful?
 cost effective M&V process could use all available               Did the energy bill go down?
 DSM project resources to verify an energy saving                 Did the Project perform as specified?
 project’s power and energy impacts.                              Did the guaranteed savings actually occur?
                                                                  How much has been saved and are the
 A case study is presented for a large mine load                  savings being sustained?
 shifting project, where a cost effective and
 accurate M&V process was developed and                  For all these question Measurement and Verification
 implemented.                                            (M&V) is the language and discipline that can provide
                                                         the answers.

 1.    BACKGROUND                                        M&V is a way of addressing both the engineering and
                                                         legal issues so that the stakeholders can mutually
 The lack of generation capacity created an electrical   agree on the results of energy investments.
 energy crisis in meeting the ever growing electricity
 demand in South Africa. The reduction of the energy     Usually the stakeholders fail to agree on the simple
 demand and peak load is one of the main ways to keep    question: “What makes M&V so difficult?”
 the electrical supply uninterruptible.                  The answer is that: ‘Things CHANGE’.
 The mining industry offers excellent opportunities to
 reduce the peak demand of the country.                  The dynamics in DSM projects make it difficult and
                                                         certainly not preferable to assign any one of the
                                                         stakeholders to deliver an objective assessment of the
 2.    MEASUREMENT AND VERIFICATION                      savings.
       (M&V) OF DSM PROJECTS
                                                         There is a critical problem for those whose
 2.1   DSM PROJECT STAKEHOLDERS AND                      responsibility is to quantify the energy savings as
       STAGES                                            opposed to energy delivered: Savings cannot be
                                                         measured directly.[3]
 The DSM project usually follows the process as
 shown in Figure 1.                                      Proper M&V processes are crucial for the success of
                                                         any DSM load management and energy efficient
                                                         project. However, the cost of M&V might reduce the
                                                         amount of savings resulted from the DSM projects.
                                                         Thus, a proper and cost-effective M&V process is
                                                         needed to identify and quantify the savings that result
                                                         from the DSM projects.


                                                         3.     CASE STUDY

                                                         3.1.   PROBLEM STATEMENT

                                                         The case study is illustrating the implementation of
 Figure 1: DSM Project Stakeholders and Stages           cost –effective measurement and verification process
                                                         in a DSM project in the mining sector.
 2.2   PERFORMANCE            MONITORING          OF     According to ‘M&V Guidelines for Pump Scheduling
       DSM PROJECTS                                      Projects in the Mining Industry’ [1], M&V has the
                                                         function of quantifying and verifying the impacts of
 For any DSM project the following questions are         the above projects. The process is structured in a
 relevant:                                               staged approach with a number of deliverable outputs
that are circulated between the project stakeholders.      To surface filter plant


The major steps are to develop the baseline and the         Pumps 16.3MW                                   2-5
                                                                                                           level
performance assessment as well the savings                                                      Pumps 3    17
calculations.                                                                                   MW         level


                                                            Pumps 3 MW                          Pumps 8    23-60
Periodical recalculations are also done for these steps                                         MW         level


to ensure that the baseline cater for the changing                                                         31
pumping activities on the mine.                                                                            level


                                                            Pumps 15 MW                                     32

Due to the scale and complexity of the project, the                                                         level


M&V option SA-B (direct measurement of the                                                       Pumps 8     39

required parameters) is adopted as recommended by                          Water storage dams    MW          level


‘The M&V Guideline for DSM Projects’ [2], since it
offers the highest possible accuracy for the M&V           Figure 2: Simplified pumping reticulation diagram
process.                                                   The mine’s simplified underground pumping layout is
                                                           shown on Figure 2.
This     process     requires  independent      power
measurements for the pumps involved, as well as
independent communication network that is using            A neural network expert system with learning
optic fibre double armoured cables all the way to the      capabilities is used to optimize, control and sustain
last level at Main Shaft of the mine. Multiple cores       savings. The total load shift target is 6.45 MW. The
should also be used for purposes of optimal reliability    project’s cost is R 10 612 748 and the rand cost per
and elimination of loss of communications down             kW is R 1645.
stream. This is approximately 6000 meters of optic
fibre installed underground.
                                                           3.3             THE PROPOSED SOLUTION
The cost implication of such kind of communication
network and the significant number of individual           The M&V team used low-cost logging equipment to
power meters / analyzers can be economically               verify the working schedule of the pumping load and
unacceptable.                                              also the accuracy of the metering installed by the
                                                           ESCO.

3.2       THE PROJECT’S BACKGROUND
                                                           3.4        BASELINE METERING
The project that has been identified for the cost-
effective      measurement        and      verification    The baseline metering is organized and performed as
implementation is targeting maximum demand control         follows:
through pumping load shifting in a large gold mine in      The metering in the project is done using 41 ‘PM 171’
South Africa. The load shift savings are achieved by       Energy Meters with data logging capabilities,
shifting the peak pumping load, half to off - peak and     installed to each pump individually.
the other half to standard time of use. This will result
in savings of approximately R 2.285 million annually.      The meters are using dedicated software for
The forty one shifted pumping loads in question are        programming and retrieving the logged data. The
located on four different underground levels:              software is provided by the supplier of the meters.
                                                           The measuring period for establishing the baseline is
                                                           for one month – between 1.04.2006 and 30.04.2006.
•     1800 kW at 2-5 level
                                                           The meters / data loggers are permanently installed on
•     1800 kW at 23-60 level
                                                           the pumps concerned, performing the baseline and the
•     1500 kW at 32 level                                  post implementation measurements.
•     1350 kW at 39 level
                                                           The metering interval is a standard half hour one as
                                                           normally used for energy saving projects.


                                                           3.5        INDEPENDENT METERING
                                                                      VERIFICATION

                                                           The independent control measurements for the
                                                           duration of the baseline determination period are
                                                           performed on each pump, using 41 HOBO H06-004-
                                                           02 motor ‘on-off’ data loggers. The loggers are
programmed and downloaded using dedicated                                                                           The above co-efficient shows a close correlation
software ‘Boxcar BCP 4.3-DL’. Since the loggers                                                                     between the shapes of the curves.
only record the ‘on-off’ state of the motors, the ‘on’
state was considered to be the motors rating as it was                                                              It is important to point out that the power assigned to
audited earlier, using installed motor protection                                                                   the HOBO curve is the sum of the pumps’ nominal
relays/meters and the amp-meters installed for each                                                                 power ratings, which may differ from the actual
pump.                                                                                                               power drawn by the pumps as it is registered by the
                                                                                                                    PM 171 meters.
The data were downloaded manually since the
communications underground would be extremely                                                                       To illustrate the trend of power vs. time, fourth order
costly. The data from the loggers was compared to the                                                               polynomials were fitted to both sets of data as
data recorded by the installed PM 171 meters and it                                                                 It is shown on Figure 4.
was established very close co-relation between the
two sets of data. The performance tracking of the                                                                                                   Independent verification
project was done on monthly basis in accordance with                                                                         kW                        Trendline 4th order
the M&V Guidelines.[2]                                                                                                  35000
                                                                                                                        30000
                                                                                                                        25000
The differences in the values for the recorded active                                                                   20000
power can be explained with some differences in the                                                                     15000

loading of the pumps throughout their hours of                                                                          10000
                                                                                                                         5000
operation.                                                                                                                 0                                                                                                    time




                                                                                                                                00:00

                                                                                                                                        02:00
                                                                                                                                                04:00
                                                                                                                                                        06:00
                                                                                                                                                                08:00

                                                                                                                                                                        10:00
                                                                                                                                                                                12:00
                                                                                                                                                                                        14:00

                                                                                                                                                                                                16:00
                                                                                                                                                                                                        18:00

                                                                                                                                                                                                                20:00
                                                                                                                                                                                                                        22:00
It is clear that the data obtained by the PM 171 power
                                                                                                                           PM171                        HOBO                       hobo trendline                           PM171 trendline
meter/data logger is more accurate and the baseline
                                                                                                                                        th
will be built using it after verifying the operation                                                                Figure 4: 4 Order Polynomial trend-lines of HOBO
hours of the pumps by the HOBO loggers.                                                                             and PM 171 data sets

A comparison of the two sets of data is given on                                                                    The modelling polynomials are:
Figure 3.
                                                                                                                    Y1 = -0.0846X14 + 9.0271X13 - 318.2X12 + 4017.7X1
                                                                                                                    + 15700 (HOBO)
                                     Independent verification
         kW
    35000                                                                                                           Y2 = -0.0868X24 + 9.1958X23 - 318.62X22 +
    30000
    25000
                                                                                                                    3903.1X2+ 14143 (PM171)
    20000
    15000                                                                                                           The deviation between the two trend-line curves can
    10000
                                                                                                                    be evaluated throughout the day.
     5000
         0                                                                                                   time
                                                                                                                    It is of a particular interest during the evening peak (
                                                                                                                    18:00 till 20:00 ), where it is:
             00:00

                     02:00

                             04:00
                                     06:00

                                             08:00

                                                     10:00
                                                             12:00

                                                                     14:00

                                                                             16:00
                                                                                     18:00

                                                                                             20:00

                                                                                                     22:00




                                                                                                                           Phobo − PPM 171
                                      PM171                                                  HOBO

Figure 3: Verification of the Measured Data for an                                                                  ΔP =                   .100 = 5.9%
Average Weekday                                                                                                                PPM 171

The correlation between the two sets of data from the                                                               The post-implementation data is based on
PM 171 and HOBO loggers can be compared using                                                                       measurements between 18.02.2007 and 21.03 2007
the correlation co-efficient as follows:                                                                            and can be considered a representative set of data that
                                                                                                                    was gathered for more than a month and reflects a
     cov( x1 , x2 )                                                                                                 time slot outside the holidays season.
ρ=                  = 0.97
      σx1 .σx2                                                                                                      The metering for the post implementation stage does
                                                                                                                    not differ from the baseline metering and uses the
where:                                                                                                              same types and numbers of data loggers on the same
                                                                                                                    locations.
ρ – correlation co-efficient
                                                                                                                    The proposed measurement and verification method is
cov(x1,x2) – co-variance of the two independent                                                                     equally applicable for the baseline development and
variables from the two sets of measurement                                                                          post-implementation verification.

σx1, σx2 – standard deviations for these variables
4.    CONCLUSIONS                                        E-mail    addresses:     dintchev@icon.co.za   or
                                                         dintchevod@tut.ac.za
Based on the independent verification of the M&V
results and their interpretation, the methodology used   Presenter: This paper will be presented by
by the M&V team proved to be successful. The             G.S.Donev.
evaluation showed acceptable correlation both in the
time and power [kW] as it is shown above.

The usage of low-cost, ‘on-off’, field detecting data
loggers allowed the M&V process to be performed at
relatively low-cost.

The methodology used by the M&V team allow to use
the metering equipment installed by the ESCO for the
needs of the client as part of the DSM project. Thus,
considerable expenses and time were avoided without
compromising the independent measurement and
verification process.


5.    REFERENCES

[1]     M&V Guidelines for Pump Scheduling
        Projects in the Mining Industry, Prof LJ
        Grobler and Dr W.L.R den Heijer, School for
        Mechanical and Materials Engineering,
        North-West University.
[2]     The M&V Guideline for DSM Projects’,
        ESKOM CTAD ,2008
[3]     CMVP         International    Performance
        Measurement & Verification Protocol
        Volume 1 DOE/GO-10202002-1554
[4]     www.eskom.co.za


6.    AUTORS

Principal author: G.S. Donev
is a Senior lecturer at the
Department       of    Electrical
Engineering of the Tshwane
University of Technology. His
fields of expertise and interests
are Renewable Energies, Energy
Management, Energy Efficiency,
Measurement & Verification,
E-mail     addresses:    gsdonev@yahoo.com         or
donevgs@tut.ac.za

Co-author: O.D. Dintchev is a
professor at the Department of
Electrical Engineering of the
Tshwane       University     of
Technology.     His fields of
expertise and interests are
Renewable Energies, Sustainable
Energy      Management,      Energy      efficiency,
Measurement & Verification, Rural Development
Projects based on application of sustainable energy
sources.

				
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