A Commentary on the use of GIS to ... - Esri South...
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A COMMENTARY ON THE USE OF GIS TO ENHANCE
THE VISUALIZATION OF DEMAND SIDE
MANAGEMENT PROJECTS IN ESKOM.
Yvonne Steenkamp
University of Salzburg UNIGIS Sub Saharan Africa
AGENDA
OUTLINE
MOTIVATION
METHOD
RESULTS
DISCUSSION
FUTURE WORK
OUTLINE South Africa was facing a power shortage that needed urgent
attention
Building traditional coal fired power stations took too long and
was/is environmentally unfriendly
Demand Response can be described as programs that offer
incentives to customers to curtail their energy usage during peak
times
Problem: Eskom’s DR program
customer and meter data was not
spatially visualized
GIS was selected as the tool that
could be used for capture,
manipulation, analysis and
visualization of the attribute data.
OUTLINE
Attribute
data initially
Data after spatial
visualization with
GIS
Current Situation
Desired Situation
EXAMPLE OF
DEMAND RESPONSE
BEING USED
Energy usage Fast Feedback for saving
energy – Consumers can see their daily
consumption on their tablets and smart
phones and money saved.
90kW saved in 2hrs by using Residential Load
Management (RLM)
Direct Load
Control device
MOTIVATION To establish whether GIS could assist in the identification of areas for Eskom’s Demand Response project and to determine if using GIS as a visualization tool would make peak load analysis easier and more efficient
METHOD
Methodology
Demand Response methods
ULM
AMI
Split metering
Peak load analysis
graphs
WORKFLOW PROCESS USED TO CONVERT TABULAR
DATA TO SPATIAL DATA
RESULTS AND ANALYSIS OF RESULTS
INPUT
DATA GATHERING
Customer and meter
data from CC&B
Cadastral data
Base maps services
from
OpenStreetMaps
Spatial maps served
on internal web
service
OUPUT
GIS analysis using
spatial analysis tools
(geocoding, Add XY
data, Display XY data)
ArcGIS 10.1
METHODOLOGY
Utility Load Management Concept Diagram
DEMAND RESPONSE METHODS
ESKOM UTILITY LOAD MANAGER DATA WHEN
PLOTTED IN ARCGIS
Advanced Metering Infrastructure Diagram
ADVANCED METERING INFRASTRUCTURE (AMI)
DATA DISPLAYED SPATIALLY IN ARCGIS
SPLIT METERING DATA DISPLAYED
SPATIALLY
Data was in the form of stand
numbers and thus Add XY data
GIS tool used.
RESULTS Expected results were
achieved in that;
A visualization and
spatial intelligence
platform was created
DR customers were
mapped and
methodologies identified
Spatial tracking of the
DR project roll-out was
now possible
The graphic view
displayed the underlying
customer database
which could now be built
upon
VISUALIZATION AND SPATIAL
INTELLIGENCE PLATFORM CREATED
DIFFICULTIES ENCOUNTERED
Installation coordinates not falling within erven boundaries
DIFFICULTIES ENCOUNTERED CONTINUED
Lack funding to purchase address
databases
Lack of integration with CC&B database
Data capture errors i.e. the same street
captured with different spellings
Minimal resources, funding and time
Lack of interest from business to push
project to completion in terms of
producing an advanced visualization and
spatial intelligence platform.
DISCUSSION • South Africa’s economy was growing too
rapidly to be satisfied by the current energy
supply and an urgent solution was needed.
• The development of a GIS visualization
platform of the DR program assisted in the
rapid roll out of this solution to ease
pressure on the grid.
• Despite the difficulties encountered during
the implementation of this case study, the
desired outcome of a spatial visualization
platform for the customer data was
achieved. http://172.24.29.173/Apps/DMR/
• This study successfully
proved that using GIS as a
visualization tool helps in
management and
monitoring of DR projects.
• The platform could be
used for high level mapping
such as time-series maps,
peak load analysis as well
as sentiment mapping.
IMPLICATIONS FOR FUTURE
RESEARCH
• The video clip on the next slide is of the current use of Lidar data in
Eskom to create 3D visualization of planned routes. Future studies could
investigate how this kind of GIS visualization can be integrated with DR
and Smart Grid technology.
• DR is one of the first steps towards a Smart Grid. It would be interesting
to investigate what role GIS can play in the implementation of a Smart
Grid especially since a large portion of the population do not make use
of high consumption appliances.
• Predictive analysis is used in Eskom in the control of 3rd party
encroachment, it could also be employed on the visualization platform to
predict areas of potential high peak usage that can be targeted for DR
programs.
REFERENCES image obtained from (Opower, 2015)
How a smart meter works. Image from (ICP, 2014)
ULM System Overview and Generic AMI Components. Images from (Khatri, 2013)
Workflow process adapted from Gouareh et al. (Gouareh, et al., 2015)
ESRI website
https://www.mapcite.com/Images/locationPlatform.jpg
OpenStreetMap Foundation (OSMF), 2012. OpenStreetMap. [Online]
Energy Business Reports, 2007. Energy Efficiency & Demand Response Programs. [Online] Energy Business Reports Available at: www.EnergyBusinessReports.com [Accessed 16 February 2015].
Chotpantarat, S., Konkul, J., Boonkaewwa, S. & Thitimakorn, T., 2015. Groundwater Recharge Potential Using GIS around the Land Development Facilities of Chulalongkorn University at Kaeng Khoi District, Saraburi Province, Thailand. Applied Environmental Research, pp.75-83
Goodchild, M. F., 1987. CIS 87: the Research Agenda,. In: R. T. Aangeenbrug & Y. M. Schiffman, eds. Towards an enumeration and classification of GIS functions. Washington DC: s.n., pp. 67-77.
Gouareh, A. et al., 2015. GIS-based analysis of hydrogen production from geothermal electricity using CO2 as working fluid in Algeria. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, pp.1-10.
Negnevitsky, M. & Wong, K., 2015. Demand response visualization tool for electric. Visualization in Engineering, pp.1-14.
CONTACT:
Yvonne Steenkamp
https://www.linkedin.com/in/yvonne-steenkamp-4796562/