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

steenkyk@eskom.co.za

https://www.linkedin.com/in/yvonne-steenkamp-4796562/