Master’s Thesis
In accordance with the course: “Geographical Information Science and Systems” (UNIGIS MSc) at the Centre for Geoinformatics (Z_GIS) the
University of Salzburg
“A Commentary on the use of GIS to enhance the Visualization of
Demand-Side Management Projects in Eskom”
Submitted by
Ms Yvonne Steenkamp
UP40261, UNIGIS MSc June 2016
For the purpose of obtaining the degree
“Master of Science (Geographical Information Science & Systems) – MSc (GIS)”
Reviewer:
Director: UNIGIS Sub-Saharan Africa: Ann Olivier
Date, 28.06.2016
FOREWORD
“Dwell on the beauty of life. Watch the stars, and see yourself running with them.”
- Marcus Aurelius, Meditations
Acknowledgements
ACKNOWLEDGEMENTS
I would like to thank the Eskom Research, Testing and Development (RTD), as well as ESI-GIS Department for allowing me the use of their pilot project data and information as well as the time that they gave me during interviews, phone calls and emails. I would also like to acknowledge Eskom Transmission Asset Management and Execution for giving me the opportunity to work on this research, as well as providing funding.
I would especially like to thank my never-tiring, always patient and available supervisor, Ann Olivier. Despite the distance, she was always there when I had questions, or when I was just lost in research – to steer me back on track. She never tired of encouraging me.
I would also like to acknowledge the rest of the UNIGIS SA Office Team for their support throughout my studies.
I would like to thank my family, my sisters and my mother, for always being there for me through the good and bad times. They have always encouraged me and supported me – especially through all my health struggles. Without them, I would not have completed my studies. I send them all my love.
Last, but not least, I want to thank my Father, which art in heaven, the Lord Jesus Christ and the Holy Spirit.
Science Pledge
SCIENCE PLEDGE
By my signature below, I certify that my thesis is entirely the result of my own work. I have cited all sources I have used in my thesis; and I have always indicated their origin.
Date: 28 June, 2016 Signature: Yvonne Steenkamp
ABSTRACT
A Commentary on the use of GIS to enhance the Visualization of Demand-Side Management Projects in Eskom
Keywords: Demand Response, GIS, Visualization, Direct Load Control
This study has examined the impact of using GIS as a visualization tool in Demand- Response roll-out monitoring and management, in order to establish whether GIS could assist in the identification of areas to be rolled out, make peak-load analyses easier, and more efficient, as well as offer possible solutions for any gaps identified. A pilot project created to produce a visualization and spatial-intelligence platform, based on a spatially enabled customer database that could be used to identify future roll-out areas for
demand-response solutions was selected as the case study for this study.
South Africa as a country is currently in the middle of a massive energy crisis that threatens to plunge the nation into a complete black-out; and since the construction of power-stations takes more than a decade, immediate measures need to be taken. The biggest South African energy utility, Eskom, carried out an investigation into possible ways to immediately alleviate pressure on the grid; and they opted for Demand Side Management (DSM) – to alleviate the demand – by encouraging consumers to switch off their power-draining appliances and minimize any power-draining activities, especially during peak hours.
One of the projects being implemented is the Demand-Response (DR) initiative. This project makes use of special meters that are mounted on geysers in households of specific areas; and then during peak times, the geysers are switched off remotely by Eskom.
The data and documentation on the project were collected; and interviews were conducted, in order to establish the outcome of the project; as it pertains to GIS
visualization. The created output of a web-based GIS viewer, showing the DR solutions was created by using ArcGIS software to convert the attributed customer-billing data to spatial data; and this was critically examined in terms of the study objectives.
The results of the study show that the GIS viewer does give the demand response data a spatial aspect. This makes it easier to track the roll-out of the DR solutions. A lack of resources to purchase address databases for geocoding did not allow all the DR solutions to be displayed on the viewer.
However, the influence of the GIS viewing application in making peak load analysis easier and more efficient was not practically established at the time. This was due to time and resource constraints.
This does leave a gap for future studies, which could be done in the area of the application of GIS in peak-load analyses.
1 INTRODUCTION ... 15
1.1 Motivation & Background ... 15
1.1.1 GIS as a Visualization Tool ... 15
1.1.2 Demand-Side Management ... 17
1.2 Research Statement & Objectives ... 24
1.3 Methodology & Approach ... 25
1.3.1 The Data Used ... 25
1.3.2 The approach ... 26
1.4 Expected Results ... 26
1.5 Topics Not Covered ... 26
1.6 Target Group ... 27
1.7 Conclusion ... 27
2 THE LITERATURE REVIEW ... 29
2.1 Introduction ... 29
2.2 Smart Grid ... 29
2.3 Demand-Side Management (DSM) ... 31
2.4 GIS Application in Visualization ... 33
2.5 Demand Response ... 37
2.6 Demand-Response Visualization... 40
2.7 Residential-Demand Response ... 41
2.8 Conclusion ... 44
3 MATERIALS AND METHODOLOGY ... 46
3.1 Materials ... 46
3.1.1 AMI Customer Data ... 46
3.1.2 ULM Customers ... 47
3.1.3 Split-Metering Customers ... 48
3.1.4 RMR Customers ... 48
3.1.5 Open-Street Map ... 51
3.1.6 Explorative-Expert Interviews... 53
3.2 Methodology ... 55
3.2.1 Background ... 55
3.2.2 Research Setting ... 56
3.2.3 Data Processing ... 57
3.2.4 Trustworthiness (Reliability and Triangulation) ... 63
3.3 Participants ... 64
3.4 Tools ... 64
3.5 Conclusion ... 65
4 RESULTS ... 67
4.1 Introduction ... 67
4.2 Findings ... 67
4.2.1 Was the provision of a visualization and spatial-intelligence platform showing the current status of the DR rolled out in the specific areas achieved? ... 67
4.2.2 Was the determination of the customers impacted and the most suitable methodologies for each area identified? ... 70
4.2.2 Was there a clear identification of the gaps in the data capturing that could enable the tracking of the data spatially? ... 71
4.2.3 Was a single consolidated geographical overview of the DR solution in implementation achieved? ... 73
4.2.4 Was a baseline method and database, on which to build a spatially enabled customer database to identify those areas suitable for the continued and future roll-out of the DR solution established? (Madhoo & Brits, 2014) ... 73
4.3 Conclusion ... 74
5 ANALYSIS OF THE RESULTS ... 75
5.1 Overview ... 75
5.1.1 Research Question/Hypothesis ... 75
5.1.2. Data ... 76
5.1.3. Methodology ... 78
5.2. Analysis ... 79
5.2.1. Was the provision of a visualization and spatial intelligence platform, showing the current status of the DR rolled out in various areas achieved? ... 79
5.2.2 Was the determination of the customers impacted and the most suitable methodologies for each area identified? ... 81
5.2.5 Was a baseline method and database, on which to build a spatially enabled customer database to identify areas suitable for the continued and future roll-out of the DR solution
established? (Madhoo & Brits, 2014) ... 89
5.3. Conclusion ... 91
6 DISCUSSION... 93
6.1 Reflection on the Research Goals... 93
6.2 Future Work ... 94
REFERENCES ... 96
LIST OF FIGURES
Figure 1 - 1: Example of an Infographic Image from (Futterman, 2013) ... 16
Figure 1 - 3: Fast Feedback for saving – image obtained from (Opower, 2015) ... 19
Figure 1 - 4: How a smart meter works. Image from (ICP, 2014) ... 20
Figure 1 - 5: Generic AMI Components. Image from (Khatri, 2013) ... 21
Figure 1 - 6: ULM System Overview. Image from (Khatri, 2013) ... 22
Figure 1 - 7: Residential Demand-Response Framework. Image from (Khatri, 2013) ... 22
Figure 1 - 8: Average weekday-demand profile (Baseline vs Actual) – April 2014. Image from (Eskom, 2014) ... 23
Figure 2 - 1: A view of the utility information systems impacted by smart-grid strategies. Image from Ipakchi and Albuyeh (2009) ... 30
Figure 2 - 3: Average daily electricity cost minimization of a single user. Image from (Khan et al., 2015) ... 33
Figure 2 - 4: Shows the methodology used with GIS to produce the maps in the paper by Gouareh et al. Image from Gouareh et al. (2015) ... 34
Figure 2 - 5: Groundwater recharge potentiality map, as seen in Chotpantarat et al. study. Image from (Chotpantarat et al., 2015) ... 36
Figure 2 - 6: Installation co-ordinates not falling within erven boundaries. Image from Madhoo & Brits, 2014) ... 36
Figures 2 - 7: A Direct Load-Control Device connected to a residential DB. Photo of personal home installation taken by Yvonne Steenkamp. ... 39
Figure 2 - 8: Time of Use. Image from (Energy Business Reports, 2007) ... 40
Figure 3 - 1: View of Paulshof suburb standard map, as seen online using OpenStreetMaps (OpenStreetMap Foundation (OSMF), 2012) ... 52
Figure 3 - 2: Same area, but showing the cycle routes as another option of base maps in OSM (OpenStreetMap Foundation (OSMF), 2012) ... 53
Figure 3 - 3: The Hyperwave graphical-user interface ... 55
Figure 3 - 4: AMI data of retirement village accessed through a web service in ArcGIS ... 58
Figure 3 - 5: AMI retirement village CC&B data without the estate lay-out plans ... 59
Figure 3 - 6: AMI data in KZN as provided by CC&B ... 60
Figure 3 - 7: ULM data in Gauteng as provided by CC&B ... 61
Figure 3 - 8: Split meters’ data plotted in Soweto Township on the formal settlement side... 62
Figure 3 - 9: Polygon centroids generated in ArcGIS to create the point layer for split meters . 62 Figure 3 - 10: Triangulation method. Image sourced from (Bowen, 2005) ... 63
Figure 3 - 11: Web service created for the Eskom Demand-Response project using OpenStreetMaps. Image from (Eskom, 2016) ... 65 Figure 4 - 1: A visualization and Spatial Intelligence platform showing the status of the AMI
Figure 4 - 3: A visualization and Spatial-Intelligence platform, showing the status of the split
metering roll-out ... 70
Figure 4 - 4: KZN stand-alone houses, as shown on the web service ... 71
Figure 4 - 5: Data outliers in the split-metering dataset ... 72
Figure 4 - 6: Cluster of those points that do not sit in the demarcated erven ... 72
Figure 4 - 7: Showing all the AMI, ULM and Split-metering point data, in Gauteng area, as plotted in a consolidated view on the GIS viewing platform. ... 73
Figure 5 - 1: Workflow process, adapted from Goureh et al. (Gouareh, et al., 2015) ... 79
Figure 5 - 2: The AMI data mapped to individual customers in a complex ... 82
Figure 5 - 3: Solitary points mapped, as part of the project from AMI data ... 83
Figure 5 - 4: Data outliers mapped as part of the project from the Split-Metering data ... 84
Figure 5 - 5: Random points falling outside the SA boundary from the ULM data ... 84
Figure 5 - 6: No meaningful attributed data discovered for the random points in Figure 5 - 5.. 85
Figure 5 - 7: Solitary points falling on the other side of the highway from the data cluster of the ULM data ... 85
Figure 5 - 8: South African electricity-demand patterns ... 88
Figure 5 - 9: Showing Peak, Standard and Off-peak time slots during the week ... 88
LIST OF TABLES
Table 1 - 1: Implemented Pilot Project (Eskom, 2014) ... 24
Table 2 - 1: Evaluation of Rates and weights of the factors and their contributing classes used for evaluating the groundwater recharge potential in Chotpantarat et al. study (Chotpantarat et al., 2015) ... 35
LIST OF ACRONYMS & ABBREVIATIONS
GIS Geographic Information Systems DSM Demand Side Management IDM Integrated Demand Management
DR Demand Response
CC&B Customer Care and Billing LSM Living Standard Measurement AMI Advanced Metering Infrastructure ULM Utility Load Manager
RMR Residential Mass Roll-out TOU Time-of-Use
DLC Direct Load Control OCGT Open-Cycle Gas Turbine RTP Real-Time Pricing EBR Energy-Business Reports RDR Residential Demand Response
ERTD Eskom Research, Testing and Development EGC Eskom Group Capital
OSM OpenStreetMap ESI-GIS Eskom Enterprise GIS
NGI National Geo-spatial Information
DRDLR Department of Rural Development and Land Reform SBC Spot-Building Count
POI Points of Interest
Introduction
1 INTRODUCTION
1.1 Motivation and Background
1.1.1 GIS as a Visualization Tool
“`Visualization' is a broad term that refers to an array of methods that are used to provide insight into the data through visual representations; and it includes the areas of geographic, information, and scientific visualization, which refer to the visual representation and exploration of the geographical data, of non-numeric datasets, and of large, multivariate datasets that use high-end computing, respectively” (Knigge & Cope, 2006).
According to the International Journal of Health Geographics, there are four main types of visualization outputs; these are: maps, graphs, such as histograms and box plots, tables and time plots. A map can be used to display spatial information; and the user can manipulate this by performing simple analysis, such as the selection for a complex analysis, such as buffering and 3D analysis, as well as creating animated GIS (Geographical Information Systems) map sequences. A histogram can graphically represent the changing value of a particular
characteristic over time; while a table would have certain values per row that would give a representation of the changes of an attribute over space or time. Finally, the time plots can be used to map a characteristic into a bivariate plot, in order to analyse the time dependencies of that particular characteristic (AvRuskin, et al., 2004).
However, more recently, it has been proven that data can be visualized in many other
different ways from those specified by the International Journal of Health Geographics, such as, in the form of a photograph, or an infogram, an example of which is given in Figure 1 - 1.These are all forms in which the data can be represented in a graphical or visual manner.
What sets GIS visualization apart is the fact that the data represented using GIS shows where the object/feature occurs on the earth’s surface, or to be more accurate in space; and thus, the term “spatial” representation is used.
Figure 1 - 1:Example of an Infographic Image from Futterman (2013)
Everything that occupies space on earth and even some distance above the earth’s surface, or below it for that matter, can be represented with a pair of co-ordinates, either in latitude and longitude, or in X and Y projected co-ordinates. These are known as 2D co-ordinates or two- dimensional co-ordinates. Points occurring above or below the earth’s surface are represented with 3D or three-dimensional co-ordinates, i.e. X, Y and Z. The Z co-ordinate measures how far above or below the earth’s surface the point being visualized is.
The absence of education in the field of data visualization using GIS may be a contributing factor to the lack of use of this technique, in order to communicate data in an easy to
understand manner (Wuerzer & Mallow, 2012). Visualization through the use of maps is an
Introduction
important tool that is available in GIS through the use of GIS software packages, such as ArcGIS, MapInfo, QGIS, GeoMedia and many more. Just as the visualization of data can enable management to make decisions concerning resource management; it can also assist planners to recognise the trends in the data and the analytical patterns that might allow them to better predict where to expand the infrastructure by building roads, or schools in
anticipation of population growth or migration. Following this thought pattern, visualization of areas that put the most pressure on a utility grid at a specified time period can be mapped – given the peak-load data, as well as the customer-location information.
1.1.2 Demand-Side Management
The world is ploughing through energy quicker than it can produce it. Consequently, the world’s natural energy sources are being depleted quickly; and better energy management must be applied. One of the ways to manage energy consumption is to encourage consumers to participate in managing their own consumption – by watching how much energy or power they consume on a monthly, weekly, daily or even on an hourly basis. This is known as Demand-Side Management (DSM); and it is the first step towards the implementation of a Smart Grid. A Smart Grid is one that automatically balances the pressure on the grid by switching off certain appliances in households during peak times. Peak times are the times when the most electricity is used; because the majority of the customers are carrying out energy-intensive activities at the same time.
This is usually in the mornings between 06:00hrs and 08:00hrs, when people are taking baths or showers, preparing food for kids and breakfast; as they rush to work; and also between 18:00hrs and 20:00hrs at night, when people arrive home from work and prepare their evening meals, watch TV and take baths or showers. At this time, when there is the most pressure on the electricity grid; because the transmission lines are working at full capacity and the generation plant is also producing at maximum capacity. At this point, if there is any fault at a generation power station, it might cause the entire grid to trip.
Utilities are increasingly focused on the conservation and management of energy demand.
Many markets and jurisdictions want to reduce carbon dioxide emissions, increase the use of renewable energy, and decrease energy consumption and demand. To achieve these goals, utilities need a much greater influence over customers’ energy efficiency, which is a function of how much energy customers use; and when they use it, through energy efficiency and demand-response programs. Utilities have now taken to investing more in DSM programs, like energy efficiency (EE) and demand-response (DR) initiatives. DR programs target the reduction of demand during peak loads, so as to protect the grid; but they can also be used to control energy costs by reducing the amount of energy bought at the high-cost times, as well as modifying the demand for the energy currently available.
EE programs target the reduction of electricity wasted, as well as to save the consumers money by lowering energy costs (SAP, 2011)
‘According to the US Federal Energy Regulatory Commission, Demand Response (DR) is defined as: “Changes in electricity usage by end-use customers from their normal
consumption patterns – in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity usage at times of high wholesale market prices, or when the system’s reliability is jeopardized” (Khatri, 2013).
An interesting case study of Opower’s (Opower, 2015) approach to demand response is cited here. Their DR program’s mandate was to drive peak reduction from 100% of their customers by making use of behavioural response. They send a postcard to an individual encouraging them to work with their neighbours to reduce energy usage during the peak seasons. On a day when peak usage is anticipated, the energy company sends the individual an sms encouraging them to reduce their usage, along with their neighbours, during a specific time-window e.g.
Wednesday between 14:00hrs and 19:00hrs. On Wednesday, the customer then decides to turn off the geyser before leaving for work and to postpone the laundry day until the weekend; so as to reduce the electricity consumption in their house. On Thursday morning, the utility sends the consumer an email thanking them and detailing how much energy they have conserved, as well as how their neighbours did.
Figure 1 - 2 shows an example of the feedback the customer receives from the utility company via email the following day. As the consumer participates in more power-saving initiatives over the season, they receive a summary showing them their improving
performance. The summary would also indicate how much money the consumer has saved on their bills; and it also suggests ways in which they can save even more money – by switching their bill to a “Time-of-Use” rate (Opower, 2015).
By mixing data from smart meters with behavioural science, it is possible for utility companies to answer this key question: How can we get people to care? They have proven that by showing someone how, when and how much electricity they use; and what their neighbours or peers use. They are then spurred on to change and do better. Even without the use of smart meters, electricity consumer engagement companies have found that comparing customers’ electricity bills with that of their neighbours is an effective way to get them to take action (Levitan, 2013).
Introduction
Figure 1 - 2:Fast Feedback for saving – image obtained from (Opower, 2015).
“Smart meters can let homeowners see a remarkably detailed picture of their home-energy use, from which appliances that are using a lot of power – to what times of day are the most expensive, in which to use electricity. Smart appliances of which fewer are in homes, can also connect to these systems and allow, say, a dishwasher to run at 3 a.m., when the power
demand and the prices are low” (Levitan, 2013). An example of how a Smart Meter works is shown in Figure 1 - 3 below.
Figure 1 - 3:How a smart meter works. Image from (ICP, 2014)
Because of the serious capacity shortage that Eskom experienced in 2008 – leading to rolling black-outs, Eskom commissioned a research report to look into Residential Demand
Response (RDR) as a cost-efficient alternative to reduce pressure on the grid, compared with sourcing new capacity. According to the report by Amal Khatri (Khatri, 2013), programs like Demand Market Participation (DMP) were being offered by Eskom to their large industrial power users; but since the bulk of the peak load came from residential users, it was
imperative for Eskom to investigate RDR as an alternative solution. The experimental projects that Eskom embarked on included: ripple control, Homeflex TOU tariff, Advanced Metering Infrastructure (AMI) and Utility-Load Manager (ULM). The goals of these projects included:
Shifting residential peak loads by reducing morning and evening peak loads;
Promoting customer-behaviour change by using incentives to encourage customers to become more energy conscious in their use of power;
Improving customer service by improving operational efficiencies, which would be achieved by reducing customer electricity costs – due to getting more accurate meter readings instead of relying on reading estimates.
Introduction
Developing a standardized solution that can be adopted as the basis for residential metering;
With the projected cost of meeting residential load requirements being taken into
consideration, it became obvious that the implementation of RDR (Figures 1 - 6) was a more sustainable and cost-effective option than meeting the supply by using generation capacity (Khatri, 2013). And so, the Demand Response project was introduced as an option; and it covers AMI (Figures 1 - 4), ULM (Figures 1 - 5) and Residential-Mass Roll-out (RMR).
Below are shown the basic components of the different systems mentioned here.
Figures 1 - 4:Generic AMI Components. Image from (Khatri, 2013)
Figures 1 - 5:ULM System Overview. Image from (Khatri, 2013)
Figures 1 - 6:Residential Demand Response Framework. Image from (Khatri, 2013)
Future Present
Residential Demand Response Framework
Dynamic interactive pricing with
customer discretion DLC/TOU
Set & Forget Limited Customer discretion
Utility Centric Centralist Command
& Control
Customer Centric Degree of
customer participation
Customer low risk Customer high risk
Smart Meters
Introduction
Eskom has embarked on one such DR Project, known as the Demand-Response project. This will involve the placing of smart meters mounted on geyser units in specific areas. During peak times, these geysers are switched off for 2 hours, thus stabilizing the grid. This interruption in power to the geysers should not have an effect on the customer’s electricity consumption. Since it is limited to 2 hours, the customer should still have enough hot water during that period.
Figures 1 - 7:Average weekday demand profile (Baseline vs Actual) – April 2014. Image from (Eskom, 2014)
Looking at Figures 1 - 7, it can be observed that the estimated peak demand for the two hours between 18:00hrs and 20:00hrs has significantly reduced from an expected amount of 120 000kW/30 minutes to 90 000kW/30 minutes. This is a saving of 30 000kW in 30
minutes; and in 2hrs this amounts a saving of 120 000kW. All this was achieved by remotely switching off the geysers for 2hrs. This is an example of the performance profile of the Residential Load Management (RLM) project. The contents of Table 1 - 1 show some of the project areas, where this has been implemented; and the total savings in MW that have been observed.
Table 1 - 1: Implemented Pilot Project (Eskom, 2014)
Project number Municipality
Technology/
Communication Type
Contracted MW
2006027 Oudtshoorn-HWLC-Radio Radio 2.20
2002052 Worcester Load Control (Breedevallei) Radio 9.00
2005075 Swellendam-HWLC-Ripple Ripple 0.57
2005156 Swartland-HWLC-Radio Radio 1.22
2005163 Hessequa-HWLC-Radio Radio 1.57
2005168 Khara Hais Local Municipality Ripple 1.96
2006078 Table View-HWLC-Ripple - PHASE I Ripple 2.00
2006108 George-HWLC-Ripple Ripple 3.24
2006161 De Zalze – HWLC Ripple 0.16
2005161 (a) Saldanha Bay HWLC-Radio Radio 4.16
2011017 Mossel Bay Municipality - Ripple Ripple 4.69
2011029 Overstrand HWLC- Ripple Ripple 3.16
1.2 Research Statement and Objectives
The primary goal of this thesis is to study how GIS can be integrated in the application of Demand-Side Management, particularly in the Demand-Response initiative project being piloted in Eskom, in order to be able to visualize the data.
The DR project makes use of special meters that are mounted onto geysers in households of specific pilot areas; and then during peak times, the geysers are switched off remotely by Eskom for a period of 2 hours. This is done in a way that does not impact the consumer. This research attempts to investigate the results of how GIS is now to be integrated into this project, in order to enable the visualization of the customers’ data, in order to more effectively plan, manage and audit the future roll-out of the DR initiatives to other areas.
The successful integration of GIS as a visualization tool for the Demand-Response initiative should result in the following:
The provision of a visualization and spatial-intelligence platform, showing the current status of the DR roll-out areas
Introduction
Determination of the customers impacted and the most suitable methodologies for each area identified.
A clear identification of the gaps in data capturing that can enable the tracking of the data spatially.
Give a single consolidated geographical overview of the DR solution in its implementation.
Establish a baseline method and database, on which to build a spatially enabled customer database to identify areas suitable for the continued and future roll-out of the DR solution (Madhoo & Brits, 2014).
The research will, upon examination of the above-mentioned outcomes, endeavour:
To establish whether GIS can assist in the identification of areas for Eskom’s Demand-Response project;
To determine whether using GIS as a visualization tool makes peak-load analysis easier and more efficient;
To investigate possible solutions for gaps identified, when applying GIS as a visualization tool in the Demand-Response project.
1.3 Methodology and Approach
1.3.1 The Data Used
The output will be a study based on the methodologies implemented in the project, which are as follows:
Obtain source data for the Eskom customers listed below, i.e. a Customer Care and Billing (CC&B) extract of all Eskom customers; and categorise these Eskom customers, according to the applicable Living Standard Measurement (LSM) groups.
Map AMI, ULM and RMR source data (obtained by the method outlined in point above) to produce a graphical representation of the DR impacted customers. This can be in the form of a web service that would be managed by the GIS department, but to which the users will have access through desktop GIS applications, such as ArcGIS Explorer or ArcGIS Reader.
Maintenance and management of data will be done by using a spatial database with an automated process implemented to get the updated data from CC&B and a monthly Q&A (Quality and Assurance) process put in place (Madhoo and Brits, 2014).
1.3.2 Approach
This research will be proving the methods used, and the results obtained in the report; by using mainly deductive reasoning, and to a lesser extent inductive reasoning. There are three common approaches to scientific reasoning: deductive, inductive and abductive reasoning.
In deductive reasoning, the scientific case is concluded from general theory, such as the theory that: (A) geographical data have x and y co-ordinates; (B) the location of houses comprises the geographical data; and (C) my house has x and y co-ordinates. Thus, the theory states that since geographical data have x and y co-ordinates; and my home location is
geographical data; then the conclusion is that my home has x and y co-ordinates, which is true. It must, however, be pointed out that deductive reasoning can sometimes be flawed (Wallentin, 2012).
Deductive reasoning will be applied to ascertain whether indeed the use of GIS as a visualization tool would assist in the identification of the areas for DR roll-out. This is because a deduction will be made from observation of the results in the report; as to whether the GIS visualization objectives were indeed obtained.
In inductive reasoning, the theory is built up by repeatedly observing a phenomenon in specific situations. An example could be: (A) This polar sea-gull lays eggs on a rock; (B) this polar seagull lays eggs on a rock, (C) … (Z). Conclusion: Polar seagulls lay eggs on rocks (Wallentin, 2012). In this form of reasoning, the theory is true until such time that it can be proven false. This is somewhat like when everyone believed the earth was flat – until
someone sailed over the horizon (the world’s edge); and he did not fall off, thus proving that the earth was not flat.
Inductive reasoning will be used to identify the gaps, where the assumption that GIS does make the planning, managing and rolling out of the DR project more effective, is proven wrong. This is because it may not be possible to actually implement the suggested solutions;
but by observing them work in other similar cases, the solutions can be reasonably deduced.
1.4 Expected Results
The intended result of this thesis is to establish that indeed GIS can assist, and has assisted, in the identification of areas for Eskom’s Demand-Response project. And also that GIS as a visualization tool, makes peak-load analysis easier and more efficient in the DR project.
Finally, the study will identify the gaps, when applying GIS as a visualization tool to the Demand Response project; and it will attempt to offerpossible solutions for these gaps.
1.5 Topics Not Covered
Introduction
This study concentrates primarily on the application of GIS as a visualization tool in the DR initiative that has been undertaken by Eskom; and even though this is a step that is necessary in the application of a Smart Grid in the future; this does not really fall within the scope of this study.
1.6 Target Group
This thesis research is a commentary of the project that is currently being carried out by Eskom to identify those areas, where the demand response (DR) initiatives have been tested, and rolled out – and to furthermore identify future areas that can be rolled out.
And as such; it would be useful to any Eskom personnel that are working in the realm of Demand-Response Management, especially the data custodian of the operational data, the business owners and the system’s architecture within Eskom; but also to the wider field of Independent Power Producers, who would be interested to know how their generated power can be plugged into such a system.
In general, Geographic Information Scientists would also find the methodologies of approach useful in their studies and the enhancement of their work experience.
Affected consumers might also find the study useful; as it might affect them to a degree; but this could mainly for interest’s sake.
1.7 Conclusion
In this chapter, we have introduced the concept of GIS as a visualization tool, by referring to its use in the past decade, to visualize the data in the form of maps, tables or 3D images. This is to provide the foundation for the DR project’s choice of using GIS to visualize the data, as opposed to other forms of visualization. Since the project is location-based and GIS deals with spatial data; it was quickly realized that GIS would be the best visualization tool for this particular case, according to the research carried out by Amal Khatri (Khatri, 2013).
Demand-Side Management was also explained by giving a background of where the concept of saving energy by changing consumer behaviour has already been introduced. This has been accepted worldwide, as a more sustainable way of meeting demand. This could be seen from the Opower case study (Opower, 2015) and the references to various other studies.
DSM is a much cheaper alternative than continuously building new power stations to meet demand. There are many complex aspects of DSM that could be examined here; but in this
study, the focus will be on Demand Response, and specifically on how visualization of the data by using GIS could improve the management and efficiency of the DR project in Eskom.
Will this application of GIS be able to improve the project performance; and thus save Eskom energy and finances? Will GIS provide an easy-to-use platform for the planning,
management, visualization and audit of new roll-outs? This remains to be seen, as we continue our study.
In the following chapter, a closer examination will be made of what has been done in the past, and in other countries.
Literature Review
2 THE LITERATURE REVIEW
2.1 Introduction
“Energy demand is determined primarily by population growth, industry and geographical distribution; whereas the number of people that can be supported at an acceptable quality of life relies heavily on the availability, costs and efficiency with which energy is produced”
(Ames & Solan, 2015).
Currently, in South, Africa the demand for energy is growing faster than the country can supply it; and the cost of producing this energy has become too high for the country to sustain it. More efficient and sustainable methods of managing the use of available capacity need to be implemented, in order to meet the demand. Demand-side management is one of the solutions that are being considered – and in particular, demand response from the consumer side – as a short-term solution to the reduction of load on the grid, especially during peak times. In order to fully understand the implications of using DR as a solution, Eskom has embarked on a DR project that has been proven to work by curtailing some of the demand by the use of direct load control, especially with respect to geyser control.
GIS is a powerful tool that can be used to visualize the data in this project. A thorough investigation of what has been done in the area of DR and GIS visualization has to be done, in order to avoid re-inventing the wheel in this area; and therefore, past research was gathered and the findings are documented here.
Since DR is a stepping stone to smart grid, a little background information on smart grids was included, and thereafter the application of DSM globally. Finally, a critical look into DR and GIS visualization was done, and compared with the Eskom DR Project; after which various conclusions were then drawn.
2.2 Smart Grid
Due to the rising issues of climate change and the stripping of the earth’s natural resources, the traditional way in which power has been produced is no longer sustainable. In order to cater for the rapidly increasing demand for power – especially in the upcoming economies of developing countries, such as China and South Africa, smart grids have been hailed as the power-delivery model of the future. Building large remote power stations by making use of fossil fuels with central dispatch via long transmission lines to transmission substations and
finally distributed to load centres, is being set aside in favour of the smart-grid approach (Ipakchi & Albuyeh, 2009). The smart grid makes use of clean energy, such as solar and wind, where solar can be localized at the load and generated energy can be injected into the grid, giving the consumer some control over how much power they consume, and how much they plug back into the grid. In order for the smart grid to be effective, the different utility information systems need to be integrated, such as Billing Systems and Asset Management.
This is best illustrated in Figure 2 - 1 below. Customers can also inject power into the grid with solar generation.
Figure 2 - 1:A view of the utility-information systems impacted by smart-grid strategies. Image from (Ipakchi &
Albuyeh, 2009)
The smart grid encourages demand-side management by allowing decentralized generation, as well as power storage from the customers, power sources being closer to the loads, and thereby reducing line losses; and an integrated information system is used to monitor generation through transmission and distribution (Ipakchi & Albuyeh, 2009). A smart grid, when implemented, can lead to massive savings in power losses and financial gain – both for the utility companies and the consumer. However, South Africa is not quite smart-grid ready;
as the infrastructure is not yet in place, as well as the technology – not to mention the cost.
Nevertheless, DSM is a good start in the direction of an integrated-smart grid; and South Africa has already started putting some DSM solutions in place.
Literature Review
2.3 Demand-Side Management (DSM)
As the world rushes towards renewable energy generation, the reality is that the building of these generation plants does not come cheap; as they are cost- and land-intensive; and they also take time to construct. Meanwhile, more and more countries are finding that they have power shortages leading to black-outs and forced outages. This means that an immediate solution is needed to reduce the demand and to decrease the pressure on the grid, especially during peak times. It is a known fact that extra peaking generation is built solely for use during peak times, such as Open Cycle Gas Turbine (OCGT) in South Africa. This type of generation does not come cheaply; since it uses fuel as the ignition agent; and the combustion thereof is not environmentally friendly.
One of the solutions to this problem is demand-side management, which encourages consumers to use electricity during off-peak times, and sparingly during peak times. In so doing, the demand on the grid is reduced during peak times, bringing stability to the grid; and the consumers are able to manage their electricity use – sometimes even bringing down their bills.
“Demand-Side Management is a proven way to meet economic goals in an environmentally sustainable way” (Hu, 2005).
According to Z. Hu et al., China has low reliability of the network; and this is mainly due to the growth rate of the economy; whereas South Africa’s problem is compounded by the ageing infrastructure, as well as the rapid economic growth, due to the expanding black middle class and the massive electrification push to the rural areas. Another significant difference between the Chinese situation and the South African situation, is the size of the grid shortages incurred, with a total demand of 220 terawatt-hours by 2020. And multiple power stations are needed to bridge the gap between supply and demand in China. South Africa’s consumption peaked in 2011 to 262 538 GWh, according to Statistics South Africa, thus only requiring the additional capacity of a couple of large capacity power stations, in order to meet the current power shortage; whereas the solutions used in China would need an average of 200 new power plants to keep up with demand.
The most efficient energy-saving strategies that were applied during the DSM programs used in California during the 2001 power shortages, included “appliance efficiency, building codes and utility efficiency programs” (Hu, 2005). The DSM efficiency programs in place locally are concerned more with appliance efficiency and utility efficiency. Not much research has gone into building codes, as a way to decrease demand. Consideration should be given to include building codes as part of the mix for long-term DSM solutions for future building
designs in South Africa.
Demand bidding is another aspect of DSM that has not been embraced in the local situation.
This could be due to the fact that there are not many sellers of electricity at the moment; and the platform for this is yet to become available. Demand bidding involves the willingness of the demand side to buy electricity. This is achieved through bids by buyers and sellers; and sometimes advanced bids from buyers are used (Hu, 2005).
According to Khan et al., DSM can be applied by the use of direct load control (DLC), where the power utility is the one in control, when the appliances are used in the homes, as a form of demand response (DR) (Khan et al., 2015). The other way DSM can be applied is through real-time pricing (RTP) and time-of-use (TOU) pricing. But Khan et al. maintain that the DLC is only effective with users that have high-powered consumption appliances; and this has barely any effect on users having low power consumption appliances.
In this study, the DLC approach to DR has been implemented in the local context; and good results have been recorded with the sampled households with geysers. However, in the light of the studies cited in the paper of Khan et al., this approach would not work to control demand in the lower-income households without high-power consumption appliances. The incentive-based DSM would work better in that situation, encouraging households to save money by the implementation of time-of-use pricing. However, this is not covered in the focus report of this paper; and it might be a useful future topic of research. In their paper, Khan et al. develop a Generic DSM model (G-DSM), based on an energy-management control unit (EMCU) that determines the optimum starting and stopping time for any given appliances, in order to obtain the best pricing, while respecting the power constraints, as illustrated in Figure 2 - 2 (Khan et al., 2015).
This algorithm is extremely useful in demonstrating savings made to the grid, as well as costs for the consumer in graphical format; but it would be even more powerful if integrated with a geographical visualization tool, such as GIS. Themes could be picked up linking location to load-management optimization with the G-DSM, and a lot more information derived for the DSM project.
Literature Review
Figure 2 - 2:Average daily electricity cost minimization of a single user. Image from (Khan et al., 2015)
2.4 GIS Application in Visualization
A geographical information system (GIS) is a very powerful application that can be used for visualization purposes. It was selected in this thesis; because it can be used to clearly indicate the geographical location of the suitable areas for the Eskom Demand Response solution to be implemented, as well as where the solution has already been rolled out, and for many other uses. By examining the paper on “Using GIS analytics and social preference data to evaluate the utility-scale solar power site suitability”, it is clear that GIS visualization also brings down the cost of planning (Ames & Solan, 2015).
The many tools that are available in the ArcGIS software by ESRI, can be used to produce a raster map that takes into account the distances to roads, rivers and power lines, as well as slope and solar irradiance, in order to find suitable solar sites. The additional fact is that social sentiment for or against solar photo-voltaic (PV) can be mapped and overlaid on the site-selection map, in order to form a complete picture for developers to consider. This demonstrates how powerful a tool GIS can be for visualization purposes.
Ames and Solan make use of a PV Mapper, which is an open-source GIS program that is web-based; and it can be used for mapping, modelling and analysis. Since it is open-source, it would be a much cheaper option to use instead of ArcGIS Server to serve the maps on the web. The PV Mapper option is worth investigating; as it has only been recently highlighted this year, in September 2015, when the paper of Ames and Solan was published (Ames &
Solan, 2015).
In the paper by Gouareh et al., GIS is applied in the visualization of maps, showing the areas of highest CO2 emissions, as well as the most suitable locations for the geothermal-heat extraction processes (Gouareh et al., 2015).
Figure 2 - 3:Shows the methodology used with GIS to produce the maps in the paper by Gouareh et al. Image from (Gouareh et al., 2015)
Similarly, this study will endeavour to confirm that the application of GIS, as used in Figure 2 - 3, will be used to establish a customer database, as well as a visualization platform that will show the areas that are most suitable for the roll-out of the DR project. Although the input layers will be different using statistical data as well as power usage data, the
methodology will be very similar; and the planned outcome should be maps of a similar nature. This will, in turn, be able to save time and money – in terms of rolling out to areas that would not produce the maximum power-saving impact on the grid.
A dynamic database should be created from these data that can be accessed via ArcReader; as was proposed in the focus report by Brits & Madhoo (Madhoo & Brits, 2014). Considering the paper by Gouareh et al., this seems to be a reasonable assumption.
Reviewing the paper by Knigge and Cope, one is able to note that at the time of publication, the visualization capabilities in ArcGIS were limited; and other software programs, like Atlas.ti were used to create statistical visualization, such as scatter plots (Knigge & Cope, 2006). However, if we consider the paper by Chotpantarat et al., on a similar topic, using GIS in the visualization of Groundwater recharge potential, it is clear that there have been huge
Literature Review
advances made in ArcGIS in the last 9 years; as is evidenced in the methodology section of the paper (Chotpantarat et al., 2015). ArcGIS was used to generate thematic layers from the statistical data in the case of surface-area drainage; and spatial analysis was employed to incorporate the sum of the weighted factors to the groundwater recharge potentiality map.
This is further illustrated by Table 2 - 1 and Figure 2 - 4 below.
Table 2 - 1: Evaluation of Rates and weights of the factors and their contributing classes used for evaluating the groundwater-recharge potential in the study of Chotpantarat et al. (Chotpantarat et al., 2015)
Figure 2 - 4:Groundwater recharge potentiality map, as seen in the study of Chotpantarat et al. Image from (Chotpantarat et al., 2015)
In the paper of Chotpantarat et al., the point-based estimation methods were abandoned in favour of distribution models because point-based estimation is more time- and cost-
demanding. It might be worth considering that the distribution models in the data-gathering part of the Eskom DR project, in order to save on time and cost; as it has already been observed that some of the point data collected during the project do not even fall in the correct location sometimes, as shown in Figure 2 - 5 below from the DR project’s Utility Load Manager (ULM) conducted by Eskom (Madhoo & Brits, 2014)
Figure 2 - 5:Installation coordinates not falling within erven boundaries. Image from (Madhoo & Brits, 2014)
For instance, a DR area-potentiality map could be created in ArcGIS, using statistical data indicating the number and positioning of households with water heaters/geysers, as
recommended in the paper by Khan et al., overlaid with population-density data and load demand data (Khan et al.). This map would show where the DR project would make a larger impact on the reduction of demand on the grid during peak times. A weighting and rating system can also be incorporated in these layers, depending on which factors contribute most to the demand on the power grid of the three suggested layers above. This approach can be used, instead of sending out resources to pick up households, as point data and risking incorrect co-ordinate data; and thus also wasting valuable financial resources and time (Chotpantarat et al., 2015).
Literature Review
2.5 Demand Response
Demand response (DR) can be described as programs that offer incentives to customers to curtail their energy usage during times of peak demand (Ozturk et al., 2013).
“Demand response can provide competitive pressure to reduce wholesale power prices; to increase the awareness of energy usage; to provide for more efficient operation of the
markets; to mitigate market power; to enhance the reliability; and in combination with certain new technologies, to support the use of renewable energy resources, distributed generation, and advanced metering. Thus, enabling demand-side resources, as well as supply-side resources, improves the economic operation of electric power markets by aligning prices more closely with the value customers place on electric power” (Ipakchi & Albuyeh, 2009).
According to Ipakchi and Albuyeh, DR has the added advantage of bringing the price of electricity down to a more competitive level, thereby giving the consumers more value for their money, in addition to the reduction of their bill brought about by the reduced spending during peak times.
In this study more emphasis is put on the curtailing of peak-load demand, and not so much bringing down the price of electricity. In actual fact, the price of electricity in South Africa at the moment is going up; and although this is due to other mitigating factors, it is interesting to note that this is the direct opposite of what Ipakchi and Albuyeh claim.
The paper by Chelmis et al. seeks to analyze the effects of different methods of selecting baselines for DR consumption-reduction measurements and the errors that might occur if the wrong baseline is used (Chelmis et al., 2015). The outcome of the study shows that the curtailment of power consumption is dependent on the baseline used; and that more effort must be used in researching an accurate baseline. In fact, Chelmis et al. suggest that instead of using estimates of what the peak consumption would have been without DR, and then calculating the difference, it would be more beneficial to use computational methods that make use of past events to predict the future load saved.
This would also enable a proper comparison of the actual peak load pre-DR to the peak load post-DR, thereby giving a more accurate performance evaluation (Chelmis et al., 2015). This is, however, based on a study done on University buildings and not residential dwellings; and thus, it does not necessarily ensure that the results would be the same; although the likely hood is quite high. In the case of a country that has no DR history, it would be difficult to apply the use of past events to establish a baseline; so perhaps the predicted future method could be used first; and after the results of a pilot project; thus history is created and can be used.
DR is often described as a part of energy efficiency; because it is usually assumed that it saves energy. However, according to the report by Energy Efficiency Business Reports (Energy Business Reports, 2007), energy efficiency saves energy; while DR only changes the time that the energy is used by shifting it or storing it, and then using it in a different time slot. DSM is the term that is used to incorporate energy efficiency; and DR, as well as other terms that could include the increased use of energy, according to the Energy Business Reports. With respect to the effects of DR on energy efficiency, the report claims that there has not been enough research conducted into integrating demand-response and energy- efficiency objectives into a single program, thus measuring the overall energy consumption.
Ways in which energy efficiency can be integrated in DR programs would be to replace lighting with CFLs, to insulate walls and ceilings, to replace old refrigerators with the newer energy-efficient models, replacing shower heads with newer water-saving models, and so on (Energy Business Reports, 2007). The focus report of this study does include energy
efficiency – via the residential mass roll-out part of the DR project. The EBR also introduces the Direct Load Control (DLC) terminology, which is when a utility shuts down a customer’s electrical appliance at short notice – in order to address the system’s reliability for an
incentive.
DLC has been in use for over 20 years; and it is most commonly applied to water heaters or air conditioners – by remotely switching off the load at the appliance during peak time, in order to reduce the peak load (Energy Business Reports, 2007). The image below in Figure 2 - 6 is an example of a DLC device that is being used to remotely control geyser usage by the Johannesburg City Council, in a multiple-unit gated-residential complex.
Literature Review
Figure 2 - 6:A Direct Load-Control Device connected to a residential DB. Photo of personal home installation taken by Yvonne Steenkamp
Another terminology introduced by the EBR report that is of interest to us is the Time-of-use (TOU) rates. These rates vary, depending on the time that the customer makes use of his/her electricity, this could be described as seasonal rates (summer vs winter) or hourly rates (peak vs off-peak). When it is peak time, the rate per kWh is higher than when it is off peak; and thus, if the customer uses less power during peak time he/she will make a saving in his/her electricity bill, as seen in Figure 2 - 7 (Energy Business Reports, 2007). As mentioned before, this is the best solution for the lower-energy consumers.
Figure 2 - 7:Time of Use. Image from (Energy Business Reports, 2007)
The Eskom DR project being studied here will make use of similar devices, as shown in
Figure 2 - 6, in order to control peak load, as a form of DLC; however, there is no direct incentive offered to the customer, except the ‘greater good” of preventing rolling black-outs.
The TOU principle will also be part of the DR project at some stage; but due to the brief nature of the power interruption to the geysers that will be implemented in the project; there would not be a noticeable saving in the customer’s bill. According to the project premise, Eskom is more concerned with minimizing the impact on the consumer in terms of the
continuous availability of hot water, rather than with saving the consumer money at this stage of the project. The areas that are mostly being targeted for DLC at this time are the formal housing areas with water-heating systems. But with the huge push for rural electrification and the large numbers added to the grid every year, this might not be so for much longer.
2.6 Demand-Response Visualization
The DR solutions that are currently being used do not make use of the powerful tools available in GIS, such as the easy and accessible interrogation of assets, adding intelligence to locations of objects, as well as the information in the databases linked to the spatial location, the interaction of features with one another, and the analysis of the relationships between objects – both spatially and via attributed data. Using GIS analysis, it is then possible to build a platform, with which to manage DR implementation, marketing and roll out. Because the DR program is missing this component, the following gaps were identified by Brits and Madhoo; locations where DR has been rolled out are not tracked or monitored;
and future rollout sites are not planned out strategically or efficiently; the optimal target customers are not identified in advance; and there is no consolidated view of the DR
Literature Review
solutions. Furthermore, the lack of a database encourages the possibility of work duplication;.
since other organizations, such as the municipalities are also rolling out DR programs; and this limits the possibilities of further research through the analysis of past DR solutions (Brits
& Madhoo, 2013). Without the analysis of past DR programs, it has already been established that the baseline for the measurement of peak reduction may be compromised; and it is then impossible to know where value needs to be added – without the feedback from past DR customers.
DR visualization on a GIS platform can only work optimally with current and accurate data to plug into the software. As referenced in Figure 2 - 1, the data required to form part of the GIS in the DR program include Customer Care and Billing (CC&B) data, assets data and grid network data, as part of the internal data within Eskom, as well as external data, like land-use data, statistical data and imagery, as referenced in Figure 2 - 3.
2.7 Residential-Demand Response
Residential-demand response (RDR), as the name suggests, is DR that is targeted specifically at residential households, in order to reduce the peak load. The more comprehensive
definition given in the Residential-Demand Response Research Report is:
“Residential electricity-demand response (RDR) programs have at their core a singular set of aims in common. These are to reduce the load during peak periods, to relieve congestion on the grid, to ensure network stability and the reliability of supply, and to ensure the most cost- effective rates for household electricity usage” (Khatri, 2013)
The aim of the report was to consider all the available forms of RDR, including the tried and tested as well as the innovative methods, in order to advise Eskom, as to which methods would be most appropriate for the current electricity problems being faced in South Africa.
South Africa is currently experiencing a serious power shortage; and it is in dire need of increased capacity. However, due to the fact that Eskom, the major power utility in South Africa has limited financial resources, the obvious solution of adding generation capacity to the grid was not very practical as a short-term solution. Other mitigating factors to the building of new coal-fired power stations to increase capacity included the time it takes to build them, 12 – 15 years, and their negative environmental impact.
This meant that new solutions would have to be considered, in order to reduce the load during peak times; when the load shedding became a necessity, because the demand exceeded the capacity. These solutions had to be relatively immediate; as the strain on the grid was
introducing regular infrastructure failure, black-outs and costing the economy a lot of money.
RDR is consumer-driven and some of the available applications can be applied in the short
term to immediately relieve the strain on the grid – especially during peak times. RDR solutions vary from automated-demand response, like ripple control and ‘set and forget’, time-of-use tariffs through to real-time pricing (RTP). However, past and present Eskom projects are centred on the ripple control (type of DLC), Homeflex TOU tariff and Advanced Metering Infrastructure (AMI) and Utility Load Manager (ULM) (Khatri, 2013).
Since this study focuses on the benefits of the visualization of Eskom’s DR project in GIS; it is only prudent to understand the DR methodologies applied in the project; and Khatri gives some good definitions of these technologies;
“Advanced Metering Infrastructure (AMI) enables the installation and operation of the electricity metering and communications, designed to transmit the data to, and receive data from, a remote meter.” (Khatri, 2013)
AMI is a solution that is heavily reliant on data exchange between the customer meter and the utility; and thus GIS can be implemented to ensure there is consistency between the data recorded on the customer’s side and what is being received at the utility data centres. As already pointed out in the introductory chapter of this study, GIS would also keep track of the location data of the AMI meters by quickly picking up any errors. Although Khatri does go into detail about AMI as a technology and the pilot programs implemented in Eskom, the use of GIS as a visualization and data-audit tool has not been considered; and therefore, the time and cost-saving implications have not been weighed with the technologies, in conjunction with GIS.
Such an investigation might change the recommendations, with respect to those which are the best RDR solutions for South Africa. In addition, since AMI relies on telecommunication signals, such as 3G and GPRS, it would be prudent to have the telecommunication signal strength hot spots mapped out in the GIS; so as to be able to eliminate these as a cause when breakdown in communications occur, or to overlay it with other data layers, in order to pick up any themes or intersections.
“The Utility Load Manager (ULM) system comprises a back-end system, field hardware and a customer-display unit. It enables the load to be limited at the end-user level via a central control point at Eskom National Control.” (Khatri, 2013)
ULM makes use of a type of warning signal sent to household meter-display units, asking them to reduce power usage; and, depending on compliance, can either switch them off or keep them on during times of constrained capacity. GIS can assist in mapping the customers that do comply versus those that do not, and also the time taken for the households to comply.
