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.
Current Situation OUTLINE Attribute data initially Desired Situation Data after spatial visualization with GIS
EXAMPLE OF DEMAND RESPONSE BEING USED Direct Load Control device 90kW saved in 2hrs by using Residential Load Management (RLM) Energy usage Fast Feedback for saving energy Consumers can see their daily consumption on their tablets and smart phones and money saved.
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
METHODOLOGY WORKFLOW PROCESS USED TO CONVERT TABULAR DATA TO SPATIAL DATA INPUT OUPUT DATA GATHERING Customer and meter data from CC&B Cadastral data Base maps services from OpenStreetMaps ArcGIS 10.1 Spatial maps served on internal web service GIS analysis using spatial analysis tools (geocoding, Add XY data, Display XY data) RESULTS AND ANALYSIS OF RESULTS
DEMAND RESPONSE METHODS Utility Load Management Concept Diagram
ESKOM UTILITY LOAD MANAGER DATA WHEN PLOTTED IN ARCGIS
Advanced Metering Infrastructure Diagram
ADVANCED METERING INFRASTRUCTURE (AMI) DATA DISPLAYED SPATIALLY IN ARCGIS
Data was in the form of stand numbers and thus Add XY data GIS tool used. SPLIT METERING DATA DISPLAYED SPATIALLY
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 RESULTS
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.
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. 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. DISCUSSION 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/
IMPLICATIONS FOR FUTURE RESEARCH 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 3 rd 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. 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.
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/