Enterprise GIS for Distribution Loss Management in Energy Sector Chandan Guha Head - GIS Reliance Energy Ltd, India Email: Chandan.Guha@rel.co.in Introduction While India has made impressive progress in the Power Sector since independence, it has not been sufficient. In terms of generation, while new capacity has been added, demand has far outstripped the supply leading to a widening gap. The primary reason of the widening gap lies in the distribution link in the value chain. It is clear that the biggest fundamental issue hampering the viability of the Indian Power Sector is the sheer volume or level of Aggregate Technical and Commercial (AT&C) losses that amount to 60%, a very high level by any global standard. Available figures reveal that national capital had T&D losses inherited by the private companies more than 50% in July 2002. After privatization of power distribution in Delhi the immediate task was to achieve the targets of T&D loss reduction. Implementation of Geographic Information system has helped significantly for reduction the distribution losses the DISCOMs of Reliance Energy both in Delhi & Mumbai. Some of the initiatives taken by GIS group established at Central Technical Services of the company are as follows: Development of the Functional Requirements and Data Model REL had appointed ESRI/M&M as GIS service providers who studied the existing system and developed functional requirements for the proposed GIS system. Apart from the commercially of the shelf (COTS) available platforms from ESRI and third party applications from Miner & Miner, this included various custom applications for GIS interfaces for integrating them with other systems: · SAP (ISU-CCU) for Consumer Information · SAP (PM) for Operations and Maintenance · Cymedist Interface for Network Analysis · GIS Interface for SCADA system Some of these tools were then developed based on the functional requirement and application design document approved by REL for ESRI/M&M. After development these tools were then tested on BSES site and approved for implementation at the cluster citrix application servers at Dhirubhai Ambani Knowledge City at Navi Mumbai. These applications were then made available for access from anywhere in the Reliance Network including that of both the DISCOMS in Delhi. Updating of Reliance Corporate Land base Maps: An Integrated large scale corporate land base map was prepared by the Reliance Digital World Pvt Limited, a wholly owned subsidiary of Reliance Infocomm for more than 600 cities in the country. This was made based on the land base data requirements of Reliance Infocomm with base data as IKONOS imagery imported from Space Imaging in the year 2001. The IKONOS imagery was digitized by RDWL team through a network of digitization vendors across the country. The land base maps were then supplemented with the field survey information conducted by the survey contractors identified by RDWL. It was found that the land base information updated by RDWL was not sufficient for the locating the electrical network and individual consumer service points required by REL data dictionary. A new guideline for land base updation was prepared in consultation with RDWL which included updation of all the buildings wherever the service line was found to be feeding (both legal and illegal tapings). All the new transport features including that of road, railways, flyovers were also included in this. As per the codification guidelines of RDWL all the buildings were given unique id for its identification and linking that further with its consumer/service line information. Multiple scenarios of building updation included adding new buildings, deleting building polygons incorrectly created, splitting for multiple buildings polygons and combining polygons of the single building. Capturing the entire EHV/HV Network: The GIS data dictionary developed included all the spatial feature classes / object classes and their relationships identified based on the functional requirements. This included the entire network of EHV and HV · EHV Grid Stations and their equipments · 11/0.44 KV Substations and their equipments · 66, 33 and 11 KV Feeders (both over head and underground) Survey agencies were identified for capturing of EHV/HV networks in the first phase. The data captured by survey agencies was then digitized using in-house digitization tools developed by RDWL and REL. The digital data thus captured was migrated to REL corporate electric data base server at Dhirubhai Ambani Knowledge City at Navi Mumbai. This database was then made available for access through ESRI COTS and custom application through from anywhere in the Reliance Wide Area Network including that of both the DISCOMS in Delhi. Capturing the entire LV Network: After migrating EHV/HV Network in the REL Corporate Electric Database, the next phase planned is for capturing LV Network. Extensive survey is now being conducted in more than 11 districts of BRPL/BYPL for capturing features including · Consumer Feeding Points o LV Support Structures o LV Feeder Pillars o Street Light Structures · LV Feeder Network (0.440 KV) connectivity All Consumer Feeding Structure Points are being codified with unique codes for linking them with respective set of consumers. This will enable linking of every consumer with its feeding point and 11/0.440 KV substation required for energy audit, NA, O&M and other applications. Capturing Consumer Information: The consumer information made available from erstwhile DVB’s electronic billing system is very erroneous. Non availability of accurate consumer records had been one of the main reasons for commercial losses. GIS based consumer indexing has been carried out by many DISCOMs/SEBs across the country but none of them have proved a substantial reduction of losses. At BSES Delhi this exercise is still at implementationl stage and different models are being evaluated for collecting consumer information. The task is most complex data collection exercise and company has encountered many issues while carrying out pilots and proof of concept for consumer data collection. As mentioned above the consumer data being collected will be integrated with for its spatial location with its building id and network connectivity with its feeding structure id in GIS as shown in Figure 1. The DT-buildingid – pillarid – K No relationship is aimed at 1) tracking of faults in minimum time 2) escalation of “most possible fault” from trouble calls 3) integrate outage management with electrical operations 4) generate fault id and historize The relationship and the benefits thereof is described in complete details in the slides .