Power Delivery Network Smart Grid and Big Data Applications Final

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Power Delivery Network Smart Grid and Big Data Applications

Presented at the 3rd Annual Indonesia Power Conference 27th to 30th November 2012, Jakarta, Indonesia

Himadri Banerji, Ex Chief Executive Reliance Energy Ltd India & MD EcoUrja

Utility business processes operating as separate legal entities in a deregulated environment. (Courtesy of ABB.)

2

Drivers for Total Network Investment

Approach to T&D Network Planning

The Mission Statement
1. "Our company will be the premium regional provider of electric power.“ Recognizing that its current financial situation prohibits competing on the basis of price, this utility has decided to make quality and service its hallmark. Achieving lowest possible cost is not the goal; achieving low cost while meeting high service standards. 2. "Provide economical electric power for the prosperity of the region.“ This utility has a long-standing tradition of low rates, a way of attracting new industry (i.e. growth) to the region. Plans that invest a good deal to improve quality are simply "not with the program.“ Marginal quality improvements in a new plan are permissible, only if they lead to lower cost.

3

Drivers for Total Network Investment

Implementing Renewable Energy Directive In order to reach the 15% overall energy target, the RES suggests that: More than 30% of electricity is to be generated from renewable sources; 12% of heat is to be generated from renewable sources such as biomass, solar and heat pump sources in homes and businesses; 10% of transport energy is to come from renewable sources. The RES recognises that increasing generation from renewable will have implications for grid investment, grid technology and grid connection policy. All of these issues have the ability to impact on T&D‟s investment plans.

4

Approach to analysis of integrating renewables

Approach to analysis of integrating renewables
5

Example of VCCVCC-HVDC transmission for a Wind Turbine Integration

6

Major Challenges

Security of electricity supply Society is becoming more and more dependent on reliable and highquality electricity supply. The power industry around the world continues to face an ever changing technological and regulatory environment. As a result of the efforts to combat climate change, deployments of wind, solar, tidal, wave and other power generators with variable and less certain power output are being installed and will continue to be installed on a large scale.

7

SHORTSHORT-AND LONGLONG-RANGE PLANNING
Load forecast for the leadlead-time year(s)

Existing system & planned additions through leadlead-time

Drivers for Total Network Investment

Power system studies

Short-range Planning process

Identified area capacity shortfalls and solutions

8

Chasing Higher IRRs Integrated Big Data and Enterprise GIS for Capital Budgeting

Expectations of Power Utility

Improve revenue by improving ‘ENS’ ( Energy Not Served). Improve performance by improving SAIFI, SAIDI. Improving ‘Customer Complaints’ logs in Trouble Call Management. Long-standing faults brought to a minimum. Limiting number of interruption per day a) Breakdown b) Preventive Maintenance c) Load-shedding.

Expectations of Power Utility

Good collection & billing System. To reduce Outage Time. To stop Power theft. To provide better services to the consumers. To have stabilized Asset Management System. Safety- Zero fatality rate.

On August 14, 2003, large portions of the Midwest and Northeast United States and Ontario, Canada, experienced an electric power blackout. The outage affected an area with an estimated 50 million people and 61,800 megawatts (MW) of electric load in the states of Ohio, Michigan, Pennsylvania, New York, Vermont, Massachusetts, Connecticut, and New Jersey and the Canadian province of Ontario. The blackout began a few minutes after 4:00 pm Eastern Daylight Time (16:00 EDT), and power was not restored for 2 days in some parts of the United States. Parts of Ontario suffered rolling blackouts for more than a week before full power was restored.

Relational database (RDB)

Rise to prominent use by utilities

However failure of traditional databases like RDBs to scale well in the face of rising data volumes, complexity, and speed has been well proven, with alternative technologies often outperforming them by more

Object-oriented databases (ODB) and emerging NoSQL technologies, HADOOP,

“Big Data” is typically considered to be a data collection that has grown so large it can’t be effectively or affordably managed (or exploited) using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines, depending on the task at hand. This can as easily occur at 1 terabyte as at 1 petabyte, though most discussions concern collections that weigh in at several terabytes at least.

To satisfy these imposing requirements constraints, Web entrepreneurs developed data management systems that achieved supercomputer power at bargain-basement cost by distributing computing tasks in parallel across large clusters of commodity servers. They also gained crucial agility – and further ramped up performance – by developing data models that were far more flexible than those of conventional RDBMS.

The best known of these WebWeb-derived technologies are nonnon-relational databases (called “NoSQL “NoSQL” NoSQL” for “Not“Not-OnlyOnly-SQL,” SQL being the standard language for querying and managing RDBMS), like the Hadoop framework (inspired by Google; developed and openopen-sourced to Apache by Yahoo!) and Cassandra (Facebook (Facebook), Facebook), and search engine platforms, CloudView (EXALEAD) Nutch (Apache).
15

situational awareness means

in real-time

having an understanding of what you need to know, have control of & conduct analysis for

If you have these things, making the right decision in the right amount of time in any context becomes much easier

to identify anomalies in normal patterns or behaviours that can affect the outcome of a business or process.

Variety

Velocity

Volume

Validity

Veracity

The utility industry's time scales vary over 15 orders of magnitude due to the unique diversity of sensors and critical business processes, and often at much faster intervals than other industries, which, when trying to create scalable situational awareness, impacts all five V‟s of the industry's Big Data pressures.

Data from Utilities’ devices and sensors has an extraordinarily broad range of relevant time durations for which they are valuable to the business, from milliseconds, to decades

There were three time windows where situational awareness would have given sufficient time to adequately respond.

Enterprise wide Geographical Information System (GIS) in REL
GIS Platform Enterprise Data Management

Network Data
ArcCatalog, ArcMap

SLDs, Layouts, Cable Routes

ArcSDE, ArcIMS

Structural Data Towers, Pillars, Poles, Plinths

Equipment Data New EHV Stations , HVDS, LTMP, O&M etc. Specifications, Diagrams, Operational History

Consumer Data
Responder OMS, ArcFM

Name, KNo., Service Line, DT No

Network Analysis Tools, Application Programs

Seeing is believing !!!!!!! We have seen it

System Architecture
Business Support Customer Care (CIS) XML XML Transmission/Distribution
WMS/Staking/IVR

XML

XML

Integration Framework

ArcFM Solution
(Models and Tools for Mapping and Network Data Management)

ArcGIS
(Core GIS Functions)

Open RDBMS

System Architecture

Need for GIS
• Impressive progress in power sector, but still insufficient.

Demand

Widening gap between demand and supply Supply due to T& D losses amounting to 25% in the distribution link.

Losses AT&C Loss % Distribution Loss % No. of Consumers

BYPL 50.71 48.11 836000

BRPL 39.68 42.7 1070000

The estimated T&D losses for the fiscal 04-05 for BRPL and BYPL, Delhi

Target – T & D loss Reduction

Solution – 1) Implementation of GIS modules 2) Distribution management thru’ GIS

Estimates of Implementing GIS at Delhi
Initiatives taken by GIS PMO group established at CEO Office of the company are as follows • Development of Functional Requirements and Data Model.

• Updating of Reliance Corporate Land base Maps. • Capturing the entire EHV/HV network. • Capturing the entire LV network. • Capturing Consumer Information. • In House Digitization and field QC. • Consultancy Services by REL

Updating of Reliance Corporate LAND base Maps
• Integrated large scale corporate land base map prepared based on RICs data requirements with base data as IKONOS imagery imported from Space Imaging. • IKONOS imagery digitized by RDWL team through network of digitization vendors. • Maps supplemented with field survey information conducted by contractors identified by RDWL. Flaw in above system – Information updated by RDWL was insufficient for locating Electrical network and individual Consumer Service points. Solution – * Updating of all buildings with service line feeding * Including all new transport features including road, railway, flyovers.
Codification Guidelines of RDWL

• Providing unique id to all building for its identification. • Linking it with its consumer/service line.

Unit Land Base Land Base Updating Sq Km Polygons

Unit Cost 20,000 8

Qty. 900 1,000,000

Total Cost (Mn) 18 8

Cost Per Consumer 9.44 4.2

Cost Estimates for Land base and Updating

Development of Functional Requirements and Data Models
• ESRI / M&M appointed as GIS service providers by REL ( on behalf of BRPL and BYPL ) for
- studying existing system. - developing functional requirement for proposed GIS. • GIS Tools – * COTS available platforms from ESRI. * Third party applications from Miner and Miner. * Customer Applications for GIS interfaces for integration with other applications - SAP (ISU-CCS for Consumer Information). - SAP (PM) for Operations and Maintenance. - Cymedist Interface for Network Analysis. - GIS Interface for SCADA system.
Development of tool based on functional requirement and application design document approved by REL for ESRI / M&M Testing and approval for implementation at cluster Citrix application servers at DAKC. Applications made available for access from anywhere in the Reliance Network including both the DISCOMS in Delhi.

Capturing Entire EHV/HV Network
Phase 1 The GIS data dictionary 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.

Survey agencies identified for capturing EHV/HV networks. Unit Digitization of captured data using in-house digitization tools developed by RDWL and REL 33/66 KV Conductor 33/66 KV Cables 11 KV Conductor Digitized data migrated to REL Corporate electric data base server at DAKC. 11 KV Cables EHV Station HV Stations Database made accessible through ESRI COTS and customer application from anywhere in Reliance WAN including both DISCOMS in Delhi. Km Km Km Km Nos Nos 650 2,500 500 1,441 124 8,000 650 476 Unit Cost Qty.

Total Cost (Mn)

Cost Per Consume r

0.3094

0.16

0.9367 0.31 4

0.49 0.16 2.1

Cost Estimates for EHV / HV Data Collection

Capturing the entire LV Network
Phase 2
Capturing LV network
Features captured – • Consumer Feeding Points * LV Support Structures * LV Feeder Pillars * Street Light Structures • LV Feeder Network (0.44 kv ) connectivity
Consumer Feeding Structure points codified with unique codes for linking them with respective set of consumers. This would enable linking of every consumer with its feeding point & 11/440 v substation, required for energy audit, NA, O&M and other applications.

Unit

Unit Cost

Qty.

Total Cost (Mn)

Cost Per Consum er

Feeder Pillars/Support Structures Cost Estimates for LV Data Collection

SS

8

800,000

6

3.36

In-House Digitization and Field QC
Data being captured is governed with stringent QA requirements based on feature being captured Team deputed for carrying out QC who conducts field checks before accepting data for digitization Data is then handed over to digitization team where digitization is done using industry standard CAD/GIS packages.

Capturing Consumer Information
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, without much of a fruitful result. At BSES, Delhi it is at a conceptual; stage and different models are being evaluated for collecting consumer information The consumer data being collected will be integrated with its building id for its spatial location and network connectivity with its feeding structure id in GIS.

Unit Consumers Consumer

Unit Cost 7

Qty. 1,906,000

Total Cost (Mn) 13

Cost Per Consume r 7

Cost Estimates for Consumer Data Collection

Skeleton of GIS
SCADA CIS

NO

OMS

CIS

DATA

SAP

INTEGRATION AS A CONCEPT

INTEGRATION AS A CONCEPT
Why integration? Use work process flows to define touch points of integration to support business processes Use enterprise and process modeling to describe how data and components service the needed business processes – Shared data doubles the accuracy and quality requirements – look at the data from each systems perspective (financial vs. operations) When Should Integration be Considered? Data/applications exist in many places Merger/acquisition of requirements Supporting thousands of users with many different requirements Scalability

Benefits of Integration

Metrics -- Measuring Integration Success Customized for the organization Tie Benefits and Metrics – Customer service measurements related to more up-to-date information How can Integration… – increase revenue? – improve customer service? – give more information about our business?

Reduces cost of connecting components and adding/changing components. Adds value to business processes Enforces process consistency Data Consistency

Integration with Other Applications
! Fault Detected

High Tension Network

SCADA

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SAP

Notification & Work order

SAP PM

ArcFM
The image cannot be display ed. Your computer may not ha

`

Analyze Fault

Low Tension Network

OMS/
!

Fault Reported
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Electric Data
/

Enterprise Wide Integrated GIS.

OMSResponder Network Analysis

Consumer Information System(CIS)

GISAM/FM

SAP SCADA-DMS Custom Tools
( Energy Audit Scheduler et al.)

Electric Network Trace

LT NETWORK

Service Line

Service Point

CONSUMER DATA

Load growth planning Using Geo-referenced data

Distribution Management thru’ integrated GIS
• Load Calibration (LC), Load Flow Calculation (LFC)

• Switching Orders based on minimum Loss path • Static spatial connection and dynamic behavior of organizational model & equipment model • Switch and switch status telemetry superimposed with cable/conductor type & length • Electrical parameters (R, X, B, G ) derived there from to give loss data and options • Voltage profiling with load data superimposed on above • Power quality and THD • Distance relay zones superimposed on map to give nearest fault location • Fault Isolation and switching restoration options

Estimated Benefits of GIS
• As per estimation, GIS will benefit by decreasing both Commercial and Technical losses.
• Based on assumption for 0.5%, 1.5% and 2.5% increase in MU billed for the first, second and third year respectively by reduction of losses and better O & M.
Pre GIS Implementation Post GIS Implementation

BYPL MU Domestic Commercial Industrial Agriculture Bulk Total Units Billed 1591 580 272 1 368 2812

BRPL MU 2801 740 406 66 1100 5113

Total 4392 1320 678 67 1468 7925

Approx Tariff 2 4 4 1 1

I Yr. (1.0% Incr MU) 43.92 13.2 6.78 0.67 14.68 79.25

Approx. Increas e in Revenu e 87.84 52.8 27.12 0.34 7.34 175.44

II Yr. (1.5% Incr MU) 66.54 20 10.27 1.02 22.24 120.06

Approx. Increas e in Revenu e 133.08 79.99 41.09 0.51 11.12 265.78

III Yr. (2.5% Incr MU) 112.56 33.83 17.38 1.72 37.62 203.11

Approx. Increas e in Revenu e 225.12 135.32 69.51 0.86 18.81 449.62

Benefits after GIS Implementation

Note: From the table, increase in revenues with prevailing tariff is 175mn, 265Mn and 450Mn respectively for three years.

Estimating Return on Investments
Based on the assumptions of the benefits made, it can be seen that the project has a pay back less than one year of its implementation.

Cost GIS Implementation GIS Implementation (End of I Yr) Maintenance Cost estimated as 25% Of total Implementation (Yr. II) Maintenance Cost estimated as 15% Of total Implementation (Yr. iii) 128 40 42 25

Increase in Revenue 0 175 266 450

Net Cash Flow -128 135 224 424

IRR

6% 95% 138%

Return on investments From the Table it can be seen that the project not only has very less payback period, but has fabulous returns over second and third year with net cash flows as 135mn, 224mn and 424Mn with IRR as 6%, 955 and 138% for first, second and third year respectively

Thank You The Project has since been commissioned and the T&D Losses have been reduced adding significantly to the IRR

Presented at the 3rd Annual Indonesia Power Conference 27th to 30th November 2012, Jakarta, Indonesia Dr Himadri Banerji, MD EcoUrja Ex Chief Executive Reliance Energy Ltd India

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