sas based analytics for electric utilities in...

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Copyright © 2012, SAS Insti tute Inc. Al l r ights reserved.

SAS BASED ANALYTICS FOR ELECTRIC

UTILITIES IN INDIA

GEO SMART INDIA, 3RD MARCH



POWER SECTOR PROCESS GLIMPSE

Fuel

Sources

• Coal

• Naptha

• Natural

Gas

• Diesel

• Bio Mass

• Hydro

• Wind

• Solar

Electricity

generation

pant

Equipment

Vendor

• Domestic

• Global

Transmission

Utilities

• Central

transmission Utility

• Power Grid

Corporation

(PGCIL)

• Power transmission

Carrier

• State transmission

Utilities

Distribution

Utilities

• State Electricity

Board

• State Distribution

Utility

• Private

Distribution

Utility

Ultimate

Consumer

• Industrial

(HT/LT)

• Commercial

• Residential

• Agriculture

• Rural

Consumers

Regulatory Body, CERC –Central : SERC-State

Electricity Accounting Body ,CEA –Central :SEA-State

Transmission/Distribution Monitoring

State – SLDC , Regional 5 – RLDCs

Central -1 NLDC, local ALDC for each state Rising

Renewable

Rising Smart

Grid

Grid

Balancing



UTILITY

CHALLENGES

DECISION MAKING NEEDS TO BE TRANSFORMED FROM

“GUT FEEL” TO “FACT BASED”

Increase in

Consumption

Peak Load

Growth

Network

Reliability

going down

Network

Loss Increasing

Quality of

Service

going down

Improper

Expansion &

Augmentation

Increased

Economic

Development

Higher Power

Purchase Cost



UTILITY

CHALLENGES FUTURE OUTLOOK FOR ANALYTICS IN UTILITIES



POWER SECTOR INDIAN LANDSCAPE

APDRP/R-APDRP(DISTRIBUTION)

•Creating IT infrastructure by binging in Information technology solution thru AMR, Asset Mgmt, CRM, Energy Billing and SCADA implementation

•Have good amount of data to analyze for analytical approach on customer load research, meter event analysis, theft, energy reconciliation, energy arrear, customer complain etc

Energy Management (DISTRIBUTION)

•Energy Forecasting for utility

•Better power procurement to curtail imbalances

SMART GRID (DISTRIBUTION)

•Smart metering

•Peak load management (PLM)

•Demand Response

•Transformer Outage Management

•Network Management

REGULATORS

• Billing Efficiency-Improvement in Billed Energy based on metered consumption

• Energy Accounting-Reduction in losses/ Energy Theft

• Long term forecasting

Transmission

•PMU initiative

•Renewable integration

•SLDC load balancing

•RLDC load balancing

•NLDC/POSOCO load balancing



TRANSFORMING

UTILITIES

BUSINESS ANALYTICS MADE SIMPLE


Copyright © 2012, SAS Insti tute Inc. Al l r ights reserved. www.SAS.com

RAPDRP /IPDS & REGULATORS

http://www.sas.com/



RAPDRP POST DEPLOYMENT

• Meter data analytics

• Fraud/ theft or non technical loss

• Network Reliability ( SAIFI, SAIDI, CAIFI etc)

• Outage analysis –transformer incidents/failure

• Load management & research, peak analysis

• Billing anomalies, subsequent billing, arrears analysis

• Energy Auditing and Reconciliation

• Customer care handling

• Customer indexing and GIS mapping verification



ENERGY MANAGEMENT



SAS ENERGY

FORECASTING HORIZON

VSTLF STLF MTLF LTLF

1 Day 2 Weeks 3 Years 30 Years



BALANCING DEMAND / SUPPLY – FORECASTING IS

CRITICAL

Data Management

Cleaning

Adjustment & Transformation

Validation

Standardization

Intraday/Short Term Forecasting

Shape Models

Day Models

Combination Model

Customer Forecast

Mid & Long Term Forecasting

Weather

Scenario •Seasonal Average

•1 in N Warm or Cold

•Historical Year

Marketing

Scenario •Customer Gains

•Customer Losses

•Load Growth

Scenario Definition

LTF

Models

Econometrics

Metered Demands

Weather

Contract Details

Demand Profiles

Data Feeds

Data Management

Po

rtfolio

Op

timiz

atio

n, S

imu

latio

n Shutdowns

Holidays

Interruption

Special Events

Settlement Curves

Forecast + Portfolio Development & Optimization Reporting & Usage

Special Effects Holiday Recency Weekend Surrounding Days

Portfolio Parameters

Special

Effects Holiday

Recency

Weekend

Surrounding

Days

ENERGY

MANAGEMENT

Central Procurement

Planning

Regulatory

Report

Forecasting

Analysis



SMART GRID/AMI (ADVANCED METERING

INFRASTRUCTURE)



How do you optimize the roll out of Smart Meters?

How can you differentiate service to the customer?

How do you minimize ‘Non-technical losses’?

How can you use all the data from the Smart Grid?

How/what can you cross/up-sell from partner organisations?

How do you reduce incumbent debt?

Can you derive insight to help improve energy trading?

TRANSFORMING

UTILITIES MAKING THE MOST OF SMART GRID DATA



PREDICTIVE INTELLIGENT

UTILITY

ASSET MANAGEMENT

DISTRIBUTION MANAGEMENT SYSTEM

WORKFORCE MANAGEMENT CUSTOMER INFORMATION SYSTEM

PARTICIPANT

REPORTS, OLAP & ALERTS

INSIGHTFUL LEADER

FORECASTING & STATISTICAL ANALYSIS

COMPETITIVE INNOVATOR

PREDICTIVE MODELING & OPTIMIZATION

VOLT-VAR OPTIMIZATION

PHASOR MEASUREMENT UNIT

OUTAGE MANAGEMENT SYSTEM

GEOGRAPHIC INFORMATION SYSTEM SCADA

FAULT LOCATION ISOLATION & RESTORATION

ETRM

LOAD FORECASTING

ADVANCED METERING INFRASTRUCTURE



PREDICTIVE INTELLIGENT

UTILITY

ASSET MANAGEMENT

DISTRIBUTION MANAGEMENT SYSTEM

WORKFORCE MANAGEMENT CUSTOMER INFORMATION SYSTEM

PARTICIPANT

REPORTS, OLAP & ALERTS

INSIGHTFUL LEADER

FORECASTING & STATISTICAL ANALYSIS

COMPETITIVE INNOVATOR

PREDICTIVE MODELING & OPTIMIZATION

VOLT-VAR OPTIMIZATION

ASSET MONITORING

OUTAGE MANAGEMENT SYSTEM

GEOGRAPHIC INFORMATION SYSTEM SCADA

FAULT LOCATION ISOLATION & RESTORATION

ETRM

LOAD FORECASTING

ADVANCED METERING INFRASTRUCTURE

SMART GRID VALUE CREATION

Load Forecasting

DISTRIBUTION OPTIMIZATION

Predictive Asset Maintenance

Volt VAR Optimization

R APDRP

Grid Analytics

PREDICTIVE INTELLIGENT UTILITY

MA

TU

RIT

Y

Demand Response Programs



RAPDRP - Application

SMART GRID

FRAMEWORK

ANALYTICS LAYER

Network

Infrastructure

Smart

Meters

Distribution

System

DMS

Communication

Network

Zigbee

GPRS

LL/MPLS

Application Layer

Energy

Accounting

Load

Research and

Load

Forecasting

Demand

Response

Peak Load

Management

Application Layer

Outage

Management

Workforce

Management

Cyber security Demand

Response

SCADA

GIS

Billing

BW / BI

Power Quality

Management

Accounting

& Loss

Modeling Load

Research

Load

Forecasting

Peak Load

Modeling

Customer

Classification

Segmentation

Alarm

Suppression

Workforce Volt Var Control

Optimziation

Spectral /

Harmonics

Analysis

Condition

Monitoring

Visualization

Layer

In Memory

Visualizer

Portal

Email

GIS

SMS

MSOffice

Asset Failure

Pattern

Modeling

ANALYTICS LAYER



SMART GRID

FRAMEWORK

PLATFORM VIEW

Alarms / Events

Asset / Network Data

Outage Data

Metered Demand

Weather / Environment Data

Workforce Data

Holidays / Special Events

Equipment Condition / Design Data

Shutdowns

Smart Grid

Analytics Information Store

Voltage / Current / Power Factor / Data

Analytics Process Flows & Business Rules

Data Management

Asset Failure

Analytics

Alarm Analytics

Workforce

Optimization

Peak Load

Modeling

Energy Accounting

and

Meter Data

Visualization

Load Research

Short Term

Load Forecasting

Generation Forecasting

Customer Profiling

Time of Use Modeling

Demand Response

Smart Grid Analytics Platform (One single platform instead of multiple visualization components, analytics components, reporting

components)

Volt VAR Control

& Optimization

Spectrum Analysis

Wavelet Analysis

Harmonics



TRANSMISSION /RENEWABLE



REMC RENEWABLE ENERGY MONITORING CENTER

• GUJARAT

• MADHYA PRADESH

• MAHARASTRA

• RAJASTHAN

• KERALA

• ANDHARA

• TAMIL NADU

• KARNATAKA

WESTERN RLDC

NORTHERN RLDC

SOUTHERN RLDC



OVERVIEW REMC

Ramps &

Volatility

Prediction

Renewable

Energy

Forecasting

REMC Renewable

Energy Management

Centre

RE Forecasters # 1

RE Forecasters # 2

RE Forecasters # n

Weather Service Provider

RE-Scheduling



STUDY CASE SPATIAL ANALYTICS FOR SOLAR SITE

ASSESSMENT



STUDY CASE SPATIAL ANALYTICS FOR SOLAR SITE

ASSESSMENT

Efficient & Robust

methodology for

geospatial solar site

assessment



STUDY CASE SPATIAL ANALYTICS FOR WIND SITE

ASSESSMENT

• Wind turbines placement:

http://support.sas.com/resources/papers/proceedin

gs15/SAS1681-2015.pdf

WHITE PAPER

Using SAS to

Optimize the Layout

of Wind Farm

Turbines

http://support.sas.com/resources/papers/proceedings15/SAS1681-2015.pdf







WIND ENERGY

ANALYTICS NWP-BASED HYBRID APPROACH

Wind Site Information

• Cluster/group windspeed distribution by angle

• Estimate correlation between individual windmills

• Assess interactions of wind direction and correlation of power generated by turbines (cross-turbine wind-shadowing effect

• Record Online Measurements from SCADA (at turbine height)

Extract Statistical Features

• Timeseries forecasting models (seasonality, trend, outliers)

• GLM

• Persistence

• Additional variables (humidity, temperature, tree-coverage, land use plan, SCADA - outages…)

Power Conversion

• Use manufacturer power curve for newly turbines without historical data

• Calculate Weibull distribution parameters for windmills with available historical data

• Train ANN on predicted power vs. actual measurements

• Refine using online data

Forecast

• Forecast windspeeds by location/direction* - hierarchically

• Using calculated conversion rates (weibull distribution in previous step) calculate power output by each turbine hierarchically

• Account for correlation effects between windmills, adjust for trend and seasonality

• Refine with online data available, re-weight the models/inputs (ensemble)

• Aggregate output for the whole windfarm/Region and assess



HIGH LEVEL ALGORITHM DIAGRAM

.

.

.

.

.

.

NWP/MOS Power Curves,

Probabiliites

Online

Measurement

(SCADA)

Probabilistic RE Forecast,

Ramps, Variability



WIND ENERGY

ANALYTICS MACHINE-LEARNING BASED



POWER CURVE

MODELLING POV WITH GLOBAL WIND FARM MANUFACTURER

Manufacturer curves e.g. for GE Turbines: http://wind-data.ch/tools/powercalc.php?lng=en



SOLAR ENERGY DATA DISCOVERY

• Automated trend identification and extraction for Timeseries (e.g.

Solar bonus trend)

TIMESERIES FEATURES

During implementation at

Origin Australia, SAS

solutions helped customer

to identify and isolate

typical known features of

timeseries, such as trend

and seasonality

components and uncover

input from larger Solar PV

installations running at

customer sites

• Automated identification and extraction of surface trend

Before: with trend After: detrended data



SAS RENEWABLE EXPERIENCE



NCBSN SOLAR DASHBOARDS



ANALYZING INFORMATION

IOT

BIG

DATA Machine

Learning

HADOOP Social

Media

Devices/

sensors

Cloud



ANALYTICS KEY DRIVER FOR



POWER SECTOR ANALYTICS SOLUTION ACROSS THE VALUE CHAIN



POWER BUSINESS MAPPED TO ANALYTICS ANALYTICS

PLATFORM

ORGANIZE STORE

Data Management

Data Management

Capabilities in SAS

Interactive data

integration development

environment

Connectivity and data

access

Data Quality, de-

duplication, cleansing

and enrichment

Extraction ,

Transformation and

Loading

Data profiling

An

aly

tical

Data

Mart

Bu

ilt

for

pu

rpo

se p

roje

ct

data

mart

s

1

Op

era

tio

nal

So

urc

e S

yste

ms

Calendar

Ev

en

ts

WE

AT

HE

R

Macro

Economy

Data

Meter

Data

Outage

Data

Sta

gin

g A

rea

SAS/ ACCESS

Software

SAS/ ACCESS

Software

ACCESS

Visualization Layer 3

VISUALIZE AND REPORT

Data Visualization capabilities include

Reliability & Outage Performance

Metrics

Peak Load Metrics / Forecasting /

Energy Savings Dashboard

Peak Season

Performance Forecast Accuracy

Dashboard

Investment Scenario

Ad Hoc dashboard

development

Ad Hoc data

exploration Data quality checks

Power Purchase Cost Dashboard

Savings optimization

Loss Reports

Advanced Analytics

SA

S M

eta

da

ta T

ier

- G

ive

s S

ecu

rity

/ A

uth

en

tica

tio

n/ A

uth

ori

za

tio

n/ A

ud

ita

bility

Advanced modeling, and forecasting

capabilities

Power

Purchase

Optimization

Peak Load

Management

/ Demand

Response

Exp

lora

tio

n a

nd

Sta

tisti

cal

An

aly

sis

Fo

recasti

ng

& D

ata

Min

ing

Seg

men

tati

on

/ C

lassif

icati

on

Ris

k A

naly

sis

Reliability

Analysis

ANALYZE, MODEL AND FORECAST

2

SAS Distribution Planning and Optimization Platform

Non

Technical

Loss

Billing

Data

Investigation / Corrective and

Preventive Action Process

Case

Initiation Investigation

Reports

Field Details Action

Analysis

Customer Care

Data

Data

Management

Advanced Analytics

(Predictive Modelling)

Visualization

(Agile Business

Intelligence)

http://www.sas.com/content/dam/SAS/en_us/doc/factsheet/sas-data-management-advanced-102451.pdf



ANALYTICS

PLATFORM CUSTOMERS WORLD WIDE



WHO WE ARE THE GLOBAL LEADER IN BUSINESS ANALYTICS

38

1

+13,800

$3.02

Years of Growth (2013=US$3.02b)

Billions USD

world revenues

+60,000

24%

+3.5 mil

Installations

R&D investment

Of the

Fortune

100

Customers

Employees

Biggest Private Software Company focus on

“Business Analytics”

2010 , 2011 &

2012, 2013

http://images.google.com/imgres?imgurl=www.bitspin.net/images/internet.jpg&imgrefurl=http://www.bitspin.net/illustration.html&h=600&w=800&sz=110&tbnid=6pvO6LS_rv0J:&tbnh=106&tbnw=141&start=3&prev=/images?q=Internet&hl=es&lr=&ie=UTF-8

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sas based analytics for electric utilities in...

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