Smart Grid Analytics

Smart Grid AnalyticsAnalytics

Data and analytics SAP HANA

Session objectives

Understand the strategic importance of smart meter data, and review data requirements and analytical methods needed to realize the business benefits around advanced metering infrastructure (AMI)

Learn more about how KPMGs approach, accelerators, combined with the power of SAP HANA technology, can shorten the go-live process and enable companies to rapidly derive the benefits of smart grid analytics.

Review tips and techniques for getting smart grid data analytics up and running quickly with KPMGs industry solutions

2012 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (KPMG International), a Swiss entity. All rights reserved. NDPPS 132878

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Classic Meter Smart Meter 15-min (96

values) per customer/day

1KB per reading ~ 400GB raw

data per year

1 reading per customer/year

1KB per reading ~ 1 GB raw data

per year

Smart metering = Big data production

Smart meter data will revolutionize the way power is managed, distributed, and used. Smart grid analytics unlocks Smart meter data and turns opportunity into business reality.


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Data production Data volume Data storage 50 Transformers 720 Reads/day 138 Days

6.9 Million Records 200 MB

5000 Transformers 720 Reads/day 90 Days

388 Million Records 9.9 GB

5000 Transformers 720 Reads/day 365 Days

1.627 Billion Records 32 GB

Big data and analytics

Data and analytics capabilities should be cultivated in lock step

Comprehensive Capture is Essential

Timely Processing is Crucial


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Connecting the Information Silos is Critical Focus on Value is Vital Discovery is Paramount

Context Matters Connectivity Matters

Decision Ready Insights

BIG DATA

Voltage Data Power quality

Data

Unstructured non-text based data

Unstructured External Text Based Data (including social)

DATA DISCOVERY ZONE Revenue data Load data Theft data Prepay data Rate data Demand data

Utilities data set sample

Data connectivity matters


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Peak demand readings

Home area networks Data

Electric vehicles Data

Supplier & Supply chain Structured Data Functional Data

Regulatory DataAdjacent/Influencing and direct Markets

Data

Cross Functional Data

DATA VALUE ZONES

Demand data Consumer data Outage data Distribution data AMI Network data Service level data Peak demand readings Home area networks data Electric vehicles data Voltage data Power quality data Others

Strategic importance of smart meter data

Top 2 causes of increasing complexity: [1] Regulation and [2] Information managementRegulations Unbundling of energy markets Promotion of renewable energy and energy efficiency Enhanced regulatory reporting and rulesMarket Increasing competition New service business revenue opportunities More demanding customers


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Innovation AMI/Sensing & Measurement Technology Distributed generation Electric VehiclesOpportunity Vs. Business Problems Increase adoption rates for demand-side management programs Increase revenue from new energy services Reduce direct energy costs via more accurate load forecasting Reduce revenue loss from theft Achieve energy savings and emissions targets Boost customer satisfaction and retention

SAP HANA technology

SAPHANA, What is it? In-memory software + hardware

(HP, IBM, Fujitsu, Cisco, Dell) Data modeling and data management Real-time data replication via Sybase Replication Server SAP BusinessObjects Data Services for ETL

capabilities from SAP Business Suite, SAP NetWeaver Business Warehouse (SAP NetWeaver BW), and Third-Party Systems

Key Functions

SAP HANA defined

SAP HANA appliance

SQL MDXBICS

SAP HANA studio

SQL

SAP BusinessObjects BI Solutions Other Applications

SAP HANA Database


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Key Functions Analyze information in real-time at unprecedented

speeds on large volumes of non-aggregated data Create flexible analytic models based on real-time and

historic business data Foundation for new category of applications

(e.g., planning, simulation) to significantly outperform current applications in category

Minimizes data duplication

3600x Faster reporting speed 460B Data records analyzed in less than a second

SLT(Real-time data

replication)

SAP NetWeaver BW

SAP Business Suite

Calculation and Planning Engine

Row & Column Storage

3rd Party

SAP Business Objects Data

Services

SAP Smart Meter Analytics

SAP HANA

Line Loss/Phase

Transformer Load Revenue and SAP Business

Real Time

SAP BusinessObjects

Tool SetsSAP NetWeaver Business Client Mobile Client

Online Portal

Analytics

Meter To Cash

SAP smart meter analytics Architecture

The first step of our approach is designed to quickly identify possible saving opportunities with a minimal impact on client resources to create reference architecture.


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SAP BusinessObjectsData Services

(Meter Data Unification) SAP BW ETL Process

(Utility Business Content)

Meter Analytics

In-Memory Engine

Loss/Phase Balancing Analytics

Load Management

Analytics

Revenue and Cost

Analytics

SAP NetWeaver BW 7.3(Smart Meter Analytics Application)

SAP Business Transactions

(ECC)

Marketing Data

Weather Data

MDUS(MDM System)

Other Utility Data

NON SAP Business

Transactions (Ex: Oracle,

File, etc.)

External Data

Un Structured Data

(Ex: Social Media, Web Blog, etc.)

Meter To Cash

SAP smart meter analytics data integration

SAP HANA(Real-time data) SAP Data Services - structured data access

SAP HANA enables in memory real-time analytics on structured business transactions and unstructured data


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SAP Data Services un structured data access

KPMG value proposition &accelerators

Profitable growth andefficient use of Capital

Market Share

Strategy Outcome Metrics Key Influencers

Number of bills related

KPMGs approach

KPMG approaches Smart Grid analytics by connecting corporate strategic objectives to outcome matrices. Our analytics models are driven from outcome metrics.


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efficient use of Capital

Grow market share

Revenue

Collection

Billing

Operating Margin

complaints % of consumers

unmetered % of bills not delivered on

time % of 33KV, 11KVand

DTR metered Number of disconnection Number of reconnection Arrears

KPMGs approach - Sample

KPMG drives Smart Grid analytics model from companies strategic objectives

Maximize Metering ,

Meter ReadingTimeliness

Accuracy

Monitoring

No of cases of delay in meter reading

Meter Reading Found Incorrect in Random Check

Incorrect meter reading complaints

Assessment of consumption due to faulty meter


Timeliness % of bills not delivered on time


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Maximize Metering , Billing and Collection

Efficiency.Increase Revenue

BillingAccuracy

time

Metering

Coverage

Accuracy

Monitoring

% of 33KV, 11KV and DTR feeders metered


% of consumers unmetered

No of meter related complaints


Number of bills related complaints

Collection

Energy Audit

InstallationPoint of Delivery (Meter)

Profile

Consumption Profile Model

Connection contains the postal and political regional structure, dependent on the service address of the business partner and others

Installation contains the rate category,

KPMGs turns smart meter data into analytical model


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Contract

Business Partner

Calendar Day

Contract Account

PremiseConnectionObjects

Installation contains the rate category, flat-rate installation, billing class, and others

Contract contains Plant or Company Consumption, Renewal Date of Contract, Start Month of Payment Plan, Payment Plan Type and others

Facts_______________

Smart Meter Data

KPMGs turns smart meter data into analytical model (continued)

SAP Data Model(Sample)

Metering, Billing, Collection and Energy Audit


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KPMGs real-time predictive analytics demo


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KPMGs real-time predictive analytics demo (continued)

Planning and Forecasting

Trend Analysis


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Trend Analysis

Improved customer service

Minimizing outages and offering data-driven demand-side management DSM programs that lower costs and energy consumption

BenefitsRealization Benefits

Maximized operational excellence and

efficiency

Reducing demand for pea energy and shifting consumption to lower-demand periods, as well as eliminating production of costly excess capacity that must be shed from transmission networks

Effective risk Detecting outages more quickly and diagnosing and fixing root causes more efficiently

KPMG analytical capabilities to attain benefits of Smart Meter


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Effective risk management Detecting outages more quickly and diagnosing and fixing root causes more efficiently

Balancing of energy demand and supply

Having granular, real-time visibility into demand so they can control loads with pinpoint accuracy

Increase adoption rates for demand side management programs by precisely segmenting and targeting customers Reduce direct energy costs via more accurate load forecasts based on energy consumption patterns Achieve energy savings and emission targets via more effective energy efficiency Programs Increase revenue by up-selling and cross-selling new energy services Reduce revenue loss via increased transparency into smart meter data and benchmarking of accounts Boost customer satisfaction and retention by providing direct

Tips and techniques

Form a data & analytics strategy to attain the maximum benefits from AMI Clearly define data requirements, and the analytical processes needed to turn smart meter raw data into insights for an

actionable decisions Data requirements

Ex: Measurement data Events and alerts Energy consumption data Power quality data (voltage & reactive power)


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Outage alerts Tamper alerts Price signals and others

Analytical process Ex:

Correlations Identify statistical relationships between various data Trending Identify patterns for any time series data Exception Analysis Identify unexpected or abnormal condition Forecasts Predictions of future events or values using historical data. Other analytical process

Leveraging meter data across your organization to discover opportunities and collaborating to develop analytics solutions.

KPMG pre-packaged Smart Grid analytics(Next steps)

Phase balancing Line loss analysis Transformer load management Asset utilization monitoring Voltage analysis Power factor analysis Reliability indices validation

KPMGs smart grid analytics Sample

Top Grid Analytics well aligned with corporate strategic objectives

Regulatory

Network Operations

Customer Operations

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2


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Momentary identification & validation Outage minutes validation Outage dollarization Vegetation management Back tracing for phase ID Load profile analysis Customer segmentation Diversion Demand response verification

Strategy

Engineering

System Planning

Asset Management

Marketing/New

Products & Services

Sensing Operations

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AnalyticsOverview

The availability of coincident per-phase consumption data from all metered loads on a distribution circuit allows comparison with coincident per-phase data collected at the feeder source. The difference between the two, (source load) indicates total circuit losses. Losses increase with phase imbalance, requiring per-phase analysis to realize benefits.

Target Business functions

Typical distribution circuit technical losses are 3% 5%. The ability to collect coincident per-phase circuit load on a continual basis enables the calculation of actual circuit losses as required. Without AMI, this process is technically impossible. Today, losses are calculated and estimated.

Benefits categories Impact

Cost Reductions

Deferred Spending

Reliability

Customer Satisfaction

Line loss analytics KPMGs accelerators Determine circuit energy losses and phase loading imbalance using AMI consumption and voltage data


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Value Proposition

A more frequent and accurate accounting of circuit losses and phase imbalance will allow prioritization of high-loss circuits, and further analysis to identify the specific line segments attributing to high losses. The data can then be used to justify circuit or equipment changes to reduce losses to a level closer to 2% 3%.

Key Consierations

The remediation of phase imbalance and line losses are pre-requisites for Volt-Var optimization or Conservation Voltage Reduction (CVR). For each 1MW of circuit peak load (assuming a load factor of 75%) a 1% 2% reduction in losses equates to a savings of 65.7MWh to 131.4MWh annually. At an average wholesale cost of $40/MWh the savings range from $2600 to $5200 annually. Specific costs and savings will vary.

Productivity Improvements

Customer Meter KWh interval reading (MDM)

Meter to Transformer to Phase connectivity (CIS or OMS)

Substation Feeder

DataCollection

Select feeder to analyze Select start and stop date and time (whole hour only) Query connectivity model to identify meters on

selected feeder Query MDM to extract meter interval data between start

and stop entries Query MDM or DSCADA or EMS to extract substation

feeder interval data between start and stop

DataPrepration

Feeder TOTAL Losses

Phase A Losses Phase B Losses Phase C Losses Feeder TOTAL

Imbalance

DataPresentation

Line loss analytics model KPMGs accelerators (continued)


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Business Usage

Output will be displayed in report tables and graphical time series with other coincident time series data

Possible to display as a dial or needle gauge indicator of present data interval Need ability to select by connectivity hierarchy down to phase. Down to phase line segment or

span if supported by connectivity model Times of most interest are at system, substation, feeder, and phase peak and minimum loads for

establishing a range of values

KWh interval reading (MDM, DSCADA, or EMS)

feeder interval data between start and stop Consolidate sub intervals into 1 hr intervals if required Perform calculations

Phase A Imbalance Phase B Imbalance Phase C Imbalance

Line loss analytics model KPMGs accelerators (continued)


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Load management analytics KPMGs accelerators

AnalyticsOverview

Historically utilities calculated loading on distribution transformers based on a sample of monthly customer use against profile metering at transformers. This profile data was used to establish a correlation between peak demand and monthly use, which in turn was used to estimate peak transformer load. This approach has significant estimating error


Enables an Asset Manager to identify transformers that are consistently approaching or exceeding their operational rating.

Identify underutilized transformers. Validation of the transformer to customer link


Cost Reductions

Deferred Spending

Reliability



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Value Proposition

Optimize transformer life-cycle spend Adjust loading to extend asset life and/or defer capital investment Early identification can prevent outages and minimize the risk of

damage and the need to repair or replace the transformer. Develop better transformer sizing standards

Key Consierations

This module supports after the fact analysis of transformer loading Ability to do what-if analysis based on temperature or

incremental load, would be a really value add. This data can also be repurposed to enable the utility to get on-peak

data for substations and feeders where SCADA cannot be justified


Load management analytics model KPMGs accelerators (continued)

Endpoint Interval Load Data (AMI/MDSI)

Transformer Rating (Asset Register)

Endpoints to Transformer Mapping (Network Model)

Date Range (to be analyzed)

DataCollection

Calculate Transformer Loading (for each interval) Aggregate load for all transformer endpoints Calculate Peak Utilization (for a date range) Select the Transformers Peak Load Interval Divide the Transformers Peak Load Interval by the

Transformers Rating Calculate Average Utilization (for a date range) Select the Transformers Peak Load Interval

DataPrepration

System Breakdown by

Load RangeTransformer Peak Utilization Average UtilizationCustomer Contribution to

DataPresentation


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Business Usage

Identify transformers that are at risk of being overloaded, so that preventative decisions can be taken. Identify transformers that are under-utilized. The data can be used to:

Prevent premature transformer upgrade. Drive transformer sizing decisions for capital expansions/upgrade projects on a go-forward basis.

Correct mapping errors: Customers to Transformers Transformers to Devices

analyzed) Scope of Analysis

(Entire System vs. Circuit)

Temperature Data

Select the Transformers Peak Load Interval Divide the Transformers Peak Load Interval by the

Transformers Rating

Contribution to Utilization

Momentaries interruption analytics KPMGs accelerators (continued)

AnalyticsOverview

AMI event data can be used to identify momentary interruptions in service at the meter level. Historically utilities have had difficulty identifying and understanding the impact of momentary interruptions.


Assist the client establish and prioritize preventive maintenance programs to prevent long term outages and increase customer satisfaction.

Value Proposition

Identify and validate customers with poor PQ supply Drive proactive customer contact; ability to have history of circuit and

transformer performance on client calls Identify and validate circuits with poor PQ performance


Cost Reductions

Deferred Spending

Reliability



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Proposition Identify and validate circuits with poor PQ performance Drive focused investigations of PQ events Enable post-corrective action monitoring for selected events

Key Consierations

Definition of Momentary Interruption: Time between Pri Pwr Dwn and Pri Pwr Up meter events

Momentaries interruption analytics model KPMGs accelerators

AMI Event data from MDM

Customer data from Customer Information System to identify transformer GLN and to feeder ID

Equipment chain table from ESRI

DataCollection

Gather all of the Event data that is received from the AMI meters and import into a database

Extract Event data only for the time period in scope Flag only the meters that have events labeled Power Up and

Power Down and compare the time difference between the Power Down and Power Up.

Any instances in which a Power Down to Power Up is less than 5 minutes, this meter would be flagged as a Momentaries.

We then extract all of the Meters with momentaries, and summarize the number of momentaries by meter.

DataPrepration

Develop approach to identify the protective device related to the transformer where the momentary occurred

Deliver weekly momentary data to service centers

DataPresentation


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Business Usage

1. Understand what processes changed and what benefits resulted.2. Preliminary evaluation of momentaries to sustained outages3. Prioritize preventive maintenance program4. Increase customer satisfaction by communication of additional service information at the meter level

summarize the number of momentaries by meter. We join the meters to the customer they correspond to in order

to provide additional demographic detail as well as provide additional detail for the meter. Detail includes circuit, transformer, and substation.

This information is then extracted and put in a standard format that the client used to map all of the momentaries by geographic region to search for correlation.

Diversion analytics KPMGs accelerators (continued) Utilization of AMI alerts to remotely detect diversion and tampering at the meter level

AnalyticsOverview

AMI meters provide the advantage of notifying utilities when the meter has been tampered with, the downside of the new technology is that the meters are very sensitive to environmental impacts resulting in false positive notifications. Diversion analytics are the key to utilizing the notifications while minimizing the cost of rolling a truck to a premise which is not experienced a meter diversion or tamper.


A larger % of customers are stealing electricity than utilities are aware of. Prior to AMI technology the utility was dependant on meter readers, user trends, and the public to report theft. AMI meters provided the ability to remotely detect the theft which is important since an employee will not be visiting the meter on a monthly basis.


Cost Reductions

Deferred Spending

Reliability



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Value Proposition

Reduced operating costs as a result of identifying theft which in the past was counted as line loss.

Deterrence in meter tampering and theft by people knowing that the meter is monitored.

Key Consierations

Diversion analytics are a strong add on service to remote outage management because both data sets need to be scrubbed through the similar criteria in the first stages of the analysis. Many diversion/tamper alerts also result in an power outage and

restoration notification because the meter is removed, and power is lost.


AMI diversion/tamper notifications

AMI Power outage and restoration notifications

Service orders Interval usage data

DataCollection

Determine if a crew was onsite Determine if the meter lost power Determine what alert patterns provide false positives Determine if there is a sudden drop in usage Develop strong evidence collection process Roll trucks to test hypothesis

DataPrepration

Premise with suspected diversion

Premise with suspected meter tampering

DataPresentation

Diversion analytics model KPMGs accelerators


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Business Usage

The output will be utilized to identify premises where there is suspected diversion or tampering with a high % of accuracy. Trucks will be rolled to the premise to verify hypothesis, collect evidence, and restore the meter to proper working condition.

The output may also be utilized as additional evidence in court cases.

Interval usage data

Appendix:KPMGs pre-packed accelerators Energy consumption analysis


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Appendix:KPMGs pre-packed accelerators Energy usage trend analysis


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Appendix: KPMGs pre-packed accelerators Unstructured utilities data analysis


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Contact information

Chris WardakPrincipalT :212-954-2083 E :[email protected]://www.kpmg.com


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Jothi PeriasamyDirectorT :916.554.1631 E :[email protected]://www.kpmg.com

2012 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (KPMG International), a Swiss entity. All rights reserved. NDPPS 132878The KPMG name, logo and cutting through complexity are registered trademarks or trademarks of KPMG International.

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Documents

Smart Grid Analytics