Meter Data Management and Analytics

Utilities have been using the data from their financial, billing and usage, SCADA (Supervisory Control and Data Acquisition), DMS (Distribution Management System), GIS (Geographic Information System) and OMS (Outage Management System) to produce reports and do analytics for years. with the advent of smart metering and smart grid technologies the follwing options are in front of Utility companies

Real time or near real time data

Much more accurate data

Huge amount of data

Many entities now want the data

Reporting moving out of IT to the client sector

Customer wanting access to the data.

The list goes on and on. But all these changes present a lot of challenges to the utility that have not really been addressed.

DATA CHALLENGES 

Size – In IT for most utilities the largest databases were in the CIS applications. The smart grid databases have tripled and sometimes quadrupled in size. IT struggles to ensure that these databases are monitored and sized properly while trying to put systems and processes in place to do the backup and archiving of this data.

Duplication of data – data is being sent to several applications in the utility and this presents a problem with keeping the data in sync.

Data extracts are being requested from these large databases. We need to identify who handles them, whether they have an effect on the performance of the database and who approves these requests.

Use of extract, transform and load (ETL) tools to extract and transform the data for other sources.

Reporting and ensuring this is not detrimental to the performance of the databases.

Leveraging data experts on the data models and the entity relationships.

Working with data architects that are constantly tuning the performance of the database.

DATA WAREHOUSES/Business Intelligence

Many utilities have turned to moving data into a data warehouse. This is probably what most utilities will have to do to keep the duplication down and deal with reporting on these large volumes of data. However, this also presents its own set of challenges:

It is important that the utility starts small and works smart. There needs to be strong governance around the data. There must be standards in place that ensure any data moved into the warehouse follow those standards. The data should be moved in manageable chunks, ensuring the presence of complete data. There needs to be a user friendly tool with security to let the clients get to the data. It is important to have statements on reports if they are not going to be official reports. Data analysts or business analysts need to be available that know the data and help users with reporting. Users must be trained on what information is in the warehouse and its benefits. Issues must be addressed one at a time. It is important to learn and adapt.

REPORTING

If reporting is done by the systems of record and those same reports are generated from a data warehouse, it is important to ensure that the results are the same. Otherwise there could be huge regulatory ramifications for the utility.

In most cases the clients that support the individual systems, for example outage, are very familiar with the data. They have canned reports that they run. They know the rules around the reports, and for years they have been what the utility bases its CAIDI and SAIDI on.

The utility could end up with multiple systems with different information with no knowledge on what is counted as an outage or not counted, pulling and publishing reports in the company from different data sources.

All that being said about the struggles with the data, there are many things that the utility can really benefit from by using this data.

INDUSTRY SOLUTIONS. Cloud-based analytics could help solve the ‘big data’  with the use cases for analytics.To name few

eMeter Analytics Foundation

eMeter (Siemens), with its meter data management (MDM) product EnergyIP, has been helping utilities collect, archive, validate, estimate, edit and process meter data for several years. eMeter Analytics Foundation for EnergyIP offers utilities develop insights into usage patterns and other information from raw data. eMeter Analytics Foundation helps leverage the raw data into meaningful charts, graphs and diagnostic reports. It uses ETL tools to retrieve data from the EnergyIP MDM and load it into a special analytics database, which is based on a star schema. This makes it possible to quickly obtain analytic information such as aggregates, means, medians, etc., rather than just obtaining transactional information such as the usage of a particular customer during a particular period of time. The eMeter Analytics Foundation runs in conjunction with the eMeter Reporting Framework, and also provides a collection of standard, ready-to-use reports.

Oracle Utilities Meter Data Analytics 

Using eight pre-configured dashboards, Oracle Utilities Meter Data Analytics helps utilities improve metering performance and analyze trends in energy consumption. The event dashboard facilitates tamper event monitoring, and helps protect revenue by identifying tamper events that need investigation and rectification. The ability to drill back to Oracle Utilities Meter Data Management allows for the checking of device history for any prior revenue protection issues for this device. Further revenue protection is achieved by helping utilities identify non-functioning or malfunctioning meters, identify common factors, and rectify them. The consumption dashboard also helps utilities protect the revenue by identifying low usage customers, and then drilling back into meter data management to compare current and historical consumption.

SAP Smart Meter Analytics

SAP Smart Meter Analytics addresses the challenges of increasing the effectiveness of demand side management programs, complying with regulatory targets, generating revenue and reducing energy costs, improving accuracy of load forecasting, and reducing churn rates in deregulated markets. Key features include instant aggregation of data and analysis of customer energy usage at any level of granularity, aggregation and dimension, precisely segmenting customers based on consumption patterns, comparing energy usage of customers against peers, using root cause analysis to improve their energy efficiency, and empowering customers with self-service access to energy usage insights.

IBM Smarter Analytics for Utilities

IBM Smarter Analytics for Utilities aims to better manage the large influx of ‘big data’ coming from smart grids and meters, by effectively using past history of weather, loads and environmental factors to predict outages and track demand patterns. Key benefits include improving generation performance, transforming the grid from a rigid analog system to a dynamic and automated energy delivery system, empowering consumers by providing them with near real time, detailed information about their energy usage and meeting greenhouse gas emissions targets while maintaining sufficient cost effective power supply.

Demand Response and Energy Efficiency

WHAT IS DEMAND RESPONSE?

Demand response refers to the policy and business area whereby electricity customers

reduce or shift their electricity use during peak demand periods in response to “price

signals” or other types of incentives. At present, the vast majority of electricity customers

are on flat, average rates that do not vary by time of day or season, no matter how much

the cost to generate or deliver electricity fluctuates as demands on the system rise and fall.

Flat rates combined with the growth in the use of air conditioning—one of the highest

demands during peak periods—has led to peak power demand growing faster than overall

growth in electricity consumption. Rising peak demand is straining the electricity system

and threatening the reliability of the power grid. It is also adding costs that all customers

pay one way or the other, while leading to increased emissions.

HOW IS DEMAND RESPONSE DIFFERENT FROM ENERGY EFFICIENCY?

Energy efficiency usually refers to devices or practices that provide the same level of output

or benefit by using less energy. Energy efficiency usually focuses on reducing overall energy

use, not just at certain times. Demand response improves the overall efficiency of the

electricity system (including transmission and distribution) but differs from traditional

energy efficiency in that it is more dynamic and controllable, meaning that it can be

“dispatched” to meet rising demand in lieu of turning on a power plant. Demand response

focuses primarily on reducing use during the peak period, and involves providing customers

with price signals or time‐based incentives to encourage them to reduce their peak use.

Demand response can react to conditions in the market or to threats to system reliability

(e.g., blackouts).

Smart Grid introduction

Smart Grid introduction

Introduction to Electric Grid

The electric grid generally refers to all or the smart grid, in a nutshell, is a way to transmit and distribute electricity by electronic means. The electric grid delivers electricity from points of generation to consumers. The electricity delivery network functions via two primary networks: the transmission system and the distribution system. The transmission systems deliver electricity from power plants to distribution substations, while distribution systems deliver electricity from distribution substations to consumers.

The grid also encompasses myriads of local area networks that use distributed energy resources to several loads and/or to meet specific application requirements for remote power, municipal or district power, premium power, and critical loads protection.

Introduction to Smart Grid

Smart grid lacks a standard definition, but enters on the use of advanced of technology to increase the reliability and efficiency of the grid, from transmission to distribution. The Smart Grid is a vision of a better electricity delivery infrastructure.

Smart Grid implementation dramatically increases the quantity, quality, connectivity, automation and Coordination between the suppliers, consumers and networks, and use of data available from advanced sensing, computing, and communications hardware and software.

In addition to being outdated, power plants and transmission lines are aging, meaning they have difficulty handling current electricity needs, while demand may not be reduced any time, but it can still be increasing continuously. One solution could be to add more power lines, but the aging system would still be overwhelmed.

So instead of a quick fix, a more reliable, permanent solution is needed. Perhaps the most fundamental aspect of transitioning to a smarter electricity system is the smart meter.

Renewable and Smart Grid

The smart grid can be seen as an alternative energy source, certainly a change from the current way of doing things. In addition to rerouting electricity, the smart grid would be able to fill in the gaps of these alternative energy power sources. One way this could be accomplished, surprisingly enough, is with another alternative energy technology – the electric car, specifically, the plug-in electric hybrid (PHEV).

This would work through the concept of energy storage, in the case of the PHEV, specifically referred to as V2G or vehicle to grid. This use of alternative energy sources, like wind and solar reduces the nation’s dependence on foreign oil and helps keep pollution from car exhaust and power plants to a minimum.

Smart Grid Knowledge sharing - Part 1

Hi,

As part of knowledge sharing sessions would be part of my blog as part series from a certified smart grid and renewable energy consultant.

Need for smart grid

Smart grid is an emerging technology that changes the way electric energy is produced, transmitted, distributed, utilized and paid for.

There is an need for training and certifying Smart grid professionals , in this process i will be sharing  my expereinces on smart grid in form of blogs/part series

What is Smart Grid
 A smart grid is a form of electricity network utilizing digital technology. A smart grid delivers electricity from suppliers to consumers using two-way digital communications to control appliances at consumers' homes; this  save energy, reduce costs and increase reliability and transparency if the risks inherent in executing massive information technology projects are avoided.

In my next blog  i will be writing about smart grid overview

Smart Grid Training - 2

Target Group

Smart grid training is targeted for a variety of professionals. Typical attendees would be:

(a)Utility personnel: Cross training for domain and IT personnel.

(b) Practicing professionals: to facilitate furthering their careers in Smart Grids.

(c) New employees in Smart Grid Companies: To get breadth training.

(d) Graduating students: Desirous of entering the Smart Grid work force.

(e) IT professional working on the Utilities/Non Utilities

Smart Grid Training

The areas covered in the program are:

(a) Introduction to Smart Metering and  Smart Grids:

(b) Field devices at Homes/ Commercial-Industrial consumer locations/ Substations (HAN Techonology)

(c) Communication systems:

(d) Central systems:

(e) Application software:

 

Additional topics covered as part of the training

Smart Grid Applications and its relevance to IT: AMI, Meter Data Management Systems, Demand Response.

Specific used cases will be dealt as part of the triaining program

Target group