Building Effective Big Data Operations

Utilities are finding different ways of optimising their data streams
Published: Tue 16 Sep 2014

The level of big data in the electricity industry is ever-increasing. In response to this wave of information, utilities and grid operators are turning to experts to help them make sense of and optimise the valuable data they are capturing. [Engerati-Big Data Analytics – Potential for Utilities to Profit.]

It is becoming clear that the use of data about utility systems and customers can vary significantly.

According to ABB Smart Grids VP Gary Rackliffe, there are three fundamental areas where utilities are working towards building effective big data operations:

  1. Improve distribution grid management - particularly around storm restoration

  2. Improve system health management by accurately assessing risks of asset failure

  3. Manage distributed energy resources integration into transmission and distribution systems

He adds that there are five “V’s” in big data Volume - how much data, Variety of data types, Velocity and rate the data comes in at, Veracity and accuracy of the data and finally Value how useful the data is. The scale of each one of these directly inputs into the complexity of the organisation’s big data challenge.

Storm response and asset health

ABB assimilates and analyses data on past storms in order to predict how the system will be impacted and to estimate restoration times. Based on situational awareness, ABB can predict the type of equipment inventory and crew resources that will be needed to handle grid restoration, says Rackliffe.

American Electric Power (AEP) is now rolling out the ABB-Ventyx Asset Health Center. With more than 50% of its transformers over 50 years old, AEP is integrating its system data with ABB’s operations technology/information technology (OT/IT) capabilities, Ventyx's business intelligence and layered-in analytic algorithms to manage its 40,000-mile, 11-state, 5-million-customer grid. [Engerati-Disruption Encourages Innovation.]

“Our customers want to drive down the total risk of failure and get better performance with the same number of people or get the same performance with fewer people,” explains Rackliffe. “There is a big wave of concern now about being able to manage aging infrastructure.”

The above two applications are one category of data analytics at utilities. Consumer analytics is the second and it includes demand response (DR).

Transactive energy

Meter data and other data on customer behaviour is used “to tease out consumer preferences and understand how different DR programs like peak-pricing or time-of-use rates or [electric vehicle] charging will affect demand,” according to Rackliffe.

ABB is working with European utility giant Vattenfall in Gotland, Sweden on a project targeting a 10% load shift at 2,000 homes and 30 commercial facilities. It is using Ventyx’s Demand Response Management System (DRMS) to manage a consumer marketplace with the full range of transactive energy services, including wind, solar PV, energy storage, electric vehicle (EV) charging, and tiered pricing. [Engerati-Smart Grid Gotland – Even Smarter.]

Rackliffe says that predicting customer responses is where data analytics is going. [Engerati- Data Analysis is Providing New Efficiencies.]

Demand response

Opower, Austin Energy and the City of Palo Alto Utilities (CPAU) have also been using data analysis to study consumers’ reactions to DR.

An Austin Energy demonstration program, funded by a Department of Energy ARPA-E grant, tested how AutoGrid’s Demand Response Optimization and Management System (DROMS) can be used to reduce peak load. The utility used the single platform to control 60 thermostats from two different manufacturers’ and 15 electric vehicle chargers from a third manufacturer, all at dispersed customer locations.

Over about a dozen peak demand periods in June and September of 2013, the AutoGrid software was able to adjust the thermostats’ temperatures up four degrees and turn off car chargers for about two hours. The platform also allowed EV owners to turn their chargers back on via email and allowed customers to push a button on the thermostat if they chose to opt out of the event.

According to Austin Energy, Autogrid is effective and easy to use as the solution has a centralized DR portal so that a signal only needs to be sent once. Engerati has recently listed Autogrid as one of the emerging energy and technology pioneers. [Engerati- Energy And Technology Pioneers - Ten Companies To Watch In 2015.]

The City of Palo Alto Utilities (CPAU) has been running a relatively small demand response with seven or eight large commercial customers for four years and according to Senior Resource Planner, Karla Dailey, during a demand response even in May, they were able to reduce the load by 5,653kW. CPAU used AutoGrid to communicate the event to its customers, to do the back end analytics and to report savings to its customers. Dailey sets up the parameters of the anticipated event and participants are then emailed. “AutoGrid is a communication tool for us,” says Dailey.

Demand response has a lot of potential but it is utility specific and depends on the customer profile, the local climate, and on costs. [Engerati-How to Win a Customer’s Interest in Demand Response.]

“There is no one-size-fits-all for DR programs,” Dailey said. “All we are trying to do is shave our summer peak. But there are lots of other things a utility can do with DR and we are going to be looking at all of them.”

Good data, in a timely fashion, will give both customers and operations the ability to make good decisions that will improve overall long-term energy efficiency, service levels, demand response, sustainability and energy reliability.

In this way, the value of data will be maximized.