Reducing Electricity Losses With In-Grid Data Analytics

There are cases where in-grid data, combined with meter analytics data, will be more effective in locating losses than the smart meter.
Published: Wed 02 Sep 2015

Over US$200 billion of energy is wasted each year across the globe. Non-technical losses are responsible for approximately half of this and continue to escalate by 2.5% annually, according to the World Bank.

Distribution losses have an enormous impact on the environment and the economic performance of power companies. Therefore it is important to find a solution that will quickly (and accurately) identify and quantify losses of all types including theft by diversion or illegal connections, or errors in metering, billing and wiring.

An effective solution has the potential to substantially improve the distribution company’s financial performance and reduce operational risk.

Locating grid losses-when the smart meter is not the best solution

Often smart meters are installed to reduce grid losses (caused mainly by meter tampering and by-passing) but there are instances where “in grid data” combined with “meter analytics data” will be more effective in locating losses quicker and more accurately.

For instance, a smart meter may have difficulties in locating losses in a low voltage diversion closer to the transformer. If there is no meter installed and there is a direct connection, there is a danger of unmetered loads and illegal MV connections. Also, sometimes meters are wired or installed incorrectly. These are mostly found in commercial and industrial areas and these losses often have a major impact on utilities’ operations and finances.

In some instances, in-grid has the potential to find inefficiencies quicker at the transformer than the smart meter can. In-grid will pick up on low efficiency from phase imbalance and heavy transformer loading.

In addition, smart meters can take a long time to install-sometimes up to four years. Added to this, making sense of the big data as a result of the installation also takes time. By combining in-grid data with meter analytics data, distribution companies may be able to recover more losses quicker than if they were to rely on smart meter and analytics alone.

Distribution companies face a number of challenges when it comes to grid losses and these include:

  • Recovering their smart meter investments

  • Correctly interpreting the meaning  of the trends  and alerts generated from analysing big data

  • Relating the customer and consumption data to the grid operating  condition

  • Determining the next best action to reduce losses

To tackle these challenges, distribution companies need a solution will help them reduce energy losses quicker, improve efficiency and identify areas of risk within their grid networks.

Creating a best in class grid data analytics solution

Awesense Inc. and Schneider Electric have developed just this. It is called the True Grid Intelligence system (TGI), and is described by Schneider as a ‘best in class grid data analytics solution’. [Schneider Electric and Awesense Inc. To Develop Joint Offers To Reduce Losses On Distribution Networks].

While the benefits of the system will be discussed in detail by Francisco Romero Pinedo, Global BD Director for Smart Meters and Analytics, Schneider Electric and Rudi Carolsfeld, Executive Vice President of Global Sales & Alliances, Schneider Electric, during the upcoming webinar Reduce losses, improve efficiency and protect revenues with analytics, the system has the potential to reduce energy losses by half within approximately three years of implementation.

TGI works in conjunction with meter data management and meter data analytics systems to identify parts of the distribution grid that are likely to have high levels of theft and losses. The solution identifies losses on all grid topologies with overhead or underground conductors. It can pinpoint the cause of losses and other problems in these high risk segments.

TGI then manages the complete field investigation process, using pre-installed and roving in-grid data collection tools to improve situational awareness of the actual grid operating conditions. The system draws on the relevant analytics of the collected in-grid data to determine the next best action to improve and maintain the situation. For long term benefits, persistent in-grid data collection is often deployed.

TGI draws on the systematic approach that industries such as insurance, finance and IT use to reduce fraud and abuse.

Beyond conventional data analytics

Most analytics vendors stop their search once they have provided the utility with a list of possible theft locations, explains Mr Carolsfeld. The utility is expected to search for the cause of theft which can prove to be daunting and highly inefficient. Many distribution companies do not have smart meters and the big data they provide and this is where the TGI solution will help.

TGI uses conventional meter data analytics as an input to the Risk Advisor to determine the highest risk Grid Segments, making field investigations more effective.  A list of high risk segments with all types of losses (not just theft) is created.

Once the list of risk areas has been created from the data collected, TGI applies risk algorithms. Various tools and methodologies are applied to each step during the investigation.

TGI dashboards provide recommendations for Next Best Actions which include verification of billing, balance phases, and upgrading of high risk transformers. TGI retains the full audit trail of all investigations so this makes the case litigation ready. Again, this is a service that many vendors do not offer, says Mr Carolsfeld.  

Through this solution, utilities can look forward to recovering lost revenue more easily and accurately, thereby helping them achieve more reliable and profitable distribution grids.

For more details on how TGI can enhance your business operations and bottom line, register for the webinar.