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September 2017

The Neighborhood Battery System: Conserving Energy and Reducing Emissions in the Netherlands

Qiyang Xu's picture
Electric cars are so popular in the Netherlands that it would not be uncommon, say, for a Tesla to roll up as a taxi outside Amsterdam’s Schiphol Airport. And it is not tough to find charging stations for these cars in neighborhoods, parking lots, or even along the streets.

To reduce carbon emissions, national and local governments are taking various approaches—and, thus, electric cars, solar home systems, and energy-efficient solutions for buildings are booming in Europe. Cities like Amsterdam are front and center of this transformation. Netherlands, for instance,  has an ambitious goal of reducing CO2 emissions by 80–95 percent by 2050 compared with 1990, making it an ideal venue for a Smart Cities Tour earlier this year, where  a group of 26 representatives, including national and municipal officials and World Bank project teams, to learn from the Netherlands’ successful experience in energy sector transformation.

For instance, during a site visit to energy network company Alliander, we saw the pilot of a neighborhood battery system (NBS) in Rijsenhout, a town in the Western Netherlands near Amsterdam. The NBS is a local, community-level energy storage system that employs one large battery to stabilize neighborhood power distribution grids, particularly during peak hours. With a significant and increasing number of electric vehicle charging stations and solar panels installed in communities, electric networks are under increasing pressure to handle the variation between solar power during the day and concentrated peak electricity demand in the evenings and nights. Maintaining stable power supply and enhancing the resilience of the electricity grid to spikes in demand are fast becoming real challenges for these communities. While overhauling the power grids to prepare for these challenges could be costly and time-consuming, these small-scale NBS provide a low-cost, smart alternative solution.
 
Housing of the pilot neighborhood battery system in Rijsenhout, Netherlands.  Credit: Alliander

Machine Learning Helps Power Down Electricity Theft in Jamaica

Anna Lerner's picture
  • In Jamaica, about a quarter of electricity produced is stolen or “lost” through non-paying customers and/or accounting errors. Manual detection has failed to make a difference in reducing this theft.
  • ESMAP’s technical assistance team implemented a machine learning model to help Jamaican utility JPS identify and decrease incidents of theft.
  • The machine learning model is based on an open source code, and is available for free to any utility.
About a quarter of the electricity produced by Jamaica’s energy utility, Jamaica Public Service (JPS) is stolen. When traditional, labor-intensive methods failed to produce lasting results, Jamaica tried a different approach: machine learning.
 
Globally, billions of dollars are lost every year due to electricity theft, wherein electricity is distributed to customers but is never paid for. In 2014 alone, Jamaica’s total power transmission and distribution system reported 27% of losses (due to technical and non-technical reasons), close to double the regional average. While the utility company absorbs a portion of the cost, it also passes some of that cost onto consumers. Both actors therefore have an incentive to want to change this.
 
To combat this, JPS would spend more than $10 million (USD) on anti-theft measures every year, only to see theft numbers temporarily dip before climbing back up again. The problem was, these measures relied primarily on human-intensive, manual detection, and customers stealing electricity used more and more sophisticated ways to go around regularly metered use. JPS employees would use their institutional knowledge of serial offenders and would spend hours poring over metering data to uncover irregular patterns in electricity usage to identify shady accounts. But it wasn’t enough to effectively quash incidents of theft.