Jamiil Touré Ali
Sustainable development constitutes an interesting framework to discuss the use of Big Data. Each Sustainable Development Goal (SDG) is global and concerns everyone. For instance, SDG7’s main objective is to provide affordable and clean energy for all. According to the UN Global Pulse 2017, Big Data helps to address the SDG 7 through smart electric metering, allowing utility companies to increase or restrict the flow of electricity, gas, or water so as to reduce waste and ensure an adequate supply at peak periods.
Smart electric meter and big data management from utility companies
In the past, the strategy to control user consumption and bills from utility companies used to be a report from important manpower: reading electric meters from each consumer and reporting it to the utility companies.
However, in 1977, Metretek, Inc. a company launched by Theodore Paraskevakos, created the first smart meter, presenting an alternative to the archaic mode of meter reading and bringing in a set of benefits. Nowadays, smart electric meters now report the same information that is usually produced by non-smart electric meters every month, while also reporting every 15 minutes, accounting for a total of 2,880 reports per month. This is a massive amount of data characterized by volume, velocity, and variety – being those three traits of Big Data.
Big Data produced by smart electric meters is used to minimize outages and deliver enhanced customer service. For instance, outages usually happen due to a failure of a transformer caused by a high peak of electricity demand in given areas. To better understand which transformers are most likely to fail, utility companies can use meter data to simulate loads over time on a test transformer. The insights from such transformers can then be used by utility companies to prevent future failures. In the short term, overloaded transformers can be replaced or upgraded to meet customer electricity demand. In the long term, additional investment can be made to add more capacity to those transformers, which typically operate beyond planned capacity. The result is a reduction in outages, a better use of maintenance resources, and ultimately, an improved customer satisfaction.
Customer targeting and rate recommendations are the other top use cases for smart electric meters data insights. Once the data on electricity consumption is collected by utility companies, customer segmentation can be implemented, to create groups of customers based on certain characteristics. This way, they can target new customers, based on their segmentation efforts. For instance, in their study of Cluster Analysis of smart Metering Data, C. Flath et al. performed a K-Means clustering analysis on real-world consumption data from a smart meter project conducted by a German regional utility company. They showed that metering service companies can offer innovative service products like energy management planning or regional load profiles.
Thus, suppliers can profit from the possibility of designing segment-specific rates which allow for a better integration of the demand side into the control of the electricity system. While smart electric meters offer all those benefits by managing this amount of data, it turns out that the change from a non-smart electric meter to a smart electric meter is only the tip of the iceberg, since smart meters are a part of something bigger: smart grids.
Smart grids and Big Data, the tip of the iceberg
The Journal of Big Data defines a “smart grid” as an intelligent electricity grid that optimizes the generation, distribution, and consumption of electricity, through the introduction of information and communication technologies. Information systems such as new electric vehicles, connected houses and communication systems such as smart meters, sensors, and remote control points, are the sources of the massive data produced in a smart grid.
Big Data architecture and analytics technologies can help leverage the manipulation of such humongous data. In fact, Big Data architecture for smart grids can help integrate, store, analyze and visualize the data collected from the various sources (sensors, substation, meter, Supervisory Control and Data Acquisition (SCADA) customer devices, historical data, etc.), in order to make the smart grids more intelligent, efficient and gainful to the Big Data analytics. The figures below exhibit the life cycle of the smart grid, using Big Data analytics and architecture.
Figure 1: Big data architecture for smart grids
Figure 2: Big data analytics for smart grids
Using Big Data in smart grids brings a significant added value to both utilities and customers in terms of quality of the services and customer service. Utilities can better optimize, control, and monitor the grid, enabling decision-making at the right time. This includes:
- A more efficient delivery of electricity.
- A quicker restoration of electricity after power disturbances or outages.
- Reduced operations and management costs for utilities, and ultimately lower power costs for consumers.
- Reduced peak demand, lowering electricity rates.
- Increased integration of large-scale renewable energy systems.
- Better integration of customer-owner power generation systems, including renewable energy systems.
- Improved security and fraud detection.
Concerning customer service analysis, smart grids provide flexibility in the energy use. This includes:
- A better electricity consumption tracking and cost, by seeing how much electricity the customer uses, when they use it, and how much it costs.
- Real-time pricing for wise electricity use when it is expensive.
- Saving money by managing the customer’s electricity use and choosing the best time to purchase.
Digitalization to facilitate smart meters and grids in Electricity
From the previous analysis, it is clear that digitalization in electricity is essential in order to make use of smart electric meters, smart grids, or Big Data technologies. It’s been the case through the SCADA system, but the industrial revolution 4.0 means new technologies such as smart electric meters, grid, etc, impose some changes in the electricity sector, and the usage of Big Data architectures and analytics technologies to leverage facilities management. Among many possible actions to tackle digitization in the electricity sector, we can list the following :
- Move from non-standard electric meter reading by utility companies to smart electric meters.
- Educate the customer on the usage of smart electric meters.
- Educate utility companies and customers on the usage of smart grids.
For instance, the Inter-American Development Bank (IDB) highlights in its technical notes on the digital revolution, the need in Latin America and the Caribbean (LAC) to digitize hydroelectric power sources – which is half of the electricity of this region and the main source of power in many countries. Furthermore, in its market analysis report, Grand View Research, highlighted that in 2018, electric utilities in the United States had nearly 86.8 million smart metering infrastructure installations, where approximately 88% were meant for residential customers.
In the end, smart electric meters and smart grids are the future of electricity, given the fact they engage electricity management from utilities or customers, while providing the framework to the applications of the Internet of Things (IoT), by having our smart devices connected. As a result, the SDG 7 goals to expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries – in particular, least developed countries, small islands developing States, and land-locked developing countries – in accordance with their respective support programmes, could be a reality by 2030.
 Big data for sustainable development, 2017
 Big data and & Analytics in the Energy & Utilities Industry, 2018
 The digital revolution of Hydropower in Latin America countries, 2019
 Harnessing the power of advanced analytics in transmission and distribution asset management, 2018
 Big Data, Big Opportunities: Energy & Utilities, 2012
 Smart metering systems use data to optimize decision-making, 2015
 Cluster Analysis of Smart Metering Data, 2012
 Big Data management in smart grid: concepts, requirements and implementation, 2017