Big Data Analytics in Power System
Big Data Analytics has become an answer to the exponential growth in data and different industries have started to make several changes with the use of modern devices. Machine learning, Cloud computing, Internet of Things (IoT) and Cyber Security are being deployed in their operations. These technologies are the present and future investment avenues for utilities and all of them will have one thing in common, a huge amount of data will be generated and will keep on growing each year as more and more smart devices and technologies are being deployed within the power systems infrastructure by both utilities and consumers. Up gradation of Electrical power system with smart devices will generate data continuously. Electric vehicles (EVs), smart home systems, grid management systems and many more subsystems will also interface with utilities and generate potentially valuable data for analysis.
As an intelligent system of both energy and information, Smart Grid is the abundant source of information, which covers the data from process of electricity generation, transmission, distribution and consumption. These data include the electrical information from distribution stations, distribution switch stations, electricity meters, and non-electrical information like marketing, meteorological as well as regional economic data. Collection and analysis of them provide essential help in scheduling of power plants, operation of subsystems, maintenance for vital power equipment and business behavior in marketing.
The data sources mentioned above can be sorted into three categories: measurement data, business data and external data. Most of the operation parameters in power system are measured through installed sensors and smart meters, indicating the system’s current and historical status. The weather conditions and social events like festivals are the external data that cannot be measured from smart meters but have an impact on the operation and planning in power system. The business data mainly includes the marketing strategies and rivals’ behavior.
The challenge for utilities is to make this data useful and generate actionable insights on aspects, such as consumer behavior and demand-supply balance, from it. Benefiting from large datasets is not straightforward and utilities need to deploy a range of new IT solutions that allow them to collect the data in consistent ways, as well as transport, secure, analyze and store it.
The characteristics of big data in are also in accordance with the universal 5 V’s big data model in many researches as below:
- Volume: Refers to the vast amount of data generated, which makes data sets too large to store and analyze using traditional database technology. In smart grid the widespread application of smart meter and advanced sensor technology provide huge amount of data.
- Velocity: Refers to the speed at which new data is generated and the speed at which data moves around.
- Variety: Refers to the types of data we can now use. In the past, we focused on structured data that neatly fitted into tables or rational databases such as financial or meteorological data. With big data technology, we need to handle different types of unstructured data including messages, social media conversations, digital images, sensor data, video or voice recordings, and bring them together with more traditional, structured data.
- Veracity: Refers to the messiness or trustworthiness of the data. The quality and accuracy are less trustworthy with such large amount of big data, which can challenge the outcome data analysis. Errors of measurements in smart grid may exist due to the imperfections in devices or mistakes in data transmission. The secure and efficient power system operation relays on the data assessment and state estimation.
- Value: Refers to our ability to extract valuable information from the huge amount of data and derive a clear understanding of the value it brings. The larger the amount of data is, the lower will be the density of valuable information.
One of the most critical risks in adopting big data applications in the power distribution system is poor quality data impacting the decision-making without the knowledge of the operator. This could happen if critical sensors failures are not detected by the data cleansing routines and the state estimation applications. Another critical risk involves data privacy and data protection. The cornerstone privacy principle “notice and consent” is no longer applicable in the big data era. Hence, to avoid backlash from customers about big data analytics, not only do we need advanced cyber-security but also enhanced laws and regulations to protect data privacy in electric utilities industry.