Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Empowering customer engagement by informative billing: a European approach

Programmes aimed at improving end-use energy efficiency are a keystone in the market strategies of leading distribution system operators (DSOs) and energy retail companies and are increasing in application, soon expected to become a mainstream practice. Informative services based on electricity meter data collected for billing are powerful tools for energy savings in scale and increase customer engagement with the energy suppliers enabling the deployment of demand response programmes helping to optimise distribution grid operation. These services are completely in line with Europe’s 2020 strategy for overall energy performance improvement (cf. directives 2006/32/EC, 2009/72/EC, 2012/27/EU). The Intelligent Energy Europe project EMPOWERING involves 4 European utilities and an international team of university researchers, social scientists and energy experts for developing and providing insight based services and tools for 344.000 residential customers in Austria, France, Italy and Spain. The project adopts a systematic iterative approach of service development based on envisaging the utilities’, customers’ and legal requirements, and incorporates the feedback from testing in the design process. The technological solution provided by the leading partner CIMNE is scalable open source Big Data Analytics System coupled with the DSO’s information systems and delivering a range of value adding services for the customer, such as: - comparison with similar households - indications of performance improvements over time - consumption-weather dependence - detailed consumption visualisation and breakdown - personalised energy saving tips - alerts (high consumption, high bill, extreme temperature, etc.) The paper presents the development approach, describes the ICT system architecture and analyses the legal and regulatory context for providing this kind of services in the European Community. The limitations for third party data access, customer consent and data privacy are discussed, and how these have been overcome with the implementation of the “privacy by design” principle is explained