On Advancing Business Intelligence in the Electricity Retail Market

G5 Doctoral dissertation (article)


Internal Authors/Editors


Publication Details

List of Authors: Hongyan Liu
Publisher: Turku Centre for Computer Science (TUCS)
Place: Turku
Publication year: 2014
ISBN: 978-952-12-3092-9


Abstract

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance.
The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market.
From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market:
(i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support;
(ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making;
(iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation;
(iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can:
(i) provide managerial implications to support the utility’s pricing decision-making;
(ii) add empirical knowledge to the landscape of BI research;
(iii) be transferred to a wide body of practice in the power sector and BI research community.

Last updated on 2019-22-10 at 04:05