Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Agent Strategies in Smart Energy Markets - PowerTAC 2016

The current energy models and infrastructures need to be restructured in order to face the changes in energy consumption, production and management. The adoption of renewable power sources combined with the capability of a more reasonable and autonomous participation on the grid lead to this energy revolution. These changes demand improvements in the way participants act, not only related to the physical electricity grid, but mainly regarding the related services, as energy markets. Since there is no real-life market to test new approaches for smart grid markets, simulations should be used. This work focus on the PowerTAC simulation framework, a top-of-the-art platform in which competitors develop broker agents to enact market companies. In this context, the tariff composition problem plays a fundamental role since customers (both real and simulated) interact with the market by selecting a tariff. Brokers should seek for creating and updating tariffs to be competitive and still profitable. Broker's performance is given by its market share and profit on the market. Current competitors use a centralized approach, with focus on single features to compose tariffs. In this work an alternative approach to this problem is presented. We propose the creation of a Broker that is inherently a Multi-Agent System - a broker composed by different specialist agents that evaluate different features to compose the final tariff. To validate the results from our work we will analyse the results from the participation of our broker on the PowerTAC, firstly in a local version of the competition against previous years public competitors and secondly, by trying to qualify to the annual competition