The goal of our project is the development and implementation in software of machine learning models for the operative surveillance of physical power markets. The developed models and software will be designed to run automatically and be used by power market regulators such as NVE, market operators, brokers and TSOs in their daily surveillance activities in physical power markets, and by market actors trading in physical markets to monitor and regulate their own trading activities. Power markets often exhibit low elasticity of demand. In combination with large and flexible power storages (e.g. hydro power and, increasingly, batteries in combination with renewables) this results in market places that are particularly vulnerable to abuse. Surveillance of markets with high renewable and storage penetration is especially complex and difficult. Auction markets where all participants face the same price may be particularly exposed, especially in periods with congestion. Currently, most models and tools used for market surveillance in the power market based on sets of rules used to define market abuse scenarios. Rule-based approaches have several weaknesses, such as a tendency to produce large volumes of alerts, an inability to assess and priories the alerts produced, and difficulty in catching new behaviours. Machine learning is well suited for such monitoring, and can execute much more effectively than rules alone. The monitoring will be more automated, produce more relevant alerts (and fewer false positives) and enable users to detect market behavior that would otherwise be obscured. Such tools will also be more robust, since they will be less dependent on specific persons. This will in turn enable more effective and efficient mitigating actions to be planned and and taken, increasing the transparency and efficiency of the power system, especially given increased use of storage and renewable generation.
Project leader: Gavin Bell
Institution: OPTIMEERING AS