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Defence of dissertation in the field of Automation, Systems and Control Engineering, M.Sc. (Tech.) Christian Giovanelli

Title of the thesis is "Aggregating domestic energy storage resources to participate in frequency containment reserves”

The increased penetration of renewable variable energy sources and electric vehicles is entailing major transformations in the power grid. The uncertainty caused by the variability in renewable energy production requires the adoption of new measures to maintain the balance between supply and demand, thus guaranteeing the reliability and stability of the power grid while avoiding blackouts and other abnormal situations. These challenges require the active engagement of consumer-side energy production and consumption to provide sufficient flexibility for the power grid through a mechanism called demand response (DR). This dissertation focuses on DR, and more specifically on enabling consumers to participate in balancing the power grid through the provision of frequency containment reserves (FCR).

This dissertation defines functional and non-functional requirements for using distributed energy resources to provide FCR. The dissertation proposes a DR system for enabling distributed energy resources to provide FCR. The work focuses on defining an ICT architecture capable of functioning during both the planning and the execution of DR. For the DR planning, the dissertation proposes a distributed optimization algorithm to schedule the day-ahead usage of consumer-owned energy storage resources. For the DR execution, the dissertation integrates the ICT architecture with a task allocation algorithm, which coordinates distributed energy resources.

To exploit a virtual power plant in ancillary markets, such as FCR markets, the dissertation identifies the key decision-making to be considered. Then, the dissertation focuses on a crucial prediction task to support reducing market participation risks and uncertainties, namely the day-ahead prediction of ancillary market prices. The proposed solution analyzes the FCR market prices and defines an artificial intelligence methodology for predicting day-ahead FCR prices.

Opponent: Professor Peter Palensky TU Delft, The Netherlands

Custos: Professor Valeriy Vyatkin, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation.

Thesis web page

Contact information: Christian Giovanelli, Aalto University, +358417086920, [email protected]

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