Présentation sur YouTube

Sector coordination between heat and electricity systems has been identified has a cornerstone in the path towards a more sustainable energy system. However, the coordination of sequential and independent markets relies on the exchange of sensitive information between the market operators, namely time series of consumers' loads.

In this talk we address the privacy concerns arising from this exchange by introducing a novel privacy-preserving Stackelberg mechanism (w-PPSM) which generates differentially-private data streams with high fidelity. The proposed w-PPSM algorithm introduces noise on the sensitive data using a classic Laplace mechanism. The novelty of the algorithm is to then restore the feasibility and fidelity of the Laplace-obfuscated data with respect to the original market-clearing solutions. This post-processing phase is implemented as a bilevel optimization problem, which redistributes the noise on the Laplace-obfuscated data to ensure a close-to-optimal operation of the markets.

We derive theoretical bounds on the cost of privacy introduced by the w-PPSM in both energy markets. Furthermore, through multiple numerical simulations in a realistic energy system, we demonstrate that the w-PPSM can achieve up to two orders of magnitude reduction in the cost of privacy compared to the traditional differentially-private Laplace mechanism.

This approach facilitates the exchange of privacy-preserving information between independent market and system operators in energy systems while ensuring near-to-optimal coordination between them. It also opens the way to quantify the value of information and design privacy-aware market mechanisms.

Bio: Lesia Mitridati is a Postdoctoral Scholar in the Institute for Power Systems & High Voltage Technology at ETH Zurich, Switzerland. She received the Ph.D. degree in Electrical Engineering from the Technical University of Denmark (DTU) in 2019. From 2019 to 2021, she was a Postdoctoral Scholar in the School of Industrial and Systems Engineering at Georgia Tech, USA. Her research interests are at the intersection between optimization, game theory, machine learning and their applications to energy markets and power systems operation and planning.