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20172021
most citedHigh-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

42 citations · 81 across the 10 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.SY2019

PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets

Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck

This paper introduces a differentially private mechanism to protect the information exchanged during the coordination of the sequential market-clearing of electricity and natural g…

math.OC2019

Privacy-Preserving Obfuscation for Distributed Power Systems

Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck

This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF…

eess.SP2019

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setp…

cs.CR2019

Privacy-Preserving Obfuscation of Critical Infrastructure Networks

Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

The paper studies how to release data about a critical infrastructure network (e.g., the power network or a transportation network) without disclosing sensitive information that ca…

cs.AI2019

Differential Privacy for Power Grid Obfuscation

Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to pr…