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Distributed Multi-Unit Privacy Assured Bidding (PAB) for Smart Grid Demand Response Programs

Publications associées (53)

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The thermodynamic and life-cycle assessments of a novel charging station for electric vehicles in dynamic and steady-state conditions

Jan Van Herle, Hossein Pourrahmani, Chengzhang Xu

The current study performs the thermodynamic and life-cycle assessments (LCA) of a novel charging station in two system designs. The goal is to design an efficient charging station for electric vehicles with high efficiencies and low environmental impacts ...

NATURE PORTFOLIO2023

On Smart-Buildings and their Integration into the Smart-Grid

Olivier Valentin Henri Van Cutsem

Today's electrical grid is undergoing deep changes, resulting from the large integration of distributed Renewable Energy Sources (RES) in an effort to decarbonize the generation of electrical energy. In addition to the emergence of this volatile electricit ...

EPFL2020

To limit global warming, the energy transition from fossil fuels to renewables is one of the main challenges of this century. This transition will help decrease our CO2 emissions, create jobs, contribute to a broader revenues distribution, improve energy s ...

EPFL2020

Cooperative energy management of a community of smart-buildings: A Blockchain approach

Maher Kayal, Olivier Valentin Henri Van Cutsem

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manag ...

ELSEVIER SCI LTD2020

Comparative Assessment of Electrical Generating Systems for a Greek Island

Lucas Mosca

This master thesis is the last part of the master studies in the department of Energy Management and Sustainability, at the Swiss Federal Institute of Technology (EPFL) in Lausanne. This work has been performed in collaboration with the company Renenig Ene ...

2019

CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning

Jérôme Henri Kämpf

Demand response has the potential of reducing peaks of electricity demand by about 20% in the US, where buildings represent roughly 70% of the total electricity demand. Buildings are dynamic systems in constant change (i.e. occupants' behavior, refurbishme ...

ACM2019

Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households

Christophe Ballif, Lionel Benjamin Bloch, Jordan Holweger, Marina Dorokhova

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power cons ...

2019

Privacy of Real-Time Pricing in Smart Grid

Maryam Kamgarpour, Christos Dimitrakakis

Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer ...

IEEE2019

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formu ...

IEEE2019

The mismatch between household electricity consumption and solar energy production is a key issue for the fast deployment of this technology in a low-voltage grid. Namely, as high photovoltaic generation occurs during the day while the load remains low, th ...

2018