Olivier Corradi

• Founder, CEO at Tomorrow (tmrow.com)

In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control electricity consumption using a one-way price signal. Estim...

Integration of fluctuating energy such as wind power becomes more and more essential in future energy systems. The reliance on it propagates risk and uncertainty to the whole electricity value chain, challenging existing market structures and balancing strategies. One solution is to take advantage of the flexibility of consumers. This is done by ac...

Using an equation-free analysis approach we identify a Hopf bifurcation point and perform a two-parameter continuation of the Hopf point for the macroscopic dynamical behavior of an interacting particle model. Due to the nature of systems with a moderate number of particles and noise, the quality of the available numerical information requires the...

With the aim of enabling effective flexible electricity demand, a machine learning algorithm is developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions. The analysis focuses on Danish bidding zone DK2 and was done on a data set comprised of a large number (473) of e...

An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimiz...

An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as input to a Model Predictive Control (MPC) - a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimize...

A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions...

Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows fro...

Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows fro...

This paper outlines the need for and the requirements of trip prediction to optimally derive the charging behavior of plug-in electric vehicles. The information required for trip prediction by a charging-service provider is shown, and a novel trip prediction model is proposed. The proposed model is a semi-Markov model that predicts the next arrival...