arXiv subjects

Philippe Drobinski

Publications and source records attributed to Philippe Drobinski.

4 recordsLinked to original sources

arXiv2024

Optimal Operation of a Building with Electricity-Heat Networks and Seasonal Storage

As seasonal thermal energy storage emerges as an efficient solution to reduce CO2 emissions of buildings, challenges appear related to its optimal operation. In a system including short-term electricity storage, long-term heat storage, and where electricity and heat networks are connected through a heat pump, it becomes crucial to operate the system on two time scales. Based on real data from a university building, we simulate the operation of such a system over a year, comparing different strategies based on model predictive control (MPC). The first objective of this paper is to determine the minimum prediction horizon to retrieve the results of the full-horizon operation problem with cost minimization. The second objective is to evaluate a method that combines MPC with setting targets on the heat storage level at the end of the prediction horizon, based on historical data. For a prediction horizon of 6 days, the suboptimality gap with the full-horizon results is 4.31%, compared to 11.42% when using a prediction horizon of 42 days and fixing the final level to be equal to the initial level, which is a common approach.

eess.SY↗

arXiv2022

Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection

We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. We use machine learning to combine outputs from numerical weather prediction models with local observations. The former provide valuable information on higher scales dynamics while the latter gives the model fresher and location-specific data. So as to make the results usable for practitioners, we focus on well-known methods which can handle a high volume of data. We study first variable selection using both a linear technique and a nonlinear one. Then we exploit these results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the indirect one (directly predict wind power).

cs.LG↗

arXiv2018

e4clim 1.0 : The Energy for CLimate Integrated Model: Description and Application to Italy

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with high shares of VRE; assessing the impact of new technologies and of climate variability; conducting sensitivity studies. Specifically, to limit the algorithm's complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production-demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time-series and fitted to available observations. We illustrate e4clim's potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate-data sources and strategies and assessing the impact of climate variability and the robustness of the results.

stat.AP↗

arXiv2012

Is the Weibull distribution really suited for wind statistics modeling and wind power evaluation?

Wind speed statistics is generally modeled using the Weibull distribution. This distribution is convenient since it fully characterizes analytically with only two parameters (the shape and scale parameters) the shape of distribution and the different moments of the wind speed (mean, standard deviation, skewness and kurtosis). This distribution is broadly used in the wind energy sector to produce maps of wind energy potential. However, the Weibull distribution is based on empirical rather than physical justification and might display strong limitations for its applications. The philosophy of this article is based on the modeling of the wind components instead of the wind speed itself. This provides more physical insights on the validity domain of the Weibull distribution as a possible relevant model for wind statistics and the quantification of the error made by using such a distribution. We thereby propose alternative expressions of more suited wind speed distribution.

physics.ao-ph↗