Seamless RES forecasting models
As Smart4RES Project Manager, Simon his in charge of the daily scientific and technical management of the project.
Beyond these coordination tasks, he is also actively involved in the research effort of ARMINES by managing the scientific activities and contributing to specific developments in forecasting and stochastic optimization.
Their work in the project has two main objectives:
- Develop a robust approach for renewable energy production which avoids discontinuity over multiple frames (from minutes ahead to days ahead horizons) and minimizes computational effort.
- Propose decision-aid tools that maximize the value of trading and control actions of RES and storage in electricity markets.
Those tools have been finalized and presented in Deliverables D3.2, D5.1 and D5.4 (to be published on the website soon).
Simon is now developing a Cost-Benefit-Analysis (CBA) that will generalize the evaluation of the value of these tools. The Figure below presents the structure of the CBA for the seamless RES forecasting tool. The benefits of predictions are assessed as a function of forecasting errors and revenues in the electricity market. All data sources potentially useful for prediction are evaluated in terms of access costs and marginal contribution to the overall benefit. The objective is to obtain combinations of data sources that maximize the revenue when trading RES production on short-term electricity markets.
Figure 1: Methodology of Cost-Benefit-Analysis applied to the seamless RES forecasting model developed by ARMINES
“A cost benefit analysis of our prediction and decision-aid tools is essential to verify that these tools bring added value to power systems under realistic scenarios.”
More about Simon Camal
On his background
Simon is an Energy and Environment Engineer from Mines Nancy and obtained an European Master in Renewable Energy EUREC.
After an experience in consulting and R&D for building energy efficiency and renewable integration, Simon defended his PhD at MINES Paris – PSL University in forecasting and optimization of the provision of ancillary services by renewable power plants (2020).
On his research interests
Prediction of renewable energy, Data science for power system management application, Forecasting of Extreme Events, Intepretable Artificial Intelligence.
On choosing Smart4RES and Mines Paris for his research
“Smart4RES is a unique Inter-disciplinary consortium, bringing together meteorologists and power system specialists. It joins cutting-edge universities and innovative companies, both SME and utilities. ARMINES is the best structure in France to develop applied research in close cooperation with industry.”
Where he sees himself in 5 years
“I’d like to continue to develop research for carbon-neutral and resilient energy systems, within international collaborations. I am convinced that European projects are pivotal to produce useful decision-aid to power system stakeholders. I think that physics-inspired, cyber-secure and interpretable solutions need to be actively developed to face the challenges of energy transition in the next decade”.
Mentors with an impact:
“I am lucky to work with George Kariniotakis, who is a pioneer in renewable energy forecasting. George is impressive by his capacity to explain clearly research concepts to various audiences and his ability to explore new paths with open mind and consistency”.