PhD opportunities

PERSEE - Short-term price forecast for ancillary services and valorization strategy for a renewable-based virtual plant

Thesis proposal

Area of expertiseEnergtique et gnie des procds
Doctoral SchoolSystems Engineering, Materials, Mechanics, Energy
SupervisorM. Georges KARINIOTAKIS
Co-supervisorM. Andrea MICHIORRI
Research unitEnergy and Processes
Starting dateJanuary 31th 2020
KeywordsSmart grids, Renewable energies, Uncertainty, Optimisation
AbstractRecent technological and regulatory developments allow non-controllable renewable power plants such as photovoltaic, wind and hydroelectric plants to provide ancillary services to grid operators. This may represent a new source of revenue for renewable power plant operators and an improvement in the integration of power stations into the electricity grid. The income obtained by ancillary services provision is added to the revenue generated by the participation in the various electricity markets (daily, intraday). To maximize this revenue it is necessary to apply a strategy that optimizes the participation of renewables (or their aggregation in the form of a virtual power plant) to each market according to the opportunities. One of the barriers identified in this context is the need to reliably predict the value of the different quantities of the electricity market and system services (daily, intraday, adjustment, primary and secondary reserve prices and activation likelihood and direction) at different horizons (from 5 minutes to 48 hours). This research is part of the European project 'REgions' which aims to manage a set of renewable virtual plants able to provide ancillary services. This project, coordinated by AIT, sees the participation of partners in France, Austria and Germany, including MINES ParisTech, Fraunhofer IEE, Engie Green and ENERCON.

Scientific Objectives: The objectives of this research are: 1) to improve the quality of forecasts of different market quantities related to system services and 2) to develop a strategy of participation in several markets of a virtual plant composed of non-controllable renewable plants. This highlights several challenges. Little attention has been given in the past to the prediction of the prices of the system services because of their reduced volume, of their variance and especially of the volatility related to their activations. The impact of activation frequency and direction, the interdependence with day-ahead and intra-day electricity prices and the impact of congestion represent sources of complexity that need to be addressed. It is also necessary to quantify the uncertainty of these forecasts. Regarding the participation in the virtual power plant market, the challenge is to develop one or more strategies for simultaneous participation in several markets that take into account: price and renewable production forecasts with their uncertainty, risk aversion of the operator, the penalties and the reliability of the control and dispatch systems of renewable plants.
ProfileTypical profile for a thesis at MINES ParisTech: Engineer and / or Master of Science - Good level of general and scientific culture. Good level of knowledge of French and English. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Teaching skills. Motivation for research activity. Coherent professional project.
Prerequisite (specific skills for this thesis): The desired profile should have a background in electrical (power systems) engineering. Skills in applied mathematics (eg optimization) and computer programming (eg MATLAB) are required. The candidate must be motivated to work in a team.
To apply: Please send a CV and a cover letter through the form ' '. For information contact AND with the subject code 'THESIS AS forecast 2019'.
FundingAutre type de financement