Job offers

  • Internship offers

    1. Subject: Strategic Bidding in Price Coupled Regions: risk and uncertainty
    2. Context
      Liberalization of electricity markets and new technologies are having a strong influence on how to organize electricity production and transmission. In this work, we consider the Bidding Problem (BP) of a GC (Generation Companies ) maximizing its profit in coupled day-ahead markets. The production of the GC is considered high enough to impact the market prices. The model defined in De Boeck et al. integrates a detailed Unit Commitment (UC) formulation of the production planning problem.

      Objective

      The goal of this project consists in relaxing the assumption made in De Boeck et al. that the bids of the competitor are known in advance. More precisely, the first step is to propose stochastic models for bids of the competitors to capture different dependencies (spatial, economic, regional, etc). Second, the randomness has to be incorporated into the optimization framework. In particular, some type of risk measure has to be added to the formulation to deal with the random parameters that were introduced. The choice will be guided by the need of a risk measure that captures risk aversion of the GC, while at the same time maintaining the structure of the problem so it can be efficiently solved for realistic instances.
      The work can be divided into 3 parts:
      – Master the model defined in De Boeck et al.
      – Define a stochastic model for competitors’ bids. Define scenarios Implement and test the model.
      – Select a risk measure and introduce it into the model. Solve the model and test it on large size instances.
      The work will be accompanied and supervised throughout the duration of the internship by Luce Brotcorne, Bernard Fortz and Bernardo Pagnoncelli. Close relations with the doctoral student’s work will take place.
      Pre-requesities
      Bac+5
      Knowledge in Linear programming, mixed-integer linear programming, Computational experience in numerical optimization is a must.
      While the focus of the work will be in decision making under uncertainty/stochastic programming, no prior knowledge is needed to work in this project. Familiarity with classical models such as two-stage and chance-constrained programming is a plus.
      Location
      The internship will last 6 months and will start in February/March 2022
      Location : INRIA Lille Nord Europe, INOCS Team https://team.inria.fr/inocs/
      Application
      In order to apply, please send to one of the contacts below, your: (1) Curriculum vitae (2) Motivation letter (3) Transcript of grades of the current year and of the M1 as well as (4) possible letters of recommendation.
      Contacts
      Luce Brotcorne : luce.Brotcorne@inria.fr, Directrice de Recherche INRIA, INOCS
      Bernard Fortz: bernard.fortz@ulb.be, Professeur, Université Libre de Bruxelles, INOCS
      Bernardo Pagnoncelli : bernardo.pagnoncelli@skema.edu, Professor, SKEMA, Lille
      References
      [1] J. De Boeck, L. Brotcorne, B. Fortz, Strategic Bidding in Price Coupled Regions, To appear in MMOR in 2022.

    3. Subject: Mechanism Design via Flexibility Disaggregation
    4. Context
      In the modelling of modern electricity systems, it is essential to consider the electricity consumption flexibilities brought by flexible appliances such as electric vehicles (EV) and water-heaters (WH). Consumption flexibilities come from individual consumers or from groups
      of consumers and are usually managed in an aggregated manner, by a particular entity (which is often called an aggregator ): for example, flexibilities from the charging of EVs can be reported by fleet/charging stations operator, flexibilities from WH by an aggregator, etc. Aggregators may have a detailed access to the constraints of their consumers, but it is not necessarily possible nor adequate to take this level of detail in the system operator’s flexibility activation problem: firstly because the dimension of the resulting problem would be prohibitive, and secondly for privacy reasons. Yet, the goal of the aggregator is to find an optimal consumption profile that can satisfy individual constraints, i.e. that can be disaggregated.
      Recently, [1] proposed a method to disaggregate an aggregate consumption profile (e.g. the aggregated power profile for the charging of a fleet of EVs) over a set of individual flexibility constraints (e.g. the parking schedule of each EV). The key point of this method is that it is privacypreserving, meaning that individual consumers keep private the set of constraints characterizing their individual electricity usage. An iterative procedure is used between an aggregator and the consumers, and the latter only provide binary information which allows the aggregator progressing in the disaggregation task . The system operator controls the flexibilities to activate on the distribution grid (as an applicative example followed in this description), in order to minimize its activation cost while taking into account a number of network constraints capturing operational aspects as well as power-flows equations modelling the laws of physics of an electricity network. The issue is that consumers can often receive monetary gains by strategically misrepresenting their usage patterns (e.g., lying about their consumption baseline) and preferences to the utility company, and many of the incentive programs in deployment today are not robust to strategic data manipulation.

      Objective
      The goal of this internship is i) to model the interactions between the consumers and the utility company as a principal–agent problem (e.g., a reverse Stackelberg game), ii) to develop a mechanism that the utility company can employ to design incentives while estimating the consumers’ utility functions/preferences, using the aggregated as well as the disaggregated flexibility data, iii) to quantify the impact of the sharing of information from the distribution system operator through the design of a network tariff reflecting the individual contribution of consumers on the congestion state of the network.

      Profile of the Candidate and Supervision
      We are looking for a highly motivated second year Master student, who would like to be involved in a 5 to 6 months internship. The internship can start as early as February but not later than June 2022. The intern will be hosted at Inria Lille-Nord Europe, in France. Some collaborations are possible with EDF R&D Lab at Paris-Saclay and with the LIA, at the University of Avignon, both located in France.
      The candidate should have a very good background in mathematical optimization/game theory/operations research, interests for economics, and basic programming skills in Python.
      The internship student will be supervized by:
      • Hélène Le Cadre, Luce Brotcorne from Inria Lille-Nord Europe,
      • Olivier Beaude, Paulin Jacquot from EDF R&D.

      Contact
      Please send your application (short motivation letter+CV) by email to
      • helene.le-cadre@inria.fr, luce.brotcorne@inria.fr,
      • olivier.beaude@edf.fr, paulin.jacquot@edf.fr.

      References
      [1] P. Jacquot, O. Beaude, P. Benchimol, S. Gaubert, and N. Oudjane, A privacy-preserving method to optimize distributed resource allocation, SIAM Journal on Optimization, vol. 30, pp. 2303-–2336, 2020.
      [2] H. Le Cadre, Y. Mou, H. Höschle, Parametrized Inexact-ADMM Based Coordination Games: a Normalized Nash Equilibrium Approach, European Journal of Operation Research, vol. 296, no. 2, pp. 696–716, 2022.

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