https://team.inria.fr/inocs/
Title of the proposal : New Models and Methods for Demand Response in a Smart Grid Context
Supervisor of the Post-doc : Luce Brotcorne, Miguel F. Anjos (INRIA International Chair), Martine Labbé.
Keywords : Smart Grid, Optimization, Bilevel problems.
Do not hesitate to send your cv and contact Luce Brotcorne : luce.brotcorne@inria.fr before april 21 2016.
Job offer description
Context:
Electricity is a critical source of energy for our society. Due to the fundamental importance of electricity, economic growth is inevitably accompanied by a corresponding growth in the demand for electricity. However, investment in the power system infrastructure almost invariably lags this growth in demand, resulting in a reduction (and sometimes near absence) of spare supply, and hence a tightly constrained operating context. The operating time period of highest power consumption are called peaks and represent a major concern for the system operator.
A smart grid is the combination of a traditional electrical power production, transmission and distribution system with two-flows of information and of energy between suppliers and consumers. This combination is expected to deliver energy savings, cost reductions, and increased reliability and security. Nevertheless it induces new challenges to operate the resulting system. These include using the power grid already in place more efficiently, integrating renewable energy sources such as wind and solar power generation, managing the flows of power and of information, and integrating loads as active participants in the grid operations.
A central challenge in the full implementation of the smart grid is the effective integration of the customers as active participants in the grid, a process generally referred to as demand response . To capture this non cooperative sequential decision making process, we use a leader-follower approach. In its simplest form, this game-theoretic framework includes two players, namely the leader and the follower. The leader intrinsically integrates the reaction of the follower in its optimization process. The resulting model is a bilevel optimization problem.
Goal:
The goal of the project is i) to develop new bilevel optimization models to represent demand response in a smart grid energy management context, ii) to design and test new algorithms to solve the problem.
More precisely the objectives consists in extending the models propose by Afsar et al. [1] by taking into account the fact that the customers may decide to adjust their consumption or decline to consume and by integrating renewable energy sources. The algorithms developed to solve the problem (exact methods, or matheuristics) will be sharply based on the structure of the problem.
Skills and profile
Good knowledge in mathematical programming, combinatorial optimization, algorithmic.
Ability in programming in C++ or java. Knowledge of optimization solvers such as CPLEX or GUROBI is an add.
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