Title : A Leader-Follower Framework for Demand Response
Keywords Optimization, operations research, Energy
The PhD thesis will be done according to a co-tutelle agreement between Ecole Polytechnique de Montreal and Centrale Lille. It will be partly supported (50%) by Canada
Scientific supervisors: Luce.Brotcorne@inria.fr , frederic.semet@inria.fr , miguelf.anjos@polymtl.ca
To apply, please send a cv and a motivation letter to the scientific supervisors
Electricity is a critical source of energy for our society. The smart grid encompasses the developments concerning the power system that are taking place, or need to take place, for the system to better meet the needs and expectations of society in the 21st century. However it also brings up 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. This includes every means available to support an active role of the loads in shaping the load curve of the grid: peak clipping, valley filling, load shifting, strategic conservation, strategic load growth, and flexible load shape. Although direct load control can be effective to handle a small number of large loads, it is certainly not practical for managing demand response in the smart grid of the future.
The objective of the proposed research is to design and implement a new framework to ensure the economic efficiency of the provision of demand response . This research is set in a context where companies providing energy via the power grid, such as utilities and virtual power plants, can use dynamic electricity pricing to incentivize their customers to shift all or part of their loads from the peak periods to off peak periods so as to adjust the load curve in a way that facilitates the operation of the power grid.
We will capture the economic aspects of this context using a leader-follower framework . This is a wellknown game-theoretic framework applicable to situations where there are two decision levels with conflicting objectives. In its simplest form, there are two players, namely the leader and the follower. The leader and fully controls a set of variables that represents its decisions, and the follower solves an optimization problem according to its own interests, but taking the leader's decisions as given. The resulting model is a bilevel optimization problem.
Bilevel optimization is a difficult class of problems; indeed all leader-follower frameworks lead to intrinsically difficult problems. Thus it is imperative to take exploit the characteristics of the speciffc application to formulate and solve these problems
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