Section: Application Domains

Energy Management

Participants: Isabelle Guyon, Marc Schoenauer, Michèle Sebag

PhD: Victor Berger, Benjamin Donnot, Balthazar Donon, Herilalaina Rakotoarison

Collaboration: Antoine Marot, Patrick Panciatici (RTE), Vincent Renault (Artelys), Olivier Teytaud (Facebook)

Energy Management has been an application domain of choice for Tao since the end 2000s, with main partners SME Artelys (METIS Ilab Inria; ADEME project POST; ADEME project NEXT) and RTE (See.4C European challenge; two CIFRE PhDs). The goals concern i) optimal planning over several spatio-temporal scales, from investments on continental Europe/North Africa grid at the decade scale (POST), to daily planning of local or regional power networks (NEXT); ii) monitoring and control of the French grid enforcing the prevention of power breaks (RTE); iii) improvement of house-made numerical methods using data-intense learning (as described in Section 3.2) in all aspects of IFPEN activities, from geological problems in oil prospection (IFPEN) to the optimal placement of eolians in eolian fields (IFPEN).

Optimal planning over long periods of time amounts to optimal sequential decision under high uncertainties, ranging from stochastic uncertainties (weather, market prices, demand prediction) handled based on massive data, to non-stochastic uncertainties (e.g., political decisions about the nuclear policy) handled through defining and selecting a tractable number of scenarios. Note that non-anticipativity constraints forbid the use of dynamic programming-related methods; this led to propose the Direct Value Search method [79] at the end of the POST project. A further recent work in the same direction [21] proposes and theoretically studies the Direct Model Predictive Control approach, a hybrid model which merges the properties of two different dynamic optimization methods, Model Predictive Control and Stochastic Dual Dynamic Programming, has robust convergence properties, and experimentally competes with both methods alone.

The daily maintainance of power grids requires the building of approximate predictive models on the top of any given network topology. Deep Networks are natural candidates for such modelling, considering the size of the French grid ($\sim$ 10000 nodes), but the representation of the topology is a challenge when, e.g. the RTE goal is to quickly ensure the "n-1" security constraint (the network should remain safe even if any of the 10000 nodes fails). Existing simulators are too slow to be used in real time, and the size of actual grids makes it intractable to train surrogate models for all possible (n-1) topologies (see Section 7.4 for more details).

Even when efficient simulators do exist, they need to be calibrated (adjusting their hyper-parameters with real data), and complemented by uncertainty propagation models. Such adaptations and extensions are at the core of the NEXT project; hyper-parameter tuning is also a challenge regarding the development plans of the local grids, that heavily rely on graph optimization algorithms.

Furthermore, predictive models of local grids are based on the estimated consumption of end-customers: Linky meters provide coarse grain information only due to privacy issues, and very few samples of fine-grained consumption are available (from volunteer customers). A first task is to transfer knowledge from small data to the whole domain of application. A second task is to directly predict the peak of consumption based on the user cluster profiles and their representativity (see Section 7.4.2).

Another research direction formulates security maintenance as a reinforcement problem, taking inspiration from the recent successes of Deep Reinforcement Learning. This direction is being investigated in Balthazar Donon's RTE CIFRE PhD with RTE (started Oct. 2018).