**Luiz A. DaSilva
**Professor of Telecommunications Trinity College Dublin

*Abstract*Wireless networks have been evolving towards great heterogeneity of radio access technologies, spectrum usage regimes, and resource ownership models. This kind of heterogeneity, combined with densification and greater decision-making autonomy for wireless devices, make game theory especially suited to model such systems. Game theory allows us to predict the outcomes of autonomous decisions by intelligent players (in our case, radios), as well as to design resource usage etiquettes in wireless networks that will lead to desirable outcomes. In this talk, we will outline some ways in which cooperative and non-cooperative game theory can help us understand the complex interactions that will characterize future wireless networks. We will discuss the use of game theory in a variety of scenarios, including dynamic sharing between incumbents and secondary users of spectrum, device-to-device communications, and resource sharing among mobile operators.

**Olivier Beaude**

(EDF)

Historically, optimization tools have been largely applied to design and operate electricity systems. A typical problem is the so-called “Unit-Commitment” [Tahanan2015]. It consists in scheduling at a minimal cost electricity production units – often before real-time – to satisfy the equality constraint between production and – often forecasted – consumption at all time. Now, the context in electricity systems is rapidly changing, leading to more competition between – newly created – stakeholders, a “more local” management of the production-consumption constraint, and stochastic aspects with the integration of – local – renewable energy sources with very uncertain generation. In this context, many new problems can be tackled using the tools of game theory [Saad2012]. This course proposes a few of such illustrative examples, linking practical crucial issues of electricity systems operators to wellknown classes of games (routing, potential [Mohsenian-Rad2010, Beaude2016, Jacquot2017], matching [Zeng2016], auctions [Bhattacharya 2014, Horta2017]). This will highlight the vitality of the field of “game-theory applied to Smart Grids”, and potential avenues to improve this link.

**[Beaude2016]** Beaude, O., Lasaulce, S., Hennebel, M., & Mohand-Kaci, I. (2016). Reducing the impact of ev charging operations on the distribution network. *IEEE Transactions on Smart Grid,* 7(6), 2666-2679.

**[Bhattacharya 2014]** Bhattacharya, S., Kar, K., Chow, J. H., & Gupta, A. (2014, June). Extended second price auctions for plug-in electric vehicle (PEV) charging in smart distribution grids. In *American Control Conference* (ACC), 2014 (pp. 908-913). IEEE.

**[Horta2017]** José Horta, Daniel Kofman, David Menga, Alonso Silva. Novel market approach for locally balancing renewable energy production and flexible demand. 8th IEEE International Conference on Smart Grid Communications (SmartGridComm 2017), 2017. arXiv:1711.09565

**[Jacquot2017]** Jacquot, P., Beaude, O., Gaubert, S., & Oudjane, N. (2017). Analysis and Implementation of a Hourly Billing Mechanism for Demand Response Management. *arXiv preprint* arXiv:1712.08622.

**[Mohsenian-Rad2010]** Mohsenian-Rad, A. H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. *IEEE transactions on Smart Grid,* 1(3), 320-331.

**[Saad 2012]** Saad, W., Han, Z., Poor, H. V., & Basar, T. (2012). Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications. *IEEE Signal Processing Magazine,* 29(5), 86-105.

**[Tahanan2015]** Tahanan, M., Van Ackooij, W., Frangioni, A., & Lacalandra, F. (2015). Large-scale Unit Commitment under uncertainty. 4OR, 13(2), 115-171.

**[Zeng 2016]** Zeng, M., Leng, S., He, J., Zhang, Y., & Qiao, G. (2016, July). Matching theory based travel plan aware charging algorithms in V2G smart grid networks. *In Communications in China (ICCC), 2016 IEEE/CIC International Conference on* (pp. 1-6). IEEE.

**Vianney Perchet**

(ENS Paris-Saclay & Criteo Research)

This tutorial will focus on auctions, that is an incredibly huge market worldwide. Indeed, each time an ad is displayed on a webpage, several auctions are run on different platforms.

I will briefly cover the game theoretic basics of auction theory (second-first price, truthfulness, optimality, etc.) and then consider the case where agents – either the seller or the bidder – want to learn epsilon-optimal strategies based on data previously gathered. A key question is to relate epsilon to the number of data point, and also to find efficient/polynomial algorithms to compute them.

This tutorial will therefore be at the junction of mechanism design (game theory) and machine learning, but it should be more or less self contained.