Distributed Learning in Hierarchical Networks

International audienceIn this article, we propose distributed learning based approaches to study the evolution of a decentralized hierarchical system, an illustration of which is the smart grid. Smart grid management requires the control of non-renewable energy production and the inegration of renewable energies which might be highly unpredictable. Indeed, their production levels rely on uncontrolable factors such as sunshine, wind strength, etc. First, we derive optimal control strategies on the non-renewable energy productions and compare competitive learning algorithms to forecast the energy needs of the end users. Second, we introduce an online learning algorithm based on regret minimization enabling the agents to forecast the production of renewable energies. Additionally, we define organizations of the market promoting collaborative learning which generate higher performance for the whole smart grid than full competition

Quantifying the Impact of Unpredictable Generation on Market Coupling

Modeling Market Coupling using an agent-based approach, we compare two organizations: centralized versus decentralized. To perform this comparison we analytically study the impact of wind farm concentration and the uncertainty resulting from the increasing penetration of renewables on the total cost of procurement, market welfare and the ratio of renewable generation to conventional supplies. We prove that the existence and uniqueness of equilibrium depend on the number of interacting demand markets. In a decentralized organization, forecast errors heavily impact the behavior of the electrical system. Simulations show that suppliers have incentives to certify the forecast uncertainty of other markets. We analytically derive the uncertainty price that might be charged by a risk certificator depending on the required confidence level

Is Energy Storage an Economic Opportunity for the Eco-Neighborhood?

International audienceIn this article, we consider houses belonging to an eco-neighborhood in which inhabitants have the capacity to optimize dynamically the energy demand and the energy storage level so as to maximize their utility. The inhabitants' preferences are characterized by their sensitivity toward comfort versus price, the optimal expected temperature in the house, thermal loss and heating efficiency of their house. At his level, the eco-neighborhood manager shares the resource produced by the eco-neighborhood according to two schemes: an equal allocation between the houses and a priority based one. The problem is modeled as a stochastic game and solved using stochastic dynamic programming. We simulate the energy consumption of the eco-neighborhood under various pricing mechanisms: flat rate, peak and off-peak hour, blue/white/red day, peak day clearing and a dynamic update of the price based on the consumption of the eco-neighborhood. We observe that economic incentives for houses to store energy depend deeply on the implemented pricing mechanism and on the homogeneity in the houses' characteristics. Furthermore, when prices are based on the consumption of the eco-neighborhood, storage appears as a compensation for the errors made by the service provider in the prediction of the consumption of the eco-neighborhood