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Section: New Results

Electricity production

Equilibria over energy markets

Participant : Benjamin Heymann.

Motivated by electricity markets we introduce in this paper a general network market model, in which agents are located on the nodes of a graph, a traded good can travel from one place to another through edges considering quadratic losses. An independent operator has to match locally production and demand at the lowest expense. As argued in our previous paper “Cost-minimizing regulations for a wholesale electricity market” this setting is relevant to describe some real electricity markets, pricing behavior and market power coming from the fact that generators can bid above their true value. In a general setting of many distributed generator agents connected by a transmission network, bidding piece-wise linear cost functions, we propose a pricing optimal mechanism model to reduce market power. Our main results are the expression of the optimal mechanism design, two algorithms for the allocation problem and market power estimations. To deduce these nice properties, we intensively use convex analysis and some monotone behaviors of the set-valued maps involved. Furthermore, these algorithms make it possible to numerically compute a Nash equilibrium for the procurement auction, which is important to compare the optimal mechanism and the standard auction setting. Finally, we also show some interesting examples. In the continuation of this work, we introduce a class of biding games for which we prove the existence of a Nash equilibrium. We give a sufficient condition for uniqueness, propose a numerical scheme to compute the extreme Nash Equilibria and show that the equilibrium strategies are convex for a subclass of games. We apply this framework to electricity auctions.

Energy management for a micro-grid

Participants : Frédéric Bonnans, Benjamin Heymann, Pierre Martinon, Olivier Tissot.

We study in [33] the energy management problem for a microgrid including a diesel generator and a photovoltaic plant with a battery storage system. The objective is to minimize the total operational cost over a certain timeframe, primarily the diesel consumption, while satisfying a prescribed power load. After reformulation, the decision variables can be reduced to the charging /discharging power for the battery system. We take into account the switching cost for the diesel generator, the non-convex objective, and the long-term aging of the batteries. We solve this problem using a continuous optimal control framework, with both a direct transcription method (time discretization) and a Dynamic Programming method (Hamilton Jacobi Bellman). This project is a collaboration between team COMMANDS (Inria Saclay, France) and Centro de Energia (Universidad de Chile, Chile). Ongoing works include more refined battery aging models, and modeling the stochastic nature of the photovoltaic power and power load.