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Bilevel Optimization Approaches to Decide the Feasibility of Bookings in the European Gas Market

Fränk Plein(frank.plein***at***ulb.ac.be)
Johannes Thürauf(johannes.thuerauf***at***fau.de)
Martine Labbé(martine.labbe***at***ulb.ac.be)
Martin Schmidt(martin.schmidt***at***uni-trier.de)

Abstract: The European gas market is organized as a so-called entry-exit system with the main goal to decouple transport and trading. To this end, gas traders and the transmission system operator (TSO) sign so-called booking contracts that grant capacity rights to traders to inject or withdraw gas at certain nodes up to this capacity. On a day-ahead basis, traders then nominate the actual amount of gas within the previously booked capacities. By signing a booking contract, the TSO guarantees that all nominations within the booking bounds can be transported through the network. This results in a highly challenging mathematical problem. Using potential-based flows to model stationary gas physics, feasible bookings on passive networks, i.e., networks without controllable elements, have been characterized in the recent literature. In this paper, we consider networks with linearly modeled active elements such as compressors and control valves that do not lie on cycles of the network. Since these active elements allow the TSO to control the gas flow, the single-level approaches from the literature are no longer applicable. We thus present a bilevel approach to decide the feasibility of bookings in networks with active elements. Besides the classical Karush-Kuhn-Tucker reformulation, we obtain three problem-specific optimal-value-function reformulations, which also lead to novel characterizations of feasible bookings in active networks. We compare the performance of our methods by a case study based on data from the GasLib.

Keywords: Gas networks, Bilevel optimization, European entry-exit market, Bookings, Active elements

Category 1: Applications -- OR and Management Sciences

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 3: Robust Optimization

Citation:

Download: [PDF]

Entry Submitted: 01/28/2021
Entry Accepted: 01/28/2021
Entry Last Modified: 01/28/2021

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