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Endogenous Price Zones and Investment Incentives in Electricity Markets: An Application of Multilevel Optimization with Graph Partitioning

Mirjam Ambrosius(mirjam.ambrosius***at***fau.de)
Veronika Grimm(veronika.grimm***at***fau.de)
Thomas Kleinert(thomas.kleinert***at***fau.de)
Frauke Liers(frauke.liers***at***fau.de)
Martin Schmidt(martin.schmidt***at***uni-trier.de)
Gregor Zöttl(Gregor.zoettl***at***fau.de)

Abstract: In the course of the energy transition, load and supply centers are growing apart in many electricity markets worldwide, rendering regional price signals even more important to provide adequate locational investment incentives. In this context, the establishment of price zones in order to at least partially price grid bottlenecks is under discussion. This paper addresses the key question of how to configure price zones on a network in order to optimally govern investment and production decisions in the long run. We extend the multilevel equilibrium model from Grimm et al. (2017a) to endogenously determine welfare-maximizing price zones for a given electricity market and analyze their impact on market outcomes. This mixed-integer nonlinear model contains a graph partitioning problem on the first level to model the zoning of the network. Using a generalized Benders decomposition and a problem-tailored scenario clustering for reducing the input data size, we are able to solve the model to global optimality even for large instances. We apply the approach to the German electricity market as an example to examine the impact of optimal zoning on key performance indicators such as welfare, generation mix and locations, or electricity prices. It turns out that already a few optimally chosen zones lead to significant welfare gains.

Keywords: Electricity Markets, Price Zones, Investment Incentives, Multilevel Optimization, Graph Partitioning

Category 1: Applications -- OR and Management Sciences

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

Category 3: Global Optimization


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Entry Submitted: 10/18/2018
Entry Accepted: 10/18/2018
Entry Last Modified: 10/18/2018

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