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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 electricity markets worldwide, rendering regional price signals even more important to provide adequate locational investment incentives. This paper focuses on electricity markets that operate under a zonal pricing market design. For a fixed number of zones, we endogenously derive the optimal configuration of price zones and available transfer capacities on a network in order to optimally govern investment and production decisions in the long run. In a multilevel mixed-integer nonlinear model that contains a graph partitioning problem on the first level, we determine welfare-maximizing price zones and available transfer capacities for a given electricity market and analyze their impact on market outcomes. Using a generalized Benders decomposition approach developed in Grimm et al. (2019) 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 even for a small number of price zones, an optimal configuration of zones induces a welfare level that almost approaches the first best.

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

Citation:

Download: [PDF]

Entry Submitted: 10/18/2018
Entry Accepted: 10/18/2018
Entry Last Modified: 01/17/2020

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